System

Abstract

An object of a system according to an embodiment is to improve the efficiency of designing presentation slides and visualizing information.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a customization unit. The reception unit receives an input of a theme or content of a presentation. The analysis unit analyzes the information received by the reception unit. The generation unit visualizes the slide design or the information based on the information analyzed by the analysis unit. The customization unit customizes the slide generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of requiring a lot of time and effort to design presentation slides and visualize information.

[0005] The system according to the embodiment aims to improve the efficiency of designing presentation slides and visualizing information. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a customization unit. The reception unit receives input of the theme or content of the presentation. The analysis unit analyzes the information received by the reception unit. The generation unit designs slides or visualizes information based on the information analyzed by the analysis unit. The customization unit customizes the slides generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of designing presentation slides and visualizing information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A presentation creation system according to an embodiment of the present invention uses a generative AI to efficiently design presentation slides and visualize information. In the presentation creation system, a user inputs the theme and content of a presentation, and the generative AI analyzes the input information and designs the slides and visualizes the information. The generative AI generates text, generates images, and processes numerical data to provide a consistent design and sophisticated charts and diagrams. This system reduces the effort required for creating materials and enhances the visual explanation power. Furthermore, users can customize the generated slides, revolutionizing the process of creating presentations. For example, in the presentation creation system, a user inputs the theme and content of a presentation. For example, the user inputs a theme such as "New Product Introduction" or "Annual Performance Report." This information is input into the generative AI. The presentation creation system then uses the generative AI to analyze the input information. The generative AI designs the slides and visualizes the information based on the theme and content. For example, the generative AI generates text and places appropriate text on each slide of the presentation. The generative AI also generates images to provide visuals appropriate for the slides. The generative AI also processes numerical data and generates visual information such as graphs and charts. The generated slides have a unified design and sophisticated diagrams. For example, the generation AI unifies the design theme of the entire presentation, giving each slide consistency. The generation AI also selects appropriate fonts and colors to enhance the visual explanation power and effectively convey information. Furthermore, the presentation creation system can customize the generated slides. For example, users can modify and add to the designs and diagrams provided by the generation AI to suit their preferences. This allows users to create slides that are optimal for their presentations. As a result, the presentation creation system reduces the effort required to create materials and improves the user experience. Users can use the generation AI to efficiently create presentation slides.Furthermore, by providing a consistent design and sophisticated diagrams, the visual explanatory power is strengthened and the quality of the presentation is improved. This will revolutionize the work of creating presentations. As a result, the presentation creation system can reduce the effort required to create materials and strengthen the visual explanatory power. For example, by utilizing generative AI, users can efficiently create presentation slides. Furthermore, by providing a consistent design and sophisticated diagrams, the visual explanatory power is strengthened and the quality of the presentation is improved. This will revolutionize the work of creating presentations.

[0029] A presentation creation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a customization unit. The reception unit receives input of a presentation theme or content. Examples of presentation themes or content include, but are not limited to, business presentations, academic presentations, and product introductions. The reception unit provides an interface for a user to input the presentation theme or content. The reception unit can also support multiple input methods, such as voice input and text input. For example, the reception unit can convert what the user speaks into text using voice recognition technology. The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis can be performed using, for example, text analysis, data analysis, image analysis, or other methods, but is not limited to these examples. For example, the analysis unit uses the generation AI to analyze the input theme or content and extract data necessary for slide design and information visualization. The analysis unit can also use the generation AI to evaluate the importance and relevance of the input information. The generation unit uses the generation AI to design slides and visualize the information based on the information analyzed by the analysis unit. The generation unit can, for example, generate text, generate images, and process numerical data. For example, the generation unit can use a generation AI to place appropriate text on each slide of a presentation. The generation unit can also use a generation AI to provide visuals appropriate for the slides. The generation unit can also use a generation AI to generate visual information such as graphs and charts. For example, the generation unit can use a generation AI to provide a unified design and sophisticated diagrams. The customization unit customizes the slides generated by the generation unit. For example, the customization unit provides an interface that allows a user to make corrections or additions based on the generated slides. For example, the customization unit provides customization options such as changing colors, selecting fonts, and adjusting layouts. The customization unit can also use a generation AI to suggest customizations that suit the user's preferences.As a result, the presentation creation system according to the embodiment can improve the efficiency of presentation slide design and information visualization, reducing the effort required for creating materials. For example, by utilizing the generation AI, users can efficiently create presentation slides. Furthermore, by providing a unified design and sophisticated diagrams, the visual explanatory power is strengthened and the quality of presentations is improved. This will revolutionize the work of creating presentations.

[0030] The reception unit can analyze the user's past presentation history and suggest an input format. The reception unit can, for example, use data analysis technology to analyze the user's past presentation history. For example, the reception unit can automatically display the themes and contents of presentations the user has used in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes and contents to be used in a specific time period based on the user's past presentation history. This can improve input efficiency by suggesting the optimal input format based on the past history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can input the user's past presentation history data into a generation AI and cause the generation AI to suggest the optimal input format.

[0031] When inputting a topic or content, the reception unit can filter the input content based on the user's current project or area of ​​interest. The reception unit can, for example, refer to data from a project management tool or the user's past activity history to identify the user's current project or area of ​​interest. For example, the reception unit can prioritize displaying topics or content related to the user's current project. The reception unit can also suggest related topics or content based on the user's area of ​​interest. Furthermore, the reception unit can filter highly relevant topics and content by referring to the user's past project history. Thus, by filtering the input content based on the user's current project or area of ​​interest, highly relevant information can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input data from a project management tool into a generation AI and cause the generation AI to filter highly relevant topics and content.

[0032] When inputting a theme or content, the reception unit can select the optimal input means according to the user's input method. The reception unit can, for example, evaluate the user's input speed and input accuracy to identify the user's input method. For example, if the user selects voice input, the reception unit can input the theme or content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also analyze the theme or content using image recognition technology. This can improve input efficiency by providing the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0033] When inputting themes and content, the reception unit can prioritize inputting highly relevant themes by taking into account the user's geographical location information. The reception unit can, for example, use analysis of GPS data or IP addresses to acquire the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting themes and content related to that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting themes and content related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize inputting themes and content related to the event. In this way, by taking the user's geographical location information into account, highly relevant themes can be prioritized. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize inputting highly relevant themes.

[0034] When inputting a theme or content, the reception unit can analyze the user's social media activity and input related themes. The reception unit can, for example, analyze posted content and followers to analyze the user's social media activity. For example, the reception unit can input related themes and content based on content shared by the user on social media. The reception unit can also analyze the user's social media posts and suggest related themes and content. Furthermore, the reception unit can input related themes and content with reference to the activities of the user's friends on social media. In this way, related themes can be input by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to input related themes.

