system

Abstract

The system according to this embodiment aims to provide comprehensive support for presentations, from preparation to the actual presentation. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a practice unit, a feedback unit, a material generation unit, a checking unit, and a follow-up unit. The reception unit inputs the presentation status. The generation unit generates an avatar based on the information entered by the reception unit. The practice unit practices the presentation based on the avatar generated by the generation unit. The feedback unit provides feedback on the practice results performed by the practice unit. The material generation unit automatically generates materials based on the feedback provided by the feedback unit. The checking unit checks the materials generated by the material generation unit. The follow-up unit provides follow-up during the actual presentation based on the materials checked by the checking unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is insufficient effective support in preparing for and practicing presentations and creating materials, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively support from preparing for a presentation to the actual performance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a practice unit, a feedback unit, a material generation unit, a checking unit, and a follow-up unit. The reception unit inputs the presentation status. The generation unit generates an avatar based on the information entered by the reception unit. The practice unit practices the presentation based on the avatar generated by the generation unit. The feedback unit provides feedback on the practice results performed by the practice unit. The material generation unit automatically generates materials based on the feedback provided by the feedback unit. The checking unit checks the materials generated by the material generation unit. The follow-up unit provides follow-up during the actual presentation based on the materials checked by the checking unit. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively support the entire process from presentation preparation to the actual presentation. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The presentation support system according to an embodiment of the present invention is a system that uses a generative AI to support presentations in order to solve various problems in presentations (sales, announcements, reports, lectures, etc.). This presentation support system takes the presentation situation (audience, purpose, presentation time, audience knowledge level, industry, etc.) as prompts into the generative AI, which generates an avatar appropriate to the presentation audience and automatically generates base materials. The generated materials are checked for appropriateness of design and expression, and to prevent violations. Furthermore, the system conducts presentation practice and provides feedback based on the results. The feedback includes consistency between materials and speech, identification of unclear parts, and generation of anticipated FAQs. Finally, during the actual presentation, follow-up is provided to alleviate tension, check time allocation, and interpret the intent of FAQs. First, the presentation situation is input into the generative AI as prompts. For example, information such as the presentation audience being executives, the presentation time being 30 minutes, and the audience having a high level of knowledge is input. This information is analyzed by the generative AI and an avatar appropriate to the presentation audience is generated. For example, an executive-level avatar is generated and used during presentation practice. Next, the generative AI automatically generates base materials. For example, if the purpose of the presentation is to introduce a new product, the generating AI will automatically generate materials that include the features and benefits of the new product. The generated materials will be checked for appropriateness in design and expression, as well as for preventing copyright infringement. For example, the colors, fonts, and layout of the materials will be checked for appropriateness, and there will be no copyright infringement. Furthermore, the presentation will be practiced, and feedback will be provided based on the results. For example, the generating AI will transcribe the content of the presentation into text and check the consistency between the materials and the spoken content. It will also point out unclear parts and suggest areas for improvement. In addition, it can generate anticipated questions (FAQs) and prepare answers. Finally, during the actual presentation, the generating AI will provide support such as easing tension, checking time management, and interpreting the intent of FAQs. For example, the generating AI will provide comments to help the presentation progress smoothly. It will also check the time management to ensure that it is proceeding according to schedule. Furthermore, it will analyze the intent of questions and provide appropriate answers.This system streamlines the entire process from presentation preparation to the actual presentation, improving the quality of the presentation. For example, the burden of creating materials is reduced, and practice provides psychological reassurance. Furthermore, feedback improves the presentation content, allowing presenters to perform without nervousness during the actual presentation. In addition, the generation AI supports the flow of the presentation, increasing the success rate of the presentation. In this way, the presentation support system can streamline the entire process from presentation preparation to the actual presentation, and improve the quality of the presentation.

[0029] The presentation support system according to this embodiment comprises a reception unit, a generation unit, a practice unit, a feedback unit, a material generation unit, a checking unit, and a follow-up unit. The reception unit inputs the presentation status. The presentation status includes, but is not limited to, the presentation theme, target audience, and time allocation. The reception unit analyzes the information entered by the user to identify the presentation status. The generation unit uses a generation AI to generate an avatar based on the information entered by the reception unit. The generation AI generates the avatar using, for example, technologies such as deep learning or generative opposite-agent networks (GANs). The generation unit generates, for example, an avatar tailored to the presentation audience and uses it during presentation practice. The practice unit uses the generated avatar to practice the presentation. The practice includes, for example, the content of the simulation, the number of practice sessions, and evaluation criteria, but is not limited to, such examples. The practice unit provides, for example, an environment in which the user can practice the presentation using the generated avatar. The feedback unit provides feedback on the practice results performed by the practice unit. Feedback includes, but is not limited to, voice analysis, gesture analysis, and evaluation criteria. The feedback unit transcribes the content of the presentation into text and checks the consistency between the materials and the spoken content. The material generation unit automatically generates materials based on the feedback provided by the feedback unit. Material generation includes, but is not limited to, templates, basic information, and layout. The material generation unit uses a generation AI to automatically generate base materials tailored to the purpose of the presentation. The checking unit checks the materials generated by the material generation unit. Checks include, but are not limited to, the appropriateness of the design, accuracy of the content, and legal compliance. The checking unit checks, for example, the appropriateness of the design and expression of the generated materials, and measures to deter violations. The follow-up unit provides support during the actual presentation based on the materials checked by the checking unit. Follow-up includes, but is not limited to, methods for reducing tension, adjusting time allocation, and answering FAQs. The follow-up unit provides support during the actual presentation, such as reducing tension, checking time allocation, and interpreting the intent of FAQs.As a result, the presentation support system according to this embodiment can streamline the entire process from presentation preparation to the actual presentation, and improve the quality of the presentation.

[0030] The reception desk inputs information about the presentation. This information includes, but is not limited to, the presentation topic, target audience, and time allocation. For example, the reception desk analyzes the information entered by the user to identify the presentation details. Specifically, the information entered by the user is provided to the reception desk in text format. The reception desk uses natural language processing technology to analyze the entered text and extract important elements such as the presentation topic, target audience, and time allocation. For example, if a user enters "I will give a 30-minute presentation introducing a new product to the management team," the reception desk recognizes "introduction of a new product" as the topic, identifies "management team" as the target audience, and extracts "30 minutes" as the time allocation. Furthermore, the reception desk can also analyze additional information provided by the user. For example, it accepts information such as the presentation's purpose, expected outcomes, and the types of materials to be used as input, and uses this information to grasp the overall picture of the presentation. The reception desk centrally manages this information and uses it as basic data to provide to subsequent generation and practice departments. This allows the reception desk to efficiently analyze the information provided by the user and accurately identify the status of the presentation.

[0031] The generation unit uses a generation AI to generate avatars based on information entered by the reception unit. The generation AI generates avatars using technologies such as deep learning and generative opposite networks (GANs). Specifically, the generation AI generates the optimal avatar based on the presentation theme and target audience information entered by the user. For example, in the case of a presentation to management, the generation AI will generate an avatar in formal attire and select an avatar with friendly facial expressions and gestures. The generation AI can also learn the characteristics and speaking style of the user's voice and set the avatar to speak with a voice similar to the user's. Furthermore, the generation AI can customize the avatar's movements and facial expressions according to the content of the presentation. For example, in the case of a technical presentation, the avatar will be set to use appropriate gestures when giving detailed explanations. The generation unit combines these functions to provide the optimal avatar for the user when practicing their presentation. In this way, the generation unit helps the user practice in an environment that is close to an actual presentation.

[0032] The practice unit uses generated avatars to practice presentations. Practice includes, but is not limited to, the content of the simulation, the number of practice sessions, and evaluation criteria. The practice unit provides an environment in which users can practice presentations using generated avatars. Specifically, the practice unit builds a simulation environment in which users present while interacting with the avatar. Users present to the avatar, and the avatar responds in real time. For example, the avatar may ask questions or respond with gestures to what the user says. The practice unit records the user's statements and gestures and saves them as data to provide feedback later. Furthermore, the practice unit evaluates the practice results based on evaluation criteria set by the user. For example, if the user sets evaluation criteria such as "clarity of pronunciation" or "gaze distribution," the practice unit evaluates the user's performance based on these criteria and assigns a score. In this way, the practice unit can provide an environment in which users can effectively practice presentations and improve their skills.

[0033] The Feedback Department provides feedback on the results of practice conducted by the Practice Department. This feedback includes, but is not limited to, voice analysis, gesture analysis, and evaluation criteria. For example, the Feedback Department transcribes presentation content into text and checks for consistency between the materials and the spoken content. Specifically, the Feedback Department uses speech recognition technology to transcribe the user's speech and analyzes its content. Voice analysis evaluates clarity of pronunciation, speaking speed, and tone of voice, and provides the user with specific areas for improvement. Gesture analysis analyzes the user's hand movements and eye gaze distribution, providing feedback on appropriate gestures and eye usage. Furthermore, the Feedback Department checks for consistency between the materials used by the user and their spoken content. For example, if a user gives a presentation using slides, the Feedback Department verifies that the slide content matches the user's spoken content and points out areas for improvement as needed. This allows the Feedback Department to provide specific advice to help users improve the quality of their presentations.

[0034] The document generation unit automatically generates documents based on feedback provided by the feedback unit. Document generation includes, but is not limited to, templates, basic information, and layouts. The document generation unit uses generation AI to automatically generate base documents tailored to the purpose of the presentation. Specifically, the document generation unit selects the optimal template and arranges basic information based on information provided by the user and feedback from the feedback unit. For example, in the case of a business presentation, the document generation unit selects a professionally designed template and appropriately places important data and graphs. The document generation unit also adjusts the layout according to the user's presentation content to create visually easy-to-understand documents. Furthermore, the document generation unit can use generation AI to automatically summarize the user's statements and reflect them in the slides. In this way, the document generation unit helps users efficiently create high-quality presentation materials.

