System

Abstract

An object of the system according to the embodiment is to generate a presentation material that is easy to understand and convey to anyone.SOLUTION: A system includes a reception unit, a generation unit, and a display unit. The reception part inputs the theme and contents of the presentation. The generation unit analyzes the information input by the reception unit and generates a presentation material. The display unit displays the presentation material generated by the generation unit in a visually easy-to-understand manner.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the quality of presentation materials depended on the skills of the individual, leading to variations in their clarity and ease of communication.

[0005] The system according to the embodiment aims to generate presentation materials that are easy to understand and communicate for anyone to see. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a display unit. The reception unit inputs the theme and content of the presentation. The generation unit analyzes the information input by the reception unit and generates presentation materials. The display unit displays the presentation materials generated by the generation unit in a visually easy-to-understand manner. [Effects of the Invention]

[0007] The system according to the embodiment can generate presentation materials that are easy to understand and communicate for anyone. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A presentation creation system according to an embodiment of the present invention utilizes a generation AI to create presentations that are easy to understand and convey for anyone. In this system, a user inputs the theme and content of the presentation, and the generation AI analyzes the content to generate optimal presentation materials and display them in a visually understandable manner. For example, in the presentation creation system, a user inputs the theme and content of the presentation. For example, the user specifically inputs the purpose, target audience, and message of the presentation. The presentation creation system then uses the generation AI to analyze the theme and content input by the user and generate optimal presentation materials based on the input. For example, if data visualization is important, graphs and charts are generated, and if a process explanation is required, a flowchart is generated. Furthermore, if video explanations are effective, animations and video clips are generated. The presentation creation system then displays the generated presentation materials in a visually understandable manner. For example, the use of colors, layout, font size, and other elements are optimized to reduce visual burden. Furthermore, the system is designed to ensure a smooth flow of information and easy understanding for the audience. This allows all business professionals to create effective presentations and help them succeed. This allows the presentation creation system to generate optimal presentation materials based on the theme and content entered by the user, and display them in a visually easy-to-understand manner. For example, in a presentation introducing a new product, the product's features and advantages can be visually conveyed in an easy-to-understand manner. In a project progress report, progress and challenges can be clearly shown. Furthermore, in a presentation for investors, business models and market analyses can be effectively communicated.

[0029] A presentation creation system according to an embodiment includes a reception unit, a generation unit, and a display unit. The reception unit allows a user to input the theme and content of a presentation. Examples of the theme and content input by the user include, but are not limited to, a business presentation, an academic presentation, and a product introduction. The reception unit allows the user to specifically input the purpose, target audience, and message of the presentation. The reception unit can also support multiple input methods, such as voice input and text input. The generation unit uses a generative AI to analyze the information input by the reception unit and generate presentation materials. For example, the generation unit generates graphs and charts when data visualization is important, generates flowcharts when process explanations are required, and generates animations and video clips when video explanations are effective. The generative AI is realized using technologies such as deep learning models, generative adversarial networks (GANs), and natural language generation (NLG). Some or all of the above-described processing in the generation unit is performed using the generative AI. The display unit displays the presentation materials generated by the generation unit in a visually easy-to-understand manner. The display unit adjusts, for example, the color usage, layout, font size, etc. to reduce visual burden. The display unit also devise ways to ensure a smooth flow of information and easy understanding for the audience. As a result, the presentation creation system according to the embodiment can generate optimal presentation materials based on the theme and content input by the user and display them in a visually easy-to-understand manner.

[0030] The generation unit can use the generation AI to generate graphs and charts when data visualization is required, generate flowcharts when a process explanation is required, and generate animations and video clips when a video explanation is required. For example, when data visualization is important, the generation unit generates graphs and charts such as bar graphs, pie charts, and scatter plots. The generation unit can also generate flowcharts and step-by-step guides when a process explanation is required. The generation unit can also generate animations and video clips when a video explanation is effective. For example, the generation unit generates optimal animations and video clips by taking into account the software used, animation style, video length, etc. This allows the generation AI to generate presentation materials in a variety of formats. Some or all of the above-mentioned processes in the generation unit are performed using the generation AI. For example, the generation unit inputs data entered by a user into the generation AI, which then analyzes the data and generates optimal presentation materials.

[0031] The display unit can reduce visual burden by adjusting color usage, layout, font size, etc. The display unit can adjust color combinations, contrast, color vision barrier-free compatibility, etc., taking into account specific standards and methods for color usage. The display unit can also adjust information placement, margins, and eye guidance, taking into account specific standards and methods for layout. The display unit can also adjust the difference in font size between headings and main text, readability standards, etc., taking into account specific standards and methods for font size. This reduces visual burden and effectively conveys information. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs the generated presentation materials into AI, which then optimizes color usage, layout, font size, etc.

[0032] The display unit can adjust the flow of information to ensure a smooth flow and easy understanding for the audience. The display unit can smooth the flow of information by, for example, creating a storyboard, structuring the information hierarchically, and using transitions. The display unit can also adjust the arrangement and order of information to smooth the flow of information. For example, the display unit can place important information first and arrange the information in order of relevance. The display unit can also adjust the order of information in line with the flow of the presentation. This provides presentation materials that are easy for the audience to understand. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs the generated presentation materials into AI, which then optimizes the flow of information.

[0033] The presentation creation system includes a feedback unit that allows a user to evaluate generated materials and provide opinions. The feedback unit allows a user to evaluate generated presentation materials and provide opinions. Evaluations include, but are not limited to, evaluation items, evaluation scales, and evaluator qualifications. The feedback unit allows a user to provide opinions such as comments, scores, and improvement suggestions for the generated materials. This allows the user to provide feedback on the generated materials. Some or all of the above-described processing in the feedback unit is performed using AI. For example, the feedback unit inputs the user's evaluation data into AI, which analyzes the evaluation results and provides feedback.

[0034] The presentation creation system includes a template unit that provides templates suitable for a specific industry or application. The template unit provides templates suitable for a specific industry or application. Examples of templates include, but are not limited to, the medical industry, educational applications, and marketing applications. For example, the template unit provides templates for visualizing medical data and patient information as templates for the medical industry. The template unit can also provide templates for creating educational materials and study guides as templates for educational applications. The template unit can also provide templates for visualizing marketing data and market analysis as templates for marketing applications. This makes it possible to provide templates suitable for a specific industry or application. Some or all of the above-described processing in the template unit is performed using AI. For example, the template unit inputs data related to the user's industry and application into AI, which then selects the optimal template.

[0035] The presentation creation system includes a learning unit that learns user opinions and reflects them in subsequent material generation. The learning unit learns user opinions and reflects them in subsequent material generation. Examples of learning include, but are not limited to, machine learning algorithms, feedback collection methods, and learning data update frequencies. For example, the learning unit learns algorithms to reflect the feedback provided by the user in subsequent material generation. The learning unit can also collect user feedback and update the learning data. This reflects the user feedback, thereby improving the accuracy of subsequent material generation. Some or all of the above-described processing in the learning unit is performed using AI. For example, the learning unit inputs user feedback data into AI, which analyzes the feedback and updates the learning data.

[0036] The reception unit can analyze the user's past presentation history and suggest the optimal input method. For example, the reception unit automatically displays themes and content that the user has frequently used in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest themes and content to be used in a specific time period based on the user's past presentation history. This improves input efficiency by suggesting the optimal input method based on the past history. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's past presentation data into AI, which analyzes the data and suggests the optimal input method.

