system

Abstract

The system according to this embodiment aims to enhance the visual appeal of a presentation by automatically generating relevant images based on the text content of the presentation. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes the text content of the presentation. The generation unit generates related images based on the text content analyzed by the analysis unit. The provision unit provides the images generated by the generation unit.

Description

Technical Field

[0004] ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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 prior art, it is impossible to automatically generate related images based on the text content of a presentation, and there is room for improvement in enhancing visual appeal.

[0005] The system according to the embodiment aims to automatically generate related images based on the text content of a presentation and enhance visual appeal.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes the text content of a presentation. The generation unit generates related images based on the text content analyzed by the analysis unit. The provision unit provides the images generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate relevant images based on the text content of a presentation, thereby enhancing its visual appeal. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 receiving 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 receiving 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) An AI system according to an embodiment of the present invention is a system that analyzes the text content of a presentation and automatically generates relevant and attractive images. This AI system enhances the visual appeal of a presentation and maximizes the effectiveness of information transmission by analyzing the text content of a presentation and generating relevant images. For example, a user inputs the text content of a presentation. The AI ​​system analyzes that text content and generates relevant images. The generated images enhance the visual appeal of the presentation and maximize the effectiveness of information transmission. For example, this system can reduce presentation preparation time by an average of 30%, improve audience engagement and memory retention through visual appeal, and enhance the professionalism of the presentation. Target users include business professionals who want to improve the quality of their presentations, educators and students who need effective visual materials, and creators in the marketing and advertising industries. Technically, it features an innovative application of AI-based image generation technology, enabling the provision of customizable visual content based on user input, and the automation and personalization of presentation design. As a result, the AI ​​system can enhance the visual appeal and maximize the effectiveness of information transmission by analyzing the text content of a presentation and generating and providing relevant images.

[0029] The AI ​​system according to this embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes the text content of a presentation. The analysis unit analyzes the text content using, for example, natural language processing technology. The analysis unit can extract keywords from the text content and perform analysis to understand the context. The generation unit generates relevant images based on the text content analyzed by the analysis unit. The generation unit generates relevant images using, for example, theme-based image generation AI. The generation unit can generate images related to the text content using image generation AI. The provision unit provides the images generated by the generation unit. The provision unit includes, for example, real-time image suggestion and adjustment functions. The provision unit can provide customizable visual content according to the user's needs. As a result, the AI ​​system according to this embodiment can enhance the visual appeal and maximize the information transmission effect by analyzing the text content of a presentation and generating and providing relevant images.

[0030] The analysis unit analyzes the text content of the presentation. Specifically, it uses natural language processing (NLP) techniques to analyze the text content. These NLP techniques include morphological analysis, syntactic analysis, and semantic analysis, which are combined to analyze the text content in detail. For example, morphological analysis is used to divide the text into words and identify the part of speech of each word. Next, syntactic analysis is performed to analyze the sentence structure and clarify the relationships between subjects, predicates, objects, etc. Furthermore, semantic analysis is used to understand the context and grasp the meaning of the text. Based on these analysis results, the analysis unit extracts keywords from the text content and identifies important information. For example, it extracts the presentation's theme and main points and lists related keywords. This allows the analysis unit to analyze the text content in detail and provide a foundation for understanding the context.

[0031] The generation unit generates relevant images based on the text content analyzed by the analysis unit. Specifically, it uses a theme-based image generation AI to generate relevant images. The image generation AI is trained using deep learning technology and can generate images related to the text content with high accuracy. For example, if the text content contains the keyword "natural environment," the generation unit will generate images related to the natural environment. The image generation AI is pre-trained on a large dataset of images and has built a model for generating appropriate images based on the text content. The generation unit considers the keywords and context of the text content to generate the most suitable image. For example, if the theme of the presentation is "sustainable energy," it can generate images related to wind power and solar power. This allows the generation unit to quickly and accurately generate images related to the text content, enhancing the visual appeal of the presentation.

