system

Abstract

The system according to this embodiment aims to generate original designs and artworks based on user text prompts and image inputs. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives text prompts and image inputs from the user. The generation unit analyzes the text prompts and image inputs received by the reception unit and generates original designs and artworks. The provision unit provides the designs and artworks 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to generate unique designs or artworks based on a user's text prompt or image input, and there is room for improvement.

[0005] The system according to the embodiment aims to generate unique designs or artworks based on a user's text prompt or image input.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives text prompts and image inputs from the user. The generation unit analyzes the text prompts and image inputs received by the reception unit and generates original designs and artworks. The provision unit provides the designs and artworks generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate unique designs and artworks based on user text prompts and image inputs. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also 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) The artistic agent according to an embodiment of the present invention is a system that accepts text prompts and image inputs from users, and a generative AI analyzes these inputs to generate unique designs and artworks. This artistic agent targets the design industry, artists, marketers, social media managers, and others. Challenges faced by these targets include the time it takes to generate new ideas, a lack of design skills, and high production costs. To solve these problems, the present invention aims to streamline the creative process and reduce production costs by having AI quickly generate high-quality designs and artworks. For example, the artistic agent receives text prompts and image inputs from the user. For example, the generative AI analyzes these inputs and generates unique designs and artworks. This generative AI utilizes technologies that generate images from text and deep learning models that generate new designs from image data. For example, models such as Generative Opposite-Guardian Networks (GANs) and DALL-E are used. This creates an environment where anyone can easily create high-quality designs and artworks, enabling innovation that goes beyond the limits of creativity. This allows the artistic agent to streamline the creative process by receiving and analyzing user input and providing generated designs and artwork.

[0029] The artistic agent according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives text prompts and image inputs from the user. User text prompts include, but are not limited to, questions, commands, and free-form text. Image inputs include, but are not limited to, image files, sketches, and photographs. The reception unit analyzes the text prompts entered by the user using natural language processing techniques. The reception unit can also analyze the image inputs entered by the user using image analysis algorithms. The generation unit uses a generation AI to analyze the text prompts and image inputs received by the reception unit and generate original designs and artworks. The generation unit utilizes, for example, techniques to generate images from text prompts. The generation unit can generate images from text prompts using, for example, a generative-opposite network (GAN). The generation unit also utilizes techniques to generate new designs from image inputs. The generation unit can generate new designs from image inputs using, for example, a deep learning model. Some or all of the above-described processing in the generation unit is performed using a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or an image generation AI, but is not limited to such examples. The provision unit provides the designs and artworks generated by the generation unit to the user. The provision unit displays the generated designs and artworks to the user, for example, through a web application or a mobile application. The provision unit can also send the generated designs and artworks to the user via email. Some or all of the above-described processing in the provision unit may be performed using AI or not. This allows the artistic agent according to the embodiment to streamline the creative process by receiving and analyzing user input and providing the generated designs and artworks.

[0030] The reception desk receives text prompts and image inputs from users. User text prompts include, but are not limited to, questions, commands, and free-form prompts. Specifically, a user might enter a command prompt such as "Please draw a sunset over the beach," or a question prompt such as "What elements are included in a beach landscape?". In free-form prompts, users enter text as they wish, and the generating AI analyzes the content. Image inputs include, but are not limited to, image files, sketches, and photographs. Users can provide these image inputs by uploading photographs they have taken or hand-drawn sketches. The reception desk analyzes user-entered text prompts using natural language processing techniques. Natural language processing techniques include morphological analysis, grammatical analysis, and semantic analysis, enabling accurate understanding of the text's content and intent. The reception desk can also analyze user-entered image inputs using image analysis algorithms. Image analysis algorithms include image recognition, object detection, and feature extraction, enabling detailed analysis of image content and characteristics. For example, specific shapes and patterns can be extracted from a user-uploaded sketch, and a generative AI can then generate a new design based on this. This allows the reception unit to accurately analyze diverse user inputs and provide appropriate data to the generation unit.