[0035] The reception unit can customize the input method by reflecting the user's past feedback when inputting a theme or content. The reception unit can, for example, refer to the user's evaluation comments and survey results to reflect the user's past feedback. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also improve the input procedure based on the user's past feedback. Furthermore, the reception unit can customize the input interface by reflecting the user's feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the input method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the theme or content. The analysis unit can use data analysis techniques, for example, to evaluate the importance of the theme or content. For example, the analysis unit can perform a detailed analysis of important themes or content. The analysis unit can also perform a concise analysis of general themes or content. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance specified by the user. As a result, important information can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the theme or content. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input importance data specified by the user to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the theme or content category. The analysis unit can use data analysis techniques, for example, to identify the theme or content category. For example, the analysis unit can apply a business analysis algorithm to a business-related theme. The analysis unit can also apply a science and technology analysis algorithm to a science and technology-related theme. Furthermore, the analysis unit can apply an entertainment analysis algorithm to an entertainment-related theme. This improves the accuracy of the analysis by applying an analysis algorithm depending on the theme or content category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input theme or content category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, refer to past reports or analysis logs to refer to the user's past analysis results. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also extract patterns for improving the accuracy of the analysis from the user's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the analysis priority based on the submission time of the theme or content. The analysis unit can, for example, refer to the submission deadline or submission date and time to obtain the submission time of the theme or content. For example, the analysis unit prioritizes analysis of the theme or content with an upcoming deadline. The analysis unit can also determine the analysis priority based on the submission time specified by the user. Furthermore, the analysis unit can postpone analysis of the theme or content with a more distant submission time. In this way, by determining the analysis priority based on the submission time of the theme or content, important information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input submission time data to the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of themes and content. For example, the analysis unit can calculate the degree of keyword matching or topic similarity to evaluate the relevance of themes and content. For example, the analysis unit prioritizes analysis of highly relevant themes and content. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. Furthermore, the analysis unit can postpone analysis of less relevant themes and content. By adjusting the order of analysis based on the relevance of themes and content, highly relevant information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input relevance data to a generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit can, for example, refer to qualifications or past achievements to evaluate the user's level of expertise. For example, if the user has specialized knowledge, the analysis unit can provide analysis results that use a lot of technical terminology. Alternatively, if the user only has general knowledge, the analysis unit can provide analysis results that avoid technical terminology. Furthermore, the analysis unit can adjust the use of technical terminology in the analysis results according to the user's level of expertise. This allows for a deeper understanding of the analysis results by providing analysis results that correspond to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0042] The generation unit can adjust the level of detail of the slides based on the importance of the theme or content during generation. The generation unit can use, for example, data analysis technology to evaluate the importance of the theme or content. For example, the generation unit can generate detailed slides for important themes or content. The generation unit can also generate concise slides for general themes or content. The generation unit can also adjust the level of detail of the slides based on the importance specified by the user. This allows important information to be displayed in detail by adjusting the level of detail of the slides based on the importance of the theme or content. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input user-specified importance data into the generation AI and cause the generation AI to adjust the level of detail of the slides.

[0043] During generation, the generation unit can apply different generation algorithms depending on the theme or content category. The generation unit can use data analysis technology, for example, to identify the theme or content category. For example, the generation unit can apply a business generation algorithm to a business-related theme. The generation unit can also apply a science and technology generation algorithm to a science and technology-related theme. Furthermore, the generation unit can apply an entertainment generation algorithm to an entertainment-related theme. In this way, by applying a generation algorithm depending on the theme or content category, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input theme or content category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0044] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit can, for example, refer to past slide designs and generation logs to refer to the user's past generation results. For example, the generation unit optimizes the generation algorithm based on the user's past generation results. The generation unit can also extract patterns for improving the accuracy of generation from the user's past generation results. Furthermore, the generation unit can improve the accuracy of generation by referring to the user's past generation results. In this way, the accuracy of generation is improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or without AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0045] The generation unit can determine the priority of slides based on the submission dates of the themes and contents during generation. The generation unit can, for example, refer to the submission deadline or submission date and time to obtain the submission dates of the themes and contents. For example, the generation unit prioritizes themes and contents with upcoming deadlines in the slides. The generation unit can also determine the priority of slides based on the submission date specified by the user. Furthermore, the generation unit can postpone the generation of slides for themes and contents with more distant submission dates. Thus, by determining the priority of slides based on the submission dates of the themes and contents, important information can be displayed preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input submission date data into the generation AI and have the generation AI determine the priority of slides.

[0046] The generation unit can adjust the order of slides based on the relevance of themes and contents during generation. For example, the generation unit can calculate the degree of keyword matching or topic similarity to evaluate the relevance of themes and contents. For example, the generation unit prioritizes reflecting highly relevant themes and contents in the slides. The generation unit can also adjust the order of slides based on the relevance specified by the user. Furthermore, the generation unit can generate slides after less relevant themes and contents. By adjusting the order of slides based on the relevance of themes and contents, highly relevant information can be displayed preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input relevance data into the generation AI and have the generation AI adjust the order of the slides.

[0047] The generation unit can adjust the use of technical terms in the slides according to the user's level of expertise during generation. The generation unit can, for example, refer to the user's qualifications or past achievements to evaluate the user's level of expertise. For example, if the user has specialized knowledge, the generation unit can generate slides that use a lot of technical terms. Alternatively, if the user only has general knowledge, the generation unit can generate slides that avoid technical terms. Furthermore, the generation unit can adjust the use of technical terms in the slides according to the user's level of expertise. This improves the visual explanatory power by providing slides that correspond to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0048] During customization, the customization unit can analyze the user's past customization history and propose an optimal customization method. The customization unit can, for example, use data analysis technology to analyze the user's past customization history. For example, the customization unit can automatically display customization options used by the user in the past as candidates. The customization unit can also propose an optimal customization method based on the user's past customization history. Furthermore, the customization unit can optimize the customization procedure based on the user's past customization history. In this way, the optimal customization method can be provided by analyzing the user's past customization history. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's past customization history data into a generation AI and cause the generation AI to propose an optimal customization method.

[0049] During customization, the customization unit can filter the customization content based on the user's current project or areas of interest. For example, the customization unit can refer to data from the project management tool or the user's past activity history to identify the user's current project or areas of interest. For example, the customization unit can preferentially display customization options related to the user's current project. The customization unit can also suggest related customization options based on the user's areas of interest. Furthermore, the customization unit can filter highly relevant customization options by referring to the user's past project history. This allows for highly relevant customization by filtering the customization content based on the user's current project or areas of interest. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input data from the project management tool into the generation AI and cause the generation AI to filter highly relevant customization options.