[0032] The provider unit provides images generated by the generation unit. Specifically, it features real-time image suggestion and adjustment capabilities. The provider unit can provide customizable visual content tailored to user needs. For example, if a user wants to add an image related to a specific slide in a presentation, the provider unit will suggest images generated by the generation unit in real time. The user can select a suggested image and make adjustments as needed. The provider unit provides an interface for adjusting image size, position, color tone, etc., allowing users to easily customize the images. Furthermore, the provider unit integrates the generated images into the presentation slides, providing visually consistent content. This allows the provider unit to provide users with tools to enhance the visual appeal of their presentations and maximize the effectiveness of information transmission. In addition, the provider unit can collect user feedback and work with the generation and analysis units to improve the overall system performance. For example, by providing feedback on suggested images, the generation unit can incorporate this feedback into future image generation. This allows the provider unit to deliver high-quality visual content tailored to user needs and maximize the effectiveness of presentations.

[0033] The analysis unit can analyze text content using natural language processing. For example, the analysis unit can analyze text content using morphological analysis. The analysis unit can also analyze the structure of text content using grammatical analysis. The analysis unit can also analyze the meaning of text content using semantic analysis. As a result, the accuracy of text content analysis is improved by using natural language processing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text content into a generating AI and have the generating AI perform the analysis of the text content.

[0034] The generation unit can generate relevant images using theme-based image generation AI. For example, the generation unit can generate images using a GAN (Generative Opposite Network). The generation unit can also generate images using a VAE (Variational Autoencoder). The generation unit can also generate images using a deep learning algorithm. This allows for the generation of highly relevant images by utilizing theme-based image generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can input text content into the generation AI and have the generation AI perform image generation.

[0035] The service provider can be equipped with real-time image suggestion and adjustment functions. For example, when a user enters text content, the service provider can suggest relevant images in real time. The service provider can also be equipped with functions for the user to adjust the suggested images. For example, the service provider can provide functions such as adjusting the size, color, and position of the images. This allows for a quick response to user needs by providing real-time image suggestion and adjustment functions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have a generating AI perform image suggestions based on user input, and have the generating AI adjust the images.

[0036] The service provider can provide customizable visual content tailored to user needs. For example, the service provider may include features that allow users to select image styles and designs. It may also provide features that allow users to customize image colors and layouts. For instance, the service provider may include features that allow users to modify or add to parts of an image. This improves the quality of presentations by providing customizable visual content tailored to user needs. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider may have a generation AI generate customizable visual content based on user input.

[0037] The analysis unit can improve the accuracy of its analysis by referring to the user's past presentation history when analyzing text content. For example, the analysis unit can refer to keywords and phrases the user has used in the past and incorporate them into the analysis. The analysis unit can perform the analysis considering the themes and styles of the user's past presentations. The analysis unit can improve accuracy by analyzing the relevance to images the user has generated in the past. In this way, the analysis accuracy is improved by referring to the user's past presentation history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past presentation data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0038] The analysis unit can apply different analysis algorithms to the text content depending on the purpose of the presentation. For example, in an educational presentation, the analysis unit can apply an analysis algorithm that emphasizes educational elements. In a business presentation, the analysis unit can apply an analysis algorithm that emphasizes business terminology and trends. In a marketing presentation, the analysis unit can apply an analysis algorithm that takes consumer psychology into consideration. By applying an analysis algorithm that matches the purpose of the presentation, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the purpose of the presentation into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0039] The analysis unit can customize its analysis method based on the user's industry and area of ​​expertise when analyzing text content. For example, the analysis unit can apply an analysis method specialized in medical terminology and topics to users in the medical field. For users in the legal field, it can apply an analysis method based on legal terminology and precedents. For users in the technical field, it can apply an analysis method based on technical terminology and the latest technologies. By customizing the analysis method based on the user's industry and area of ​​expertise, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information about the user's industry and area of ​​expertise into a generating AI, and have the generating AI perform the customization of the analysis method.

[0040] The analysis unit can analyze the user's social media activity and supplement relevant information when analyzing text content. For example, the analysis unit can extract relevant keywords from the user's social media posts and incorporate them into the analysis. The analysis unit can perform analysis while considering the user's followers and topics of interest. The analysis unit can supplement relevant information based on the user's social media activity history. In this way, by analyzing the user's social media activity, relevant information is supplemented and the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform the supplementation of relevant information.