[0035] The checking unit checks the materials generated by the material generation unit. This checking includes, but is not limited to, the appropriateness of the design, the accuracy of the content, and legal compliance. For example, the checking unit checks the appropriateness of the design and expression of the generated materials, and whether they deter violations. Specifically, the checking unit verifies that the material's design is visually appealing and aligns with the presentation's purpose. For example, it evaluates the use of color, font selection, and layout balance, and points out areas for improvement as needed. Regarding accuracy, it verifies that the data and information contained in the material are accurate and that there are no misleading expressions. Regarding legal compliance, it checks that the material meets legal requirements such as copyright and trademark rights. This allows the checking unit to provide high-quality materials that users can confidently use for their presentations.

[0036] The follow-up team provides support during the actual presentation based on the materials checked by the checking team. This support includes, but is not limited to, methods for reducing anxiety, adjusting time allocation, and addressing frequently asked questions (FAQs). Specifically, the follow-up team provides support during the presentation, such as reducing anxiety, checking time allocation, and interpreting the intent of FAQs. They also provide breathing exercises and simple stretches to help users relax during the presentation. Furthermore, they monitor the progress of the presentation in real time to check if the time allocation is appropriate. For example, if the presentation is running ahead of schedule, they prompt for additional explanations; conversely, if it is running behind, they advise focusing on key points. In addition, the follow-up team assists with appropriate responses to audience questions. For example, they provide an FAQ list and example answers to common questions to help users answer with confidence. In this way, the follow-up team can support users in delivering their best performance during the actual presentation.

[0037] The generation unit can generate avatars tailored to the presentation audience using generative AI. For example, the generation unit uses generative AI to generate avatars appropriate to the presentation audience. The generative AI uses technologies such as deep learning and generative opposite-agent networks (GANs) to generate avatars. For example, the generation unit can generate avatars for executive-level presentations for use during presentation practice. Furthermore, the generation unit can use generative AI to generate avatars that reflect the characteristics of the presentation audience. For example, the generation unit can generate avatars based on information such as the audience's age, gender, and occupation. This allows the generation unit to improve the effectiveness of practice by generating avatars tailored to the presentation audience.

[0038] The document generation unit can automatically generate base materials tailored to the purpose of a presentation using generation AI. For example, the document generation unit uses generation AI to automatically generate base materials that match the presentation's objectives. The generation AI generates materials using technologies such as template-based generation and data-driven generation. For example, in the case of a presentation introducing a new product, the document generation unit automatically generates materials that include the features and benefits of the new product. Furthermore, the document generation unit can also use generation AI to generate materials tailored to the presentation's theme. For example, in the case of an educational presentation, the document generation unit generates materials related to the educational content. This allows the document generation unit to reduce the burden of material creation by automatically generating base materials tailored to the presentation's objectives.

[0039] The checking unit can verify the appropriateness of the design and expression of the generated materials, as well as prevent violations. For example, the checking unit checks the appropriateness of the design and expression of the generated materials, as well as prevent violations. The checks include, but are not limited to, the appropriateness of the design, the accuracy of the content, and legal compliance. For example, the checking unit checks whether the colors, fonts, and layout of the materials are appropriate, and whether there are any copyright infringements. The checking unit can also use generation AI to automatically check the appropriateness of the design and expression of the materials. For example, the checking unit can use generation AI to evaluate whether the design of the materials is visually appealing and easy to read. In this way, the checking unit can improve the quality of the materials by checking the appropriateness of the design and expression and prevent violations.

[0040] The practice unit allows users to practice presentations using generated avatars. For example, the practice unit uses generated avatars to practice presentations. Practice includes, but is not limited to, simulation content, number of practice sessions, and evaluation criteria. The practice unit provides an environment for users to practice presentations using generated avatars. Furthermore, the practice unit can automatically evaluate the content of presentation practice using generative AI. For example, the practice unit uses generative AI to analyze the user's speech and gestures and evaluate the effectiveness of the practice. This allows the practice unit to improve the effectiveness of practice by using generated avatars for presentation practice.

[0041] The feedback unit can transcribe the content of a presentation into text and check the consistency between the materials and the spoken content. For example, the feedback unit transcribes the content of a presentation into text and checks the consistency between the materials and the spoken content. Feedback includes, but is not limited to, voice analysis, gesture analysis, and evaluation criteria. For example, the feedback unit can use generative AI to transcribe the content of a presentation into text and check the consistency between the materials and the spoken content. Furthermore, the feedback unit can use generative AI to point out unclear parts of the presentation and suggest improvements. For example, the feedback unit can use generative AI to analyze the content of a presentation, identify unclear parts, and suggest improvements. In this way, the feedback unit can improve the quality of the presentation by transcribing the content of the presentation into text and checking the consistency between the materials and the spoken content.

[0042] The support unit can provide assistance during the actual presentation, such as easing tension, checking time allocation, and interpreting the intent of FAQs. For example, the support unit can provide assistance during the actual presentation, such as easing tension, checking time allocation, and interpreting the intent of FAQs. This support includes, but is not limited to, methods for easing tension, adjusting time allocation, and answering FAQs. For example, the support unit can use generative AI to provide relaxing comments during the presentation, supporting the flow of the presentation. It can also use generative AI to check the presentation's time allocation and ensure it is progressing according to schedule. Furthermore, the support unit can use generative AI to analyze the intent of questions and provide appropriate answers. In this way, the support unit can increase the success rate of the presentation by providing assistance during the actual presentation, such as easing tension, checking time allocation, and interpreting the intent of FAQs.

[0043] The reception desk can analyze the user's past presentation history and select the optimal input method. For example, the reception desk may prioritize suggesting the input method used in the user's past presentations. The reception desk can also automatically display frequently used input items based on the user's past presentation history. Furthermore, the reception desk can analyze the user's past presentation history and customize the input method based on specific patterns. In this way, the reception desk can provide the optimal input method by analyzing the user's past presentation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0044] The reception system can filter presentation status input based on the user's current projects and areas of interest. For example, the reception system prioritizes inputting information related to the user's current projects. The reception system can also automatically suggest relevant presentation statuses based on the user's areas of interest. Furthermore, the reception system can integrate with the user's project management tools to automatically retrieve and input information related to the current project. This allows the reception system to provide highly relevant information by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not using AI.

[0045] The reception system can prioritize inputting highly relevant information when the user inputs presentation details, taking into account their geographical location. For example, if the user is giving a presentation in a specific region, the reception system will prioritize inputting information relevant to that region. The reception system can also automatically input nearby competitor information and market data based on the user's current location. Furthermore, the reception system can input region-specific topics and interests based on the user's geographical location. In this way, the reception system can provide highly relevant information by taking the user's geographical location into consideration. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI.

[0046] The reception desk can analyze the user's social media activity and input relevant information when inputting the presentation status. For example, the reception desk can analyze the content of the user's social media posts and automatically input relevant presentation status information. The reception desk can also customize the presentation status based on the interests of the user's followers and friends. Furthermore, the reception desk can input trends and trending themes from the user's social media activity. In this way, the reception desk can provide relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0047] The generation unit can adjust the level of detail of the avatar based on the purpose of the presentation when generating the avatar. For example, if the purpose of the presentation is to introduce a new product, the generation unit will generate an avatar that emphasizes the product's features. The generation unit can also generate a trustworthy avatar if the purpose of the presentation is to explain something to investors. Furthermore, if the purpose of the presentation is educational, the generation unit can generate a friendly avatar. In this way, the generation unit can generate a more appropriate avatar by adjusting the level of detail based on the purpose of the presentation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0048] The generation unit can apply different generation algorithms depending on the presentation category when generating avatars. For example, in the case of a business presentation, the generation unit can apply an algorithm that generates a formal avatar. In the case of an educational presentation, the generation unit can also apply an algorithm that generates a friendly avatar. Furthermore, in the case of an entertainment presentation, the generation unit can apply an algorithm that generates a visually appealing avatar. In this way, the generation unit can generate more effective avatars by applying different generation algorithms depending on the presentation category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0049] The generation unit can determine the priority of avatars based on the presentation submission deadline when generating avatars. For example, if the presentation submission deadline is approaching, the generation unit will prioritize avatars that can be generated quickly. Conversely, if the presentation submission deadline is far away, the generation unit can allocate time to generate detailed avatars. Furthermore, the generation unit can adjust the order of avatar generation according to the presentation submission deadline. This enables efficient avatar generation by determining the priority of avatars based on the presentation submission deadline. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0050] The generation unit can adjust the order of avatars based on the relevance of the presentation during avatar generation. For example, if the content of the presentation is important, the generation unit will prioritize generating the most relevant avatars. The generation unit can also generate standard avatars if the content of the presentation is general. Furthermore, if the content of the presentation is specific to a particular industry, the generation unit can generate avatars relevant to that industry. In this way, the generation unit can generate more effective avatars by adjusting the order of avatars based on the relevance of the presentation. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI.

[0051] The practice team can adjust the level of detail in practice sessions based on the importance of the presentation. For example, for important presentations, the practice team can conduct detailed practice sessions, checking every detail. For general presentations, the practice team can also conduct basic practice sessions. Furthermore, for short presentations, the practice team can conduct concise practice sessions focusing on the key points. This allows the practice team to enable efficient practice by adjusting the level of detail based on the importance of the presentation. Some or all of the above processes in the practice team may be performed using AI, for example, or not.

[0052] The practice unit can apply different practice algorithms depending on the presentation category during practice. For example, for business presentations, the practice unit can apply a formal practice algorithm. For educational presentations, it can also apply a more approachable practice algorithm. Furthermore, for entertainment presentations, it can apply a visually appealing practice algorithm. In this way, the practice unit can enable more effective practice by applying different practice algorithms depending on the presentation category. Some or all of the above processing in the practice unit may be performed using AI, for example, or without AI.

[0053] The practice team can prioritize practice sessions based on the presentation submission deadline. For example, if the deadline is approaching, the practice team will conduct practice sessions quickly. Conversely, if the deadline is far away, the practice team can allocate time for detailed practice sessions. Furthermore, the practice team can adjust the order of practice sessions according to the presentation submission deadline. This allows the practice team to conduct efficient practice sessions by prioritizing them based on the presentation submission deadline. Some or all of the above processes in the practice team may be performed using AI, for example, or not.