[0037] When inputting the presentation theme and content, the reception unit can select them based on the user's current project and areas of interest. For example, the reception unit can prioritize displaying themes and content related to the user's current project. The reception unit can also suggest related themes and content based on the user's areas of interest. The reception unit can also analyze the user's past project history and filter and display related themes and content. This makes it possible to provide highly relevant themes and content by filtering based on the user's current project and areas of interest. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's project data into AI, which analyzes the data and suggests related themes and content.

[0038] When inputting the theme and content of a presentation, the reception unit can select the optimal input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the theme and content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also analyze and input the theme and content using image recognition technology. This improves input efficiency by providing the optimal means according to the user's input method. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's input data into AI, which analyzes the data and selects the optimal input means.

[0039] When inputting the theme or content of a presentation, the reception unit can prioritize inputting highly relevant themes by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize displaying themes and content related to that area. Furthermore, if the user is on a business trip, the reception unit can also suggest themes and content related to the user's business trip destination. Furthermore, the reception unit can provide relevant business information based on the user's current location. In this way, highly relevant themes and content can be provided by taking geographical location information into consideration. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's location information data into AI, which analyzes the data and suggests relevant themes and content.

[0040] When the user inputs the theme or content of a presentation, the reception unit can analyze the user's social media activity and suggest related themes. For example, the reception unit can suggest related themes or content based on information shared by the user on social media. The reception unit can also analyze the user's social media activity history and display related themes or content. The reception unit can also suggest related themes or content by referring to the activity of the user's friends on social media. In this way, related themes and content can be provided by analyzing social media activity. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's social media data into AI, which analyzes the data and suggests related themes and content.

[0041] The reception unit can customize the input method by reflecting the user's past feedback when inputting the presentation theme or content. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface by reflecting the user's past feedback. The reception unit can also analyze the user's feedback history and optimize the input method. In this way, the input method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's feedback data into AI, which analyzes the data and customizes the input method.

[0042] The generation unit can adjust the level of detail of the materials based on the importance of the presentation when generating them. For example, for an important presentation, the generation unit generates materials including detailed data and graphs. The generation unit can also generate concise materials that focus on the main points for a simple report. The generation unit can also generate materials with an appropriate level of detail for a presentation of medium importance. This improves the effectiveness of the materials by providing a level of detail appropriate to the importance of the presentation. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs user input data into the generation AI, which then analyzes the data and generates materials with the optimal level of detail.

[0043] During generation, the generation unit can apply different generation algorithms depending on the category of the presentation. For example, in the case of a technical presentation, the generation unit can apply an algorithm that emphasizes technical data. In the case of a marketing presentation, the generation unit can also apply an algorithm that emphasizes visuals. In the case of an educational presentation, the generation unit can also apply an algorithm that enhances educational effectiveness. In this way, by applying a generation algorithm depending on the category of the presentation, the effectiveness of the materials is improved. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs user input data into the generation AI, which analyzes the data and applies the optimal generation algorithm.

[0044] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit analyzes presentation materials generated by the user in the past to improve the accuracy of generation. The generation unit can also learn and reflect the user's preferred designs and layouts from the user's past generation results. The generation unit can also select the optimal generation algorithm based on the user's past generation results. In this way, by referring to the past generation results, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's past generation data into the generation AI, and the generation AI analyzes the data to improve the accuracy of generation.

[0045] The generation unit can determine the priority of materials based on the submission date of the presentation at the time of generation. For example, in the case of an urgent presentation, the generation unit generates materials as a top priority. The generation unit can also generate materials as a priority for presentations with an upcoming submission deadline. The generation unit can also postpone generating materials for presentations with a distant submission deadline. In this way, determining the priority based on the submission date makes the generation of materials more efficient. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's submission date data into the generation AI, and the generation AI analyzes the data to determine the priority of materials.

[0046] The generation unit can adjust the order of materials based on the relevance of the presentation during generation. For example, the generation unit places important information first and arranges the materials in order of relevance. The generation unit can also adjust the order of information in line with the flow of the presentation. The generation unit can also place interesting information first to attract the audience's attention. This improves the effectiveness of the presentation by providing an order of materials based on relevance. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs user input data into the generation AI, which analyzes the data and adjusts the order of the materials.

[0047] During generation, the generation unit can adjust the use of technical terminology in the materials according to the user's level of expertise. For example, the generation unit generates materials that use a lot of technical terminology for users with high levels of expertise. The generation unit can also generate materials that explain things in simple language for users with low levels of expertise. The generation unit can also adjust the use of appropriate technical terminology according to the user's level of expertise. This improves understanding of the materials by providing the use of technical terminology according to the level of expertise. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's technical terminology data into the generation AI, which analyzes the data and adjusts the use of technical terminology.

[0048] The display unit can adjust the level of detail of the display based on the importance of the presentation when displaying it. For example, in the case of an important presentation, the display unit provides a display including detailed data and graphs. In addition, in the case of a simple report, the display unit can provide a concise display that focuses on the main points. In addition, in the case of a presentation of medium importance, the display unit can provide a display with an appropriate level of detail. This improves the effectiveness of the materials by providing a level of detail according to the importance. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs user input data into AI, which analyzes the data and provides a display with the optimal level of detail.

[0049] The display unit can apply different display algorithms depending on the category of the presentation when displaying. For example, in the case of a technical presentation, the display unit can apply a display algorithm that emphasizes technical data. In addition, in the case of a marketing presentation, the display unit can apply a display algorithm that emphasizes visuals. In addition, in the case of an educational presentation, the display unit can apply a display algorithm that enhances the educational effect. In this way, by providing a display algorithm according to the category, the effectiveness of the material is improved. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs user input data into AI, which analyzes the data and applies the optimal display algorithm.

[0050] The display unit can improve the accuracy of the display by referring to the user's past display results when displaying. For example, the display unit analyzes presentation materials that the user has previously displayed to improve the accuracy of the display. The display unit can also learn and reflect the user's preferred layout and design from the user's past display results. The display unit can also select the optimal display algorithm based on the user's past display results. In this way, the accuracy of the display is improved by referring to the past display results. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs the user's past display data into AI, which analyzes the data and improves the accuracy of the display.

[0051] The display unit can determine the display priority based on the submission date of the presentation when displaying. For example, in the case of an urgent presentation, the display unit displays the materials as a top priority. The display unit can also display materials as a priority for a presentation with an upcoming submission deadline. The display unit can also display materials later for a presentation with a distant submission deadline. In this way, determining the priority based on the submission date improves display efficiency. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs the user's submission date data into AI, which analyzes the data and determines the display priority.

[0052] The display unit can adjust the order of materials based on the relevance of the presentation when displaying them. For example, the display unit places important information first and arranges the materials in order of relevance. The display unit can also adjust the order of information in line with the flow of the presentation. The display unit can also place interesting information first to attract the audience's attention. This improves the effectiveness of the presentation by providing an order of materials based on relevance. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs user input data into AI, which analyzes the data and adjusts the order of the materials.