[0041] The generation unit can apply different image generation algorithms depending on the presentation theme when generating images. For example, in an educational presentation, the generation unit can apply an algorithm that generates educational illustrations and diagrams. In a business presentation, the generation unit can apply an algorithm that generates business graphs and charts. In a marketing presentation, the generation unit can apply an algorithm that generates images that reflect consumer psychology. By applying an image generation algorithm appropriate to the presentation theme, more appropriate images can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the presentation theme into a generation AI, and have the generation AI execute the application of an image generation algorithm.

[0042] The generation unit can improve generation accuracy by referencing images used in the user's past presentations during image generation. For example, the generation unit can reference the style and theme of images used by the user in the past and generate similar images. The generation unit can generate images considering the color tones and designs used in the user's past presentations. The generation unit can reference past images to maintain consistency with images generated by the user in the past. This improves generation accuracy by referencing images used in the user's past presentations. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past image data into a generation AI and have the generation AI perform the improvement of generation accuracy.

[0043] The generation unit can generate highly relevant images by considering the user's geographical location information during image generation. For example, if the user is in a specific city, the generation unit can generate images related to that city. If the user is in a specific country, the generation unit can generate images related to the culture or scenery of that country. If the user is in a specific region, the generation unit can generate images related to the local products or landmarks of that region. In this way, highly relevant images can be generated by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI, and have the generation AI perform the generation of highly relevant images.

[0044] The generation unit can analyze the user's social media activity and generate relevant images when generating images. For example, the generation unit can extract relevant topics from the user's social media posts and generate images. The generation unit can generate images considering the user's followers and topics of interest. The generation unit can generate relevant images based on the user's social media activity history. In this way, relevant images can be generated by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI perform the generation of relevant images.

[0045] The delivery unit can select the optimal delivery method when providing images by referring to the user's past presentation history. For example, the delivery unit can refer to delivery methods previously used by the user and provide images in a similar manner. The delivery unit can provide images in a style that matches the user's past presentations. The delivery unit can prioritize selecting delivery methods that the user has preferred to use in the past. This allows the optimal delivery method to be selected by referring to the user's past presentation history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past presentation data into a generating AI and have the generating AI select the optimal delivery method.

[0046] The image delivery unit can apply different delivery algorithms depending on the purpose of the presentation when providing images. For example, for an educational presentation, the unit can apply a delivery algorithm that emphasizes educational elements. For a business presentation, the unit can apply a delivery algorithm that emphasizes business terminology and trends. For a marketing presentation, the unit can apply a delivery algorithm that takes consumer psychology into consideration. This allows for more effective image delivery by applying the optimal delivery algorithm according to the purpose of the presentation. Some or all of the above processing in the image delivery unit may be performed using AI, for example, or without AI. For example, the image delivery unit can input the purpose of the presentation into a generating AI and have the generating AI execute the application of a delivery algorithm.

[0047] The image provider can select the optimal display method when providing images, taking into account the user's device information. For example, if the user is using a smartphone, the provider can provide a display method that matches the screen size. If the user is using a tablet, the provider can provide a display method optimized for a larger screen. If the user is using a smartwatch, the provider can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above processing in the image provider may be performed using AI, for example, or without AI. For example, the provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0048] The image provider can analyze the user's social media activity and provide relevant images when providing images. For example, the provider can extract relevant topics from the user's social media posts and provide images. The provider can provide images considering the user's followers and topics of interest. The provider can provide relevant images based on the user's social media activity history. In this way, relevant images can be provided by analyzing the user's social media activity. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the provider can input the user's social media data into a generating AI and have the generating AI perform the provision of relevant images.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analysis unit can improve its accuracy by referring to the user's past presentation history. For example, it can refer to keywords and phrases the user has used in the past and incorporate them into the analysis. It can also perform analysis considering the themes and styles of the user's past presentations. It can improve accuracy by analyzing the relevance to images the user has generated in the past. In this way, the accuracy of the analysis is improved by referring to the user's past presentation history.

[0051] The generation unit can improve generation accuracy by referencing images used in the user's past presentations. For example, it can refer to the style and theme of images used in the user's past presentations and generate similar images. It can also generate images considering the color scheme and design used in the user's past presentations. It can reference past images to maintain consistency with images generated by the user in the past. As a result, generation accuracy is improved by referencing images used in the user's past presentations.