[0054] The practice unit can adjust the order of practice sessions based on the relevance of the presentation. For example, in the case of an important presentation, the practice unit can start practicing with the most relevant content. Alternatively, in the case of a general presentation, the practice unit can start practicing with standard content. Furthermore, in the case of a presentation specific to a particular industry, the practice unit can start practicing with content relevant to that industry. This allows the practice unit to enable more effective practice by adjusting the order of practice sessions based on the relevance of the presentation. Some or all of the above processes in the practice unit may be performed using AI, for example, or not.

[0055] The feedback unit can adjust the level of detail in the feedback based on the importance of the presentation. For example, in the case of an important presentation, the feedback unit will provide detailed feedback, checking every detail. In the case of a general presentation, the feedback unit can also provide basic feedback. Furthermore, in the case of a short presentation, the feedback unit can provide concise feedback. In this way, the feedback unit can provide efficient feedback by adjusting the level of detail in the feedback based on the importance of the presentation. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI.

[0056] The feedback unit can apply different feedback algorithms depending on the presentation category during the feedback process. For example, in the case of a business presentation, the feedback unit can apply a formal feedback algorithm. It can also apply a more approachable feedback algorithm for educational presentations. Furthermore, for entertainment presentations, it can apply a visually appealing feedback algorithm. This allows the feedback unit to provide more effective feedback by applying different feedback algorithms depending on the presentation category. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or without AI.

[0057] The feedback unit can prioritize feedback based on the presentation submission deadline. For example, if the presentation deadline is approaching, the feedback unit will provide feedback quickly. Conversely, if the deadline is far away, the feedback unit can allocate time to provide detailed feedback. Furthermore, the feedback unit can adjust the order of feedback depending on the presentation submission deadline. This allows the feedback unit to provide efficient feedback by prioritizing feedback based on the presentation submission deadline. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not.

[0058] The feedback unit can adjust the order of feedback based on the relevance of the presentation. For example, in the case of an important presentation, the feedback unit can start with the most relevant content. In the case of a general presentation, the feedback unit can also start with standard content. Furthermore, in the case of a presentation specific to a particular industry, the feedback unit can start with content relevant to that industry. This allows the feedback unit to provide more effective feedback by adjusting the order of feedback based on the relevance of the presentation. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI.

[0059] The document generation unit can adjust the level of detail in the documents based on the importance of the presentation during document generation. For example, in the case of an important presentation, the document generation unit can generate detailed documents and review every detail. In addition, the document generation unit can generate basic documents for general presentations. Furthermore, in the case of a short presentation, the document generation unit can generate documents that highlight the key points. In this way, the document generation unit can efficiently generate documents by adjusting the level of detail in the documents based on the importance of the presentation. Some or all of the above processing in the document generation unit may be performed using AI, for example, or without using AI.

[0060] The document generation unit can apply different document generation algorithms depending on the presentation category when generating documents. For example, in the case of a business presentation, the document generation unit can apply a formal document generation algorithm. In the case of an educational presentation, the document generation unit can also apply a more approachable document generation algorithm. Furthermore, in the case of an entertainment presentation, the document generation unit can apply a visually appealing document generation algorithm. In this way, the document generation unit can generate more effective documents by applying different document generation algorithms depending on the presentation category. Some or all of the above processing in the document generation unit may be performed using AI, for example, or without using AI.

[0061] The document generation unit can prioritize documents based on the presentation submission deadline when generating them. For example, if the presentation deadline is approaching, the document generation unit will prioritize documents that can be generated quickly. Conversely, if the presentation deadline is far away, the document generation unit can allocate time to generate detailed documents. Furthermore, the document generation unit can adjust the order in which documents are generated according to the presentation submission deadline. This enables efficient document generation by prioritizing documents based on the presentation submission deadline. Some or all of the above-described processes in the document generation unit may be performed using AI, for example, or without using AI.

[0062] The document generation unit can adjust the order of documents based on their relevance in the presentation. For example, if the content of the presentation is important, the document generation unit will prioritize generating the most relevant documents. It can also generate standard documents if the content of the presentation is general. Furthermore, if the content of the presentation is specific to a particular industry, the document generation unit can generate industry-specific documents. This allows the document generation unit to generate more effective documents by adjusting the order of documents based on their relevance in the presentation. Some or all of the above processing in the document generation unit may be performed using AI, for example, or without AI.

[0063] The checking unit can adjust the level of detail of its checks based on the importance of the presentation. For example, in the case of an important presentation, the checking unit will perform a detailed check, verifying every detail. In addition, in the case of a general presentation, the checking unit can perform a basic check. Furthermore, in the case of a short presentation, the checking unit can perform a check that focuses on the key points. In this way, the checking unit can efficiently perform checks by adjusting the level of detail based on the importance of the presentation. Some or all of the above processing in the checking unit may be performed using AI, for example, or without the use of AI.

[0064] The checking unit can apply different checking algorithms depending on the category of the presentation during the checking process. For example, in the case of a business presentation, the checking unit can apply a formal checking algorithm. In addition, in the case of an educational presentation, the checking unit can apply a more approachable checking algorithm. Furthermore, in the case of an entertainment presentation, the checking unit can apply a visually appealing checking algorithm. In this way, the checking unit can enable more effective checking by applying different checking algorithms depending on the category of the presentation. Some or all of the above processing in the checking unit may be performed using AI, for example, or without the use of AI.

[0065] The checking unit can determine the priority of checks based on the presentation submission deadline. For example, if the presentation submission deadline is approaching, the checking unit will perform a quick check. Conversely, if the submission deadline is far away, the checking unit can allocate time for a detailed check. Furthermore, the checking unit can adjust the order of checks according to the presentation submission deadline. This allows the checking unit to perform efficient checks by determining the priority of checks based on the presentation submission deadline. Some or all of the above processes in the checking unit may be performed using AI, for example, or without using AI.

[0066] The checking unit can adjust the order of checks based on the relevance of the presentation during the checking process. For example, in the case of an important presentation, the checking unit can start checking with the most relevant content. In the case of a general presentation, the checking unit can also start checking with standard content. Furthermore, in the case of a presentation specific to a particular industry, the checking unit can start checking with content relevant to that industry. This allows the checking unit to perform more effective checks by adjusting the order of checks based on the relevance of the presentation. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI.

[0067] The follow-up unit can adjust the level of detail in the follow-up based on the importance of the presentation. For example, in the case of an important presentation, the follow-up unit will provide detailed follow-up, checking every detail. In addition, in the case of a general presentation, the follow-up unit can provide basic follow-up. Furthermore, in the case of a short presentation, the follow-up unit can provide concise follow-up that focuses on the key points. In this way, the follow-up unit can enable efficient follow-up by adjusting the level of detail in the follow-up based on the importance of the presentation. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or not using AI.

[0068] The follow-up function can apply different follow-up algorithms depending on the presentation category. For example, in the case of a business presentation, the follow-up function can apply a formal follow-up algorithm. In the case of an educational presentation, the follow-up function can also apply a more approachable follow-up algorithm. Furthermore, in the case of an entertainment presentation, the follow-up function can apply a visually appealing follow-up algorithm. In this way, the follow-up function can enable more effective follow-up by applying different follow-up algorithms depending on the presentation category. Some or all of the above processing in the follow-up function may be performed using AI, for example, or not using AI.

[0069] The follow-up unit can prioritize follow-ups based on the presentation submission deadline. For example, if the presentation submission deadline is approaching, the follow-up unit will provide prompt follow-up. Conversely, if the presentation submission deadline is far away, the follow-up unit can allocate time to provide detailed follow-up. Furthermore, the follow-up unit can adjust the order of follow-ups according to the presentation submission deadline. This allows the follow-up unit to perform follow-ups efficiently by prioritizing them based on the presentation submission deadline. Some or all of the above processes in the follow-up unit may be performed using AI, for example, or not.

[0070] The follow-up unit can adjust the order of follow-up based on the relevance of the presentation. For example, in the case of an important presentation, the follow-up unit will start with the most relevant content. In the case of a general presentation, the follow-up unit can also start with standard content. Furthermore, in the case of a presentation specific to a particular industry, the follow-up unit can start with content relevant to that industry. This allows the follow-up unit to enable more effective follow-up by adjusting the order of follow-up based on the relevance of the presentation. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or not using AI.

[0071] The follow-up unit can adjust its follow-up content in real time according to the progress of the presentation. For example, if the presentation is ahead of schedule, the follow-up unit can provide additional information. If the presentation is behind schedule, the follow-up unit can also provide concise follow-up. Furthermore, if an unexpected question arises during the presentation, the follow-up unit can provide an appropriate answer in real time. In this way, the follow-up unit can enable more effective follow-up by adjusting its follow-up content in real time according to the progress of the presentation. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or not using AI.

[0072] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0073] The reception desk can analyze the user's past presentation history and select the optimal input method. For example, the reception desk may prioritize suggesting the input method used in the user's past presentations. The reception desk can also automatically display frequently used input items based on the user's past presentation history. Furthermore, the reception desk can analyze the user's past presentation history and customize the input method based on specific patterns. In this way, the reception desk can provide the optimal input method by analyzing the user's past presentation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0074] The reception system can filter presentation status input based on the user's current projects and areas of interest. For example, the reception system prioritizes inputting information related to the user's current projects. The reception system can also automatically suggest relevant presentation statuses based on the user's areas of interest. Furthermore, the reception system can integrate with the user's project management tools to automatically retrieve and input information related to the current project. This allows the reception system to provide highly relevant information by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not using AI.

[0075] The generation unit can adjust the level of detail of the avatar based on the purpose of the presentation when generating the avatar. For example, if the purpose of the presentation is to introduce a new product, the generation unit will generate an avatar that emphasizes the product's features. The generation unit can also generate a trustworthy avatar if the purpose of the presentation is to explain something to investors. Furthermore, if the purpose of the presentation is educational, the generation unit can generate a friendly avatar. In this way, the generation unit can generate a more appropriate avatar by adjusting the level of detail based on the purpose of the presentation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0076] The practice team can adjust the level of detail in practice sessions based on the importance of the presentation. For example, for important presentations, the practice team can conduct detailed practice sessions, checking every detail. For general presentations, the practice team can also conduct basic practice sessions. Furthermore, for short presentations, the practice team can conduct concise practice sessions focusing on the key points. This allows the practice team to enable efficient practice by adjusting the level of detail based on the importance of the presentation. Some or all of the above processes in the practice team may be performed using AI, for example, or not.

