system

Abstract

The system according to this embodiment aims to efficiently create slides for presentations and announcements. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a modification unit. The reception unit receives input of the content to be conveyed in a presentation and the presentation time. The analysis unit analyzes the information received by the reception unit and extracts important points. The generation unit generates slides based on the important points extracted by the analysis unit. The modification unit modifies the slides generated by the generation unit based on user feedback.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult and time-consuming to efficiently create slides for presentations or lectures.

[0005] The system according to the embodiment aims to efficiently create slides for presentations or lectures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a modification unit. The reception unit receives input of the content to be conveyed in a presentation and the presentation time. The analysis unit analyzes the information received by the reception unit and extracts important points. The generation unit generates slides based on the important points extracted by the analysis unit. The modification unit modifies the slides generated by the generation unit based on user feedback. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently create slides for presentations and announcements. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The slide generation system according to an embodiment of the present invention is a system for streamlining the preparation of presentations and speeches. This slide generation system automatically generates slides when the user inputs the content they want to convey and the presentation time, and the generation AI skillfully summarizes that content. The specific steps involve the user inputting information, the generation AI analyzing it and generating slides, and making revisions as needed. First, the user inputs the content they want to convey in their presentation and the presentation time. For example, they might input information such as "Introduction of a new product" and "30 minutes." This information is input into the generation AI. Next, the generation AI analyzes the input information and skillfully summarizes the content of the presentation. Based on the input content, the generation AI extracts key points and considers an appropriate slide structure. For example, it generates slides for the introduction, main points, and conclusion. The generated slides are not overly textual and have a visually easy-to-understand design. The generation AI selects appropriate font sizes, colors, and layouts to create visually appealing slides. This allows the user to deliver an effective presentation in a short amount of time. Furthermore, the generation AI can also revise the slides based on user feedback. For example, if a user inputs a request such as "I want to explain the content of this slide in more detail," the generation AI will revise the slide accordingly. This system streamlines the preparation of presentations and allows users to create high-quality slides in a short amount of time. It can be used in various situations, such as business presentations and academic presentations. In short, the automatic slide generation system streamlines the preparation of presentations and allows users to create high-quality slides in a short amount of time.

[0029] The slide generation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a modification unit. The reception unit accepts input of the content to be conveyed in a presentation and the presentation time. For example, the user can input information such as "Introduction of a new product" and "30 minutes" into the reception unit. The reception unit transmits the input information to the generation AI. The analysis unit analyzes the information received by the reception unit and extracts important points. For example, the analysis unit extracts important keywords and phrases based on the input content. The analysis unit can understand the context and identify important information using natural language processing technology. The generation unit generates slides based on the important points extracted by the analysis unit. For example, the generation unit generates slides such as an introduction, main points, and conclusion. The generation unit selects appropriate font sizes, colors, and layouts to create visually appealing slides. The generation unit can automatically adjust the slide design using the generation AI. The modification unit modifies the slides generated by the generation unit based on user feedback. The editing unit, for example, modifies the slide when a user inputs a request such as "I want to explain the content of this slide in more detail." The editing unit can automatically modify the slide using generation AI. As a result, the automatic slide generation system according to this embodiment can streamline the preparation of presentations and create high-quality slides in a short amount of time.

[0030] The reception desk accepts input on the content and duration of the presentation. For example, the reception desk allows users to input information such as "Introduction of a new product" and "30 minutes." The reception desk then sends the entered information to the generation AI. Specifically, users can input detailed information such as the presentation theme and purpose, the characteristics of the target audience, and the components of the presentation through a dedicated interface. For example, users can input specific items such as "Technical features of the new product," "Market needs," "Comparison with competing products," and "Case studies." Regarding the presentation time, users can also finely adjust the overall time allocation and the time allocated to each section. This allows the reception desk to collect detailed information tailored to the user's needs and prepare to send it to the generation AI. Furthermore, the reception desk has a function to check the information entered by the user in real time and prompt corrections or additional input as needed. For example, if there are deficiencies in the input or if more specific information is needed, an alert will be displayed to notify the user. In this way, the reception desk can support users in entering accurate and detailed information and provide the foundational data for the generation AI to generate high-quality slides.

[0031] The analysis unit analyzes the information received by the reception unit and extracts key points. For example, the analysis unit extracts important keywords and phrases based on the input content. The analysis unit can understand the context and identify important information using natural language processing technology. Specifically, the generation AI analyzes the input text and uses topic modeling and keyword extraction algorithms to understand the context and meaning. For example, topic modeling is used to identify the main topics from the input text and extract related keywords. Keyword extraction algorithms are also used to identify frequently occurring words and phrases and analyze their meaning in context. Furthermore, the analysis unit performs importance scoring to select points that should be particularly emphasized and information that is important to the audience from the input information. For example, scores are assigned to specific keywords and phrases, and important points are ranked based on those scores. This allows the analysis unit to accurately grasp what the user wants to convey and provide basic data for slide generation. In addition, the analysis unit can refer to past data and similar presentations to perform more accurate analysis. This allows the analysis unit to support the generation of high-quality slides that meet the user's needs.