[0053] The display unit can adjust the use of technical terminology in the material during display according to the user's level of expertise. For example, the display unit displays material that uses a lot of technical terminology for users with high levels of expertise. The display unit can also display material that explains things in simpler terms for users with low levels of expertise. The display unit can also adjust the use of appropriate technical terminology according to the user's level of expertise. This improves understanding of the material by providing technical terminology appropriate to the user's level of expertise. Some or all of the above-described processing in the display unit is performed using AI. For example, the display unit inputs the user's technical terminology data into AI, which analyzes the data and adjusts the use of technical terminology.

[0054] The feedback unit can adjust the level of detail of the feedback based on the importance of the presentation when providing feedback. For example, the feedback unit provides detailed feedback for an important presentation. The feedback unit can also provide concise feedback that focuses on the main points for a simple report. The feedback unit can also provide feedback with an appropriate level of detail for a presentation of medium importance. This improves the effectiveness of feedback by providing feedback with a level of detail that corresponds to the importance. Some or all of the above-mentioned processing in the feedback unit is performed using AI. For example, the feedback unit inputs user input data into AI, which analyzes the data and provides feedback with an optimal level of detail.

[0055] The feedback unit can apply different feedback algorithms depending on the category of the presentation when providing feedback. For example, in the case of a technical presentation, the feedback unit can provide feedback based on technical data. In addition, in the case of a marketing presentation, the feedback unit can provide feedback based on visuals. In addition, in the case of an educational presentation, the feedback unit can provide feedback that enhances the educational effectiveness. In this way, by providing a feedback algorithm according to the category, the effectiveness of the feedback is improved. Some or all of the above-mentioned processing in the feedback unit is performed using AI. For example, the feedback unit inputs user input data into AI, which analyzes the data and applies the optimal feedback algorithm.

[0056] The feedback unit can determine the priority of feedback based on the submission date of the presentation when providing feedback. For example, in the case of an urgent presentation, the feedback unit provides feedback as a top priority. The feedback unit can also provide feedback as a priority for a presentation with an upcoming submission deadline. The feedback unit can also provide feedback later for a presentation with a distant submission deadline. In this way, determining the priority based on the submission date improves the efficiency of feedback. Some or all of the above-mentioned processing in the feedback unit is performed using AI. For example, the feedback unit inputs the user's submission date data into AI, which analyzes the data and determines the priority of feedback.

[0057] When providing feedback, the feedback unit can adjust the order of feedback based on the relevance of the presentation. For example, the feedback unit places important information first and provides feedback in order of relevance. The feedback unit can also adjust the order of feedback in line with the flow of the presentation. The feedback unit can also place interesting information first to attract the audience's attention. This improves the effectiveness of feedback by providing a feedback order based on relevance. Some or all of the above-mentioned processing in the feedback unit is performed using AI. For example, the feedback unit inputs user input data into AI, which analyzes the data and adjusts the order of feedback.

[0058] When selecting a template, the template unit can adjust the level of detail of the template based on the importance of the presentation. For example, for an important presentation, the template unit can provide a template including detailed data and graphs. For a simple report, the template unit can also provide a concise template that focuses on the main points. For a presentation of medium importance, the template unit can also provide a template with moderate level of detail. This improves the effectiveness of the presentation by providing a template with a level of detail appropriate to the importance. Some or all of the above-mentioned processing in the template unit is performed using AI. For example, the template unit inputs user input data into AI, which then analyzes the data and provides a template with the optimal level of detail.

[0059] When selecting a template, the template unit can provide different templates depending on the category of the presentation. For example, for a technical presentation, the template unit can provide a template that emphasizes technical data. For a marketing presentation, the template unit can also provide a template that emphasizes visuals. For an educational presentation, the template unit can also provide a template that enhances the educational effect. In this way, by providing templates according to the category, the effectiveness of the presentation is improved. Some or all of the above-mentioned processing in the template unit is performed using AI. For example, the template unit inputs user input data into AI, which analyzes the data and provides the optimal template.

[0060] When selecting templates, the template unit can determine the priority of templates based on the submission date of the presentation. For example, in the case of an urgent presentation, the template unit can provide templates with the highest priority. The template unit can also provide templates with priority for presentations with an upcoming submission deadline. The template unit can also provide templates with a later deadline for presentations with a distant submission deadline. In this way, by providing template priorities based on submission dates, the efficiency of presentations is improved. Some or all of the above-mentioned processing in the template unit is performed using AI. For example, the template unit inputs the user's submission date data into AI, which analyzes the data and determines the priority of templates.

[0061] When selecting templates, the template unit can adjust the order of templates based on the relevance of the presentation. For example, the template unit may place important information first and arrange the templates in order of relevance. The template unit can also adjust the order of templates in line with the flow of the presentation. The template unit can also place interesting information first to attract the audience's attention. This improves the effectiveness of the presentation by providing a template order based on relevance. Some or all of the above-mentioned processing in the template unit is performed using AI. For example, the template unit inputs user input data into AI, which analyzes the data and adjusts the order of the templates.

[0062] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit analyzes past learning data and selects the optimal learning algorithm. The learning unit can also adjust the learning algorithm based on past learning results. The learning unit can also extract effective learning patterns from past learning data and reflect them in the algorithm. In this way, by referring to past learning data, the accuracy of the learning algorithm is improved. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit inputs past learning data into AI, which analyzes the data and optimizes the learning algorithm.

[0063] During learning, the learning unit can update the learning data by reflecting user feedback. For example, the learning unit updates the learning data based on user feedback. The learning unit can also adjust the learning algorithm by reflecting user feedback. The learning unit can also analyze the user's feedback history and optimize the learning data. This improves the accuracy of the learning data by reflecting user feedback. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit inputs user feedback data into AI, which analyzes the data and updates the learning data.

[0064] During learning, the learning unit can weight the learning data based on the submission date of the presentation. For example, in the case of a presentation with an upcoming submission deadline, the learning unit weights important data and prioritizes learning. Furthermore, in the case of a presentation with a distant submission deadline, the learning unit can weight detailed data and learn. Furthermore, in the case of a presentation with a medium submission deadline, the learning unit can weight the data appropriately and learn. In this way, weighting of the learning data based on the submission date improves learning efficiency. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit inputs submission date data into AI, which analyzes the data and weights the learning data.

[0065] During learning, the learning unit can integrate information from different data sources to enrich the training data. For example, the learning unit integrates information from different data sources to enrich the training data. The learning unit can also integrate data from different industries to diversify the training data. The learning unit can also integrate data in different formats (text, images, audio, etc.) to enrich the training data. This improves the diversity of the training data by integrating information from different data sources. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit inputs data from different data sources into AI, which analyzes the data to enrich the training data.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The reception unit can analyze the user's past presentation history and suggest the optimal input method. For example, themes and content frequently used by the user in the past can be automatically displayed as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest themes and content to be used at a specific time period based on the user's past presentation history. This improves input efficiency by suggesting the optimal input method based on the past history. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's past presentation data into AI, which analyzes the data and suggests the optimal input method.

[0068] During generation, the generation unit can apply different generation algorithms depending on the category of the presentation. For example, for a technical presentation, an algorithm that emphasizes technical data can be applied. The generation unit can also apply an algorithm that emphasizes visuals for a marketing presentation. The generation unit can also apply an algorithm that enhances educational effectiveness for an educational presentation. In this way, by applying a generation algorithm depending on the presentation category, the effectiveness of the materials is improved. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs user input data into the generation AI, which analyzes the data and applies the optimal generation algorithm.