[0077] The feedback unit can adjust the level of detail in the feedback based on the importance of the presentation. For example, in the case of an important presentation, the feedback unit will provide detailed feedback, checking every detail. In the case of a general presentation, the feedback unit can also provide basic feedback. Furthermore, in the case of a short presentation, the feedback unit can provide concise feedback. In this way, the feedback unit can provide efficient feedback by adjusting the level of detail in the feedback based on the importance of the presentation. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI.

[0078] The following briefly describes the processing flow for example form 1.

[0079] Step 1: The reception desk enters the presentation status. This includes, for example, the presentation topic, target audience, and time allocation. The reception desk analyzes the information entered by the user to determine the presentation status. Step 2: The generation unit uses a generation AI to generate avatars based on the information entered by the reception unit. The generation AI uses technologies such as deep learning and generative opposite networks (GANs) to generate avatars. The generation unit generates avatars tailored to the presentation audience and uses them during presentation practice. Step 3: The practice team conducts presentation practice using the generated avatars. This practice includes the content of the simulation, the number of practice sessions, and the evaluation criteria. The practice team provides an environment in which users can practice their presentations using the generated avatars. Step 4: The Feedback team provides feedback on the practice results conducted by the Practice team. This feedback includes voice analysis, gesture analysis, and evaluation criteria. The Feedback team transcribes the presentation content into text and checks for consistency between the materials and the spoken content. Step 5: The document generation unit automatically generates documents based on the feedback provided by the feedback unit. Document generation includes templates, basic information, and layout. The document generation unit uses a generation AI to automatically generate base documents tailored to the purpose of the presentation. Step 6: The checking unit checks the materials generated by the material generation unit. The checks include the appropriateness of the design, the accuracy of the content, and legal compliance. The checking unit checks the appropriateness of the design and expression of the generated materials, as well as measures to deter violations. Step 7: The follow-up team provides support during the actual presentation based on the materials checked by the checking team. This support includes methods for reducing anxiety, adjusting time allocation, and addressing FAQs. The follow-up team provides support during the actual presentation, such as reducing anxiety, checking time allocation, and interpreting the intent of FAQs.

[0080] (Example of form 2) The presentation support system according to an embodiment of the present invention is a system that uses a generative AI to support presentations in order to solve various problems in presentations (sales, announcements, reports, lectures, etc.). This presentation support system takes the presentation situation (audience, purpose, presentation time, audience knowledge level, industry, etc.) as prompts into the generative AI, which generates an avatar appropriate to the presentation audience and automatically generates base materials. The generated materials are checked for appropriateness of design and expression, and to prevent violations. Furthermore, the system conducts presentation practice and provides feedback based on the results. The feedback includes consistency between materials and speech, identification of unclear parts, and generation of anticipated FAQs. Finally, during the actual presentation, follow-up is provided to alleviate tension, check time allocation, and interpret the intent of FAQs. First, the presentation situation is input into the generative AI as prompts. For example, information such as the presentation audience being executives, the presentation time being 30 minutes, and the audience having a high level of knowledge is input. This information is analyzed by the generative AI and an avatar appropriate to the presentation audience is generated. For example, an executive-level avatar is generated and used during presentation practice. Next, the generative AI automatically generates base materials. For example, if the purpose of the presentation is to introduce a new product, the generating AI will automatically generate materials that include the features and benefits of the new product. The generated materials will be checked for appropriateness in design and expression, as well as for preventing copyright infringement. For example, the colors, fonts, and layout of the materials will be checked for appropriateness, and there will be no copyright infringement. Furthermore, the presentation will be practiced, and feedback will be provided based on the results. For example, the generating AI will transcribe the content of the presentation into text and check the consistency between the materials and the spoken content. It will also point out unclear parts and suggest areas for improvement. In addition, it can generate anticipated questions (FAQs) and prepare answers. Finally, during the actual presentation, the generating AI will provide support such as easing tension, checking time management, and interpreting the intent of FAQs. For example, the generating AI will provide comments to help the presentation progress smoothly. It will also check the time management to ensure that it is proceeding according to schedule. Furthermore, it will analyze the intent of questions and provide appropriate answers.This system streamlines the entire process from presentation preparation to the actual presentation, improving the quality of the presentation. For example, the burden of creating materials is reduced, and practice provides psychological reassurance. Furthermore, feedback improves the presentation content, allowing presenters to perform without nervousness during the actual presentation. In addition, the generation AI supports the flow of the presentation, increasing the success rate of the presentation. In this way, the presentation support system can streamline the entire process from presentation preparation to the actual presentation, and improve the quality of the presentation.

[0081] The presentation support system according to this embodiment comprises a reception unit, a generation unit, a practice unit, a feedback unit, a material generation unit, a checking unit, and a follow-up unit. The reception unit inputs the presentation status. The presentation status includes, but is not limited to, the presentation theme, target audience, and time allocation. The reception unit analyzes the information entered by the user to identify the presentation status. The generation unit uses a generation AI to generate an avatar based on the information entered by the reception unit. The generation AI generates the avatar using, for example, technologies such as deep learning or generative opposite-agent networks (GANs). The generation unit generates, for example, an avatar tailored to the presentation audience and uses it during presentation practice. The practice unit uses the generated avatar to practice the presentation. The practice includes, for example, the content of the simulation, the number of practice sessions, and evaluation criteria, but is not limited to, such examples. The practice unit provides, for example, an environment in which the user can practice the presentation using the generated avatar. The feedback unit provides feedback on the practice results performed by the practice unit. Feedback includes, but is not limited to, voice analysis, gesture analysis, and evaluation criteria. The feedback unit transcribes the content of the presentation into text and checks the consistency between the materials and the spoken content. The material generation unit automatically generates materials based on the feedback provided by the feedback unit. Material generation includes, but is not limited to, templates, basic information, and layout. The material generation unit uses a generation AI to automatically generate base materials tailored to the purpose of the presentation. The checking unit checks the materials generated by the material generation unit. Checks include, but are not limited to, the appropriateness of the design, accuracy of the content, and legal compliance. The checking unit checks, for example, the appropriateness of the design and expression of the generated materials, and measures to deter violations. The follow-up unit provides support during the actual presentation based on the materials checked by the checking unit. Follow-up includes, but is not limited to, methods for reducing tension, adjusting time allocation, and answering FAQs. The follow-up unit provides support during the actual presentation, such as reducing tension, checking time allocation, and interpreting the intent of FAQs.As a result, the presentation support system according to this embodiment can streamline the entire process from presentation preparation to the actual presentation, and improve the quality of the presentation.

[0082] The reception desk inputs information about the presentation. This information includes, but is not limited to, the presentation topic, target audience, and time allocation. For example, the reception desk analyzes the information entered by the user to identify the presentation details. Specifically, the information entered by the user is provided to the reception desk in text format. The reception desk uses natural language processing technology to analyze the entered text and extract important elements such as the presentation topic, target audience, and time allocation. For example, if a user enters "I will give a 30-minute presentation introducing a new product to the management team," the reception desk recognizes "introduction of a new product" as the topic, identifies "management team" as the target audience, and extracts "30 minutes" as the time allocation. Furthermore, the reception desk can also analyze additional information provided by the user. For example, it accepts information such as the presentation's purpose, expected outcomes, and the types of materials to be used as input, and uses this information to grasp the overall picture of the presentation. The reception desk centrally manages this information and uses it as basic data to provide to subsequent generation and practice departments. This allows the reception desk to efficiently analyze the information provided by the user and accurately identify the status of the presentation.

[0083] The generation unit uses a generation AI to generate avatars based on information entered by the reception unit. The generation AI generates avatars using technologies such as deep learning and generative opposite networks (GANs). Specifically, the generation AI generates the optimal avatar based on the presentation theme and target audience information entered by the user. For example, in the case of a presentation to management, the generation AI will generate an avatar in formal attire and select an avatar with friendly facial expressions and gestures. The generation AI can also learn the characteristics and speaking style of the user's voice and set the avatar to speak with a voice similar to the user's. Furthermore, the generation AI can customize the avatar's movements and facial expressions according to the content of the presentation. For example, in the case of a technical presentation, the avatar will be set to use appropriate gestures when giving detailed explanations. The generation unit combines these functions to provide the optimal avatar for the user when practicing their presentation. In this way, the generation unit helps the user practice in an environment that is close to an actual presentation.

[0084] The practice unit uses generated avatars to practice presentations. Practice includes, but is not limited to, the content of the simulation, the number of practice sessions, and evaluation criteria. The practice unit provides an environment in which users can practice presentations using generated avatars. Specifically, the practice unit builds a simulation environment in which users present while interacting with the avatar. Users present to the avatar, and the avatar responds in real time. For example, the avatar may ask questions or respond with gestures to what the user says. The practice unit records the user's statements and gestures and saves them as data to provide feedback later. Furthermore, the practice unit evaluates the practice results based on evaluation criteria set by the user. For example, if the user sets evaluation criteria such as "clarity of pronunciation" or "gaze distribution," the practice unit evaluates the user's performance based on these criteria and assigns a score. In this way, the practice unit can provide an environment in which users can effectively practice presentations and improve their skills.

[0085] The Feedback Department provides feedback on the results of practice conducted by the Practice Department. This feedback includes, but is not limited to, voice analysis, gesture analysis, and evaluation criteria. For example, the Feedback Department transcribes presentation content into text and checks for consistency between the materials and the spoken content. Specifically, the Feedback Department uses speech recognition technology to transcribe the user's speech and analyzes its content. Voice analysis evaluates clarity of pronunciation, speaking speed, and tone of voice, and provides the user with specific areas for improvement. Gesture analysis analyzes the user's hand movements and eye gaze distribution, providing feedback on appropriate gestures and eye usage. Furthermore, the Feedback Department checks for consistency between the materials used by the user and their spoken content. For example, if a user gives a presentation using slides, the Feedback Department verifies that the slide content matches the user's spoken content and points out areas for improvement as needed. This allows the Feedback Department to provide specific advice to help users improve the quality of their presentations.