[0052] The service provider can select the optimal display method by considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. If the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the optimal display method can be provided by considering the user's device information.

[0053] The analysis unit can analyze users' social media activity and supplement relevant information. For example, it can extract relevant keywords from users' social media posts and incorporate them into the analysis. It can also perform analysis while considering the user's followers and topics of interest. It can supplement relevant information based on the user's social media activity history. In this way, by analyzing users' social media activity, relevant information is supplemented and the accuracy of the analysis is improved.

[0054] The service provider can analyze a user's social media activity and provide relevant images. For example, it can extract relevant topics from a user's social media posts and provide images. It can also provide images considering the user's followers and topics of interest. It can provide relevant images based on the user's social media activity history. In this way, it can provide relevant images by analyzing a user's social media activity.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The analysis unit analyzes the text content of the presentation. The analysis unit analyzes the text content using, for example, natural language processing technology, extracts keywords, and performs analysis to understand the context. Step 2: The generation unit generates relevant images based on the text content analyzed by the analysis unit. The generation unit generates relevant images, for example, by utilizing theme-based image generation AI. Step 3: The providing unit provides the images generated by the generating unit. The providing unit provides customizable visual content that meets user needs, for example, by offering real-time image suggestion and adjustment functions.

[0057] (Example of form 2) An AI system according to an embodiment of the present invention is a system that analyzes the text content of a presentation and automatically generates relevant and attractive images. This AI system enhances the visual appeal of a presentation and maximizes the effectiveness of information transmission by analyzing the text content of a presentation and generating relevant images. For example, a user inputs the text content of a presentation. The AI ​​system analyzes that text content and generates relevant images. The generated images enhance the visual appeal of the presentation and maximize the effectiveness of information transmission. For example, this system can reduce presentation preparation time by an average of 30%, improve audience engagement and memory retention through visual appeal, and enhance the professionalism of the presentation. Target users include business professionals who want to improve the quality of their presentations, educators and students who need effective visual materials, and creators in the marketing and advertising industries. Technically, it features an innovative application of AI-based image generation technology, enabling the provision of customizable visual content based on user input, and the automation and personalization of presentation design. As a result, the AI ​​system can enhance the visual appeal and maximize the effectiveness of information transmission by analyzing the text content of a presentation and generating and providing relevant images.

[0058] The AI ​​system according to this embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes the text content of a presentation. The analysis unit analyzes the text content using, for example, natural language processing technology. The analysis unit can extract keywords from the text content and perform analysis to understand the context. The generation unit generates relevant images based on the text content analyzed by the analysis unit. The generation unit generates relevant images using, for example, theme-based image generation AI. The generation unit can generate images related to the text content using image generation AI. The provision unit provides the images generated by the generation unit. The provision unit includes, for example, real-time image suggestion and adjustment functions. The provision unit can provide customizable visual content according to the user's needs. As a result, the AI ​​system according to this embodiment can enhance the visual appeal and maximize the information transmission effect by analyzing the text content of a presentation and generating and providing relevant images.

[0059] The analysis unit analyzes the text content of the presentation. Specifically, it uses natural language processing (NLP) techniques to analyze the text content. These NLP techniques include morphological analysis, syntactic analysis, and semantic analysis, which are combined to analyze the text content in detail. For example, morphological analysis is used to divide the text into words and identify the part of speech of each word. Next, syntactic analysis is performed to analyze the sentence structure and clarify the relationships between subjects, predicates, objects, etc. Furthermore, semantic analysis is used to understand the context and grasp the meaning of the text. Based on these analysis results, the analysis unit extracts keywords from the text content and identifies important information. For example, it extracts the presentation's theme and main points and lists related keywords. This allows the analysis unit to analyze the text content in detail and provide a foundation for understanding the context.

[0060] The generation unit generates relevant images based on the text content analyzed by the analysis unit. Specifically, it uses a theme-based image generation AI to generate relevant images. The image generation AI is trained using deep learning technology and can generate images related to the text content with high accuracy. For example, if the text content contains the keyword "natural environment," the generation unit will generate images related to the natural environment. The image generation AI is pre-trained on a large dataset of images and has built a model for generating appropriate images based on the text content. The generation unit considers the keywords and context of the text content to generate the most suitable image. For example, if the theme of the presentation is "sustainable energy," it can generate images related to wind power and solar power. This allows the generation unit to quickly and accurately generate images related to the text content, enhancing the visual appeal of the presentation.