[0069] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, it can analyze presentation materials generated by the user in the past to improve the accuracy of generation. The generation unit can also learn and reflect the user's preferred designs and layouts from the user's past generation results. The generation unit can also select the optimal generation algorithm based on the user's past generation results. In this way, by referring to past generation results, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's past generation data into the generation AI, which analyzes the data to improve the accuracy of generation.

[0070] The display unit can determine the display priority based on the submission date of the presentation when displaying. For example, in the case of an urgent presentation, the display unit displays the materials as the highest priority. The display unit can also display materials as a priority for presentations with an upcoming submission deadline. The display unit can also display materials later for presentations with a distant submission deadline. In this way, display efficiency is improved by determining the priority based on the submission date. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs the user's submission date data into AI, which analyzes the data and determines the display priority.

[0071] The feedback unit can apply different feedback algorithms depending on the category of the presentation when providing feedback. For example, in the case of a technical presentation, feedback based on technical data is provided. In addition, the feedback unit can provide feedback based on visuals in the case of a marketing presentation. In addition, in the case of an educational presentation, the feedback unit can provide feedback that enhances the educational effectiveness. In this way, by providing a feedback algorithm according to the category, the effectiveness of the feedback is improved. Some or all of the above-mentioned processing in the feedback unit is performed using AI. For example, the feedback unit inputs user input data into AI, which analyzes the data and applies the optimal feedback algorithm.

[0072] When selecting templates, the template unit can determine the priority of templates based on the submission date of the presentation. For example, in the case of an urgent presentation, the template is provided with the highest priority. The template unit can also provide templates with priority for presentations with an upcoming submission deadline. The template unit can also provide templates with a later deadline for presentations with a distant submission deadline. This improves the efficiency of presentations by providing a priority of templates based on the submission date. Some or all of the above-mentioned processing in the template unit is performed using AI. For example, the template unit inputs the user's submission date data into AI, which analyzes the data and determines the priority of templates.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The reception unit allows the user to input the theme and content of the presentation. The themes and content input by the user include business presentations, academic presentations, product introductions, etc. The reception unit allows the user to specifically input the purpose and target audience of the presentation, the message they want to convey, etc. The reception unit also supports multiple input methods, such as voice input and text input. Step 2: The generation unit uses generative AI to analyze the information entered by the reception unit and generate presentation materials. The generation unit generates graphs and charts when data visualization is important, flowcharts when process explanations are required, and animations and video clips when video explanations are effective. Generative AI is realized using technologies such as deep learning models, generative adversarial networks (GAN), and natural language generation (NLG). Step 3: The display unit displays the presentation materials generated by the generator in a visually easy-to-understand manner. The display unit adjusts the color usage, layout, font size, etc. to reduce visual strain. It also makes efforts to ensure a smooth flow of information and easy understanding for the audience.

[0075] (Example 2) A presentation creation system according to an embodiment of the present invention utilizes a generation AI to create presentations that are easy to understand and convey for anyone. In this system, a user inputs the theme and content of the presentation, and the generation AI analyzes the content to generate optimal presentation materials and display them in a visually understandable manner. For example, in the presentation creation system, a user inputs the theme and content of the presentation. For example, the user specifically inputs the purpose, target audience, and message of the presentation. The presentation creation system then uses the generation AI to analyze the theme and content input by the user and generate optimal presentation materials based on the input. For example, if data visualization is important, graphs and charts are generated, and if a process explanation is required, a flowchart is generated. Furthermore, if video explanations are effective, animations and video clips are generated. The presentation creation system then displays the generated presentation materials in a visually understandable manner. For example, the use of colors, layout, font size, and other elements are optimized to reduce visual burden. Furthermore, the system is designed to ensure a smooth flow of information and easy understanding for the audience. This allows all business professionals to create effective presentations and help them succeed. This allows the presentation creation system to generate optimal presentation materials based on the theme and content entered by the user, and display them in a visually easy-to-understand manner. For example, in a presentation introducing a new product, the product's features and advantages can be visually conveyed in an easy-to-understand manner. In a project progress report, progress and challenges can be clearly shown. Furthermore, in a presentation for investors, business models and market analyses can be effectively communicated.

[0076] A presentation creation system according to an embodiment includes a reception unit, a generation unit, and a display unit. The reception unit allows a user to input the theme and content of a presentation. Examples of the theme and content input by the user include, but are not limited to, a business presentation, an academic presentation, and a product introduction. The reception unit allows the user to specifically input the purpose, target audience, and message of the presentation. The reception unit can also support multiple input methods, such as voice input and text input. The generation unit uses a generative AI to analyze the information input by the reception unit and generate presentation materials. For example, the generation unit generates graphs and charts when data visualization is important, generates flowcharts when process explanations are required, and generates animations and video clips when video explanations are effective. The generative AI is realized using technologies such as deep learning models, generative adversarial networks (GANs), and natural language generation (NLG). Some or all of the above-described processing in the generation unit is performed using the generative AI. The display unit displays the presentation materials generated by the generation unit in a visually easy-to-understand manner. The display unit adjusts, for example, the color usage, layout, font size, etc. to reduce visual burden. The display unit also devise ways to ensure a smooth flow of information and easy understanding for the audience. As a result, the presentation creation system according to the embodiment can generate optimal presentation materials based on the theme and content input by the user and display them in a visually easy-to-understand manner.

[0077] The generation unit can use the generation AI to generate graphs and charts when data visualization is required, generate flowcharts when a process explanation is required, and generate animations and video clips when a video explanation is required. For example, when data visualization is important, the generation unit generates graphs and charts such as bar graphs, pie charts, and scatter plots. The generation unit can also generate flowcharts and step-by-step guides when a process explanation is required. The generation unit can also generate animations and video clips when a video explanation is effective. For example, the generation unit generates optimal animations and video clips by taking into account the software used, animation style, video length, etc. This allows the generation AI to generate presentation materials in a variety of formats. Some or all of the above-mentioned processes in the generation unit are performed using the generation AI. For example, the generation unit inputs data entered by a user into the generation AI, which then analyzes the data and generates optimal presentation materials.

[0078] The display unit can reduce visual burden by adjusting color usage, layout, font size, etc. The display unit can adjust color combinations, contrast, color vision barrier-free compatibility, etc., taking into account specific standards and methods for color usage. The display unit can also adjust information placement, margins, and eye guidance, taking into account specific standards and methods for layout. The display unit can also adjust the difference in font size between headings and main text, readability standards, etc., taking into account specific standards and methods for font size. This reduces visual burden and effectively conveys information. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs the generated presentation materials into AI, which then optimizes color usage, layout, font size, etc.

[0079] The display unit can adjust the flow of information to ensure a smooth flow and easy understanding for the audience. The display unit can smooth the flow of information by, for example, creating a storyboard, structuring the information hierarchically, and using transitions. The display unit can also adjust the arrangement and order of information to smooth the flow of information. For example, the display unit can place important information first and arrange the information in order of relevance. The display unit can also adjust the order of information in line with the flow of the presentation. This provides presentation materials that are easy for the audience to understand. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs the generated presentation materials into AI, which then optimizes the flow of information.