[0086] The document generation unit automatically generates documents based on feedback provided by the feedback unit. Document generation includes, but is not limited to, templates, basic information, and layouts. The document generation unit uses generation AI to automatically generate base documents tailored to the purpose of the presentation. Specifically, the document generation unit selects the optimal template and arranges basic information based on information provided by the user and feedback from the feedback unit. For example, in the case of a business presentation, the document generation unit selects a professionally designed template and appropriately places important data and graphs. The document generation unit also adjusts the layout according to the user's presentation content to create visually easy-to-understand documents. Furthermore, the document generation unit can use generation AI to automatically summarize the user's statements and reflect them in the slides. In this way, the document generation unit helps users efficiently create high-quality presentation materials.

[0087] The checking unit checks the materials generated by the material generation unit. This checking includes, but is not limited to, the appropriateness of the design, the accuracy of the content, and legal compliance. For example, the checking unit checks the appropriateness of the design and expression of the generated materials, and whether they deter violations. Specifically, the checking unit verifies that the material's design is visually appealing and aligns with the presentation's purpose. For example, it evaluates the use of color, font selection, and layout balance, and points out areas for improvement as needed. Regarding accuracy, it verifies that the data and information contained in the material are accurate and that there are no misleading expressions. Regarding legal compliance, it checks that the material meets legal requirements such as copyright and trademark rights. This allows the checking unit to provide high-quality materials that users can confidently use for their presentations.

[0088] The follow-up team provides support during the actual presentation based on the materials checked by the checking team. This support includes, but is not limited to, methods for reducing anxiety, adjusting time allocation, and addressing frequently asked questions (FAQs). Specifically, the follow-up team provides support during the presentation, such as reducing anxiety, checking time allocation, and interpreting the intent of FAQs. They also provide breathing exercises and simple stretches to help users relax during the presentation. Furthermore, they monitor the progress of the presentation in real time to check if the time allocation is appropriate. For example, if the presentation is running ahead of schedule, they prompt for additional explanations; conversely, if it is running behind, they advise focusing on key points. In addition, the follow-up team assists with appropriate responses to audience questions. For example, they provide an FAQ list and example answers to common questions to help users answer with confidence. In this way, the follow-up team can support users in delivering their best performance during the actual presentation.

[0089] The generation unit can generate avatars tailored to the presentation audience using generative AI. For example, the generation unit uses generative AI to generate avatars appropriate to the presentation audience. The generative AI uses technologies such as deep learning and generative opposite-agent networks (GANs) to generate avatars. For example, the generation unit can generate avatars for executive-level presentations for use during presentation practice. Furthermore, the generation unit can use generative AI to generate avatars that reflect the characteristics of the presentation audience. For example, the generation unit can generate avatars based on information such as the audience's age, gender, and occupation. This allows the generation unit to improve the effectiveness of practice by generating avatars tailored to the presentation audience.

[0090] The document generation unit can automatically generate base materials tailored to the purpose of a presentation using generation AI. For example, the document generation unit uses generation AI to automatically generate base materials that match the presentation's objectives. The generation AI generates materials using technologies such as template-based generation and data-driven generation. For example, in the case of a presentation introducing a new product, the document generation unit automatically generates materials that include the features and benefits of the new product. Furthermore, the document generation unit can also use generation AI to generate materials tailored to the presentation's theme. For example, in the case of an educational presentation, the document generation unit generates materials related to the educational content. This allows the document generation unit to reduce the burden of material creation by automatically generating base materials tailored to the presentation's objectives.

[0091] The checking unit can verify the appropriateness of the design and expression of the generated materials, as well as prevent violations. For example, the checking unit checks the appropriateness of the design and expression of the generated materials, as well as prevent violations. The checks include, but are not limited to, the appropriateness of the design, the accuracy of the content, and legal compliance. For example, the checking unit checks whether the colors, fonts, and layout of the materials are appropriate, and whether there are any copyright infringements. The checking unit can also use generation AI to automatically check the appropriateness of the design and expression of the materials. For example, the checking unit can use generation AI to evaluate whether the design of the materials is visually appealing and easy to read. In this way, the checking unit can improve the quality of the materials by checking the appropriateness of the design and expression and prevent violations.

[0092] The practice unit allows users to practice presentations using generated avatars. For example, the practice unit uses generated avatars to practice presentations. Practice includes, but is not limited to, simulation content, number of practice sessions, and evaluation criteria. The practice unit provides an environment for users to practice presentations using generated avatars. Furthermore, the practice unit can automatically evaluate the content of presentation practice using generative AI. For example, the practice unit uses generative AI to analyze the user's speech and gestures and evaluate the effectiveness of the practice. This allows the practice unit to improve the effectiveness of practice by using generated avatars for presentation practice.

[0093] The feedback unit can transcribe the content of a presentation into text and check the consistency between the materials and the spoken content. For example, the feedback unit transcribes the content of a presentation into text and checks the consistency between the materials and the spoken content. Feedback includes, but is not limited to, voice analysis, gesture analysis, and evaluation criteria. For example, the feedback unit can use generative AI to transcribe the content of a presentation into text and check the consistency between the materials and the spoken content. Furthermore, the feedback unit can use generative AI to point out unclear parts of the presentation and suggest improvements. For example, the feedback unit can use generative AI to analyze the content of a presentation, identify unclear parts, and suggest improvements. In this way, the feedback unit can improve the quality of the presentation by transcribing the content of the presentation into text and checking the consistency between the materials and the spoken content.

[0094] The support unit can provide assistance during the actual presentation, such as easing tension, checking time allocation, and interpreting the intent of FAQs. For example, the support unit can provide assistance during the actual presentation, such as easing tension, checking time allocation, and interpreting the intent of FAQs. This support includes, but is not limited to, methods for easing tension, adjusting time allocation, and answering FAQs. For example, the support unit can use generative AI to provide relaxing comments during the presentation, supporting the flow of the presentation. It can also use generative AI to check the presentation's time allocation and ensure it is progressing according to schedule. Furthermore, the support unit can use generative AI to analyze the intent of questions and provide appropriate answers. In this way, the support unit can increase the success rate of the presentation by providing assistance during the actual presentation, such as easing tension, checking time allocation, and interpreting the intent of FAQs.

[0095] The reception system can estimate the user's emotions and adjust the timing of presentation status input based on the estimated emotions. For example, if the user is nervous, the reception system can provide time for relaxation before prompting for presentation status input. If the user is anxious, the reception system can also provide a simplified input screen to allow for quick completion. Furthermore, if the user is relaxed, the reception system can provide detailed input options that the user can customize. This allows the reception system to reduce the user's burden by adjusting the timing of presentation status input based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The reception desk can analyze the user's past presentation history and select the optimal input method. For example, the reception desk may prioritize suggesting the input method used in the user's past presentations. The reception desk can also automatically display frequently used input items based on the user's past presentation history. Furthermore, the reception desk can analyze the user's past presentation history and customize the input method based on specific patterns. In this way, the reception desk can provide the optimal input method by analyzing the user's past presentation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0097] The reception system can filter presentation status input based on the user's current projects and areas of interest. For example, the reception system prioritizes inputting information related to the user's current projects. The reception system can also automatically suggest relevant presentation statuses based on the user's areas of interest. Furthermore, the reception system can integrate with the user's project management tools to automatically retrieve and input information related to the current project. This allows the reception system to provide highly relevant information by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not using AI.

[0098] The reception desk can estimate the user's emotions and, based on those emotions, prioritize the presentation situation to be entered. For example, if the user is nervous, the reception desk can prioritize entering important items and allow for later addition of details. If the user is relaxed, the reception desk can also allow for entering detailed items in order. Furthermore, if the user is anxious, the reception desk can allow for entering only the most important items and allow for additional information to be entered later. This enables efficient input by prioritizing the presentation situation based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The reception system can prioritize inputting highly relevant information when the user inputs presentation details, taking into account their geographical location. For example, if the user is giving a presentation in a specific region, the reception system will prioritize inputting information relevant to that region. The reception system can also automatically input nearby competitor information and market data based on the user's current location. Furthermore, the reception system can input region-specific topics and interests based on the user's geographical location. In this way, the reception system can provide highly relevant information by taking the user's geographical location into consideration. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI.