[0032] The generation unit generates slides based on key points extracted by the analysis unit. For example, it generates slides for the introduction, main points, and conclusion. The generation unit selects appropriate font sizes, colors, and layouts to create visually appealing slides. Using generation AI, the generation unit can automatically adjust the slide design. Specifically, the generation AI structures the content of each slide and selects a visually effective design based on the key points provided by the analysis unit. For example, the introduction briefly explains the purpose and background of the presentation and includes catchy phrases and images to capture the audience's attention. The main points section provides detailed explanations for each topic and conveys information visually using graphs and charts. The conclusion section summarizes the key points of the presentation and presents the next steps or suggestions. The generation unit automatically generates these slides, enabling users to create high-quality slides in a short time. Furthermore, the generation unit also has a function to customize the slide design according to user preferences and brand guidelines. For example, by selecting a specific color palette or font style, slides that match the company's brand image can be generated. This allows the generation unit to create slides flexibly according to the user's needs, streamlining the preparation of presentations and speeches.

[0033] The editing unit modifies slides generated by the generation unit based on user feedback. For example, if a user inputs a request such as "I want to explain the content of this slide in more detail," the editing unit modifies the slide accordingly. The editing unit can automatically modify slides using generation AI. Specifically, when a user inputs the parts they want to modify or the information they want to add, the generation AI analyzes the content and makes appropriate modifications. For example, if a user inputs "I want to add more detailed data to this graph," the generation AI searches for the additional data and reflects it in the graph. Also, if a user inputs "I want to change the layout of this slide," the generation AI suggests an optimal layout and automatically rearranges the slides. Furthermore, the editing unit can improve the accuracy and quality of slide generation by continuously collecting user feedback and using it as training data for the generation AI. For example, based on past modifications and feedback from users, the generation AI can make more appropriate suggestions when generating slides next time. This allows the editing unit to achieve flexible slide modifications in response to user requests, further streamlining the preparation of presentations and presentations.

[0034] The reception desk can analyze the user's past presentation and presentation history and suggest the optimal input method. For example, the reception desk can suggest relevant templates based on the themes and content of presentations the user has used in the past. It can also automatically apply fonts and colors that the user has previously preferred. Furthermore, the reception desk can extract frequently used keywords from the user's past presentation history and provide an auto-completion function during input. This allows for the suggestion of the optimal input method based on past history, thereby streamlining the input process. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past presentation data into a generating AI and have the generating AI suggest the optimal input method.

[0035] The reception system can automatically customize input fields according to the content of the presentation or announcement. For example, if a user enters "Introduction of a new product," the reception system can automatically display input fields such as product features, price, and comparison with competing products. Similarly, if a user enters "Research presentation," the reception system can automatically display input fields such as research background, methods, results, and discussion. Furthermore, if a user enters "Project report," the reception system can automatically display input fields such as project objectives, progress, challenges, and next steps. This allows for the automatic display of input fields appropriate to the content, streamlining the input process. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input the user's input content into a generating AI and have the generating AI customize the input fields.

[0036] The reception desk can add region-specific information to input fields, taking into account the user's geographical location. For example, if the user is giving a presentation in a specific region, the reception desk can automatically add market data and competitor information for that region to the input fields. Furthermore, if the user is giving a presentation overseas, the reception desk can add information about the culture and business etiquette of that country to the input fields. Additionally, if the user is giving a presentation in a specific city, the reception desk can add information about the city's economic situation and major industries to the input fields. This allows for the addition of region-specific information to the input fields, enriching the presentation content. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI add region-specific information.

[0037] The reception desk can analyze a user's social media activity and automatically reflect relevant information in the input fields. For example, the reception desk can add relevant topics to the input fields based on articles and posts that the user has shared on social media. It can also suggest input fields that reflect the opinions of industry leaders and influencers that the user follows. Furthermore, the reception desk can extract themes and topics of interest from the user's social media activity history and reflect them in the input fields. This allows for the reflection of relevant information based on social media activity and streamlines the input process. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI perform the extraction and reflection of relevant information.

[0038] The analysis unit can evaluate the reliability of the input information and prioritize the analysis of highly reliable information. For example, the analysis unit can verify the source of the input information and prioritize the analysis of highly reliable information. Furthermore, the analysis unit can cross-check the content of the input information and prioritize the analysis of matching information. In addition, the analysis unit can calculate a reliability score for the input information and prioritize the analysis of information with high scores. This allows for the prioritization of highly reliable information and improves the accuracy of the analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the input information into a generating AI and have the generating AI perform the reliability evaluation and analysis.

[0039] The analysis unit can apply different analytical methods depending on the theme of the presentation or presentation. For example, in the case of a business presentation, the analysis unit can apply market analysis and competitor analysis methods. In the case of an academic presentation, the analysis unit can apply literature review and data analysis methods. Furthermore, in the case of a project report, the analysis unit can apply progress management and risk assessment methods. This allows for the application of analytical methods appropriate to the theme, improving the accuracy of the analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the theme of the presentation or presentation into a generating AI, and have the generating AI select and apply an appropriate analytical method.

[0040] The analysis unit can determine the priority of analysis based on the submission timing of the input information. For example, the analysis unit may prioritize the analysis of information with an approaching deadline. Furthermore, the analysis unit can determine the priority of analysis based on a deadline specified by the user. Additionally, the analysis unit may prioritize the analysis of information with an earlier submission date. This allows for efficient analysis by determining the priority of analysis based on the submission date. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0041] The analysis unit can improve the accuracy of the analysis results by referring to relevant literature and databases. For example, the analysis unit can supplement the analysis results by referring to relevant academic papers. Furthermore, the analysis unit can improve the reliability of the analysis results by referring to industry databases. In addition, the analysis unit can refine the analysis results by referring to publicly available statistical data. This allows for improved accuracy of the analysis results by referring to relevant literature and databases. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from relevant literature and databases into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis results.