[0050] During customization, the customization unit can improve the customization method by reflecting user feedback. For example, the customization unit can refer to user evaluation comments and survey results to reflect user feedback. For example, the customization unit can suggest an optimal customization method based on feedback provided by the user in the past. The customization unit can also improve the customization procedure based on the user's past feedback. Furthermore, the customization unit can improve the customization interface by reflecting user feedback. In this way, an optimal customization method can be provided by reflecting user feedback. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input user feedback data into a generation AI and cause the generation AI to improve the customization method.

[0051] During customization, the customization unit can select a customization method based on the user's geographical location information. The customization unit can, for example, use analysis of GPS data or an IP address to obtain the user's geographical location information. For example, if the user is in a specific area, the customization unit can prioritize providing customization options related to that area. Furthermore, if the user is traveling, the customization unit can prioritize providing customization options related to the travel destination. Furthermore, if the user is participating in a specific event, the customization unit can prioritize providing customization options related to the event. This allows for highly relevant customization to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the customization unit can be performed using, for example, AI, or without AI. For example, the customization unit can input the user's geographical location information data into a generation AI and cause the generation AI to select the optimal customization method.

[0052] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. For example, the customization unit can analyze the user's social media activity by analyzing the content of posts and followers. For example, the customization unit can suggest relevant customization options based on the content the user has shared on social media. The customization unit can also analyze the content of the user's social media posts and suggest relevant customization options. Furthermore, the customization unit can suggest relevant customization options based on the activity of the user's friends on social media. This makes it possible to provide highly relevant customization by analyzing the user's social media activity. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest customization methods.

[0053] During customization, the customization unit can customize the customization method by reflecting the user's past feedback. The customization unit can, for example, refer to the user's evaluation comments and survey results to reflect the user's past feedback. For example, the customization unit can suggest an optimal customization method based on the user's past feedback. The customization unit can also improve the customization procedure based on the user's past feedback. Furthermore, the customization unit can customize the customization interface by reflecting the user's past feedback. In this way, the optimal customization method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the customization unit may be performed, for example, using AI or without AI. For example, the customization unit can input the user's feedback data into the generation AI and cause the generation AI to customize the customization method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can analyze the user's past presentation history and suggest an input format. For example, the reception unit can automatically display the themes and contents of presentations used by the user in the past as candidates. The reception unit can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes and contents to be used in a specific time period based on the user's past presentation history. This can improve input efficiency by suggesting the optimal input format based on the past history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past presentation history data into a generation AI and have the generation AI suggest the optimal input format.

[0056] When inputting a topic or content, the reception unit can filter the input content based on the user's current project or area of ​​interest. For example, the reception unit can refer to data from the project management tool or the user's past activity history to identify the user's current project or area of ​​interest. For example, the reception unit can preferentially display topics and content related to the user's current project. The reception unit can also suggest related topics and content based on the user's area of ​​interest. Furthermore, the reception unit can filter highly relevant topics and content by referring to the user's past project history. This makes it possible to provide highly relevant information by filtering the input content based on the user's current project or area of ​​interest. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data from the project management tool to the generation AI and cause the generation AI to filter highly relevant topics and content.

[0057] When inputting a theme or content, the reception unit can select the optimal input means according to the user's input method. For example, the reception unit can evaluate the user's input speed and input accuracy to identify the user's input method. For example, if the user selects voice input, the reception unit can input the theme or content using voice recognition technology. Also, if the user selects text input, the reception unit can provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can analyze the theme or content using image recognition technology. This can improve input efficiency by providing the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0058] When inputting themes and content, the reception unit can prioritize inputting highly relevant themes by taking into account the user's geographical location information. For example, the reception unit can use analysis of GPS data or IP addresses to obtain the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting themes and content related to that area. Also, if the user is traveling, the reception unit can prioritize inputting themes and content related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize inputting themes and content related to the event. In this way, by taking the user's geographical location information into account, highly relevant themes can be prioritized. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize inputting highly relevant themes.

[0059] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the theme or content. For example, the analysis unit can use data analysis techniques to evaluate the importance of the theme or content. For example, the analysis unit can perform a detailed analysis of important themes or content. The analysis unit can also perform a concise analysis of general themes or content. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance specified by the user. As a result, important information can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the theme or content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data specified by the user to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0060] During analysis, the analysis unit can apply different analysis algorithms depending on the theme or content category. For example, the analysis unit can use data analysis techniques to identify the theme or content category. For example, the analysis unit can apply a business analysis algorithm to a business-related theme. The analysis unit can also apply a science and technology analysis algorithm to a science and technology-related theme. Furthermore, the analysis unit can apply an entertainment analysis algorithm to an entertainment-related theme. This improves the accuracy of the analysis by applying an analysis algorithm depending on the theme or content category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input theme or content category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0061] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can refer to past reports and analysis logs to refer to the user's past analysis results. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also extract patterns for improving the accuracy of the analysis from the user's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0062] The generation unit can adjust the level of detail of the slides based on the importance of the theme or content during generation. For example, the generation unit can use data analysis technology to evaluate the importance of the theme or content. For example, the generation unit can generate detailed slides for important themes or content. The generation unit can also generate concise slides for general themes or content. The generation unit can also adjust the level of detail of the slides based on the importance specified by the user. This allows important information to be displayed in detail by adjusting the level of detail of the slides based on the importance of the theme or content. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input importance data specified by the user into the generation AI and cause the generation AI to adjust the level of detail of the slides.

[0063] During generation, the generation unit can apply different generation algorithms depending on the theme or content category. For example, the generation unit can use data analysis technology to identify the theme or content category. For example, the generation unit can apply a business generation algorithm to a business-related theme. The generation unit can also apply a science and technology generation algorithm to a science and technology-related theme. Furthermore, the generation unit can apply an entertainment generation algorithm to an entertainment-related theme. In this way, by applying a generation algorithm depending on the theme or content category, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input theme or content category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0064] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit can refer to past slide designs and generation logs to refer to the user's past generation results. For example, the generation unit can optimize the generation algorithm based on the user's past generation results. The generation unit can also extract patterns for improving the accuracy of generation from the user's past generation results. Furthermore, the generation unit can improve the accuracy of generation by referring to the user's past generation results. In this way, the accuracy of generation is improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0065] During customization, the customization unit can analyze the user's past customization history and propose an optimal customization method. For example, the customization unit can use data analysis technology to analyze the user's past customization history. For example, the customization unit can automatically display customization options used by the user in the past as candidates. The customization unit can also propose an optimal customization method based on the user's past customization history. Furthermore, the customization unit can optimize the customization procedure based on the user's past customization history. In this way, the optimal customization method can be provided by analyzing the user's past customization history. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's past customization history data into a generation AI and cause the generation AI to propose an optimal customization method.