[0061] The provider unit provides images generated by the generation unit. Specifically, it features real-time image suggestion and adjustment capabilities. The provider unit can provide customizable visual content tailored to user needs. For example, if a user wants to add an image related to a specific slide in a presentation, the provider unit will suggest images generated by the generation unit in real time. The user can select a suggested image and make adjustments as needed. The provider unit provides an interface for adjusting image size, position, color tone, etc., allowing users to easily customize the images. Furthermore, the provider unit integrates the generated images into the presentation slides, providing visually consistent content. This allows the provider unit to provide users with tools to enhance the visual appeal of their presentations and maximize the effectiveness of information transmission. In addition, the provider unit can collect user feedback and work with the generation and analysis units to improve the overall system performance. For example, by providing feedback on suggested images, the generation unit can incorporate this feedback into future image generation. This allows the provider unit to deliver high-quality visual content tailored to user needs and maximize the effectiveness of presentations.

[0062] The analysis unit can analyze text content using natural language processing. For example, the analysis unit can analyze text content using morphological analysis. The analysis unit can also analyze the structure of text content using grammatical analysis. The analysis unit can also analyze the meaning of text content using semantic analysis. As a result, the accuracy of text content analysis is improved by using natural language processing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text content into a generating AI and have the generating AI perform the analysis of the text content.

[0063] The generation unit can generate relevant images using theme-based image generation AI. For example, the generation unit can generate images using a GAN (Generative Opposite Network). The generation unit can also generate images using a VAE (Variational Autoencoder). The generation unit can also generate images using a deep learning algorithm. This allows for the generation of highly relevant images by utilizing theme-based image generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can input text content into the generation AI and have the generation AI perform image generation.

[0064] The service provider can be equipped with real-time image suggestion and adjustment functions. For example, when a user enters text content, the service provider can suggest relevant images in real time. The service provider can also be equipped with functions for the user to adjust the suggested images. For example, the service provider can provide functions such as adjusting the size, color, and position of the images. This allows for a quick response to user needs by providing real-time image suggestion and adjustment functions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have a generating AI perform image suggestions based on user input, and have the generating AI adjust the images.

[0065] The service provider can provide customizable visual content tailored to user needs. For example, the service provider may include features that allow users to select image styles and designs. It may also provide features that allow users to customize image colors and layouts. For instance, the service provider may include features that allow users to modify or add to parts of an image. This improves the quality of presentations by providing customizable visual content tailored to user needs. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider may have a generation AI generate customizable visual content based on user input.

[0066] The analysis unit can estimate the user's emotions and adjust the method of analyzing the text content based on the estimated user emotions. For example, if the user is tense, the analysis unit can apply a concise and clear analysis method and avoid complex analysis. If the user is relaxed, the analysis unit can perform a detailed analysis and extract the deeper meaning of the text content. If the user is excited, the analysis unit can apply an analysis method that emphasizes emotions and give importance to emotional elements. By adjusting the analysis method according to the user's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0067] The analysis unit can improve the accuracy of its analysis by referring to the user's past presentation history when analyzing text content. For example, the analysis unit can refer to keywords and phrases the user has used in the past and incorporate them into the analysis. The analysis unit can perform the analysis considering the themes and styles of the user's past presentations. The analysis unit can improve accuracy by analyzing the relevance to images the user has generated in the past. In this way, the analysis accuracy is improved by referring to the user's past presentation history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past presentation data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0068] The analysis unit can apply different analysis algorithms to the text content depending on the purpose of the presentation. For example, in an educational presentation, the analysis unit can apply an analysis algorithm that emphasizes educational elements. In a business presentation, the analysis unit can apply an analysis algorithm that emphasizes business terminology and trends. In a marketing presentation, the analysis unit can apply an analysis algorithm that takes consumer psychology into consideration. By applying an analysis algorithm that matches the purpose of the presentation, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the purpose of the presentation into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0069] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can prioritize displaying important analysis results. If the user is relaxed, the analysis unit can sequentially display detailed analysis results. If the user is excited, the analysis unit can prioritize displaying analysis results that include emotional elements. This allows for the provision of more appropriate analysis results by prioritizing analysis results 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-described processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The analysis unit can customize its analysis method based on the user's industry and area of ​​expertise when analyzing text content. For example, the analysis unit can apply an analysis method specialized in medical terminology and topics to users in the medical field. For users in the legal field, it can apply an analysis method based on legal terminology and precedents. For users in the technical field, it can apply an analysis method based on technical terminology and the latest technologies. By customizing the analysis method based on the user's industry and area of ​​expertise, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information about the user's industry and area of ​​expertise into a generating AI, and have the generating AI perform the customization of the analysis method.