[0031] The generation unit uses generative AI to analyze text prompts and image inputs received by the reception unit and generate original designs and artworks. For example, the generation unit utilizes techniques to generate images from text prompts. Specifically, it can generate images from text prompts using a Generative Opposite Network (GAN). A GAN consists of two neural networks: a generator and a discriminator. The generator produces a new image, and the discriminator determines whether the image is real or fake. By repeating this process, the generator improves its ability to generate more realistic images. The generation unit also utilizes techniques to generate new designs from image inputs. For example, it can generate new designs from image inputs using a deep learning model. Deep learning models have the ability to extract image features and generate new designs by learning from large amounts of image data. For example, based on a sketch provided by a user, it can generate a design that retains the style of the sketch while adding new elements. Some or all of the above-described processes in the generation unit are performed using generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and image generation AI. The text generation AI analyzes the user's text prompts and generates instructions to create appropriate images based on their content. The image generation AI then generates new images based on these instructions. This allows the generation unit to produce high-quality designs and artwork based on user input.

[0032] The delivery unit provides users with designs and artwork generated by the generation unit. For example, the delivery unit displays the generated designs and artwork to users through web applications or mobile applications. Specifically, the generated designs and artwork are displayed on the interface of the website or mobile application accessed by the user, allowing the user to view and download them. The delivery unit can also send the generated designs and artwork to users via email. An email containing image files and links to the generated artwork is sent to the email address specified by the user. Some or all of the above processing in the delivery unit may be performed using AI, or not. For example, if AI is used, the generated artwork can be automatically categorized based on the user's past usage history and preferences and provided in the most optimal format. If AI is not used, the generated artwork can be provided to the user as is. This allows the delivery unit to provide users with generated designs and artwork quickly and efficiently. Furthermore, the delivery unit can improve the overall system performance by collecting user feedback and providing it to the generation and reception units. For example, when users rate and comment on the provided artwork, this feedback is used as training data for the generation AI, improving the quality of future generation. This allows the service provider to increase user satisfaction and continuously improve the overall system performance.

[0033] The generation unit can generate images from text prompts. For example, the generation unit takes a text prompt as input and generates an image using a Generative Opposite Network (GAN). The generation unit can also generate abstract designs or concrete works of art based on text prompts. For example, the generation unit can extract keywords contained in a text prompt and generate an image based on them. This allows for the provision of designs based on user text input by generating images from text prompts. Some or all of the above processing in the generation unit is performed using a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or an image generation AI, but is not limited to these examples.

[0034] The generation unit can generate new designs from image input. For example, the generation unit can receive image input and generate new designs using a deep learning model. The generation unit can also generate abstract designs or concrete works of art based on image input. For example, the generation unit can extract features contained in image input and generate new designs based on them. In this way, by generating new designs from image input, it is possible to provide designs based on the user's image input. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, for example, an image generation AI or a deep learning model, but is not limited to such examples.

[0035] The service provider can provide the generated designs and artwork to the user. For example, the service provider can display the generated designs and artwork to the user through a web application or a mobile application. For example, the service provider can also send the generated designs and artwork to the user via email. For example, the service provider can store the generated designs and artwork in cloud storage and make it accessible to the user. This allows the user to verify the generated results by providing them with the generated designs and artwork. Some or all of the above processes in the service provider may be performed using AI or not.

[0036] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk may prioritize displaying input methods (text, images, etc.) that the user has frequently used in the past. For example, the reception desk may also suggest the optimal input method for a specific time period based on the user's past input history. For example, the reception desk may analyze the user's past input history and automatically select the most efficient input method. In this way, by analyzing the user's past input history, the optimal input method can be provided. Some or all of the above processing in the reception desk may be performed using AI or not.

[0037] The reception unit can filter input based on the user's current projects and areas of interest. For example, the reception unit can prioritize accepting only input related to the user's current project. The reception unit can also filter and display relevant input based on the user's areas of interest. For example, the reception unit can suggest appropriate input based on the progress of the user's project. This allows for the priority acceptance of highly relevant input by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not.

[0038] The reception unit can prioritize accepting inputs that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit will prioritize accepting inputs related to that region. The reception unit can also suggest the optimal input method based on the user's current location. The reception unit can also filter relevant inputs, taking into account the user's geographical location information. This allows for the priority acceptance of highly relevant inputs by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI or not.

[0039] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can prioritize accepting inputs related to the user's current interests from their social media activity. The reception unit can also analyze the content of the user's social media posts and suggest relevant inputs. The reception unit can also filter relevant inputs by referring to the activities of the user's social media followers and friends. This allows the reception unit to prioritize accepting relevant inputs by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not.