[0080] The presentation creation system includes a feedback unit that allows a user to evaluate generated materials and provide opinions. The feedback unit allows a user to evaluate generated presentation materials and provide opinions. Evaluations include, but are not limited to, evaluation items, evaluation scales, and evaluator qualifications. The feedback unit allows a user to provide opinions such as comments, scores, and improvement suggestions for the generated materials. This allows the user to provide feedback on the generated materials. Some or all of the above-described processing in the feedback unit is performed using AI. For example, the feedback unit inputs the user's evaluation data into AI, which analyzes the evaluation results and provides feedback.

[0081] The presentation creation system includes a template unit that provides templates suitable for a specific industry or application. The template unit provides templates suitable for a specific industry or application. Examples of templates include, but are not limited to, the medical industry, educational applications, and marketing applications. For example, the template unit provides templates for visualizing medical data and patient information as templates for the medical industry. The template unit can also provide templates for creating educational materials and study guides as templates for educational applications. The template unit can also provide templates for visualizing marketing data and market analysis as templates for marketing applications. This makes it possible to provide templates suitable for a specific industry or application. Some or all of the above-described processing in the template unit is performed using AI. For example, the template unit inputs data related to the user's industry and application into AI, which then selects the optimal template.

[0082] The presentation creation system includes a learning unit that learns user opinions and reflects them in subsequent material generation. The learning unit learns user opinions and reflects them in subsequent material generation. Examples of learning include, but are not limited to, machine learning algorithms, feedback collection methods, and learning data update frequencies. For example, the learning unit learns algorithms to reflect the feedback provided by the user in subsequent material generation. The learning unit can also collect user feedback and update the learning data. This reflects the user feedback, thereby improving the accuracy of subsequent material generation. Some or all of the above-described processing in the learning unit is performed using AI. For example, the learning unit inputs user feedback data into AI, which analyzes the feedback and updates the learning data.

[0083] The reception unit can estimate the user's emotions and change the input method for the presentation theme and content based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of the theme and content. This improves input efficiency by providing an input method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the input method.

[0084] The reception unit can analyze the user's past presentation history and suggest the optimal input method. For example, the reception unit automatically displays themes and content that the user has frequently used in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest themes and content to be used in a specific time period based on the user's past presentation history. This improves input efficiency by suggesting the optimal input method based on the past history. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's past presentation data into AI, which analyzes the data and suggests the optimal input method.

[0085] When inputting the presentation theme and content, the reception unit can select them based on the user's current project and areas of interest. For example, the reception unit can prioritize displaying themes and content related to the user's current project. The reception unit can also suggest related themes and content based on the user's areas of interest. The reception unit can also analyze the user's past project history and filter and display related themes and content. This makes it possible to provide highly relevant themes and content by filtering based on the user's current project and areas of interest. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's project data into AI, which analyzes the data and suggests related themes and content.

[0086] When inputting the theme and content of a presentation, the reception unit can select the optimal input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the theme and content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also analyze and input the theme and content using image recognition technology. This improves input efficiency by providing the optimal means according to the user's input method. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's input data into AI, which analyzes the data and selects the optimal input means.

[0087] The reception unit can estimate the user's emotions and determine the priority of the themes and contents to be input based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit prioritizes input of important themes and contents. Furthermore, when the user is relaxed, the reception unit can also input detailed themes and contents. Furthermore, when the user is in a hurry, the reception unit can also prioritize input of the most important themes and contents. Thus, by determining priorities according to the user's emotions, important themes and contents can be input preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and determines the priority.

[0088] When inputting the theme or content of a presentation, the reception unit can prioritize inputting highly relevant themes by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize displaying themes and content related to that area. Furthermore, if the user is on a business trip, the reception unit can also suggest themes and content related to the user's business trip destination. Furthermore, the reception unit can provide relevant business information based on the user's current location. In this way, highly relevant themes and content can be provided by taking geographical location information into consideration. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's location information data into AI, which analyzes the data and suggests relevant themes and content.

[0089] When the user inputs the theme or content of a presentation, the reception unit can analyze the user's social media activity and suggest related themes. For example, the reception unit can suggest related themes or content based on information shared by the user on social media. The reception unit can also analyze the user's social media activity history and display related themes or content. The reception unit can also suggest related themes or content by referring to the activity of the user's friends on social media. In this way, related themes and content can be provided by analyzing social media activity. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's social media data into AI, which analyzes the data and suggests related themes and content.

[0090] The reception unit can customize the input method by reflecting the user's past feedback when inputting the presentation theme or content. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface by reflecting the user's past feedback. The reception unit can also analyze the user's feedback history and optimize the input method. In this way, the input method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's feedback data into AI, which analyzes the data and customizes the input method.

[0091] The generation unit can estimate the user's emotions and adjust the expression method of the generated presentation materials based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate materials with a visually calming design. If the user is in a hurry, the generation unit can also generate concise materials that emphasize the main points. If the user is excited, the generation unit can also generate materials with a visually stimulating design. This improves the effectiveness of the materials by providing an expression method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the expression method.

[0092] The generation unit can adjust the level of detail of the materials based on the importance of the presentation when generating them. For example, for an important presentation, the generation unit generates materials including detailed data and graphs. The generation unit can also generate concise materials that focus on the main points for a simple report. The generation unit can also generate materials with an appropriate level of detail for a presentation of medium importance. This improves the effectiveness of the materials by providing a level of detail appropriate to the importance of the presentation. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs user input data into the generation AI, which then analyzes the data and generates materials with the optimal level of detail.

[0093] During generation, the generation unit can apply different generation algorithms depending on the category of the presentation. For example, in the case of a technical presentation, the generation unit can apply an algorithm that emphasizes technical data. In the case of a marketing presentation, the generation unit can also apply an algorithm that emphasizes visuals. In the case of an educational presentation, the generation unit can also apply an algorithm that enhances educational effectiveness. In this way, by applying a generation algorithm depending on the category of the presentation, the effectiveness of the materials is improved. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs user input data into the generation AI, which analyzes the data and applies the optimal generation algorithm.

[0094] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit analyzes presentation materials generated by the user in the past to improve the accuracy of generation. The generation unit can also learn and reflect the user's preferred designs and layouts from the user's past generation results. The generation unit can also select the optimal generation algorithm based on the user's past generation results. In this way, by referring to the past generation results, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's past generation data into the generation AI, and the generation AI analyzes the data to improve the accuracy of generation.

[0095] The generation unit can estimate the user's emotions and adjust the length of the generated presentation materials based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate short, concise materials. If the user is relaxed, the generation unit can generate longer materials with detailed explanations. If the user is excited, the generation unit can generate materials with visually stimulating effects. This improves the effectiveness of the materials by providing a length that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the length of the materials.

[0096] The generation unit can determine the priority of materials based on the submission date of the presentation at the time of generation. For example, in the case of an urgent presentation, the generation unit generates materials as a top priority. The generation unit can also generate materials as a priority for presentations with an upcoming submission deadline. The generation unit can also postpone generating materials for presentations with a distant submission deadline. In this way, determining the priority based on the submission date makes the generation of materials more efficient. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's submission date data into the generation AI, and the generation AI analyzes the data to determine the priority of materials.

[0097] The generation unit can adjust the order of materials based on the relevance of the presentation during generation. For example, the generation unit places important information first and arranges the materials in order of relevance. The generation unit can also adjust the order of information in line with the flow of the presentation. The generation unit can also place interesting information first to attract the audience's attention. This improves the effectiveness of the presentation by providing an order of materials based on relevance. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs user input data into the generation AI, which analyzes the data and adjusts the order of the materials.