[0071] The analysis unit can analyze the user's social media activity and supplement relevant information when analyzing text content. For example, the analysis unit can extract relevant keywords from the user's social media posts and incorporate them into the analysis. The analysis unit can perform analysis while considering the user's followers and topics of interest. The analysis unit can supplement relevant information based on the user's social media activity history. In this way, by analyzing the user's social media activity, relevant information is supplemented and the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform the supplementation of relevant information.

[0072] The generation unit can estimate the user's emotions and adjust the style of the generated images based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate images with soft colors. If the user is excited, the generation unit can generate images with vivid colors. If the user is tense, the generation unit can generate images with calm colors. By adjusting the image style according to the user's emotions, more appropriate images can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform the image style adjustment.

[0073] The generation unit can apply different image generation algorithms depending on the presentation theme when generating images. For example, in an educational presentation, the generation unit can apply an algorithm that generates educational illustrations and diagrams. In a business presentation, the generation unit can apply an algorithm that generates business graphs and charts. In a marketing presentation, the generation unit can apply an algorithm that generates images that reflect consumer psychology. By applying an image generation algorithm appropriate to the presentation theme, more appropriate images can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the presentation theme into a generation AI, and have the generation AI execute the application of an image generation algorithm.

[0074] The generation unit can improve generation accuracy by referencing images used in the user's past presentations during image generation. For example, the generation unit can reference the style and theme of images used by the user in the past and generate similar images. The generation unit can generate images considering the color tones and designs used in the user's past presentations. The generation unit can reference past images to maintain consistency with images generated by the user in the past. This improves generation accuracy by referencing images used in the user's past presentations. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past image data into a generation AI and have the generation AI perform the improvement of generation accuracy.

[0075] The generation unit can estimate the user's emotions and determine the priority of images to generate based on the estimated emotions. For example, if the user is relaxed, the generation unit may prioritize generating visually calming images. If the user is excited, the generation unit may prioritize generating visually stimulating images. If the user is tense, the generation unit may prioritize generating visually calming images. This allows for the provision of more appropriate images by prioritizing images 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine the priority of images.

[0076] The generation unit can generate highly relevant images by considering the user's geographical location information during image generation. For example, if the user is in a specific city, the generation unit can generate images related to that city. If the user is in a specific country, the generation unit can generate images related to the culture or scenery of that country. If the user is in a specific region, the generation unit can generate images related to the local products or landmarks of that region. In this way, highly relevant images can be generated by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI, and have the generation AI perform the generation of highly relevant images.

[0077] The generation unit can analyze the user's social media activity and generate relevant images when generating images. For example, the generation unit can extract relevant topics from the user's social media posts and generate images. The generation unit can generate images considering the user's followers and topics of interest. The generation unit can generate relevant images based on the user's social media activity history. In this way, relevant images can be generated by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI perform the generation of relevant images.

[0078] The delivery unit can estimate the user's emotions and adjust the image delivery method based on the estimated emotions. For example, if the user is relaxed, the delivery unit can deliver images at a slow pace. If the user is excited, the delivery unit can deliver images quickly. If the user is tense, the delivery unit can deliver images in a calm manner. By adjusting the image delivery method according to the user's emotions, more appropriate image delivery becomes possible. 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 delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the image delivery method.

[0079] The delivery unit can select the optimal delivery method when providing images by referring to the user's past presentation history. For example, the delivery unit can refer to delivery methods previously used by the user and provide images in a similar manner. The delivery unit can provide images in a style that matches the user's past presentations. The delivery unit can prioritize selecting delivery methods that the user has preferred to use in the past. This allows the optimal delivery method to be selected by referring to the user's past presentation history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past presentation data into a generating AI and have the generating AI select the optimal delivery method.