[0040] The generation unit can adjust the level of detail of the generated output based on the importance of the input text prompts and image inputs. For example, the generation unit can generate a detailed design based on a high-importance text prompt. It can also generate a simple design based on a low-importance image input. For example, the generation unit can compare the importance of text prompts and image inputs and generate a design with the optimal balance. This allows for the generation of optimal designs and artworks by adjusting the level of detail of the output based on the importance of the inputs. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or an image generation AI.

[0041] The generation unit can apply different generation algorithms depending on the input category during generation. For example, it can apply a natural language processing algorithm to text prompts. For example, it can apply an image generation algorithm to image inputs. For example, it can apply a hybrid generation algorithm to inputs containing both text and images. This allows for the generation of optimal designs and artworks by applying different generation algorithms depending on the input category. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM), image generation AI, or hybrid generation AI.

[0042] The generation unit can determine the generation priority based on the submission date of the inputs during generation. For example, the generation unit can prioritize generating inputs with approaching deadlines. For example, the generation unit can postpone generating inputs with distant submission dates. For example, the generation unit can also adjust the level of detail of the generation according to the submission date. This enables efficient generation by determining the generation priority based on the input submission date. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or image generation AI.

[0043] The generation unit can adjust the generation order based on the relevance of the inputs during generation. For example, the generation unit can prioritize generating inputs with high relevance. For example, the generation unit can postpone generating inputs with low relevance. For example, the generation unit can also adjust the level of detail of generation according to relevance. This allows for efficient generation by adjusting the generation order based on the relevance of the inputs. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or image generation AI.

[0044] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider may prioritize selecting service delivery methods that the user has preferred to use in the past. For example, the service provider may also suggest the optimal service delivery method based on the user's past usage history. For example, the service provider may analyze the user's past usage history and automatically select the most efficient service delivery method. This allows the service provider to deliver the optimal service delivery method by referring to the user's past usage history. Some or all of the above-described processes in the service provider may be performed using AI or not.

[0045] The service provider can customize the content offered based on the user's current projects and areas of interest at the time of delivery. For example, the service provider may prioritize providing designs related to the project the user is currently working on. For example, the service provider may also customize and provide relevant designs based on the user's areas of interest. For example, the service provider may suggest appropriate designs according to the progress of the user's project. This allows for highly relevant deliveries by customizing the content based on the user's current projects and areas of interest. Some or all of the above processing in the service provider may be performed using AI or not.

[0046] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the delivery unit will prioritize providing designs related to that region. The delivery unit can also suggest the optimal delivery method based on the user's current location. The delivery unit can also customize and provide relevant designs, taking into account the user's geographical location information. This allows for the provision of the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0047] The service provider can analyze the user's social media activity and customize the content offered at the time of delivery. For example, the service provider can prioritize providing designs related to the user's current interests based on their social media activity. The service provider can also analyze the user's social media posts and suggest relevant designs. The service provider can also customize relevant designs by referencing the activities of the user's social media followers and friends. In this way, the service provider can customize relevant content by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not.

[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 reception desk can analyze the user's past input history when receiving user input and suggest the most suitable input method. For example, it can prioritize displaying input methods (text, images, etc.) that the user has frequently used in the past. It can also suggest the most suitable input method for a specific time period based on the user's past input history. Furthermore, it can analyze the user's past input history and automatically select the most efficient input method. In this way, by analyzing the user's past input history, the system can provide the most optimal input reception method.

[0050] The service provider can prioritize providing highly relevant designs and artwork to users by considering the user's geographical location. For example, if a user is in a specific region, designs related to that region can be prioritized. The service can also suggest the optimal delivery method based on the user's current location. Furthermore, it can customize and deliver relevant designs by considering the user's geographical location. This allows for the provision of the most optimal delivery method by taking the user's geographical location into account.

[0051] The generation unit can apply different generation algorithms depending on the input category during generation. For example, a natural language processing algorithm can be applied to text prompts. An image generation algorithm can also be applied to image inputs. Furthermore, a hybrid generation algorithm can be applied to inputs containing both text and images. This allows for the generation of optimal designs and artworks by applying different generation algorithms depending on the input category.