[0098] During generation, the generation unit can adjust the use of technical terminology in the materials according to the user's level of expertise. For example, the generation unit generates materials that use a lot of technical terminology for users with high levels of expertise. The generation unit can also generate materials that explain things in simple language for users with low levels of expertise. The generation unit can also adjust the use of appropriate technical terminology according to the user's level of expertise. This improves understanding of the materials by providing the use of technical terminology according to the level of expertise. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's technical terminology data into the generation AI, which analyzes the data and adjusts the use of technical terminology.

[0099] The display unit can estimate the user's emotions and adjust the layout of the presentation materials to be displayed based on the estimated user's emotions. For example, if the user is nervous, the display unit can provide a simple, highly visible layout. Furthermore, if the user is relaxed, the display unit can provide a layout that includes detailed information. Furthermore, if the user is in a hurry, the display unit can provide a layout that focuses on the main points. This improves the visibility of the materials by providing a layout that corresponds to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the display unit is performed using AI. For example, the display unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the layout.

[0100] The display unit can adjust the level of detail of the display based on the importance of the presentation when displaying it. For example, in the case of an important presentation, the display unit provides a display including detailed data and graphs. In addition, in the case of a simple report, the display unit can provide a concise display that focuses on the main points. In addition, in the case of a presentation of medium importance, the display unit can provide a display with an appropriate level of detail. This improves the effectiveness of the materials by providing a level of detail according to the importance. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs user input data into AI, which analyzes the data and provides a display with the optimal level of detail.

[0101] The display unit can apply different display algorithms depending on the category of the presentation when displaying. For example, in the case of a technical presentation, the display unit can apply a display algorithm that emphasizes technical data. In addition, in the case of a marketing presentation, the display unit can apply a display algorithm that emphasizes visuals. In addition, in the case of an educational presentation, the display unit can apply a display algorithm that enhances the educational effect. In this way, by providing a display algorithm according to the category, the effectiveness of the material is improved. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs user input data into AI, which analyzes the data and applies the optimal display algorithm.

[0102] The display unit can improve the accuracy of the display by referring to the user's past display results when displaying. For example, the display unit analyzes presentation materials that the user has previously displayed to improve the accuracy of the display. The display unit can also learn and reflect the user's preferred layout and design from the user's past display results. The display unit can also select the optimal display algorithm based on the user's past display results. In this way, the accuracy of the display is improved by referring to the past display results. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs the user's past display data into AI, which analyzes the data and improves the accuracy of the display.

[0103] The display unit can estimate the user's emotions and adjust the length of the presentation materials to be displayed based on the estimated user emotions. For example, if the user is in a hurry, the display unit can display short, concise materials. Alternatively, if the user is relaxed, the display unit can display longer materials with detailed explanations. Alternatively, if the user is excited, the display unit can display materials with visually stimulating effects. This improves the effectiveness of the materials by providing a length of material that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit is performed using AI. For example, the display unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the length of the materials.

[0104] The display unit can determine the display priority based on the submission date of the presentation when displaying. For example, in the case of an urgent presentation, the display unit displays the materials as a top priority. The display unit can also display materials as a priority for a presentation with an upcoming submission deadline. The display unit can also display materials later for a presentation with a distant submission deadline. In this way, determining the priority based on the submission date improves display efficiency. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs the user's submission date data into AI, which analyzes the data and determines the display priority.

[0105] The display unit can adjust the order of materials based on the relevance of the presentation when displaying them. For example, the display unit places important information first and arranges the materials in order of relevance. The display unit can also adjust the order of information in line with the flow of the presentation. The display unit can also place interesting information first to attract the audience's attention. This improves the effectiveness of the presentation by providing an order of materials based on relevance. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs user input data into AI, which analyzes the data and adjusts the order of the materials.

[0106] The display unit can adjust the use of technical terminology in the material during display according to the user's level of expertise. For example, the display unit displays material that uses a lot of technical terminology for users with high levels of expertise. The display unit can also display material that explains things in simpler terms for users with low levels of expertise. The display unit can also adjust the use of appropriate technical terminology according to the user's level of expertise. This improves understanding of the material by providing technical terminology appropriate to the user's level of expertise. Some or all of the above-described processing in the display unit is performed using AI. For example, the display unit inputs the user's technical terminology data into AI, which analyzes the data and adjusts the use of technical terminology.

[0107] The feedback unit can estimate the user's emotions and adjust the way the feedback is expressed based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can provide gentle feedback. If the user is relaxed, the feedback unit can also provide detailed feedback. If the user is in a hurry, the feedback unit can also provide concise feedback that focuses on the main points. This improves the effectiveness of feedback by providing feedback that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the feedback unit is performed using AI. For example, the feedback unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the way the feedback is expressed.

[0108] The feedback unit can adjust the level of detail of the feedback based on the importance of the presentation when providing feedback. For example, the feedback unit provides detailed feedback for an important presentation. The feedback unit can also provide concise feedback that focuses on the main points for a simple report. The feedback unit can also provide feedback with an appropriate level of detail for a presentation of medium importance. This improves the effectiveness of feedback by providing feedback with a level of detail that corresponds to the importance. Some or all of the above-mentioned processing in the feedback unit is performed using AI. For example, the feedback unit inputs user input data into AI, which analyzes the data and provides feedback with an optimal level of detail.

[0109] The feedback unit can apply different feedback algorithms depending on the category of the presentation when providing feedback. For example, in the case of a technical presentation, the feedback unit can provide feedback based on technical data. In addition, in the case of a marketing presentation, the feedback unit can provide feedback based on visuals. In addition, in the case of an educational presentation, the feedback unit can provide feedback that enhances the educational effectiveness. In this way, by providing a feedback algorithm according to the category, the effectiveness of the feedback is improved. Some or all of the above-mentioned processing in the feedback unit is performed using AI. For example, the feedback unit inputs user input data into AI, which analyzes the data and applies the optimal feedback algorithm.

[0110] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated user emotions. For example, if the user is in a hurry, the feedback unit can provide short, to-the-point feedback. If the user is relaxed, the feedback unit can provide longer feedback with detailed explanations. If the user is excited, the feedback unit can provide feedback with visually stimulating effects. This improves the effectiveness of the feedback by providing feedback lengths that correspond to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit is performed using AI. For example, the feedback unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the length of the feedback.

[0111] The feedback unit can determine the priority of feedback based on the submission date of the presentation when providing feedback. For example, in the case of an urgent presentation, the feedback unit provides feedback as a top priority. The feedback unit can also provide feedback as a priority for a presentation with an upcoming submission deadline. The feedback unit can also provide feedback later for a presentation with a distant submission deadline. In this way, determining the priority based on the submission date improves the efficiency of feedback. Some or all of the above-mentioned processing in the feedback unit is performed using AI. For example, the feedback unit inputs the user's submission date data into AI, which analyzes the data and determines the priority of feedback.

[0112] When providing feedback, the feedback unit can adjust the order of feedback based on the relevance of the presentation. For example, the feedback unit places important information first and provides feedback in order of relevance. The feedback unit can also adjust the order of feedback in line with the flow of the presentation. The feedback unit can also place interesting information first to attract the audience's attention. This improves the effectiveness of feedback by providing a feedback order based on relevance. Some or all of the above-mentioned processing in the feedback unit is performed using AI. For example, the feedback unit inputs user input data into AI, which analyzes the data and adjusts the order of feedback.