[0066] During customization, the customization unit can filter the customization content based on the user's current project or areas of interest. For example, the customization unit can refer to data from the project management tool or the user's past activity history to identify the user's current project or areas of interest. For example, the customization unit can preferentially display customization options related to the user's current project. The customization unit can also suggest related customization options based on the user's areas of interest. Furthermore, the customization unit can filter highly relevant customization options by referring to the user's past project history. This makes it possible to provide highly relevant customization by filtering the customization content based on the user's current project or areas of interest. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input data from the project management tool to the generation AI and cause the generation AI to filter highly relevant customization options.

[0067] During customization, the customization unit can improve the customization method by reflecting user feedback. For example, the customization unit can refer to user evaluation comments and survey results to reflect user feedback. For example, the customization unit can suggest an optimal customization method based on feedback provided by the user in the past. The customization unit can also improve the customization procedure based on the user's past feedback. Furthermore, the customization unit can improve the customization interface by reflecting user feedback. In this way, an optimal customization method can be provided by reflecting user feedback. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input user feedback data into a generation AI and cause the generation AI to improve the customization method.

[0068] During customization, the customization unit can select a customization method based on the user's geographic location information. For example, the customization unit can use analysis of GPS data or IP address to obtain the user's geographic location information. For example, if the user is in a specific area, the customization unit can prioritize providing customization options related to that area. Also, if the user is traveling, the customization unit can prioritize providing customization options related to the travel destination. Furthermore, if the user is participating in a specific event, the customization unit can prioritize providing customization options related to the event. In this way, by taking the user's geographic location information into consideration, highly relevant customization can be provided. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's geographic location information data into the generation AI and cause the generation AI to select the optimal customization method.

[0069] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. For example, the customization unit can analyze the user's social media activity by analyzing the content of posts and followers. For example, the customization unit can suggest relevant customization options based on the content the user has shared on social media. The customization unit can also analyze the content of the user's social media posts and suggest relevant customization options. Furthermore, the customization unit can suggest relevant customization options based on the activity of the user's friends on social media. In this way, highly relevant customization can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest customization methods.

[0070] During customization, the customization unit can customize the customization method by reflecting the user's past feedback. For example, the customization unit can refer to the user's evaluation comments and survey results to reflect the user's past feedback. For example, the customization unit can suggest an optimal customization method based on the user's past feedback. The customization unit can also improve the customization procedure based on the user's past feedback. Furthermore, the customization unit can customize the customization interface by reflecting the user's past feedback. In this way, the optimal customization method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's feedback data into the generation AI and cause the generation AI to customize the customization method.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit receives input of the topic or content of the presentation. The topic or content of the presentation may include a business presentation, an academic presentation, a product introduction, etc. The reception unit provides an interface for the user to input the topic or content of the presentation and supports multiple input methods, such as voice input and text input. For example, speech recognition technology can be used to convert what the user says into text. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit. Analysis is performed using methods such as text analysis, data analysis, and image analysis. The analysis unit analyzes the input themes and content and extracts the data necessary for slide design and information visualization. It can also evaluate the importance and relevance of the input information. Step 3: The generator uses generative AI to design slides and visualize information based on the information analyzed by the analyzer. The generator can generate text, images, and numerical data. For example, it places appropriate text on each slide of the presentation and generates visual information such as graphs and charts appropriate for the slide. Step 4: The customization unit customizes the slides generated by the generation unit. The customization unit provides an interface for users to make corrections or additions based on the generated slides, and offers customization options such as changing colors, selecting fonts, and adjusting layouts. It can also use the generation AI to suggest customizations that suit the user's preferences.

[0073] (Example 2) A presentation creation system according to an embodiment of the present invention uses a generative AI to efficiently design presentation slides and visualize information. In the presentation creation system, a user inputs the theme and content of a presentation, and the generative AI analyzes the input information and designs the slides and visualizes the information. The generative AI generates text, generates images, and processes numerical data to provide a consistent design and sophisticated charts and diagrams. This system reduces the effort required for creating materials and enhances the visual explanation power. Furthermore, users can customize the generated slides, revolutionizing the process of creating presentations. For example, in the presentation creation system, a user inputs the theme and content of a presentation. For example, the user inputs a theme such as "New Product Introduction" or "Annual Performance Report." This information is input into the generative AI. The presentation creation system then uses the generative AI to analyze the input information. The generative AI designs the slides and visualizes the information based on the theme and content. For example, the generative AI generates text and places appropriate text on each slide of the presentation. The generative AI also generates images to provide visuals appropriate for the slides. The generative AI also processes numerical data and generates visual information such as graphs and charts. The generated slides have a unified design and sophisticated diagrams. For example, the generation AI unifies the design theme of the entire presentation, giving each slide consistency. The generation AI also selects appropriate fonts and colors to enhance the visual explanation power and effectively convey information. Furthermore, the presentation creation system can customize the generated slides. For example, users can modify and add to the designs and diagrams provided by the generation AI to suit their preferences. This allows users to create slides that are optimal for their presentations. As a result, the presentation creation system reduces the effort required to create materials and improves the user experience. Users can use the generation AI to efficiently create presentation slides.Furthermore, by providing a consistent design and sophisticated diagrams, the visual explanatory power is strengthened and the quality of the presentation is improved. This will revolutionize the work of creating presentations. As a result, the presentation creation system can reduce the effort required to create materials and strengthen the visual explanatory power. For example, by utilizing generative AI, users can efficiently create presentation slides. Furthermore, by providing a consistent design and sophisticated diagrams, the visual explanatory power is strengthened and the quality of the presentation is improved. This will revolutionize the work of creating presentations.

[0074] A presentation creation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a customization unit. The reception unit receives input of a presentation theme or content. Examples of presentation themes or content include, but are not limited to, business presentations, academic presentations, and product introductions. The reception unit provides an interface for a user to input the presentation theme or content. The reception unit can also support multiple input methods, such as voice input and text input. For example, the reception unit can convert what the user speaks into text using voice recognition technology. The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis can be performed using, for example, text analysis, data analysis, image analysis, or other methods, but is not limited to these examples. For example, the analysis unit uses the generation AI to analyze the input theme or content and extract data necessary for slide design and information visualization. The analysis unit can also use the generation AI to evaluate the importance and relevance of the input information. The generation unit uses the generation AI to design slides and visualize the information based on the information analyzed by the analysis unit. The generation unit can, for example, generate text, generate images, and process numerical data. For example, the generation unit can use a generation AI to place appropriate text on each slide of a presentation. The generation unit can also use a generation AI to provide visuals appropriate for the slides. The generation unit can also use a generation AI to generate visual information such as graphs and charts. For example, the generation unit can use a generation AI to provide a unified design and sophisticated diagrams. The customization unit customizes the slides generated by the generation unit. For example, the customization unit provides an interface that allows a user to make corrections or additions based on the generated slides. For example, the customization unit provides customization options such as changing colors, selecting fonts, and adjusting layouts. The customization unit can also use a generation AI to suggest customizations that suit the user's preferences.As a result, the presentation creation system according to the embodiment can improve the efficiency of presentation slide design and information visualization, reducing the effort required for creating materials. For example, by utilizing the generation AI, users can efficiently create presentation slides. Furthermore, by providing a unified design and sophisticated diagrams, the visual explanatory power is strengthened and the quality of presentations is improved. This will revolutionize the work of creating presentations.