[0052] The input receiving system can filter input based on the user's current projects and areas of interest. For example, it can prioritize receiving only input related to the user's current project. It can also filter and display relevant input based on the user's areas of interest. Furthermore, it can suggest appropriate input based on the user's project progress. This allows for the priority of receiving highly relevant input by filtering based on the user's current projects and areas of interest.

[0053] The service provider can analyze the user's social media activity and customize the content offered at the time of delivery. For example, it can prioritize providing designs related to the user's current interests based on their social media activity. It can also analyze the user's social media posts and suggest relevant designs. Furthermore, it can customize relevant designs by referencing the activities of the user's social media followers and friends. In this way, the service provider can customize the content offered by analyzing the user's social media activity.

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

[0055] Step 1: The reception unit receives text prompts and image inputs from the user. Text prompts from the user can be in question form, command form, free form, etc., and image inputs can be image files, sketches, photographs, etc. The reception unit can analyze text prompts using natural language processing technology and image inputs using image analysis algorithms. Step 2: The generation unit analyzes the text prompts and image inputs received by the reception unit and generates original designs and artworks. The generation unit utilizes technologies such as generating images from text prompts using generative AI, and generating images from text prompts using generative opponent networks (GANs). In addition, the generation unit utilizes technologies such as generating new designs from image inputs using deep learning models. Step 3: The provider delivers the designs and artwork generated by the generator to the user. The provider displays the generated designs and artwork to the user through web applications and mobile applications. The provider can also send the generated designs and artwork to the user via email.

[0056] (Example of form 2) The artistic agent according to an embodiment of the present invention is a system that accepts text prompts and image inputs from users, and a generative AI analyzes these inputs to generate unique designs and artworks. This artistic agent targets the design industry, artists, marketers, social media managers, and others. Challenges faced by these targets include the time it takes to generate new ideas, a lack of design skills, and high production costs. To solve these problems, the present invention aims to streamline the creative process and reduce production costs by having AI quickly generate high-quality designs and artworks. For example, the artistic agent receives text prompts and image inputs from the user. For example, the generative AI analyzes these inputs and generates unique designs and artworks. This generative AI utilizes technologies that generate images from text and deep learning models that generate new designs from image data. For example, models such as Generative Opposite-Guardian Networks (GANs) and DALL-E are used. This creates an environment where anyone can easily create high-quality designs and artworks, enabling innovation that goes beyond the limits of creativity. This allows the artistic agent to streamline the creative process by receiving and analyzing user input and providing generated designs and artwork.

[0057] The artistic agent according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives text prompts and image inputs from the user. User text prompts include, but are not limited to, questions, commands, and free-form text. Image inputs include, but are not limited to, image files, sketches, and photographs. The reception unit analyzes the text prompts entered by the user using natural language processing techniques. The reception unit can also analyze the image inputs entered by the user using image analysis algorithms. The generation unit uses a generation AI to analyze the text prompts and image inputs received by the reception unit and generate original designs and artworks. The generation unit utilizes, for example, techniques to generate images from text prompts. The generation unit can generate images from text prompts using, for example, a generative-opposite network (GAN). The generation unit also utilizes techniques to generate new designs from image inputs. The generation unit can generate new designs from image inputs using, for example, a deep learning model. Some or all of the above-described processing in the generation unit is performed using a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or an image generation AI, but is not limited to such examples. The provision unit provides the designs and artworks generated by the generation unit to the user. The provision unit displays the generated designs and artworks to the user, for example, through a web application or a mobile application. The provision unit can also send the generated designs and artworks to the user via email. Some or all of the above-described processing in the provision unit may be performed using AI or not. This allows the artistic agent according to the embodiment to streamline the creative process by receiving and analyzing user input and providing the generated designs and artworks.

[0058] The reception desk receives text prompts and image inputs from users. User text prompts include, but are not limited to, questions, commands, and free-form prompts. Specifically, a user might enter a command prompt such as "Please draw a sunset over the beach," or a question prompt such as "What elements are included in a beach landscape?". In free-form prompts, users enter text as they wish, and the generating AI analyzes the content. Image inputs include, but are not limited to, image files, sketches, and photographs. Users can provide these image inputs by uploading photographs they have taken or hand-drawn sketches. The reception desk analyzes user-entered text prompts using natural language processing techniques. Natural language processing techniques include morphological analysis, grammatical analysis, and semantic analysis, enabling accurate understanding of the text's content and intent. The reception desk can also analyze user-entered image inputs using image analysis algorithms. Image analysis algorithms include image recognition, object detection, and feature extraction, enabling detailed analysis of image content and characteristics. For example, specific shapes and patterns can be extracted from a user-uploaded sketch, and a generative AI can then generate a new design based on this. This allows the reception unit to accurately analyze diverse user inputs and provide appropriate data to the generation unit.