[0113] The template unit can estimate the user's emotions and select a template based on the estimated user's emotions. For example, if the user is nervous, the template unit can provide a simple, highly visible template. If the user is relaxed, the template unit can also provide a template containing detailed information. If the user is in a hurry, the template unit can also provide a template that focuses on the main points. This improves the effectiveness of the presentation by providing a template that matches the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the template unit is performed using AI. For example, the template unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and selects a template.

[0114] When selecting a template, the template unit can adjust the level of detail of the template based on the importance of the presentation. For example, for an important presentation, the template unit can provide a template including detailed data and graphs. For a simple report, the template unit can also provide a concise template that focuses on the main points. For a presentation of medium importance, the template unit can also provide a template with moderate level of detail. This improves the effectiveness of the presentation by providing a template with a level of detail appropriate to the importance. Some or all of the above-mentioned processing in the template unit is performed using AI. For example, the template unit inputs user input data into AI, which then analyzes the data and provides a template with the optimal level of detail.

[0115] When selecting a template, the template unit can provide different templates depending on the category of the presentation. For example, for a technical presentation, the template unit can provide a template that emphasizes technical data. For a marketing presentation, the template unit can also provide a template that emphasizes visuals. For an educational presentation, the template unit can also provide a template that enhances the educational effect. In this way, by providing templates according to the category, the effectiveness of the presentation is improved. Some or all of the above-mentioned processing in the template unit is performed using AI. For example, the template unit inputs user input data into AI, which analyzes the data and provides the optimal template.

[0116] The template unit can estimate the user's emotions and prioritize templates based on the estimated user emotions. For example, if the user is nervous, the template unit can prioritize providing simple, highly visible templates. Furthermore, if the user is relaxed, the template unit can prioritize providing templates containing detailed information. Furthermore, if the user is in a hurry, the template unit can prioritize templates that focus on the main points. This prioritizes templates according to the user's emotions, improving the effectiveness of the presentation. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the template unit is performed using AI. For example, the template unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and prioritizes the templates.

[0117] When selecting templates, the template unit can determine the priority of templates based on the submission date of the presentation. For example, in the case of an urgent presentation, the template unit can provide templates with the highest priority. The template unit can also provide templates with priority for presentations with an upcoming submission deadline. The template unit can also provide templates with a later deadline for presentations with a distant submission deadline. In this way, by providing template priorities based on submission dates, the efficiency of presentations is improved. Some or all of the above-mentioned processing in the template unit is performed using AI. For example, the template unit inputs the user's submission date data into AI, which analyzes the data and determines the priority of templates.

[0118] When selecting templates, the template unit can adjust the order of templates based on the relevance of the presentation. For example, the template unit may place important information first and arrange the templates in order of relevance. The template unit can also adjust the order of templates in line with the flow of the presentation. The template unit can also place interesting information first to attract the audience's attention. This improves the effectiveness of the presentation by providing a template order based on relevance. Some or all of the above-mentioned processing in the template unit is performed using AI. For example, the template unit inputs user input data into AI, which analyzes the data and adjusts the order of the templates.

[0119] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit selects detailed training data. Furthermore, if the user is in a hurry, the learning unit can select training data that focuses on the main points. Furthermore, if the user is excited, the learning unit can select visually stimulating training data. This improves the effectiveness of learning by providing training data that corresponds to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit is performed using AI. For example, the learning unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and selects training data.

[0120] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit analyzes past learning data and selects the optimal learning algorithm. The learning unit can also adjust the learning algorithm based on past learning results. The learning unit can also extract effective learning patterns from past learning data and reflect them in the algorithm. In this way, by referring to past learning data, the accuracy of the learning algorithm is improved. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit inputs past learning data into AI, which analyzes the data and optimizes the learning algorithm.

[0121] During learning, the learning unit can update the learning data by reflecting user feedback. For example, the learning unit updates the learning data based on user feedback. The learning unit can also adjust the learning algorithm by reflecting user feedback. The learning unit can also analyze the user's feedback history and optimize the learning data. This improves the accuracy of the learning data by reflecting user feedback. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit inputs user feedback data into AI, which analyzes the data and updates the learning data.

[0122] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, when the user is relaxed, the learning unit increases the learning frequency to learn detailed data. Furthermore, when the user is in a hurry, the learning unit can reduce the learning frequency to learn data that focuses on the main points. Furthermore, when the user is excited, the learning unit can adjust the frequency of learning visually stimulating data. This improves the effectiveness of learning by providing a learning frequency that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit is performed using AI. For example, the learning unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the learning frequency.

[0123] During learning, the learning unit can weight the learning data based on the submission date of the presentation. For example, in the case of a presentation with an upcoming submission deadline, the learning unit weights important data and prioritizes learning. Furthermore, in the case of a presentation with a distant submission deadline, the learning unit can weight detailed data and learn. Furthermore, in the case of a presentation with a medium submission deadline, the learning unit can weight the data appropriately and learn. In this way, weighting of the learning data based on the submission date improves learning efficiency. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit inputs submission date data into AI, which analyzes the data and weights the learning data.

[0124] During learning, the learning unit can integrate information from different data sources to enrich the training data. For example, the learning unit integrates information from different data sources to enrich the training data. The learning unit can also integrate data from different industries to diversify the training data. The learning unit can also integrate data in different formats (text, images, audio, etc.) to enrich the training data. This improves the diversity of the training data by integrating information from different data sources. Some or all of the above-mentioned processing in the learning unit is performed using AI. For example, the learning unit inputs data from different data sources into AI, which analyzes the data to enrich the training data. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, generation unit, display unit, feedback unit, template unit, and learning unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, and allows a user to input the theme and content of the presentation. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates presentation materials using a generation AI. The display unit is implemented, for example, by the output device 40 of the smart device 14, and displays the generated presentation materials in a visually easy-to-understand manner. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects and analyzes user evaluations and opinions. The template unit is implemented, for example, by the control unit 46A of the smart device 14, and provides templates suitable for specific industries or applications. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and learns user feedback and reflects it in subsequent material generation. === Hard Collateral 1-2 === Each of the above-described elements, including the reception unit, generation unit, display unit, feedback unit, template unit, and learning unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, and a user inputs the theme and content of the presentation. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates presentation materials using a generation AI. The display unit is implemented, for example, by the output device 40 of the smart glasses 214, and displays the generated presentation materials in a visually easy-to-understand manner. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects and analyzes user evaluations and opinions. The template unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides templates suitable for specific industries or applications. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and learns user feedback and reflects it in subsequent material generation. === Hard Collateral 1-3 === Each of the above-described elements, including the reception unit, generation unit, display unit, feedback unit, template unit, and learning unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset-type terminal 314, and allows a user to input the theme and content of the presentation. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates presentation materials using a generation AI. The display unit is implemented, for example, by the output device 40 of the headset-type terminal 314, and displays the generated presentation materials in a visually easy-to-understand manner. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects and analyzes user evaluations and opinions. The template unit is implemented, for example, by the control unit 46A of the headset-type terminal 314, and provides templates suitable for specific industries or applications. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and learns user feedback and reflects it in subsequent material generation. === Hard Collateral 1-4 === Each of the above-described elements, including the reception unit, generation unit, display unit, feedback unit, template unit, and learning unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, and a user inputs the theme and content of the presentation. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates presentation materials using a generation AI. The display unit is implemented, for example, by the output device 40 of the robot 414, and displays the generated presentation materials in a visually easy-to-understand manner. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects and analyzes user evaluations and opinions. The template unit is implemented, for example, by the control unit 46A of the robot 414, and provides templates suitable for specific industries or applications. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and learns user feedback and reflects it in subsequent material generation.