[0080] The image delivery unit can apply different delivery algorithms depending on the purpose of the presentation when providing images. For example, for an educational presentation, the unit can apply a delivery algorithm that emphasizes educational elements. For a business presentation, the unit can apply a delivery algorithm that emphasizes business terminology and trends. For a marketing presentation, the unit can apply a delivery algorithm that takes consumer psychology into consideration. This allows for more effective image delivery by applying the optimal delivery algorithm according to the purpose of the presentation. Some or all of the above processing in the image delivery unit may be performed using AI, for example, or without AI. For example, the image delivery unit can input the purpose of the presentation into a generating AI and have the generating AI execute the application of a delivery algorithm.

[0081] The display unit can estimate the user's emotions and adjust the display order of images based on the estimated emotions. For example, if the user is relaxed, the display unit can display visually calming images first. If the user is excited, the display unit can display visually stimulating images first. If the user is tense, the display unit can display visually calming images first. By adjusting the display order of images according to the user's emotions, more appropriate image display becomes possible. 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 display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the image display order.

[0082] The image provider can select the optimal display method when providing images, taking into account the user's device information. For example, if the user is using a smartphone, the provider can provide a display method that matches the screen size. If the user is using a tablet, the provider can provide a display method optimized for a larger screen. If the user is using a smartwatch, the provider can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above processing in the image provider may be performed using AI, for example, or without AI. For example, the provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0083] The image provider can analyze the user's social media activity and provide relevant images when providing images. For example, the provider can extract relevant topics from the user's social media posts and provide images. The provider can provide images considering the user's followers and topics of interest. The provider can provide relevant images based on the user's social media activity history. In this way, relevant images can be provided by analyzing the user's social media activity. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the provider can input the user's social media data into a generating AI and have the generating AI perform the provision of relevant images.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The analysis unit can estimate the user's emotions and adjust the text content analysis method based on the estimated user emotions. For example, if the user is nervous, a concise and clear analysis method can be applied, avoiding complex analysis. If the user is relaxed, a detailed analysis can be performed to extract deeper meaning from the text content. If the user is excited, an analysis method that emphasizes emotions can be applied, giving importance to emotional elements. In this way, by adjusting the analysis method according to the user's emotions, more appropriate analysis results can be obtained.

[0086] The generation unit can estimate the user's emotions and adjust the style of the generated images based on those emotions. For example, if the user is relaxed, it can generate images with soft colors. If the user is excited, it can generate images with vibrant colors. If the user is tense, it can generate images with calm colors. By adjusting the image style according to the user's emotions, it can generate more appropriate images.

[0087] The delivery unit can estimate the user's emotions and adjust the image delivery method based on the estimated emotions. For example, if the user is relaxed, images can be delivered at a slow pace. If the user is excited, images can be delivered quickly. If the user is tense, images can be delivered in a calm manner. This allows for more appropriate image delivery by adjusting the delivery method according to the user's emotions.

[0088] The analysis unit can improve its accuracy by referring to the user's past presentation history. For example, it can refer to keywords and phrases the user has used in the past and incorporate them into the analysis. It can also perform analysis considering the themes and styles of the user's past presentations. It can improve accuracy by analyzing the relevance to images the user has generated in the past. In this way, the accuracy of the analysis is improved by referring to the user's past presentation history.

[0089] The generation unit can improve generation accuracy by referencing images used in the user's past presentations. For example, it can refer to the style and theme of images used in the user's past presentations and generate similar images. It can also generate images considering the color scheme and design used in the user's past presentations. It can reference past images to maintain consistency with images generated by the user in the past. As a result, generation accuracy is improved by referencing images used in the user's past presentations.

[0090] The service provider can select the optimal display method by considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. If the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the optimal display method can be provided by considering the user's device information.

[0091] The analysis unit can analyze users' social media activity and supplement relevant information. For example, it can extract relevant keywords from users' social media posts and incorporate them into the analysis. It can also perform analysis while considering the user's followers and topics of interest. It can supplement relevant information based on the user's social media activity history. In this way, by analyzing users' social media activity, relevant information is supplemented and the accuracy of the analysis is improved.