[0059] The generation unit uses generative AI to analyze text prompts and image inputs received by the reception unit and generate original designs and artworks. For example, the generation unit utilizes techniques to generate images from text prompts. Specifically, it can generate images from text prompts using a Generative Opposite Network (GAN). A GAN consists of two neural networks: a generator and a discriminator. The generator produces a new image, and the discriminator determines whether the image is real or fake. By repeating this process, the generator improves its ability to generate more realistic images. The generation unit also utilizes techniques to generate new designs from image inputs. For example, it can generate new designs from image inputs using a deep learning model. Deep learning models have the ability to extract image features and generate new designs by learning from large amounts of image data. For example, based on a sketch provided by a user, it can generate a design that retains the style of the sketch while adding new elements. Some or all of the above-described processes in the generation unit are performed using generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and image generation AI. The text generation AI analyzes the user's text prompts and generates instructions to create appropriate images based on their content. The image generation AI then generates new images based on these instructions. This allows the generation unit to produce high-quality designs and artwork based on user input.

[0060] The delivery unit provides users with designs and artwork generated by the generation unit. For example, the delivery unit displays the generated designs and artwork to users through web applications or mobile applications. Specifically, the generated designs and artwork are displayed on the interface of the website or mobile application accessed by the user, allowing the user to view and download them. The delivery unit can also send the generated designs and artwork to users via email. An email containing image files and links to the generated artwork is sent to the email address specified by the user. Some or all of the above processing in the delivery unit may be performed using AI, or not. For example, if AI is used, the generated artwork can be automatically categorized based on the user's past usage history and preferences and provided in the most optimal format. If AI is not used, the generated artwork can be provided to the user as is. This allows the delivery unit to provide users with generated designs and artwork quickly and efficiently. Furthermore, the delivery unit can improve the overall system performance by collecting user feedback and providing it to the generation and reception units. For example, when users rate and comment on the provided artwork, this feedback is used as training data for the generation AI, improving the quality of future generation. This allows the service provider to increase user satisfaction and continuously improve the overall system performance.

[0061] The generation unit can generate images from text prompts. For example, the generation unit takes a text prompt as input and generates an image using a Generative Opposite Network (GAN). The generation unit can also generate abstract designs or concrete works of art based on text prompts. For example, the generation unit can extract keywords contained in a text prompt and generate an image based on them. This allows for the provision of designs based on user text input by generating images from text prompts. Some or all of the above processing in the generation unit is performed using a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or an image generation AI, but is not limited to these examples.

[0062] The generation unit can generate new designs from image input. For example, the generation unit can receive image input and generate new designs using a deep learning model. The generation unit can also generate abstract designs or concrete works of art based on image input. For example, the generation unit can extract features contained in image input and generate new designs based on them. In this way, by generating new designs from image input, it is possible to provide designs based on the user's image input. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, for example, an image generation AI or a deep learning model, but is not limited to such examples.

[0063] The service provider can provide the generated designs and artwork to the user. For example, the service provider can display the generated designs and artwork to the user through a web application or a mobile application. For example, the service provider can also send the generated designs and artwork to the user via email. For example, the service provider can store the generated designs and artwork in cloud storage and make it accessible to the user. This allows the user to verify the generated results by providing them with the generated designs and artwork. Some or all of the above processes in the service provider may be performed using AI or not.

[0064] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the timing of input acceptance to provide time to relax. For example, if the user is excited, the reception unit can accept input immediately to ensure that creative ideas are not missed. For example, if the user is tired, the reception unit can adjust the timing of input acceptance and display a message encouraging them to take a break. This allows for input acceptance tailored to the user's state by adjusting the timing of input acceptance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not.