[0075] The presentation creation system further includes a reception unit that estimates a user's emotion and adjusts a theme or content input method based on the estimated emotion. The reception unit can use, for example, facial expression recognition technology to estimate the user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the theme or content. This improves the user experience by providing an input method that matches the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0076] The reception unit can analyze the user's past presentation history and suggest an input format. The reception unit can, for example, use data analysis technology to analyze the user's past presentation history. For example, the reception unit can automatically display the themes and contents of presentations the user has used in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes and contents to be used in a specific time period based on the user's past presentation history. This can improve input efficiency by suggesting the optimal input format based on the past history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can input the user's past presentation history data into a generation AI and cause the generation AI to suggest the optimal input format.

[0077] When inputting a topic or content, the reception unit can filter the input content based on the user's current project or area of ​​interest. The reception unit can, for example, refer to data from a project management tool or the user's past activity history to identify the user's current project or area of ​​interest. For example, the reception unit can prioritize displaying topics or content related to the user's current project. The reception unit can also suggest related topics or content based on the user's area of ​​interest. Furthermore, the reception unit can filter highly relevant topics and content by referring to the user's past project history. Thus, by filtering the input content based on the user's current project or area of ​​interest, highly relevant information can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input data from a project management tool into a generation AI and cause the generation AI to filter highly relevant topics and content.

[0078] When inputting a theme or content, the reception unit can select the optimal input means according to the user's input method. The reception unit can, for example, evaluate the user's input speed and input accuracy to identify the user's input method. For example, if the user selects voice input, the reception unit can input the theme or content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also analyze the theme or content using image recognition technology. This can improve input efficiency by providing the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0079] The reception unit can estimate the user's emotions and prioritize input content based on the estimated emotions. The reception unit can use, for example, facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, when the user is stressed, the reception unit can prioritize input of important topics or content. Furthermore, when the user is relaxed, the reception unit can prioritize input of detailed topics or content. Furthermore, when the user is in a hurry, the reception unit can prioritize input of the most important topics or content. This allows important information to be prioritized by prioritizing input content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input image data of the user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0080] When inputting themes and content, the reception unit can prioritize inputting highly relevant themes by taking into account the user's geographical location information. The reception unit can, for example, use analysis of GPS data or IP addresses to acquire the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting themes and content related to that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting themes and content related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize inputting themes and content related to the event. In this way, by taking the user's geographical location information into account, highly relevant themes can be prioritized. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize inputting highly relevant themes.

[0081] When inputting a theme or content, the reception unit can analyze the user's social media activity and input related themes. The reception unit can, for example, analyze posted content and followers to analyze the user's social media activity. For example, the reception unit can input related themes and content based on content shared by the user on social media. The reception unit can also analyze the user's social media posts and suggest related themes and content. Furthermore, the reception unit can input related themes and content with reference to the activities of the user's friends on social media. In this way, related themes can be input by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to input related themes.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when inputting a theme or content. The reception unit can, for example, refer to the user's evaluation comments and survey results to reflect the user's past feedback. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also improve the input procedure based on the user's past feedback. Furthermore, the reception unit can customize the input interface by reflecting the user's feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the input method.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis presentation method based on the estimated emotions. The analysis unit can use, for example, facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide visually stimulating analysis results when the user is excited. This allows for a deeper understanding of the analysis results by providing an analysis presentation method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the theme or content. The analysis unit can use data analysis techniques, for example, to evaluate the importance of the theme or content. For example, the analysis unit can perform a detailed analysis of important themes or content. The analysis unit can also perform a concise analysis of general themes or content. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance specified by the user. As a result, important information can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the theme or content. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input importance data specified by the user to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the theme or content category. The analysis unit can use data analysis techniques, for example, to identify the theme or content category. For example, the analysis unit can apply a business analysis algorithm to a business-related theme. The analysis unit can also apply a science and technology analysis algorithm to a science and technology-related theme. Furthermore, the analysis unit can apply an entertainment analysis algorithm to an entertainment-related theme. This improves the accuracy of the analysis by applying an analysis algorithm depending on the theme or content category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input theme or content category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, refer to past reports or analysis logs to refer to the user's past analysis results. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also extract patterns for improving the accuracy of the analysis from the user's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit can use, for example, facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. This allows the analysis result to be better understood by providing the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0088] During analysis, the analysis unit can determine the analysis priority based on the submission time of the theme or content. The analysis unit can, for example, refer to the submission deadline or submission date and time to obtain the submission time of the theme or content. For example, the analysis unit prioritizes analysis of the theme or content with an upcoming deadline. The analysis unit can also determine the analysis priority based on the submission time specified by the user. Furthermore, the analysis unit can postpone analysis of the theme or content with a more distant submission time. In this way, by determining the analysis priority based on the submission time of the theme or content, important information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input submission time data to the generation AI and have the generation AI determine the analysis priority.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of themes and content. For example, the analysis unit can calculate the degree of keyword matching or topic similarity to evaluate the relevance of themes and content. For example, the analysis unit prioritizes analysis of highly relevant themes and content. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. Furthermore, the analysis unit can postpone analysis of less relevant themes and content. By adjusting the order of analysis based on the relevance of themes and content, highly relevant information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input relevance data to a generation AI and have the generation AI adjust the order of analysis.