[0092] The generation unit can estimate the user's emotions and determine the priority of images to generate based on those estimated emotions. For example, if the user is relaxed, it can prioritize generating visually calming images. If the user is excited, it can prioritize generating visually stimulating images. If the user is tense, it can prioritize generating visually calming images. By prioritizing images according to the user's emotions, it can provide more appropriate images.

[0093] The display unit can estimate the user's emotions and adjust the display order of images based on those emotions. For example, if the user is relaxed, visually calming images can be displayed first. If the user is excited, visually stimulating images can be displayed first. If the user is stressed, visually calming images can be displayed first. By adjusting the display order of images according to the user's emotions, more appropriate image display becomes possible.

[0094] The service provider can analyze a user's social media activity and provide relevant images. For example, it can extract relevant topics from a user's social media posts and provide images. It can also provide images considering the user's followers and topics of interest. It can provide relevant images based on the user's social media activity history. In this way, it can provide relevant images by analyzing a user's social media activity.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The analysis unit analyzes the text content of the presentation. The analysis unit analyzes the text content using, for example, natural language processing technology, extracts keywords, and performs analysis to understand the context. Step 2: The generation unit generates relevant images based on the text content analyzed by the analysis unit. The generation unit generates relevant images, for example, by utilizing theme-based image generation AI. Step 3: The providing unit provides the images generated by the generating unit. The providing unit provides customizable visual content that meets user needs, for example, by offering real-time image suggestion and adjustment functions.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes the text content of the presentation. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a related image based on the analyzed text content. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated image. 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.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the text content of the presentation. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates a related image based on the analyzed text content. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated image. 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.

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

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0132] Each of the multiple elements, including the analysis unit, generation unit, and provision unit described above, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the text content of the presentation. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a related image based on the analyzed text content. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated image. 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.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements, including the analysis unit, generation unit, and provision unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 and analyzes the text content of the presentation. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a related image based on the analyzed text content. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated image. 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) An analysis unit that analyzes the text content of the presentation, A generation unit generates related images based on the text content analyzed by the analysis unit, The system includes a providing unit that provides the image generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze text content using natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Use theme-based image generation AI to generate related images. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Features real-time image suggestion and adjustment capabilities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide customizable visual content tailored to user needs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, It estimates the user's emotions and adjusts the text content analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, When analyzing text content, the system improves analysis accuracy by referencing the user's past presentation history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing text content, different analysis algorithms are applied depending on the purpose of the presentation. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing text content, the analysis method is customized based on the user's industry and area of ​​expertise. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing text content, the system analyzes the user's social media activity and supplements it with relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the style of the generated images based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating images, different image generation algorithms are applied depending on the presentation theme. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating images, the system references images used in the user's past presentations to improve generation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and determines the priority of images to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating images, the system considers the user's geographical location to generate highly relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating images, the system analyzes the user's social media activity and generates relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how images are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing images, the system will refer to the user's past presentation history to select the most suitable method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing images, different presentation algorithms are applied depending on the purpose of the presentation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the display order of images based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing images, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing images, we analyze the user's social media activity and provide relevant images. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 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. An analysis unit that analyzes the text content of the presentation, A generation unit generates related images based on the text content analyzed by the analysis unit, The system includes a providing unit that provides the image generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze text content using natural language processing. The system according to feature 1.

3. The generating unit is Use theme-based image generation AI to generate related images. The system according to feature 1.

4. The aforementioned supply unit is, Features real-time image suggestion and adjustment capabilities. The system according to feature 1.

5. The aforementioned supply unit is, Provide customizable visual content tailored to user needs. The system according to feature 1.

6. The aforementioned analysis unit, It estimates the user's emotions and adjusts the text content analysis method based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit, When analyzing text content, the system improves analysis accuracy by referencing the user's past presentation history. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing text content, different analysis algorithms are applied depending on the purpose of the presentation. The system according to feature 1.

9. The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system according to feature 1.

10. The aforementioned analysis unit, When analyzing text content, the analysis method is customized based on the user's industry and area of ​​expertise. The system according to feature 1.

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