[0100] The reception desk can analyze the user's social media activity and input relevant information when inputting the presentation status. For example, the reception desk can analyze the content of the user's social media posts and automatically input relevant presentation status information. The reception desk can also customize the presentation status based on the interests of the user's followers and friends. Furthermore, the reception desk can input trends and trending themes from the user's social media activity. In this way, the reception desk can provide relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0101] The generation unit can estimate the user's emotions and adjust the avatar's expression based on the estimated emotions. For example, if the user is nervous, the generation unit can generate an avatar with a relaxed expression. It can also generate an avatar with a bright and cheerful expression if the user is relaxed. Furthermore, if the user is anxious, the generation unit can generate an avatar with a calm expression. This allows the generation unit to enable more effective practice by adjusting the avatar's expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] The generation unit can adjust the level of detail of the avatar based on the purpose of the presentation when generating the avatar. For example, if the purpose of the presentation is to introduce a new product, the generation unit will generate an avatar that emphasizes the product's features. The generation unit can also generate a trustworthy avatar if the purpose of the presentation is to explain something to investors. Furthermore, if the purpose of the presentation is educational, the generation unit can generate a friendly avatar. In this way, the generation unit can generate a more appropriate avatar by adjusting the level of detail based on the purpose of the presentation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0103] The generation unit can apply different generation algorithms depending on the presentation category when generating avatars. For example, in the case of a business presentation, the generation unit can apply an algorithm that generates a formal avatar. In the case of an educational presentation, the generation unit can also apply an algorithm that generates a friendly avatar. Furthermore, in the case of an entertainment presentation, the generation unit can apply an algorithm that generates a visually appealing avatar. In this way, the generation unit can generate more effective avatars by applying different generation algorithms depending on the presentation category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0104] The generation unit can estimate the user's emotions and adjust the length of the avatar based on the estimated emotions. For example, if the user is nervous, the generation unit will generate a short, concise avatar. If the user is relaxed, the generation unit can also generate a longer avatar with detailed explanations. Furthermore, if the user is anxious, the generation unit can generate a short avatar that conveys information quickly. This allows the generation unit to enable more effective practice by adjusting the length of the avatar based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The generation unit can determine the priority of avatars based on the presentation submission deadline when generating avatars. For example, if the presentation submission deadline is approaching, the generation unit will prioritize avatars that can be generated quickly. Conversely, if the presentation submission deadline is far away, the generation unit can allocate time to generate detailed avatars. Furthermore, the generation unit can adjust the order of avatar generation according to the presentation submission deadline. This enables efficient avatar generation by determining the priority of avatars based on the presentation submission deadline. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0106] The generation unit can adjust the order of avatars based on the relevance of the presentation during avatar generation. For example, if the content of the presentation is important, the generation unit will prioritize generating the most relevant avatars. The generation unit can also generate standard avatars if the content of the presentation is general. Furthermore, if the content of the presentation is specific to a particular industry, the generation unit can generate avatars relevant to that industry. In this way, the generation unit can generate more effective avatars by adjusting the order of avatars based on the relevance of the presentation. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI.

[0107] The practice unit can estimate the user's emotions and adjust the way the practice is presented based on those emotions. For example, if the user is nervous, the practice unit will conduct the practice in a relaxed atmosphere. If the user is relaxed, the practice unit can also provide detailed feedback. Furthermore, if the user is anxious, the practice unit can help them complete the practice quickly. In this way, the practice unit can enable more effective practice by adjusting the way the practice is presented based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The practice team can adjust the level of detail in practice sessions based on the importance of the presentation. For example, for important presentations, the practice team can conduct detailed practice sessions, checking every detail. For general presentations, the practice team can also conduct basic practice sessions. Furthermore, for short presentations, the practice team can conduct concise practice sessions focusing on the key points. This allows the practice team to enable efficient practice by adjusting the level of detail based on the importance of the presentation. Some or all of the above processes in the practice team may be performed using AI, for example, or not.

[0109] The practice unit can apply different practice algorithms depending on the presentation category during practice. For example, for business presentations, the practice unit can apply a formal practice algorithm. For educational presentations, it can also apply a more approachable practice algorithm. Furthermore, for entertainment presentations, it can apply a visually appealing practice algorithm. In this way, the practice unit can enable more effective practice by applying different practice algorithms depending on the presentation category. Some or all of the above processing in the practice unit may be performed using AI, for example, or without AI.

[0110] The practice unit can estimate the user's emotions and adjust the length of the practice session based on those emotions. For example, if the user is nervous, the practice unit can conduct a short, concise practice session. If the user is relaxed, the practice unit can conduct a longer practice session with more detailed explanations. Furthermore, if the user is anxious, the practice unit can enable the user to complete the practice session quickly. In this way, the practice unit can enable more effective practice by adjusting the length of the practice session based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The practice team can prioritize practice sessions based on the presentation submission deadline. For example, if the deadline is approaching, the practice team will conduct practice sessions quickly. Conversely, if the deadline is far away, the practice team can allocate time for detailed practice sessions. Furthermore, the practice team can adjust the order of practice sessions according to the presentation submission deadline. This allows the practice team to conduct efficient practice sessions by prioritizing them based on the presentation submission deadline. Some or all of the above processes in the practice team may be performed using AI, for example, or not.

[0112] The practice unit can adjust the order of practice sessions based on the relevance of the presentation. For example, in the case of an important presentation, the practice unit can start practicing with the most relevant content. Alternatively, in the case of a general presentation, the practice unit can start practicing with standard content. Furthermore, in the case of a presentation specific to a particular industry, the practice unit can start practicing with content relevant to that industry. This allows the practice unit to enable more effective practice by adjusting the order of practice sessions based on the relevance of the presentation. Some or all of the above processes in the practice unit may be performed using AI, for example, or not.

[0113] The feedback unit can estimate the user's emotions and adjust the way it expresses feedback based on those emotions. For example, if the user is nervous, the feedback unit can provide feedback in gentle language. If the user is relaxed, the feedback unit can also provide detailed feedback. Furthermore, if the user is anxious, the feedback unit can provide quick and concise feedback. This allows the feedback unit to provide more effective feedback by adjusting its expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The feedback unit can adjust the level of detail in the feedback based on the importance of the presentation. For example, in the case of an important presentation, the feedback unit will provide detailed feedback, checking every detail. In the case of a general presentation, the feedback unit can also provide basic feedback. Furthermore, in the case of a short presentation, the feedback unit can provide concise feedback. In this way, the feedback unit can provide efficient feedback by adjusting the level of detail in the feedback based on the importance of the presentation. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI.

[0115] The feedback unit can apply different feedback algorithms depending on the presentation category during the feedback process. For example, in the case of a business presentation, the feedback unit can apply a formal feedback algorithm. It can also apply a more approachable feedback algorithm for educational presentations. Furthermore, for entertainment presentations, it can apply a visually appealing feedback algorithm. This allows the feedback unit to provide more effective feedback by applying different feedback algorithms depending on the presentation category. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or without AI.

[0116] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit can provide short, concise feedback. If the user is relaxed, the feedback unit can also provide longer feedback with more detailed explanations. Furthermore, if the user is anxious, the feedback unit can enable the feedback to be completed quickly. In this way, the feedback unit can provide more effective feedback by adjusting the length of the feedback based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The feedback unit can prioritize feedback based on the presentation submission deadline. For example, if the presentation deadline is approaching, the feedback unit will provide feedback quickly. Conversely, if the deadline is far away, the feedback unit can allocate time to provide detailed feedback. Furthermore, the feedback unit can adjust the order of feedback depending on the presentation submission deadline. This allows the feedback unit to provide efficient feedback by prioritizing feedback based on the presentation submission deadline. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not.

[0118] The feedback unit can adjust the order of feedback based on the relevance of the presentation. For example, in the case of an important presentation, the feedback unit can start with the most relevant content. In the case of a general presentation, the feedback unit can also start with standard content. Furthermore, in the case of a presentation specific to a particular industry, the feedback unit can start with content relevant to that industry. This allows the feedback unit to provide more effective feedback by adjusting the order of feedback based on the relevance of the presentation. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI.

[0119] The document generation unit can estimate the user's emotions and adjust the presentation of the document based on those emotions. For example, if the user is nervous, the document generation unit can generate simple, highly visual documents. If the user is relaxed, the document generation unit can also generate documents containing detailed information. Furthermore, if the user is anxious, the document generation unit can generate documents that convey information quickly. In this way, the document generation unit can generate more effective documents by adjusting the presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The document generation unit can adjust the level of detail in the documents based on the importance of the presentation during document generation. For example, in the case of an important presentation, the document generation unit can generate detailed documents and review every detail. In addition, the document generation unit can generate basic documents for general presentations. Furthermore, in the case of a short presentation, the document generation unit can generate documents that highlight the key points. In this way, the document generation unit can efficiently generate documents by adjusting the level of detail in the documents based on the importance of the presentation. Some or all of the above processing in the document generation unit may be performed using AI, for example, or without using AI.

[0121] The document generation unit can apply different document generation algorithms depending on the presentation category when generating documents. For example, in the case of a business presentation, the document generation unit can apply a formal document generation algorithm. In the case of an educational presentation, the document generation unit can also apply a more approachable document generation algorithm. Furthermore, in the case of an entertainment presentation, the document generation unit can apply a visually appealing document generation algorithm. In this way, the document generation unit can generate more effective documents by applying different document generation algorithms depending on the presentation category. Some or all of the above processing in the document generation unit may be performed using AI, for example, or without using AI.

[0122] The document generation unit can estimate the user's emotions and adjust the length of the document based on the estimated emotions. For example, if the user is nervous, the document generation unit can generate a short, concise document. If the user is relaxed, the document generation unit can also generate a longer document with detailed explanations. Furthermore, if the user is anxious, the document generation unit can generate a short document that conveys information quickly. In this way, the document generation unit can generate more effective documents by adjusting the length of the document based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] The document generation unit can prioritize documents based on the presentation submission deadline when generating them. For example, if the presentation deadline is approaching, the document generation unit will prioritize documents that can be generated quickly. Conversely, if the presentation deadline is far away, the document generation unit can allocate time to generate detailed documents. Furthermore, the document generation unit can adjust the order in which documents are generated according to the presentation submission deadline. This enables efficient document generation by prioritizing documents based on the presentation submission deadline. Some or all of the above-described processes in the document generation unit may be performed using AI, for example, or without using AI.

[0124] The document generation unit can adjust the order of documents based on their relevance in the presentation. For example, if the content of the presentation is important, the document generation unit will prioritize generating the most relevant documents. It can also generate standard documents if the content of the presentation is general. Furthermore, if the content of the presentation is specific to a particular industry, the document generation unit can generate industry-specific documents. This allows the document generation unit to generate more effective documents by adjusting the order of documents based on their relevance in the presentation. Some or all of the above processing in the document generation unit may be performed using AI, for example, or without AI.

[0125] The checking unit can estimate the user's emotions and adjust the way the check is presented based on the estimated emotions. For example, if the user is nervous, the checking unit can provide the check results in gentle language. It can also provide detailed check results if the user is relaxed. Furthermore, if the user is anxious, it can provide quick and concise check results. This allows the checking unit to enable more effective checks by adjusting the way the check is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0126] The checking unit can adjust the level of detail of its checks based on the importance of the presentation. For example, in the case of an important presentation, the checking unit will perform a detailed check, verifying every detail. In addition, in the case of a general presentation, the checking unit can perform a basic check. Furthermore, in the case of a short presentation, the checking unit can perform a check that focuses on the key points. In this way, the checking unit can efficiently perform checks by adjusting the level of detail based on the importance of the presentation. Some or all of the above processing in the checking unit may be performed using AI, for example, or without the use of AI.