[0090] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit can, for example, refer to qualifications or past achievements to evaluate the user's level of expertise. For example, if the user has specialized knowledge, the analysis unit can provide analysis results that use a lot of technical terminology. Alternatively, if the user only has general knowledge, the analysis unit can provide analysis results that avoid technical terminology. Furthermore, the analysis unit can adjust the use of technical terminology in the analysis results according to the user's level of expertise. This allows for a deeper understanding of the analysis results by providing analysis results that correspond to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0091] The generation unit can estimate the user's emotions and adjust the design of the generated slides based on the estimated emotions. The generation unit can use, for example, facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, if the user is relaxed, the generation unit can generate slides with a calm design. If the user is in a hurry, the generation unit can also generate slides with a simple, highly visible design. Furthermore, if the user is excited, the generation unit can generate slides with a visually stimulating design. This improves the visual explanatory power by providing slide designs that correspond to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0092] The generation unit can adjust the level of detail of the slides based on the importance of the theme or content during generation. The generation unit can use, for example, data analysis technology to evaluate the importance of the theme or content. For example, the generation unit can generate detailed slides for important themes or content. The generation unit can also generate concise slides for general themes or content. The generation unit can also adjust the level of detail of the slides based on the importance specified by the user. This allows important information to be displayed in detail by adjusting the level of detail of the slides based on the importance of the theme or content. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input user-specified importance data into the generation AI and cause the generation AI to adjust the level of detail of the slides.

[0093] During generation, the generation unit can apply different generation algorithms depending on the theme or content category. The generation unit can use data analysis technology, for example, to identify the theme or content category. For example, the generation unit can apply a business generation algorithm to a business-related theme. The generation unit can also apply a science and technology generation algorithm to a science and technology-related theme. Furthermore, the generation unit can apply an entertainment generation algorithm to an entertainment-related theme. In this way, by applying a generation algorithm depending on the theme or content category, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input theme or content category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0094] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit can, for example, refer to past slide designs and generation logs to refer to the user's past generation results. For example, the generation unit optimizes the generation algorithm based on the user's past generation results. The generation unit can also extract patterns for improving the accuracy of generation from the user's past generation results. Furthermore, the generation unit can improve the accuracy of generation by referring to the user's past generation results. In this way, the accuracy of generation is improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or without AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0095] The generation unit can estimate the user's emotions and adjust the length of the generated slides based on the estimated emotions. The generation unit can use, for example, facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, if the user is in a hurry, the generation unit can generate short, concise slides. If the user is relaxed, the generation unit can generate longer slides with detailed explanations. Furthermore, if the user is excited, the generation unit can generate slides with visually stimulating effects. This improves the visual explanation by providing slide lengths that correspond to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0096] The generation unit can determine the priority of slides based on the submission dates of the themes and contents during generation. The generation unit can, for example, refer to the submission deadline or submission date and time to obtain the submission dates of the themes and contents. For example, the generation unit prioritizes themes and contents with upcoming deadlines in the slides. The generation unit can also determine the priority of slides based on the submission date specified by the user. Furthermore, the generation unit can postpone the generation of slides for themes and contents with more distant submission dates. Thus, by determining the priority of slides based on the submission dates of the themes and contents, important information can be displayed preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input submission date data into the generation AI and have the generation AI determine the priority of slides.

[0097] The generation unit can adjust the order of slides based on the relevance of themes and contents during generation. For example, the generation unit can calculate the degree of keyword matching or topic similarity to evaluate the relevance of themes and contents. For example, the generation unit prioritizes reflecting highly relevant themes and contents in the slides. The generation unit can also adjust the order of slides based on the relevance specified by the user. Furthermore, the generation unit can generate slides after less relevant themes and contents. By adjusting the order of slides based on the relevance of themes and contents, highly relevant information can be displayed preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input relevance data into the generation AI and have the generation AI adjust the order of the slides.

[0098] The generation unit can adjust the use of technical terms in the slides according to the user's level of expertise during generation. The generation unit can, for example, refer to the user's qualifications or past achievements to evaluate the user's level of expertise. For example, if the user has specialized knowledge, the generation unit can generate slides that use a lot of technical terms. Alternatively, if the user only has general knowledge, the generation unit can generate slides that avoid technical terms. Furthermore, the generation unit can adjust the use of technical terms in the slides according to the user's level of expertise. This improves the visual explanatory power by providing slides that correspond to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0099] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated emotions. The customization unit can use, for example, facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the customization unit can provide detailed customization options when the user is relaxed. The customization unit can also provide concise customization options when the user is in a hurry. Furthermore, the customization unit can provide visually stimulating customization options when the user is excited. This improves the user experience by providing a customization method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit can be performed using, for example, AI, or without AI. For example, the customization unit can input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0100] During customization, the customization unit can analyze the user's past customization history and propose an optimal customization method. The customization unit can, for example, use data analysis technology to analyze the user's past customization history. For example, the customization unit can automatically display customization options used by the user in the past as candidates. The customization unit can also propose an optimal customization method based on the user's past customization history. Furthermore, the customization unit can optimize the customization procedure based on the user's past customization history. In this way, the optimal customization method can be provided by analyzing the user's past customization history. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's past customization history data into a generation AI and cause the generation AI to propose an optimal customization method.

[0101] During customization, the customization unit can filter the customization content based on the user's current project or areas of interest. For example, the customization unit can refer to data from the project management tool or the user's past activity history to identify the user's current project or areas of interest. For example, the customization unit can preferentially display customization options related to the user's current project. The customization unit can also suggest related customization options based on the user's areas of interest. Furthermore, the customization unit can filter highly relevant customization options by referring to the user's past project history. This allows for highly relevant customization by filtering the customization content based on the user's current project or areas of interest. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input data from the project management tool into the generation AI and cause the generation AI to filter highly relevant customization options.

[0102] During customization, the customization unit can improve the customization method by reflecting user feedback. For example, the customization unit can refer to user evaluation comments and survey results to reflect user feedback. For example, the customization unit can suggest an optimal customization method based on feedback provided by the user in the past. The customization unit can also improve the customization procedure based on the user's past feedback. Furthermore, the customization unit can improve the customization interface by reflecting user feedback. In this way, an optimal customization method can be provided by reflecting user feedback. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input user feedback data into a generation AI and cause the generation AI to improve the customization method.

[0103] The customization unit can estimate the user's emotions and determine customization priorities based on the estimated emotions. The customization unit can use, for example, facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the customization unit can prioritize providing important customization options when the user is stressed. The customization unit can also prioritize providing detailed customization options when the user is relaxed. Furthermore, the customization unit can prioritize providing the most important customization options when the user is in a hurry. This allows for prioritizing customization options based on the user's emotions, thereby prioritizing important customizations. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit can be performed using, for example, AI, or without AI. For example, the customization unit can input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0104] During customization, the customization unit can select a customization method based on the user's geographical location information. The customization unit can, for example, use analysis of GPS data or an IP address to obtain the user's geographical location information. For example, if the user is in a specific area, the customization unit can prioritize providing customization options related to that area. Furthermore, if the user is traveling, the customization unit can prioritize providing customization options related to the travel destination. Furthermore, if the user is participating in a specific event, the customization unit can prioritize providing customization options related to the event. This allows for highly relevant customization to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the customization unit can be performed using, for example, AI, or without AI. For example, the customization unit can input the user's geographical location information data into a generation AI and cause the generation AI to select the optimal customization method.