[0042] The generation unit can apply different design templates depending on the content of the slides to be generated. For example, in the case of a business presentation, the generation unit can apply a professional design template. In the case of an academic presentation, the generation unit can apply a simple yet information-rich design template. Furthermore, in the case of a project report, the generation unit can apply a design template that visually shows the progress. This allows for the application of a design template appropriate to the content, thereby improving the quality of the slides. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the slide content into a generation AI and have the generation AI select and apply an appropriate design template.

[0043] The generation unit can automatically add visual elements (images, graphs, icons, etc.) when generating slides. For example, the generation unit can automatically add appropriate graphs to show important data. It can also automatically add relevant images to supplement explanations. Furthermore, it can automatically add visually striking icons to highlight key points. This allows for the automatic addition of visual elements, improving the visual appeal of the slides. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the slide content into a generation AI and have the generation AI perform the addition of visual elements.

[0044] The generation unit can optimize the design of slides by referencing the user's past presentation materials during slide generation. For example, the generation unit can generate slides based on design templates previously used by the user. Furthermore, the generation unit can automatically apply frequently used fonts and colors from the user's past presentation materials. In addition, the generation unit can analyze the user's past presentation materials and incorporate the most effective design elements. This allows for improved slide quality by referencing past presentation materials and optimizing the design. Some or all of the above processes in the generation unit may be performed using AI, or not. For example, the generation unit can input past presentation materials into a generation AI and have the generation AI perform the design optimization.

[0045] The generation unit can reflect industry-specific information of the user when generating slides. For example, the generation unit can generate slides that reflect the latest trends in the user's industry. It can also reflect industry-specific terminology and data in the slides. Furthermore, the generation unit can add examples and case studies related to the user's industry to the slides. This allows for the reflection of industry-specific information and enriches the content of the slides. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input industry-specific information into a generation AI and have the generation AI perform the process of reflecting it in the slides.

[0046] The editing unit can automatically modify slides by reflecting user feedback in real time. For example, if a user provides feedback such as "I want the font size on this slide to be larger," the editing unit can immediately adjust the font size. Furthermore, if a user provides feedback such as "I want more detailed information added to this slide," the editing unit can automatically add the relevant information. Additionally, if a user provides feedback such as "I want the color of this slide changed," the editing unit can immediately change it to the specified color. This allows for real-time feedback and improvement of slide quality. Some or all of the above-described processes in the editing unit may be performed using AI, for example, or without AI. For example, the editing unit can input user feedback into a generating AI and have the generating AI perform real-time modifications.

[0047] The editing unit can suggest the optimal editing method by referring to the user's past feedback history during the editing process. For example, the editing unit can suggest editing based on fonts and colors that the user has previously preferred. It can also suggest similar editing methods by referring to editing content the user has previously made. Furthermore, the editing unit can extract and suggest frequently used editing patterns from the user's past feedback history. This makes the editing process more efficient by referring to past feedback history and suggesting the optimal editing method. Some or all of the above processes in the editing unit may be performed using AI, for example, or without AI. For example, the editing unit can input past feedback history into a generating AI and have the generating AI suggest the optimal editing method.

[0048] The correction unit can propose the optimal correction method while considering the user's device information. For example, if the user is using a smartphone, the correction unit can propose a correction method that matches the screen size. If the user is using a tablet, the correction unit can propose a correction method optimized for a larger screen. Furthermore, if the user is using a desktop, the correction unit can propose a detailed correction method. This allows for the proposal of a correction method that takes device information into account, thereby streamlining the correction process. Some or all of the above-described processes in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input the user's device information into a generating AI and have the generating AI propose the optimal correction method.

[0049] The editing function can incorporate industry-specific feedback from the user during the editing process. For example, the editing function can make revisions that reflect the latest trends in the user's industry. It can also incorporate industry-specific terminology and data into the revisions. Furthermore, the editing function can add examples and case studies related to the user's industry to the revisions. This allows for the incorporation of industry-specific feedback and enriches the content of the slides. Some or all of the above processes in the editing function may be performed using AI, for example, or not. For example, the editing function can input industry-specific feedback into a generating AI and have the generating AI perform the process of reflecting it in the slides.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The analysis unit can automatically refer to relevant external databases based on user input to supplement the analysis results. For example, if a user inputs a new product introduction, it can refer to relevant market databases and automatically obtain information on competing products. Similarly, if a user inputs a research presentation, it can refer to relevant academic paper databases and automatically obtain the latest research findings. Furthermore, if a user inputs a project report, it can refer to relevant industry databases and automatically obtain data to supplement the project's progress. This allows for supplementing analysis results by referencing external databases and improving the quality of presentation slides.

[0052] The reception system can automatically suggest relevant video tutorials based on user input. For example, if a user enters a new product introduction, it can suggest a relevant product demo video. Similarly, if a user enters a research presentation, it can suggest a video tutorial on relevant research methods. Furthermore, if a user enters a project report, it can suggest a video tutorial on relevant project management. This allows for the suggestion of relevant video tutorials, thereby deepening the user's understanding.

[0053] The analysis unit can automatically retrieve relevant news articles based on user input and reflect them in the analysis results. For example, if a user enters a new product introduction, it can retrieve the latest relevant news articles and reflect information on competing products in the analysis results. Similarly, if a user enters a research presentation, it can retrieve the latest relevant research news and reflect it in the analysis results. Furthermore, if a user enters a project report, it can retrieve relevant industry news and reflect it in the analysis results. This allows for the incorporation of the latest news articles to supplement the analysis results and improve the quality of the slides.