[0127] The checking unit can apply different checking algorithms depending on the category of the presentation during the checking process. For example, in the case of a business presentation, the checking unit can apply a formal checking algorithm. In addition, in the case of an educational presentation, the checking unit can apply a more approachable checking algorithm. Furthermore, in the case of an entertainment presentation, the checking unit can apply a visually appealing checking algorithm. In this way, the checking unit can enable more effective checking by applying different checking algorithms depending on the category of the presentation. Some or all of the above processing in the checking unit may be performed using AI, for example, or without the use of AI.

[0128] The checking unit can estimate the user's emotions and adjust the length of the check based on the estimated emotions. For example, if the user is nervous, the checking unit will perform a short, concise check. If the user is relaxed, the checking unit can perform a longer check with more detailed explanations. Furthermore, if the user is anxious, the checking unit can enable the check to be completed quickly. In this way, the checking unit can enable more effective checks by adjusting the length of the check based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0129] The checking unit can determine the priority of checks based on the presentation submission deadline. For example, if the presentation submission deadline is approaching, the checking unit will perform a quick check. Conversely, if the submission deadline is far away, the checking unit can allocate time for a detailed check. Furthermore, the checking unit can adjust the order of checks according to the presentation submission deadline. This allows the checking unit to perform efficient checks by determining the priority of checks based on the presentation submission deadline. Some or all of the above processes in the checking unit may be performed using AI, for example, or without using AI.

[0130] The checking unit can adjust the order of checks based on the relevance of the presentation during the checking process. For example, in the case of an important presentation, the checking unit can start checking with the most relevant content. In the case of a general presentation, the checking unit can also start checking with standard content. Furthermore, in the case of a presentation specific to a particular industry, the checking unit can start checking with content relevant to that industry. This allows the checking unit to perform more effective checks by adjusting the order of checks based on the relevance of the presentation. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI.

[0131] The follow-up function can estimate the user's emotions and adjust the way it expresses follow-up based on the estimated emotions. For example, if the user is nervous, the follow-up function can provide a relaxing comment. If the user is relaxed, the follow-up function can also provide a more detailed follow-up. Furthermore, if the user is anxious, the follow-up function can provide a quick and concise follow-up. In this way, the follow-up function can provide more effective follow-up by adjusting the way it expresses follow-up based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0132] The follow-up unit can adjust the level of detail in the follow-up based on the importance of the presentation. For example, in the case of an important presentation, the follow-up unit will provide detailed follow-up, checking every detail. In addition, in the case of a general presentation, the follow-up unit can provide basic follow-up. Furthermore, in the case of a short presentation, the follow-up unit can provide concise follow-up that focuses on the key points. In this way, the follow-up unit can enable efficient follow-up by adjusting the level of detail in the follow-up based on the importance of the presentation. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or not using AI.

[0133] The follow-up function can apply different follow-up algorithms depending on the presentation category. For example, in the case of a business presentation, the follow-up function can apply a formal follow-up algorithm. In the case of an educational presentation, the follow-up function can also apply a more approachable follow-up algorithm. Furthermore, in the case of an entertainment presentation, the follow-up function can apply a visually appealing follow-up algorithm. In this way, the follow-up function can enable more effective follow-up by applying different follow-up algorithms depending on the presentation category. Some or all of the above processing in the follow-up function may be performed using AI, for example, or not using AI.

[0134] The follow-up function can estimate the user's emotions and adjust the length of the follow-up based on the estimated emotions. For example, if the user is nervous, the follow-up function can provide a short, to-the-point follow-up. If the user is relaxed, the follow-up function can also provide a longer follow-up with more detailed explanations. Furthermore, if the user is anxious, the follow-up function can complete the follow-up quickly. In this way, the follow-up function can enable more effective follow-ups by adjusting the length of the follow-up based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0135] The follow-up unit can prioritize follow-ups based on the presentation submission deadline. For example, if the presentation submission deadline is approaching, the follow-up unit will provide prompt follow-up. Conversely, if the presentation submission deadline is far away, the follow-up unit can allocate time to provide detailed follow-up. Furthermore, the follow-up unit can adjust the order of follow-ups according to the presentation submission deadline. This allows the follow-up unit to perform follow-ups efficiently by prioritizing them based on the presentation submission deadline. Some or all of the above processes in the follow-up unit may be performed using AI, for example, or not.

[0136] The follow-up unit can adjust the order of follow-up based on the relevance of the presentation. For example, in the case of an important presentation, the follow-up unit will start with the most relevant content. In the case of a general presentation, the follow-up unit can also start with standard content. Furthermore, in the case of a presentation specific to a particular industry, the follow-up unit can start with content relevant to that industry. This allows the follow-up unit to enable more effective follow-up by adjusting the order of follow-up based on the relevance of the presentation. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or not using AI.

[0137] The follow-up unit can adjust its follow-up content in real time according to the progress of the presentation. For example, if the presentation is ahead of schedule, the follow-up unit can provide additional information. If the presentation is behind schedule, the follow-up unit can also provide concise follow-up. Furthermore, if an unexpected question arises during the presentation, the follow-up unit can provide an appropriate answer in real time. In this way, the follow-up unit can enable more effective follow-up by adjusting its follow-up content in real time according to the progress of the presentation. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or not using AI.

[0138] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0139] The reception system can estimate the user's emotions and adjust the timing of presentation status input based on the estimated emotions. For example, if the user is nervous, the reception system can provide time for relaxation before prompting for presentation status input. If the user is anxious, the reception system can also provide a simplified input screen to allow for quick completion. Furthermore, if the user is relaxed, the reception system can provide detailed input options that the user can customize. This allows the reception system to reduce the user's burden by adjusting the timing of presentation status input based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0140] The generation unit can estimate the user's emotions and adjust the avatar's expression based on the estimated emotions. For example, if the user is nervous, the generation unit can generate an avatar with a relaxed expression. It can also generate an avatar with a bright and cheerful expression if the user is relaxed. Furthermore, if the user is anxious, the generation unit can generate an avatar with a calm expression. This allows the generation unit to enable more effective practice by adjusting the avatar's expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0141] The practice unit can estimate the user's emotions and adjust the way the practice is presented based on those emotions. For example, if the user is nervous, the practice unit will conduct the practice in a relaxed atmosphere. If the user is relaxed, the practice unit can also provide detailed feedback. Furthermore, if the user is anxious, the practice unit can help them complete the practice quickly. In this way, the practice unit can enable more effective practice by adjusting the way the practice is presented based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0142] The feedback unit can estimate the user's emotions and adjust the way it expresses feedback based on those emotions. For example, if the user is nervous, the feedback unit can provide feedback in gentle language. If the user is relaxed, the feedback unit can also provide detailed feedback. Furthermore, if the user is anxious, the feedback unit can provide quick and concise feedback. This allows the feedback unit to provide more effective feedback by adjusting its expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0143] The follow-up function can estimate the user's emotions and adjust the way it expresses follow-up based on the estimated emotions. For example, if the user is nervous, the follow-up function can provide a relaxing comment. If the user is relaxed, the follow-up function can also provide a more detailed follow-up. Furthermore, if the user is anxious, the follow-up function can provide a quick and concise follow-up. In this way, the follow-up function can provide more effective follow-up by adjusting the way it expresses follow-up based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0144] The reception desk can analyze the user's past presentation history and select the optimal input method. For example, the reception desk may prioritize suggesting the input method used in the user's past presentations. The reception desk can also automatically display frequently used input items based on the user's past presentation history. Furthermore, the reception desk can analyze the user's past presentation history and customize the input method based on specific patterns. In this way, the reception desk can provide the optimal input method by analyzing the user's past presentation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0145] The reception system can filter presentation status input based on the user's current projects and areas of interest. For example, the reception system prioritizes inputting information related to the user's current projects. The reception system can also automatically suggest relevant presentation statuses based on the user's areas of interest. Furthermore, the reception system can integrate with the user's project management tools to automatically retrieve and input information related to the current project. This allows the reception system to provide highly relevant information by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not using AI.

[0146] The generation unit can adjust the level of detail of the avatar based on the purpose of the presentation when generating the avatar. For example, if the purpose of the presentation is to introduce a new product, the generation unit will generate an avatar that emphasizes the product's features. The generation unit can also generate a trustworthy avatar if the purpose of the presentation is to explain something to investors. Furthermore, if the purpose of the presentation is educational, the generation unit can generate a friendly avatar. In this way, the generation unit can generate a more appropriate avatar by adjusting the level of detail based on the purpose of the presentation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0147] The practice team can adjust the level of detail in practice sessions based on the importance of the presentation. For example, for important presentations, the practice team can conduct detailed practice sessions, checking every detail. For general presentations, the practice team can also conduct basic practice sessions. Furthermore, for short presentations, the practice team can conduct concise practice sessions focusing on the key points. This allows the practice team to enable efficient practice by adjusting the level of detail based on the importance of the presentation. Some or all of the above processes in the practice team may be performed using AI, for example, or not.

[0148] The feedback unit can adjust the level of detail in the feedback based on the importance of the presentation. For example, in the case of an important presentation, the feedback unit will provide detailed feedback, checking every detail. In the case of a general presentation, the feedback unit can also provide basic feedback. Furthermore, in the case of a short presentation, the feedback unit can provide concise feedback. In this way, the feedback unit can provide efficient feedback by adjusting the level of detail in the feedback based on the importance of the presentation. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI.

[0149] The following briefly describes the processing flow for example form 2.