[0105] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. For example, the customization unit can analyze the user's social media activity by analyzing the content of posts and followers. For example, the customization unit can suggest relevant customization options based on the content the user has shared on social media. The customization unit can also analyze the content of the user's social media posts and suggest relevant customization options. Furthermore, the customization unit can suggest relevant customization options based on the activity of the user's friends on social media. This makes it possible to provide highly relevant customization by analyzing the user's social media activity. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest customization methods.

[0106] During customization, the customization unit can customize the customization method by reflecting the user's past feedback. The customization unit can, for example, refer to the user's evaluation comments and survey results to reflect the user's past feedback. For example, the customization unit can suggest an optimal customization method based on the user's past feedback. The customization unit can also improve the customization procedure based on the user's past feedback. Furthermore, the customization unit can customize the customization interface by reflecting the user's past feedback. In this way, the optimal customization method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the customization unit may be performed, for example, using AI or without AI. For example, the customization unit can input the user's feedback data into the generation AI and cause the generation AI to customize the customization method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and customization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface for the user to input the theme and content of the presentation. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and designs slides and visualizes information based on the analyzed information. The customization unit is realized by the control unit 46A of the smart device 14 and provides an interface for the user to customize the generated slides. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and customization unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for the user to input the theme and content of the presentation. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and performs slide design and information visualization based on the analyzed information. The customization unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for the user to customize the generated slides. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and customization unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface for the user to input the theme and content of the presentation. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and designs slides and visualizes information based on the analyzed information. The customization unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface for the user to customize the generated slides. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and customization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for the user to input the theme and content of the presentation. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and designs slides and visualizes information based on the analyzed information. The customization unit is realized by the control unit 46A of the robot 414 and provides an interface for the user to customize the generated slides.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can analyze the user's past presentation history and suggest an input format. For example, the reception unit can automatically display the themes and contents of presentations used by the user in the past as candidates. The reception unit can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes and contents to be used in a specific time period based on the user's past presentation history. This can improve input efficiency by suggesting the optimal input format based on the past history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past presentation history data into a generation AI and have the generation AI suggest the optimal input format.

[0109] When inputting a topic or content, the reception unit can filter the input content based on the user's current project or area of ​​interest. For example, the reception unit can refer to data from the project management tool or the user's past activity history to identify the user's current project or area of ​​interest. For example, the reception unit can preferentially display topics and content related to the user's current project. The reception unit can also suggest related topics and content based on the user's area of ​​interest. Furthermore, the reception unit can filter highly relevant topics and content by referring to the user's past project history. This makes it possible to provide highly relevant information by filtering the input content based on the user's current project or area of ​​interest. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data from the project management tool to the generation AI and cause the generation AI to filter highly relevant topics and content.

[0110] When inputting a theme or content, the reception unit can select the optimal input means according to the user's input method. For example, the reception unit can evaluate the user's input speed and input accuracy to identify the user's input method. For example, if the user selects voice input, the reception unit can input the theme or content using voice recognition technology. Also, if the user selects text input, the reception unit can provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can analyze the theme or content using image recognition technology. This can improve input efficiency by providing the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0111] The reception unit can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, the reception unit can use facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, when the user is stressed, the reception unit can prioritize input of important topics or content. Furthermore, when the user is relaxed, the reception unit can prioritize input of detailed topics or content. Furthermore, when the user is in a hurry, the reception unit can prioritize input of the most important topics or content. This allows important information to be prioritized 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 a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input image data of the user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0112] When inputting themes and content, the reception unit can prioritize inputting highly relevant themes by taking into account the user's geographical location information. For example, the reception unit can use analysis of GPS data or IP addresses to obtain the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting themes and content related to that area. Also, if the user is traveling, the reception unit can prioritize inputting themes and content related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize inputting themes and content related to the event. In this way, by taking the user's geographical location information into account, highly relevant themes can be prioritized. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize inputting highly relevant themes.

[0113] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the theme or content. For example, the analysis unit can use data analysis techniques to evaluate the importance of the theme or content. For example, the analysis unit can perform a detailed analysis of important themes or content. The analysis unit can also perform a concise analysis of general themes or content. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance specified by the user. As a result, important information can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the theme or content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data specified by the user to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0114] During analysis, the analysis unit can apply different analysis algorithms depending on the theme or content category. For example, the analysis unit can use data analysis techniques to identify the theme or content category. For example, the analysis unit can apply a business analysis algorithm to a business-related theme. The analysis unit can also apply a science and technology analysis algorithm to a science and technology-related theme. Furthermore, the analysis unit can apply an entertainment analysis algorithm to an entertainment-related theme. This improves the accuracy of the analysis by applying an analysis algorithm depending on the theme or content category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input theme or content category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0115] The analysis unit can estimate the user's emotions and adjust the analysis presentation method based on the estimated emotions. For example, the analysis unit can use facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide visually stimulating analysis results when the user is excited. This allows for a deeper understanding of the analysis results by providing an analysis presentation method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input image data of the user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0116] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can refer to past reports and analysis logs to refer to the user's past analysis results. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also extract patterns for improving the accuracy of the analysis from the user's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0117] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can use facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. This allows the analysis result to be understood more clearly by providing the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input image data of the user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0118] The generation unit can estimate the user's emotions and adjust the design of the generated slides based on the estimated emotions. For example, the generation unit can use facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, if the user is relaxed, the generation unit can generate slides with a calm design. If the user is in a hurry, the generation unit can generate slides with a simple, highly visible design. Furthermore, if the user is excited, the generation unit can generate slides with a visually stimulating design. This improves the visual explanation power by providing slide designs that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0119] The generation unit can adjust the level of detail of the slides based on the importance of the theme or content during generation. For example, the generation unit can use data analysis technology to evaluate the importance of the theme or content. For example, the generation unit can generate detailed slides for important themes or content. The generation unit can also generate concise slides for general themes or content. The generation unit can also adjust the level of detail of the slides based on the importance specified by the user. This allows important information to be displayed in detail by adjusting the level of detail of the slides based on the importance of the theme or content. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input importance data specified by the user into the generation AI and cause the generation AI to adjust the level of detail of the slides.