[0054] The reception system can automatically suggest relevant books and materials based on user input. For example, if a user enters a new product introduction, it can suggest relevant marketing books. Similarly, if a user enters a research presentation, it can suggest relevant academic books. Furthermore, if a user enters a project report, it can suggest relevant project management books. This allows for the suggestion of relevant books and materials, deepening the user's understanding.

[0055] The analysis unit can automatically obtain opinions from relevant experts based on user input and incorporate them into the analysis results. For example, if a user inputs a new product introduction, it can obtain opinions from relevant marketing experts and incorporate them into the analysis results. Similarly, if a user inputs a research presentation, it can obtain opinions from relevant academic experts and incorporate them into the analysis results. Furthermore, if a user inputs a project report, it can obtain opinions from relevant project management experts and incorporate them into the analysis results. This allows for the incorporation of expert opinions to enhance the analysis results and improve the quality of the slides.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk accepts input from users regarding the content of their presentation and the presentation time. For example, a user can input information such as "Introduction of a new product" and "30 minutes." The reception desk then sends the entered information to the generation AI. Step 2: The analysis unit analyzes the information received by the reception unit and extracts key points. For example, it extracts important keywords and phrases based on the input content. The analysis unit can use natural language processing technology to understand the context and identify important information. Step 3: The generation unit generates slides based on the key points extracted by the analysis unit. For example, it generates slides for the introduction, main points, and conclusion. The generation unit selects appropriate font sizes, colors, and layouts to create visually appealing slides. The generation unit can automatically adjust the slide design using generation AI. Step 4: The editing unit modifies the slides generated by the generation unit based on user feedback. For example, if a user inputs a request such as "I'd like to explain the content of this slide in more detail," the editing unit modifies the slide accordingly. The editing unit can automatically modify slides using generation AI.

[0058] (Example of form 2) The slide generation system according to an embodiment of the present invention is a system for streamlining the preparation of presentations and speeches. This slide generation system automatically generates slides when the user inputs the content they want to convey and the presentation time, and the generation AI skillfully summarizes that content. The specific steps involve the user inputting information, the generation AI analyzing it and generating slides, and making revisions as needed. First, the user inputs the content they want to convey in their presentation and the presentation time. For example, they might input information such as "Introduction of a new product" and "30 minutes." This information is input into the generation AI. Next, the generation AI analyzes the input information and skillfully summarizes the content of the presentation. Based on the input content, the generation AI extracts key points and considers an appropriate slide structure. For example, it generates slides for the introduction, main points, and conclusion. The generated slides are not overly textual and have a visually easy-to-understand design. The generation AI selects appropriate font sizes, colors, and layouts to create visually appealing slides. This allows the user to deliver an effective presentation in a short amount of time. Furthermore, the generation AI can also revise the slides based on user feedback. For example, if a user inputs a request such as "I want to explain the content of this slide in more detail," the generation AI will revise the slide accordingly. This system streamlines the preparation of presentations and allows users to create high-quality slides in a short amount of time. It can be used in various situations, such as business presentations and academic presentations. In short, the automatic slide generation system streamlines the preparation of presentations and allows users to create high-quality slides in a short amount of time.

[0059] The slide generation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a modification unit. The reception unit accepts input of the content to be conveyed in a presentation and the presentation time. For example, the user can input information such as "Introduction of a new product" and "30 minutes" into the reception unit. The reception unit transmits the input information to the generation AI. The analysis unit analyzes the information received by the reception unit and extracts important points. For example, the analysis unit extracts important keywords and phrases based on the input content. The analysis unit can understand the context and identify important information using natural language processing technology. The generation unit generates slides based on the important points extracted by the analysis unit. For example, the generation unit generates slides such as an introduction, main points, and conclusion. The generation unit selects appropriate font sizes, colors, and layouts to create visually appealing slides. The generation unit can automatically adjust the slide design using the generation AI. The modification unit modifies the slides generated by the generation unit based on user feedback. The editing unit, for example, modifies the slide when a user inputs a request such as "I want to explain the content of this slide in more detail." The editing unit can automatically modify the slide using generation AI. As a result, the automatic slide generation system according to this embodiment can streamline the preparation of presentations and create high-quality slides in a short amount of time.

[0060] The reception desk accepts input on the content and duration of the presentation. For example, the reception desk allows users to input information such as "Introduction of a new product" and "30 minutes." The reception desk then sends the entered information to the generation AI. Specifically, users can input detailed information such as the presentation theme and purpose, the characteristics of the target audience, and the components of the presentation through a dedicated interface. For example, users can input specific items such as "Technical features of the new product," "Market needs," "Comparison with competing products," and "Case studies." Regarding the presentation time, users can also finely adjust the overall time allocation and the time allocated to each section. This allows the reception desk to collect detailed information tailored to the user's needs and prepare to send it to the generation AI. Furthermore, the reception desk has a function to check the information entered by the user in real time and prompt corrections or additional input as needed. For example, if there are deficiencies in the input or if more specific information is needed, an alert will be displayed to notify the user. In this way, the reception desk can support users in entering accurate and detailed information and provide the foundational data for the generation AI to generate high-quality slides.