[0125] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0126] The reception unit can analyze the user's past presentation history and suggest the optimal input method. For example, themes and content frequently used by the user in the past can be automatically displayed as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest themes and content to be used at a specific time period based on the user's past presentation history. This improves input efficiency by suggesting the optimal input method based on the past history. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's past presentation data into AI, which analyzes the data and suggests the optimal input method.

[0127] During generation, the generation unit can apply different generation algorithms depending on the category of the presentation. For example, for a technical presentation, an algorithm that emphasizes technical data can be applied. The generation unit can also apply an algorithm that emphasizes visuals for a marketing presentation. The generation unit can also apply an algorithm that enhances educational effectiveness for an educational presentation. In this way, by applying a generation algorithm depending on the presentation category, the effectiveness of the materials is improved. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs user input data into the generation AI, which analyzes the data and applies the optimal generation algorithm.

[0128] The display unit can estimate the user's emotions and adjust the layout of the presentation materials to be displayed based on the estimated user's emotions. For example, if the user is nervous, the display unit can provide a simple, highly visible layout. If the user is relaxed, the display unit can provide a layout including detailed information. If the user is in a hurry, the display unit can provide a layout that focuses on the main points. This improves the visibility of the materials by providing a layout that corresponds 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit is performed using AI. For example, the display unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the layout.

[0129] The feedback unit can estimate the user's emotions and adjust the way the feedback is expressed based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can provide gentle feedback. If the user is relaxed, the feedback unit can provide detailed feedback. If the user is in a hurry, the feedback unit can provide concise feedback that focuses on the main points. This improves the effectiveness of feedback by providing feedback that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the feedback unit is performed using AI. For example, the feedback unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the way the feedback is expressed.

[0130] The template unit can estimate the user's emotions and select a template based on the estimated user's emotions. For example, if the user is nervous, it can provide a simple, highly visible template. If the user is relaxed, the template unit can provide a template containing detailed information. If the user is in a hurry, the template unit can provide a template that focuses on the main points. This improves the effectiveness of the presentation by providing a template that matches the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the template unit is performed using AI. For example, the template unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and selects a template.

[0131] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, it can analyze presentation materials generated by the user in the past to improve the accuracy of generation. The generation unit can also learn and reflect the user's preferred designs and layouts from the user's past generation results. The generation unit can also select the optimal generation algorithm based on the user's past generation results. In this way, by referring to past generation results, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's past generation data into the generation AI, which analyzes the data to improve the accuracy of generation.

[0132] The display unit can determine the display priority based on the submission date of the presentation when displaying. For example, in the case of an urgent presentation, the display unit displays the materials as the highest priority. The display unit can also display materials as a priority for presentations with an upcoming submission deadline. The display unit can also display materials later for presentations with a distant submission deadline. In this way, display efficiency is improved by determining the priority based on the submission date. Some or all of the above-mentioned processing in the display unit is performed using AI. For example, the display unit inputs the user's submission date data into AI, which analyzes the data and determines the display priority.

[0133] The feedback unit can apply different feedback algorithms depending on the category of the presentation when providing feedback. For example, in the case of a technical presentation, feedback based on technical data is provided. In addition, the feedback unit can provide feedback based on visuals in the case of a marketing presentation. In addition, in the case of an educational presentation, the feedback unit can provide feedback that enhances the educational effectiveness. In this way, by providing a feedback algorithm according to the category, the effectiveness of the feedback is improved. Some or all of the above-mentioned processing in the feedback unit is performed using AI. For example, the feedback unit inputs user input data into AI, which analyzes the data and applies the optimal feedback algorithm.

[0134] When selecting templates, the template unit can determine the priority of templates based on the submission date of the presentation. For example, in the case of an urgent presentation, the template is provided with the highest priority. The template unit can also provide templates with priority for presentations with an upcoming submission deadline. The template unit can also provide templates with a later deadline for presentations with a distant submission deadline. This improves the efficiency of presentations by providing a priority of templates based on the submission date. Some or all of the above-mentioned processing in the template unit is performed using AI. For example, the template unit inputs the user's submission date data into AI, which analyzes the data and determines the priority of templates.

[0135] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. For example, if the user is relaxed, the learning frequency can be increased to learn detailed data. Furthermore, if the user is in a hurry, the learning unit can also reduce the learning frequency to learn data that focuses on the main points. Furthermore, if the user is excited, the learning unit can also adjust the frequency of learning visually stimulating data. This improves the effectiveness of learning by providing a learning frequency that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit is performed using AI. For example, the learning unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the learning frequency.

[0136] The processing flow of the second embodiment will be briefly explained below.

[0137] Step 1: The reception unit allows the user to input the theme and content of the presentation. The themes and content input by the user include business presentations, academic presentations, product introductions, etc. The reception unit allows the user to specifically input the purpose and target audience of the presentation, the message they want to convey, etc. The reception unit also supports multiple input methods, such as voice input and text input. Step 2: The generation unit uses generative AI to analyze the information entered by the reception unit and generate presentation materials. The generation unit generates graphs and charts when data visualization is important, flowcharts when process explanations are required, and animations and video clips when video explanations are effective. Generative AI is realized using technologies such as deep learning models, generative adversarial networks (GAN), and natural language generation (NLG). Step 3: The display unit displays the presentation materials generated by the generator in a visually easy-to-understand manner. The display unit adjusts the color usage, layout, font size, etc. to reduce visual strain. It also makes efforts to ensure a smooth flow of information and easy understanding for the audience.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0143] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0159] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0161] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0162] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0165] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0175] 7, a 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.

[0176] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0177] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0178] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0180] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0181] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0182] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0183] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0184] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0185] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0186] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0187] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0188] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0189] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0190] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0191] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0192] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0193] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0194] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0195] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0196] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0197] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0198] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0199] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0200] 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.

[0201] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0202] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0203] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0204] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0205] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0206] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0207] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0208] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0209] [Explanation of symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where you can input the theme and content of your presentation, a generation unit that analyzes the information input by the reception unit and generates presentation materials; a display unit that displays the presentation materials generated by the generation unit in a visually easy-to-understand manner; Equipped with A system characterized by:

2. The generation unit Generative AI generates graphs and charts when data visualization is needed, flowcharts when process explanations are needed, and animations and video clips when animated explanations are needed.

2. The system of claim 1.

3. The display unit Adjust color usage, layout, font size, etc. to reduce visual strain 2. The system of claim 1.

4. The display unit Make sure the information flows smoothly and is easy for the audience to understand 2. The system of claim 1.

5. A feedback section is provided where users can evaluate the generated materials and provide their opinions.

2. The system of claim 1.

6. It has a template section that provides templates suitable for specific industries and applications.

2. The system of claim 1.

7. Equipped with a learning section that learns user opinions and reflects them in subsequent document generation 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and change the presentation theme and content input method based on the estimated user emotions.

2. The system of claim 1.

Images