[0120] During generation, the generation unit can apply different generation algorithms depending on the theme or content category. For example, the generation unit can use data analysis technology to identify the theme or content category. For example, the generation unit can apply a business generation algorithm to a business-related theme. The generation unit can also apply a science and technology generation algorithm to a science and technology-related theme. Furthermore, the generation unit can apply an entertainment generation algorithm to an entertainment-related theme. In this way, by applying a generation algorithm depending on the theme or content category, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input theme or content category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0121] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit can refer to past slide designs and generation logs to refer to the user's past generation results. For example, the generation unit can optimize the generation algorithm based on the user's past generation results. The generation unit can also extract patterns for improving the accuracy of generation from the user's past generation results. Furthermore, the generation unit can improve the accuracy of generation by referring to the user's past generation results. In this way, the accuracy of generation is improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0122] The generation unit can estimate the user's emotions and adjust the length of the generated slides based on the estimated emotions. For example, the generation unit can use facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point slides. If the user is relaxed, the generation unit can generate longer slides with detailed explanations. Furthermore, if the user is excited, the generation unit can generate slides with visually stimulating effects. This improves the visual explanation by providing slide lengths that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0123] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated emotions. For example, the customization unit can use facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the customization unit can provide detailed customization options when the user is relaxed. The customization unit can also provide concise customization options when the user is in a hurry. Furthermore, the customization unit can provide visually stimulating customization options when the user is excited. This improves the user experience by providing a customization method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit can be performed using AI, for example, or without AI. For example, the customization unit can input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0124] During customization, the customization unit can analyze the user's past customization history and propose an optimal customization method. For example, the customization unit can use data analysis technology to analyze the user's past customization history. For example, the customization unit can automatically display customization options used by the user in the past as candidates. The customization unit can also propose an optimal customization method based on the user's past customization history. Furthermore, the customization unit can optimize the customization procedure based on the user's past customization history. In this way, the optimal customization method can be provided by analyzing the user's past customization history. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's past customization history data into a generation AI and cause the generation AI to propose an optimal customization method.

[0125] During customization, the customization unit can filter the customization content based on the user's current project or areas of interest. For example, the customization unit can refer to data from the project management tool or the user's past activity history to identify the user's current project or areas of interest. For example, the customization unit can preferentially display customization options related to the user's current project. The customization unit can also suggest related customization options based on the user's areas of interest. Furthermore, the customization unit can filter highly relevant customization options by referring to the user's past project history. This makes it possible to provide highly relevant customization by filtering the customization content based on the user's current project or areas of interest. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input data from the project management tool to the generation AI and cause the generation AI to filter highly relevant customization options.

[0126] During customization, the customization unit can improve the customization method by reflecting user feedback. For example, the customization unit can refer to user evaluation comments and survey results to reflect user feedback. For example, the customization unit can suggest an optimal customization method based on feedback provided by the user in the past. The customization unit can also improve the customization procedure based on the user's past feedback. Furthermore, the customization unit can improve the customization interface by reflecting user feedback. In this way, an optimal customization method can be provided by reflecting user feedback. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input user feedback data into a generation AI and cause the generation AI to improve the customization method.

[0127] The customization unit can estimate the user's emotions and determine customization priorities based on the estimated emotions. For example, the customization unit can use facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the customization unit can prioritize providing important customization options when the user is stressed. The customization unit can also prioritize providing detailed customization options when the user is relaxed. Furthermore, the customization unit can prioritize providing the most important customization options when the user is in a hurry. This allows for prioritizing customization options according to the user's emotions, thereby prioritizing important customizations. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit can be performed using AI, for example, or without AI. For example, the customization unit can input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0128] During customization, the customization unit can select a customization method based on the user's geographic location information. For example, the customization unit can use analysis of GPS data or IP address to obtain the user's geographic location information. For example, if the user is in a specific area, the customization unit can prioritize providing customization options related to that area. Also, if the user is traveling, the customization unit can prioritize providing customization options related to the travel destination. Furthermore, if the user is participating in a specific event, the customization unit can prioritize providing customization options related to the event. In this way, by taking the user's geographic location information into consideration, highly relevant customization can be provided. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's geographic location information data into the generation AI and cause the generation AI to select the optimal customization method.

[0129] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. For example, the customization unit can analyze the user's social media activity by analyzing the content of posts and followers. For example, the customization unit can suggest relevant customization options based on the content the user has shared on social media. The customization unit can also analyze the content of the user's social media posts and suggest relevant customization options. Furthermore, the customization unit can suggest relevant customization options based on the activity of the user's friends on social media. In this way, highly relevant customization can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest customization methods.

[0130] During customization, the customization unit can customize the customization method by reflecting the user's past feedback. For example, the customization unit can refer to the user's evaluation comments and survey results to reflect the user's past feedback. For example, the customization unit can suggest an optimal customization method based on the user's past feedback. The customization unit can also improve the customization procedure based on the user's past feedback. Furthermore, the customization unit can customize the customization interface by reflecting the user's past feedback. In this way, the optimal customization method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's feedback data into the generation AI and cause the generation AI to customize the customization method.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit receives input of the topic or content of the presentation. The topic or content of the presentation may include a business presentation, an academic presentation, a product introduction, etc. The reception unit provides an interface for the user to input the topic or content of the presentation and supports multiple input methods, such as voice input and text input. For example, speech recognition technology can be used to convert what the user says into text. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit. Analysis is performed using methods such as text analysis, data analysis, and image analysis. The analysis unit analyzes the input themes and content and extracts the data necessary for slide design and information visualization. It can also evaluate the importance and relevance of the input information. Step 3: The generator uses generative AI to design slides and visualize information based on the information analyzed by the analyzer. The generator can generate text, images, and numerical data. For example, it places appropriate text on each slide of the presentation and generates visual information such as graphs and charts appropriate for the slide. Step 4: The customization unit customizes the slides generated by the generation unit. The customization unit provides an interface for users to make corrections or additions based on the generated slides, and offers customization options such as changing colors, selecting fonts, and adjusting layouts. It can also use the generation AI to suggest customizations that suit the user's preferences.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

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

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving input of a theme or content of a presentation; an analysis unit that analyzes the information received by the reception unit; a generation unit that designs slides or visualizes information based on the information analyzed by the analysis unit; a customization unit that customizes the slides generated by the generation unit. A system characterized by:

2. The reception unit Inferring a user's emotions and adjusting the subject or content input method based on the inferred emotions 2. The system of claim 1.

3. The reception unit Analyzes the user's past presentation history and suggests input formats 2. The system of claim 1.

4. The reception unit As you type a topic or subject, filter your input based on your current projects or areas of interest 2. The system of claim 1.

5. The reception unit When entering a theme or content, select an input method according to the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize input content based on the estimated emotions 2. The system of claim 1.

7. The reception unit When entering a topic or subject, prioritize the most relevant topics based on the user's geographic location.

2. The system of claim 1.

8. The reception unit When entering a topic or content, analyze the user's social media activity and enter related topics.

2. The system of claim 1.

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