[0061] The analysis unit analyzes the information received by the reception unit and extracts key points. For example, the analysis unit extracts important keywords and phrases based on the input content. The analysis unit can understand the context and identify important information using natural language processing technology. Specifically, the generation AI analyzes the input text and uses topic modeling and keyword extraction algorithms to understand the context and meaning. For example, topic modeling is used to identify the main topics from the input text and extract related keywords. Keyword extraction algorithms are also used to identify frequently occurring words and phrases and analyze their meaning in context. Furthermore, the analysis unit performs importance scoring to select points that should be particularly emphasized and information that is important to the audience from the input information. For example, scores are assigned to specific keywords and phrases, and important points are ranked based on those scores. This allows the analysis unit to accurately grasp what the user wants to convey and provide basic data for slide generation. In addition, the analysis unit can refer to past data and similar presentations to perform more accurate analysis. This allows the analysis unit to support the generation of high-quality slides that meet the user's needs.

[0062] The generation unit generates slides based on key points extracted by the analysis unit. For example, it generates slides for the introduction, main points, and conclusion. The generation unit selects appropriate font sizes, colors, and layouts to create visually appealing slides. Using generation AI, the generation unit can automatically adjust the slide design. Specifically, the generation AI structures the content of each slide and selects a visually effective design based on the key points provided by the analysis unit. For example, the introduction briefly explains the purpose and background of the presentation and includes catchy phrases and images to capture the audience's attention. The main points section provides detailed explanations for each topic and conveys information visually using graphs and charts. The conclusion section summarizes the key points of the presentation and presents the next steps or suggestions. The generation unit automatically generates these slides, enabling users to create high-quality slides in a short time. Furthermore, the generation unit also has a function to customize the slide design according to user preferences and brand guidelines. For example, by selecting a specific color palette or font style, slides that match the company's brand image can be generated. This allows the generation unit to create slides flexibly according to the user's needs, streamlining the preparation of presentations and speeches.

[0063] The editing unit modifies slides generated by the generation unit based on user feedback. For example, if a user inputs a request such as "I want to explain the content of this slide in more detail," the editing unit modifies the slide accordingly. The editing unit can automatically modify slides using generation AI. Specifically, when a user inputs the parts they want to modify or the information they want to add, the generation AI analyzes the content and makes appropriate modifications. For example, if a user inputs "I want to add more detailed data to this graph," the generation AI searches for the additional data and reflects it in the graph. Also, if a user inputs "I want to change the layout of this slide," the generation AI suggests an optimal layout and automatically rearranges the slides. Furthermore, the editing unit can improve the accuracy and quality of slide generation by continuously collecting user feedback and using it as training data for the generation AI. For example, based on past modifications and feedback from users, the generation AI can make more appropriate suggestions when generating slides next time. This allows the editing unit to achieve flexible slide modifications in response to user requests, further streamlining the preparation of presentations and presentations.

[0064] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. This allows for the provision of an interface tailored to the user's emotions, making the input process more comfortable. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0065] The reception desk can analyze the user's past presentation and presentation history and suggest the optimal input method. For example, the reception desk can suggest relevant templates based on the themes and content of presentations the user has used in the past. It can also automatically apply fonts and colors that the user has previously preferred. Furthermore, the reception desk can extract frequently used keywords from the user's past presentation history and provide an auto-completion function during input. This allows for the suggestion of the optimal input method based on past history, thereby streamlining the input process. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past presentation data into a generating AI and have the generating AI suggest the optimal input method.

[0066] The reception system can automatically customize input fields according to the content of the presentation or announcement. For example, if a user enters "Introduction of a new product," the reception system can automatically display input fields such as product features, price, and comparison with competing products. Similarly, if a user enters "Research presentation," the reception system can automatically display input fields such as research background, methods, results, and discussion. Furthermore, if a user enters "Project report," the reception system can automatically display input fields such as project objectives, progress, challenges, and next steps. This allows for the automatic display of input fields appropriate to the content, streamlining the input process. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input the user's input content into a generating AI and have the generating AI customize the input fields.

[0067] The reception desk can estimate the user's emotions and prioritize the input content based on the estimated emotions. For example, if the user is in a hurry, the reception desk will prioritize inputting important items and postpone detailed items. If the user is relaxed, the reception desk can allow inputting all items, including detailed ones, in order. Furthermore, if the user is stressed, the reception desk can allow inputting the easiest items first and gradually increase the difficulty. This allows for efficient input by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0068] The reception desk can add region-specific information to input fields, taking into account the user's geographical location. For example, if the user is giving a presentation in a specific region, the reception desk can automatically add market data and competitor information for that region to the input fields. Furthermore, if the user is giving a presentation overseas, the reception desk can add information about the culture and business etiquette of that country to the input fields. Additionally, if the user is giving a presentation in a specific city, the reception desk can add information about the city's economic situation and major industries to the input fields. This allows for the addition of region-specific information to the input fields, enriching the presentation content. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI add region-specific information.

[0069] The reception desk can analyze a user's social media activity and automatically reflect relevant information in the input fields. For example, the reception desk can add relevant topics to the input fields based on articles and posts that the user has shared on social media. It can also suggest input fields that reflect the opinions of industry leaders and influencers that the user follows. Furthermore, the reception desk can extract themes and topics of interest from the user's social media activity history and reflect them in the input fields. This allows for the reflection of relevant information based on social media activity and streamlines the input process. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI perform the extraction and reflection of relevant information.