[0065] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk may prioritize displaying input methods (text, images, etc.) that the user has frequently used in the past. For example, the reception desk may also suggest the optimal input method for a specific time period based on the user's past input history. For example, the reception desk may analyze the user's past input history and automatically select the most efficient input method. In this way, by analyzing the user's past input history, the optimal input method can be provided. Some or all of the above processing in the reception desk may be performed using AI or not.

[0066] The reception unit can filter input based on the user's current projects and areas of interest. For example, the reception unit can prioritize accepting only input related to the user's current project. The reception unit can also filter and display relevant input based on the user's areas of interest. For example, the reception unit can suggest appropriate input based on the progress of the user's project. This allows for the priority acceptance of highly relevant input by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not.

[0067] The reception unit can estimate the user's emotions and determine the priority of input to be received based on the estimated emotions. For example, if the user is stressed, the reception unit may prioritize simple inputs. For example, if the user is relaxed, the reception unit may also prioritize complex inputs. For example, if the user is in a hurry, the reception unit may also prioritize inputs that can be processed quickly. This allows for input reception tailored to the user's state by prioritizing inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not.

[0068] The reception unit can prioritize accepting inputs that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit will prioritize accepting inputs related to that region. The reception unit can also suggest the optimal input method based on the user's current location. The reception unit can also filter relevant inputs, taking into account the user's geographical location information. This allows for the priority acceptance of highly relevant inputs by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI or not.

[0069] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can prioritize accepting inputs related to the user's current interests from their social media activity. The reception unit can also analyze the content of the user's social media posts and suggest relevant inputs. The reception unit can also filter relevant inputs by referring to the activities of the user's social media followers and friends. This allows the reception unit to prioritize accepting relevant inputs by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not.

[0070] The generation unit can estimate the user's emotions and adjust the style of the designs and artwork it generates based on those estimated emotions. For example, if the user is relaxed, the generation unit can generate designs with calm colors. If the user is excited, for example, the generation unit can also generate designs with vibrant colors. If the user is sad, for example, the generation unit can also generate designs with soft tones. This allows for generation tailored to the user's state by adjusting the style of designs and artwork 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. Some or all of the above processing in the generation unit is performed using a generation AI.

[0071] The generation unit can adjust the level of detail of the generated output based on the importance of the input text prompts and image inputs. For example, the generation unit can generate a detailed design based on a high-importance text prompt. It can also generate a simple design based on a low-importance image input. For example, the generation unit can compare the importance of text prompts and image inputs and generate a design with the optimal balance. This allows for the generation of optimal designs and artworks by adjusting the level of detail of the output based on the importance of the inputs. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or an image generation AI.

[0072] The generation unit can apply different generation algorithms depending on the input category during generation. For example, it can apply a natural language processing algorithm to text prompts. For example, it can apply an image generation algorithm to image inputs. For example, it can apply a hybrid generation algorithm to inputs containing both text and images. This allows for the generation of optimal designs and artworks by applying different generation algorithms depending on the input category. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM), image generation AI, or hybrid generation AI.

[0073] The generation unit can estimate the user's emotions and adjust the colors of the designs and artworks it generates based on those estimated emotions. For example, if the user is relaxed, the generation unit can generate designs with calm colors. If the user is excited, for example, the generation unit can also generate designs with vibrant colors. If the user is sad, for example, the generation unit can also generate designs with soft tones. This allows for generation tailored to the user's state by adjusting the colors of designs and artworks 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. Some or all of the above-described processes in the generation unit are performed using a generation AI.

[0074] The generation unit can determine the generation priority based on the submission date of the inputs during generation. For example, the generation unit can prioritize generating inputs with approaching deadlines. For example, the generation unit can postpone generating inputs with distant submission dates. For example, the generation unit can also adjust the level of detail of the generation according to the submission date. This enables efficient generation by determining the generation priority based on the input submission date. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or image generation AI.

[0075] The generation unit can adjust the generation order based on the relevance of the inputs during generation. For example, the generation unit can prioritize generating inputs with high relevance. For example, the generation unit can postpone generating inputs with low relevance. For example, the generation unit can also adjust the level of detail of generation according to relevance. This allows for efficient generation by adjusting the generation order based on the relevance of the inputs. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or image generation AI.