[0150] Step 1: The reception desk enters the presentation status. This includes, for example, the presentation topic, target audience, and time allocation. The reception desk analyzes the information entered by the user to determine the presentation status. Step 2: The generation unit uses a generation AI to generate avatars based on the information entered by the reception unit. The generation AI uses technologies such as deep learning and generative opposite networks (GANs) to generate avatars. The generation unit generates avatars tailored to the presentation audience and uses them during presentation practice. Step 3: The practice team conducts presentation practice using the generated avatars. This practice includes the content of the simulation, the number of practice sessions, and the evaluation criteria. The practice team provides an environment in which users can practice their presentations using the generated avatars. Step 4: The Feedback team provides feedback on the practice results conducted by the Practice team. This feedback includes voice analysis, gesture analysis, and evaluation criteria. The Feedback team transcribes the presentation content into text and checks for consistency between the materials and the spoken content. Step 5: The document generation unit automatically generates documents based on the feedback provided by the feedback unit. Document generation includes templates, basic information, and layout. The document generation unit uses a generation AI to automatically generate base documents tailored to the purpose of the presentation. Step 6: The checking unit checks the materials generated by the material generation unit. The checks include the appropriateness of the design, the accuracy of the content, and legal compliance. The checking unit checks the appropriateness of the design and expression of the generated materials, as well as measures to deter violations. Step 7: The follow-up team provides support during the actual presentation based on the materials checked by the checking team. This support includes methods for reducing anxiety, adjusting time allocation, and addressing FAQs. The follow-up team provides support during the actual presentation, such as reducing anxiety, checking time allocation, and interpreting the intent of FAQs.

[0151] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0153] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the reception unit, generation unit, practice unit, feedback unit, material generation unit, checking unit, and follow-up unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which analyzes the information entered by the user and identifies the presentation status. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which generates an avatar using generation AI. The practice unit is implemented by, for example, the control unit 46A of the smart device 14, which allows the user to practice the presentation using the generated avatar. The feedback unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which provides feedback on the practice results. The material generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which automatically generates materials based on the feedback. The checking unit is implemented by, for example, the control unit 46A of the smart device 14, which checks the appropriateness of the design and expression of the generated materials. The follow-up function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides follow-up during the actual presentation. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0156] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0163] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0164] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0165] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0167] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0170] Each of the multiple elements described above, including the reception unit, generation unit, practice unit, feedback unit, material generation unit, checking unit, and follow-up unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the information entered by the user and identifies the presentation status. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which generates an avatar using generation AI. The practice unit is implemented by, for example, the control unit 46A of the smart glasses 214, which allows the user to practice the presentation using the generated avatar. The feedback unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which provides feedback on the practice results. The material generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which automatically generates materials based on the feedback. The checking unit is implemented by, for example, the control unit 46A of the smart glasses 214, which checks the appropriateness of the design and expression of the generated materials. The follow-up function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides follow-up during the actual presentation. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0171] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0172] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0173] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0174] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0175] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0177] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0178] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0179] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0180] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0181] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0182] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0183] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0184] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0185] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0186] Each of the multiple elements described above, including the reception unit, generation unit, practice unit, feedback unit, material generation unit, checking unit, and follow-up unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the information entered by the user and identifies the presentation status. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which generates an avatar using a generation AI. The practice unit is implemented by, for example, the control unit 46A of the headset terminal 314, which allows the user to practice the presentation using the generated avatar. The feedback unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which provides feedback on the practice results. The material generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which automatically generates materials based on the feedback. The checking unit is implemented by, for example, the control unit 46A of the headset terminal 314, which checks the appropriateness of the design and expression of the generated materials. The follow-up function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides follow-up during the actual presentation. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0187] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0188] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0189] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0190] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0191] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0192] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0193] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0194] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0195] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0196] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0197] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0198] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0199] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0200] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0201] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0202] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0203] Each of the multiple elements described above, including the reception unit, generation unit, practice unit, feedback unit, material generation unit, checking unit, and follow-up unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, which analyzes the information entered by the user and identifies the presentation situation. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates an avatar using a generation AI. The practice unit is implemented by, for example, the control unit 46A of the robot 414, which practices the presentation using the generated avatar. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides feedback on the practice results. The material generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates materials based on the feedback. The checking unit is implemented by, for example, the control unit 46A of the robot 414, which checks the appropriateness of the design and expression of the generated materials. The follow-up function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides follow-up during the actual presentation. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0204] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0205] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0206] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0207] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0208] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0209] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0210] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0211] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0212] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0213] 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.

[0214] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0215] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0216] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0217] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0218] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0219] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0220] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0221] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0222] (Note 1) The reception desk where the presentation status is entered, A generation unit that generates an avatar based on the information input by the reception unit, A practice unit that performs presentation practice based on the avatar generated by the generation unit, A feedback unit that provides feedback on the results of the practice conducted by the aforementioned practice unit, A document generation unit that automatically generates documents based on the feedback provided by the aforementioned feedback unit, A checking unit that checks the data generated by the aforementioned data generation unit, The system includes a follow-up unit that provides support during the actual presentation based on the materials checked by the aforementioned checking unit. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates avatars tailored to the audience you are presenting to. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned data generation unit, The AI ​​automatically generates base materials tailored to the purpose of the presentation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned checking unit is The design and expression of the generated materials are checked for appropriateness, and measures are taken to deter violations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned practice unit, Practice your presentation using the generated avatar. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Transcribe the presentation content into text and check for consistency between the materials and the spoken material. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned follow-up section is During the actual presentation, we provide support such as helping to alleviate anxiety, checking time allocation, and interpreting the intent of FAQs. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of presentation status inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past presentation history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When inputting the presentation status, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of presentation situations to input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When inputting presentation status, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When inputting the presentation status, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the avatar's representation based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 15) The generating unit is When generating an avatar, adjust the level of detail based on the purpose of the presentation. The system described in Appendix 2, characterized by the features described herein. (Note 16) The generating unit is When generating avatars, different generation algorithms are applied depending on the presentation category. The system described in Appendix 2, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the length of the avatar based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 18) The generating unit is When generating avatars, the priority of the avatars is determined based on the presentation submission deadline. The system described in Appendix 2, characterized by the features described herein. (Note 19) The generating unit is When generating avatars, adjust the order of avatars based on their relevance in the presentation. The system described in Appendix 2, characterized by the features described herein. (Note 20) The aforementioned practice unit, The system estimates the user's emotions and adjusts the way the practice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned practice unit, During practice, adjust the level of detail based on the importance of the presentation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned practice unit, During practice, different practice algorithms are applied depending on the presentation category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned practice unit, It estimates the user's emotions and adjusts the length of the practice session based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned practice unit, When practicing, prioritize your practice sessions based on the presentation submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned practice unit, During practice, adjust the order of practice based on the relevance of the presentation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the importance of the presentation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the presentation category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, prioritize the feedback based on the presentation submission date. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is During feedback, adjust the order of feedback based on the relevance of the presentation. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned data generation unit, It estimates the user's emotions and adjusts the way the material is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned data generation unit, When generating materials, adjust the level of detail based on the importance of the presentation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned data generation unit, When generating materials, different material generation algorithms are applied depending on the presentation category. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned data generation unit, It estimates the user's emotions and adjusts the length of the material based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned data generation unit, When generating materials, prioritize them based on the presentation submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned data generation unit, When generating materials, adjust the order of the materials based on their relevance in the presentation. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned checking unit is The system estimates the user's emotions and adjusts the way the checks are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned checking unit is When checking, adjust the detail level of the check based on the importance of the presentation. The system according to appended note 1, characterized in that. (Appended note 40) The checking unit When checking, apply different checking algorithms according to the category of the presentation. The system according to appended note 1, characterized in that. (Appended note 41) The checking unit Estimate the user's emotion and adjust the length of the check based on the estimated user's emotion. The system according to appended note 1, characterized in that. (Appended note 42) The checking unit When checking, determine the priority of the check based on the submission time of the presentation. The system according to appended note 1, characterized in that. (Appended note 43) The checking unit When checking, adjust the order of the check based on the relevance of the presentation. The system according to appended note 1, characterized in that. (Appended note 44) The following-up unit Estimate the user's emotion and adjust the expression method of following up based on the estimated user's emotion. The system according to appended note 1, characterized in that. (Appended note 45) The following-up unit When following up, adjust the detail level of following up based on the importance of the presentation. The system according to appended note 1, characterized in that. (Appended note 46) The following-up unit When following up, apply different following-up algorithms according to the category of the presentation. The system according to appended note 1, characterized in that. (Appended note 47) The following-up unit It estimates the user's emotions and adjusts the length of follow-up based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned follow-up section is, When following up, prioritize follow-ups based on the presentation submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 49) The aforementioned follow-up section is, When following up, adjust the order of follow-ups based on the relevance of the presentation. The system described in Appendix 1, characterized by the features described herein. (Note 50) The aforementioned follow-up section is, During follow-up, the content of the follow-up will be adjusted in real time according to the progress of the presentation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0223] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk where the presentation status is entered, A generation unit that generates an avatar based on the information input by the reception unit, A practice unit that performs presentation practice based on the avatar generated by the generation unit, A feedback unit that provides feedback on the results of the practice conducted by the aforementioned practice unit, A document generation unit that automatically generates documents based on the feedback provided by the aforementioned feedback unit, A checking unit that checks the data generated by the aforementioned data generation unit, The system includes a follow-up unit that provides support during the actual presentation based on the materials checked by the aforementioned checking unit. A system characterized by the following features.

2. The generating unit is The AI ​​generates avatars tailored to the audience you are presenting to. The system according to feature 1.

3. The aforementioned data generation unit, The AI ​​generates base materials tailored to the purpose of the presentation. The system according to feature 1.

4. The aforementioned checking unit is The design and expression of the generated materials are checked for appropriateness, and measures are taken to deter violations. The system according to feature 1.

5. The aforementioned practice unit, Practice your presentation using the generated avatar. The system according to feature 1.

6. The aforementioned feedback unit is Transcribe the presentation content into text and check for consistency between the materials and the spoken material. The system according to feature 1.

7. The aforementioned follow-up section is, During the actual presentation, we provide support such as helping to alleviate anxiety, checking time allocation, and interpreting the intent of FAQs. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of presentation status inputs based on the estimated user emotions. The system according to feature 1.

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

10. The aforementioned reception unit is When inputting the presentation status, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

Images