[0070] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is tense, the analysis unit can simplify the analysis algorithm and extract only the important points. If the user is relaxed, the analysis unit can perform a more detailed analysis and extract more points. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis and prioritize extracting the most important points. This allows the analysis algorithm to be applied according to the user's emotions, improving the accuracy of the analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0071] The analysis unit can evaluate the reliability of the input information and prioritize the analysis of highly reliable information. For example, the analysis unit can verify the source of the input information and prioritize the analysis of highly reliable information. Furthermore, the analysis unit can cross-check the content of the input information and prioritize the analysis of matching information. In addition, the analysis unit can calculate a reliability score for the input information and prioritize the analysis of information with high scores. This allows for the prioritization of highly reliable information and improves the accuracy of the analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the input information into a generating AI and have the generating AI perform the reliability evaluation and analysis.

[0072] The analysis unit can apply different analytical methods depending on the theme of the presentation or presentation. For example, in the case of a business presentation, the analysis unit can apply market analysis and competitor analysis methods. In the case of an academic presentation, the analysis unit can apply literature review and data analysis methods. Furthermore, in the case of a project report, the analysis unit can apply progress management and risk assessment methods. This allows for the application of analytical methods appropriate to the theme, improving the accuracy of the analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the theme of the presentation or presentation into a generating AI, and have the generating AI select and apply an appropriate analytical method.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. This allows for the provision of a display method that is appropriate to the user's emotions and facilitates the understanding of the analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0074] The analysis unit can determine the priority of analysis based on the submission timing of the input information. For example, the analysis unit may prioritize the analysis of information with an approaching deadline. Furthermore, the analysis unit can determine the priority of analysis based on a deadline specified by the user. Additionally, the analysis unit may prioritize the analysis of information with an earlier submission date. This allows for efficient analysis by determining the priority of analysis based on the submission date. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0075] The analysis unit can improve the accuracy of the analysis results by referring to relevant literature and databases. For example, the analysis unit can supplement the analysis results by referring to relevant academic papers. Furthermore, the analysis unit can improve the reliability of the analysis results by referring to industry databases. In addition, the analysis unit can refine the analysis results by referring to publicly available statistical data. This allows for improved accuracy of the analysis results by referring to relevant literature and databases. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from relevant literature and databases into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis results.

[0076] The generation unit can estimate the user's emotions and adjust the slide design based on the estimated emotions. For example, if the user is nervous, the generation unit can provide a design with calm colors. If the user is enjoying themselves, the generation unit can provide a design with bright colors. Furthermore, if the user is tired, the generation unit can provide a simple and highly visible design. This allows for the creation of visually appealing slides with designs that respond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] The generation unit can apply different design templates depending on the content of the slides to be generated. For example, in the case of a business presentation, the generation unit can apply a professional design template. In the case of an academic presentation, the generation unit can apply a simple yet information-rich design template. Furthermore, in the case of a project report, the generation unit can apply a design template that visually shows the progress. This allows for the application of a design template appropriate to the content, thereby improving the quality of the slides. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the slide content into a generation AI and have the generation AI select and apply an appropriate design template.

[0078] The generation unit can automatically add visual elements (images, graphs, icons, etc.) when generating slides. For example, the generation unit can automatically add appropriate graphs to show important data. It can also automatically add relevant images to supplement explanations. Furthermore, it can automatically add visually striking icons to highlight key points. This allows for the automatic addition of visual elements, improving the visual appeal of the slides. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the slide content into a generation AI and have the generation AI perform the addition of visual elements.

[0079] The generation unit can estimate the user's emotions and adjust the order of slides based on the estimated emotions. For example, if the user is nervous, the generation unit can place important points first to provide a sense of reassurance. If the user is relaxed, the generation unit can arrange the slides in order from the introduction. Furthermore, if the user is in a hurry, the generation unit can prioritize slides that get straight to the point. This provides a slide order that matches the user's emotions and enhances the effectiveness of the presentation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0080] The generation unit can optimize the design of slides by referencing the user's past presentation materials during slide generation. For example, the generation unit can generate slides based on design templates previously used by the user. Furthermore, the generation unit can automatically apply frequently used fonts and colors from the user's past presentation materials. In addition, the generation unit can analyze the user's past presentation materials and incorporate the most effective design elements. This allows for improved slide quality by referencing past presentation materials and optimizing the design. Some or all of the above processes in the generation unit may be performed using AI, or not. For example, the generation unit can input past presentation materials into a generation AI and have the generation AI perform the design optimization.

[0081] The generation unit can reflect industry-specific information of the user when generating slides. For example, the generation unit can generate slides that reflect the latest trends in the user's industry. It can also reflect industry-specific terminology and data in the slides. Furthermore, the generation unit can add examples and case studies related to the user's industry to the slides. This allows for the reflection of industry-specific information and enriches the content of the slides. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input industry-specific information into a generation AI and have the generation AI perform the process of reflecting it in the slides.

[0082] The editing unit can estimate the user's emotions and make editing suggestions based on those emotions. For example, if the user is nervous, the editing unit can make simple editing suggestions to reduce stress. If the user is relaxed, the editing unit can make detailed editing suggestions to improve the quality of the slides. Furthermore, if the user is in a hurry, the editing unit can make suggestions that allow for quick editing. This makes editing more efficient by providing editing suggestions that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit may be performed using AI, or not using AI. For example, the editing unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0083] The editing unit can automatically modify slides by reflecting user feedback in real time. For example, if a user provides feedback such as "I want the font size on this slide to be larger," the editing unit can immediately adjust the font size. Furthermore, if a user provides feedback such as "I want more detailed information added to this slide," the editing unit can automatically add the relevant information. Additionally, if a user provides feedback such as "I want the color of this slide changed," the editing unit can immediately change it to the specified color. This allows for real-time feedback and improvement of slide quality. Some or all of the above-described processes in the editing unit may be performed using AI, for example, or without AI. For example, the editing unit can input user feedback into a generating AI and have the generating AI perform real-time modifications.