[0076] The service provider can estimate the user's emotions and adjust how designs and artworks are displayed based on the estimated emotions. For example, if the user is relaxed, the service provider can display the design against a background of calming colors. If the user is excited, the service provider can also display the design against a background of vibrant colors. If the user is sad, the service provider can also display the design against a background of soft tones. This allows for a service tailored to the user's state by adjusting the display method according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not.

[0077] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider may prioritize selecting service delivery methods that the user has preferred to use in the past. For example, the service provider may also suggest the optimal service delivery method based on the user's past usage history. For example, the service provider may analyze the user's past usage history and automatically select the most efficient service delivery method. This allows the service provider to deliver the optimal service delivery method by referring to the user's past usage history. Some or all of the above-described processes in the service provider may be performed using AI or not.

[0078] The service provider can customize the content offered based on the user's current projects and areas of interest at the time of delivery. For example, the service provider may prioritize providing designs related to the project the user is currently working on. For example, the service provider may also customize and provide relevant designs based on the user's areas of interest. For example, the service provider may suggest appropriate designs according to the progress of the user's project. This allows for highly relevant deliveries by customizing the content based on the user's current projects and areas of interest. Some or all of the above processing in the service provider may be performed using AI or not.

[0079] The service provider can estimate the user's emotions and determine the priority of designs and artworks to offer based on those estimated emotions. For example, if the user is relaxed, the service provider may prioritize calming designs. If the user is excited, the service provider may prioritize vibrant designs. If the user is sad, the service provider may prioritize soft designs. This allows for services tailored to the user's state by prioritizing designs and artworks according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI or not.

[0080] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the delivery unit will prioritize providing designs related to that region. The delivery unit can also suggest the optimal delivery method based on the user's current location. The delivery unit can also customize and provide relevant designs, taking into account the user's geographical location information. This allows for the provision of the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0081] The service provider can analyze the user's social media activity and customize the content offered at the time of delivery. For example, the service provider can prioritize providing designs related to the user's current interests based on their social media activity. The service provider can also analyze the user's social media posts and suggest relevant designs. The service provider can also customize relevant designs by referencing the activities of the user's social media followers and friends. In this way, the service provider can customize relevant content by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not.

[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 reception desk can analyze the user's past input history when receiving user input and suggest the most suitable input method. For example, it can prioritize displaying input methods (text, images, etc.) that the user has frequently used in the past. It can also suggest the most suitable input method for a specific time period based on the user's past input history. Furthermore, it can analyze the user's past input history and automatically select the most efficient input method. In this way, by analyzing the user's past input history, the system can provide the most optimal input reception method.

[0084] The generation unit can estimate the user's emotions and adjust the style of the generated designs and artwork based on those emotions. For example, if the user is relaxed, it can generate designs with calming colors. If the user is excited, it can generate designs with vibrant colors. Furthermore, if the user is sad, it can generate designs with soft tones. This allows for the generation of designs and artwork tailored to the user's state by adjusting the style of the artwork according to the user's emotions.

[0085] The service provider can prioritize providing highly relevant designs and artwork to users by considering the user's geographical location. For example, if a user is in a specific region, designs related to that region can be prioritized. The service can also suggest the optimal delivery method based on the user's current location. Furthermore, it can customize and deliver relevant designs by considering the user's geographical location. This allows for the provision of the most optimal delivery method by taking the user's geographical location into account.

[0086] The reception system can estimate the user's emotions and determine the priority of input to be received based on those emotions. For example, if the user is stressed, simple input can be prioritized. Conversely, if the user is relaxed, complex input can be prioritized. Furthermore, if the user is in a hurry, input that can be processed quickly can be prioritized. In this way, by determining the priority of input according to the user's emotions, it becomes possible to receive input in a way that is tailored to the user's state.

[0087] The generation unit can apply different generation algorithms depending on the input category during generation. For example, a natural language processing algorithm can be applied to text prompts. An image generation algorithm can also be applied to image inputs. Furthermore, a hybrid generation algorithm can be applied to inputs containing both text and images. This allows for the generation of optimal designs and artworks by applying different generation algorithms depending on the input category.

[0088] The service provider can estimate the user's emotions and adjust how designs and artworks are displayed based on those estimated emotions. For example, if the user is relaxed, the design can be displayed against a background of calming colors. If the user is excited, the design can be displayed against a background of vibrant colors. Furthermore, if the user is sad, the design can be displayed against a background of soft tones. By adjusting the display method according to the user's emotions, the service can be tailored to the user's state.