[0084] The editing unit can suggest the optimal editing method by referring to the user's past feedback history during the editing process. For example, the editing unit can suggest editing based on fonts and colors that the user has previously preferred. It can also suggest similar editing methods by referring to editing content the user has previously made. Furthermore, the editing unit can extract and suggest frequently used editing patterns from the user's past feedback history. This makes the editing process more efficient by referring to past feedback history and suggesting the optimal editing method. Some or all of the above processes in the editing unit may be performed using AI, for example, or without AI. For example, the editing unit can input past feedback history into a generating AI and have the generating AI suggest the optimal editing method.

[0085] The editing unit can estimate the user's emotions and determine the priority of corrections based on the estimated emotions. For example, if the user is in a hurry, the editing unit will prioritize important corrections. If the user is relaxed, the editing unit can perform corrections sequentially, including detailed corrections. Furthermore, if the user is stressed, the editing unit can start with the simplest corrections and gradually increase the difficulty. This allows for the determination of correction priorities according to the user's emotions, resulting in efficient corrections. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit may be performed using AI or not. For example, the editing unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The correction unit can propose the optimal correction method while considering the user's device information. For example, if the user is using a smartphone, the correction unit can propose a correction method that matches the screen size. If the user is using a tablet, the correction unit can propose a correction method optimized for a larger screen. Furthermore, if the user is using a desktop, the correction unit can propose a detailed correction method. This allows for the proposal of a correction method that takes device information into account, thereby streamlining the correction process. Some or all of the above-described processes in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input the user's device information into a generating AI and have the generating AI propose the optimal correction method.

[0087] The editing function can incorporate industry-specific feedback from the user during the editing process. For example, the editing function can make revisions that reflect the latest trends in the user's industry. It can also incorporate industry-specific terminology and data into the revisions. Furthermore, the editing function can add examples and case studies related to the user's industry to the revisions. This allows for the incorporation of industry-specific feedback and enriches the content of the slides. Some or all of the above processes in the editing function may be performed using AI, for example, or not. For example, the editing function can input industry-specific feedback into a generating AI and have the generating AI perform the process of reflecting it in the slides.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The reception desk can analyze the user's voice tone and estimate their emotions. For example, if the user speaks in a high-pitched voice, it can estimate that they are nervous and provide an interface with calming colors. Conversely, if the user speaks in a low-pitched voice, it can estimate that they are relaxed and provide an interface with bright colors. Furthermore, if the user speaks quickly, it can estimate that they are in a hurry and prioritize inputting important items. This allows the interface to be adjusted based on the user's voice tone, making the input process more comfortable.

[0090] The analysis unit can automatically refer to relevant external databases based on user input to supplement the analysis results. For example, if a user inputs a new product introduction, it can refer to relevant market databases and automatically obtain information on competing products. Similarly, if a user inputs a research presentation, it can refer to relevant academic paper databases and automatically obtain the latest research findings. Furthermore, if a user inputs a project report, it can refer to relevant industry databases and automatically obtain data to supplement the project's progress. This allows for supplementing analysis results by referencing external databases and improving the quality of presentation slides.

[0091] The generation unit can estimate the user's emotions and adjust the slide animation effects based on those emotions. For example, if the user is nervous, it can provide simple and calming animation effects. If the user is enjoying themselves, it can provide dynamic animation effects. Furthermore, if the user is tired, it can provide highly visible animation effects. This allows for the creation of visually appealing slides with animation effects that respond to the user's emotions.

[0092] The editing unit can estimate the user's emotions and adjust the editing suggestions based on those emotions. For example, if the user is stressed, it can offer simple and intuitive editing suggestions. If the user is relaxed, it can offer more detailed suggestions. Furthermore, if the user is in a hurry, it can offer suggestions that allow for quick editing. This allows for editing suggestions tailored to the user's emotions, thereby streamlining the editing process.

[0093] The reception system can automatically suggest relevant video tutorials based on user input. For example, if a user enters a new product introduction, it can suggest a relevant product demo video. Similarly, if a user enters a research presentation, it can suggest a video tutorial on relevant research methods. Furthermore, if a user enters a project report, it can suggest a video tutorial on relevant project management. This allows for the suggestion of relevant video tutorials, thereby deepening the user's understanding.

[0094] The analysis unit can automatically retrieve relevant news articles based on user input and reflect them in the analysis results. For example, if a user enters a new product introduction, it can retrieve the latest relevant news articles and reflect information on competing products in the analysis results. Similarly, if a user enters a research presentation, it can retrieve the latest relevant research news and reflect it in the analysis results. Furthermore, if a user enters a project report, it can retrieve relevant industry news and reflect it in the analysis results. This allows for the incorporation of the latest news articles to supplement the analysis results and improve the quality of the slides.

[0095] The generation unit can estimate the user's emotions and adjust the audio narration of the slides based on those emotions. For example, if the user is nervous, it can provide a calm-toned audio narration. If the user is enjoying themselves, it can provide a cheerful-toned audio narration. Furthermore, if the user is tired, it can provide a simple and easy-to-read audio narration. This allows for the creation of visually appealing slides with audio narration that responds to the user's emotions.