[0089] The input receiving system can filter input based on the user's current projects and areas of interest. For example, it can prioritize receiving only input related to the user's current project. It can also filter and display relevant input based on the user's areas of interest. Furthermore, it can suggest appropriate input based on the user's project progress. This allows for the priority of receiving highly relevant input by filtering based on the user's current projects and areas of interest.

[0090] The generation unit can estimate the user's emotions and adjust the colors of the generated designs and artwork based on those estimated emotions. For example, if the user is relaxed, it can generate designs with calming colors. If the user is excited, it can generate designs with vibrant colors. Furthermore, if the user is sad, it can generate designs with soft tones. By adjusting the colors of designs and artwork according to the user's emotions, it becomes possible to generate content that matches the user's state.

[0091] The service provider can analyze the user's social media activity and customize the content offered at the time of delivery. For example, it can prioritize providing designs related to the user's current interests based on their social media activity. It can also analyze the user's social media posts and suggest relevant designs. Furthermore, it can customize relevant designs by referencing the activities of the user's social media followers and friends. In this way, the service provider can customize the content offered by analyzing the user's social media activity.

[0092] The service provider can estimate the user's emotions and prioritize the designs and artwork offered based on those emotions. For example, if the user is relaxed, calming designs can be prioritized. If the user is excited, vibrant designs can be prioritized. Furthermore, if the user is sad, soft designs can be prioritized. This allows for personalized service tailored to the user's state by prioritizing designs and artwork according to their emotions.

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

[0094] Step 1: The reception unit receives text prompts and image inputs from the user. Text prompts from the user can be in question form, command form, free form, etc., and image inputs can be image files, sketches, photographs, etc. The reception unit can analyze text prompts using natural language processing technology and image inputs using image analysis algorithms. Step 2: The generation unit analyzes the text prompts and image inputs received by the reception unit and generates original designs and artworks. The generation unit utilizes technologies such as generating images from text prompts using generative AI, and generating images from text prompts using generative opponent networks (GANs). In addition, the generation unit utilizes technologies such as generating new designs from image inputs using deep learning models. Step 3: The provider delivers the designs and artwork generated by the generator to the user. The provider displays the generated designs and artwork to the user through web applications and mobile applications. The provider can also send the generated designs and artwork to the user via email.

[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 reception unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives text prompts and image inputs from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates unique designs and artworks using generation AI. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated designs and artworks to the user. The correspondence between each unit and the devices and control units 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 reception unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives text prompts and image inputs from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates unique designs and artworks using generation AI. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated designs and artworks to the user. The correspondence between each unit and the device or control unit is not limited to the example 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 reception unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives text prompts and image inputs from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates unique designs and artworks using generation AI. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the generated designs and artworks to the user. The correspondence between each unit and the device or control unit is not limited to the example 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 reception unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives text prompts and image inputs from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates unique designs and artworks using generation AI. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated designs and artworks to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[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 reception unit that accepts text prompts and image inputs from the user, A generation unit analyzes text prompts and image inputs received by the reception unit and generates original designs and artworks. The system comprises a providing unit that provides designs and artworks generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generate images from text prompts The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generating new designs from image input The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provides users with generated designs and artwork. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving input, filtering is performed 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 reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs 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 reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. 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 designs and artwork generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, the level of detail of the generated output is adjusted based on the importance of the entered text prompts and image inputs. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, different generation algorithms are applied depending on the input category. 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 adjusts the colors of the designs and artworks generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the generation priority is determined based on when the input was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the generation order is adjusted based on the relevance of the inputs. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts how designs and artwork are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, the content will be customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the designs and artwork to be offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity to customize the content offered. 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 reception unit that accepts text prompts and image inputs from the user, A generation unit analyzes text prompts and image inputs received by the reception unit and generates original designs and artworks. The system comprises a providing unit that provides designs and artworks generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generate images from text prompts The system according to feature 1.

3. The generating unit is Generating new designs from image input The system according to feature 1.

4. The aforementioned supply unit is, Provides users with generated designs and artwork. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When receiving input, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. The system according to feature 1.

Images