[0096] The correction unit can estimate the user's emotions and determine the priority of corrections based on those emotions. For example, if the user is in a hurry, important corrections will be prioritized. If the user is relaxed, corrections, including detailed ones, can be made sequentially. Furthermore, if the user is stressed, the easiest corrections can be made first, and the difficulty level can be gradually increased. This allows for the determination of correction priorities according to the user's emotions, resulting in efficient corrections.

[0097] The reception system can automatically suggest relevant books and materials based on user input. For example, if a user enters a new product introduction, it can suggest relevant marketing books. Similarly, if a user enters a research presentation, it can suggest relevant academic books. Furthermore, if a user enters a project report, it can suggest relevant project management books. This allows for the suggestion of relevant books and materials, deepening the user's understanding.

[0098] The analysis unit can automatically obtain opinions from relevant experts based on user input and incorporate them into the analysis results. For example, if a user inputs a new product introduction, it can obtain opinions from relevant marketing experts and incorporate them into the analysis results. Similarly, if a user inputs a research presentation, it can obtain opinions from relevant academic experts and incorporate them into the analysis results. Furthermore, if a user inputs a project report, it can obtain opinions from relevant project management experts and incorporate them into the analysis results. This allows for the incorporation of expert opinions to enhance the analysis results and improve the quality of the slides.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The reception desk accepts input from users regarding the content of their presentation and the presentation time. For example, a user can input information such as "Introduction of a new product" and "30 minutes." The reception desk then sends the entered information to the generation AI. Step 2: The analysis unit analyzes the information received by the reception unit and extracts key points. For example, it extracts important keywords and phrases based on the input content. The analysis unit can use natural language processing technology to understand the context and identify important information. Step 3: The generation unit generates slides based on the key points extracted by the analysis unit. For example, it generates slides for the introduction, main points, and conclusion. The generation unit selects appropriate font sizes, colors, and layouts to create visually appealing slides. The generation unit can automatically adjust the slide design using generation AI. Step 4: The editing unit modifies the slides generated by the generation unit based on user feedback. For example, if a user inputs a request such as "I'd like to explain the content of this slide in more detail," the editing unit modifies the slide accordingly. The editing unit can automatically modify slides using generation AI.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and modification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing the user to input the presentation content and presentation time. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, analyzing the input information and extracting important points. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, generating slides based on the extracted points. The modification unit is implemented by the control unit 46A of the smart device 14, modifying the slides based on user feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and modification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input the presentation content and presentation time by voice. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the input information and extracts important points. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates slides based on the extracted points. The modification unit is implemented, for example, by the control unit 46A of the smart glasses 214, which modifies the slides based on user feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and modification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input the presentation content and presentation time by voice. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the input information and extracts important points. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates slides based on the extracted points. The modification unit is implemented by the control unit 46A of the headset terminal 314, which modifies the slides based on user feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and modification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input the presentation content and presentation time by voice. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the input information and extracts important points. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which generates slides based on the extracted points. The modification unit is implemented by, for example, the control unit 46A of the robot 414, which modifies the slides based on user feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) A reception desk that accepts input of the content to be conveyed in presentations and the presentation time, An analysis unit analyzes the information received by the reception unit and extracts important points, A generation unit that generates slides based on the key points extracted by the analysis unit, The system includes a modification unit that modifies the slides generated by the generation unit based on user feedback. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It analyzes the user's past presentation and presentation history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The input fields are automatically customized according to the content of the presentation or announcement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Consider the user's geographical location and add region-specific information to the input fields. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze users' social media activity and automatically reflect relevant information in input fields. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system evaluates the reliability of the input information and prioritizes the analysis of highly reliable information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, Depending on the topic of the presentation, different analytical methods will be applied. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The analysis priority is determined based on when the entered information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Referencing relevant literature and databases will improve the accuracy of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the slide design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is Apply different design templates depending on the content of the slides to be generated. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is Automatically add visual elements when generating slides. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the order of the slides based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating slides, the design is optimized by referencing the user's past presentation materials. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating slides, reflect information specific to the user's industry. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned modification section is, It estimates the user's emotions and makes correction suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned modification section is, It incorporates user feedback in real time and automatically corrects slides. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned modification section is, When making corrections, we refer to the user's past feedback history to suggest the most suitable correction method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned modification section is, It estimates the user's emotions and determines the priority of modifications based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned modification section is, When making corrections, we will suggest the optimal correction method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned modification section is, When making revisions, we will incorporate industry-specific feedback from users. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts input of the content to be conveyed in presentations and the presentation time, An analysis unit analyzes the information received by the reception unit and extracts important points, A generation unit that generates slides based on the key points extracted by the analysis unit, The system includes a modification unit that modifies the slides generated by the generation unit based on user feedback. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is It analyzes the user's past presentation and presentation history and suggests the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is The input fields are automatically customized according to the content of the presentation or announcement. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Consider the user's geographical location and add region-specific information to the input fields. The system according to feature 1.

7. The aforementioned reception unit is Analyze users' social media activity and automatically reflect relevant information in input fields. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit, The system evaluates the reliability of the input information and prioritizes the analysis of highly reliable information. The system according to feature 1.

10. The aforementioned analysis unit, Depending on the topic of the presentation, different analytical methods will be applied. The system according to feature 1.

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