system

Abstract

The system according to this embodiment aims to efficiently provide feedback on areas for improvement in presentation content and slides, and to simulate the reactions of rehearsal partners and the audience. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a rehearsal unit, and a simulation unit. The reception unit uploads the content and slides of the presentation. The analysis unit analyzes the content uploaded by the reception unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The rehearsal unit asks questions as a partner in the presentation rehearsal. The simulation unit simulates the reactions of the target audience.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently provide feedback on the content of a presentation and improvement points of slides, and it is difficult to simulate the reactions of a rehearsal partner or an audience.

[0005] The system according to the embodiment aims to efficiently provide feedback on the content of a presentation and improvement points of slides, and simulate the reactions of a rehearsal partner or an audience.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a rehearsal unit, and a simulation unit. The reception unit uploads the content and slides of the presentation. The analysis unit analyzes the content uploaded by the reception unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The rehearsal unit asks questions as a partner in the presentation rehearsal. The simulation unit simulates the reactions of the target audience. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently provide feedback on areas for improvement in the presentation content and slides, and can simulate the reactions of rehearsal partners and the audience. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The presentation feedback system according to an embodiment of the present invention is a system that uses generative AI to provide feedback on areas for improvement in the content and slide design of a user's presentation. This presentation feedback system allows the user to upload the content and slides of their presentation, and the generative AI automatically analyzes them and provides advice to make the user's presentation more engaging and effective. It also provides a function where an AI agent autonomously asks questions as a presentation rehearsal partner and simulates the reactions of a target audience. For example, the user uploads the content and slides of their presentation. In this process, the user inputs the text and slide data of the presentation into the system. For example, a business person can upload their own presentation materials. Next, the generative AI automatically analyzes the uploaded presentation content and slides. The generative AI evaluates the structure and expression of the presentation, the visibility of the slide design, etc., and identifies areas for improvement. For example, this includes suggestions for improving the flow and expression of the speech, and suggestions for slide layout and color scheme. Furthermore, the generative AI provides specific advice to the user. For example, it provides specific feedback such as, "Changing the expression in this section like this would make it more effective," or "Changing the background color of the slide will improve visibility." Furthermore, the system provides a feature where an AI agent autonomously asks questions as a partner in presentation rehearsals. When a user gives a mock presentation, the AI ​​agent simulates actual questions, providing the user with a realistic rehearsal environment. For example, it can ask questions such as, "Please explain this point in more detail." In addition, it provides a function to simulate the reactions of a target audience. The generative AI simulates the reactions of a virtual audience and provides feedback to the user. For example, it can simulate reactions such as, "The audience's interest increases at this point."This system allows users to receive professional feedback, improving the quality and effectiveness of their presentations. Furthermore, by providing a rehearsal environment, it enables effective preparation for the actual presentation. In this way, the presentation feedback system can improve the quality and effectiveness of users' presentations.

[0029] The presentation feedback system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a rehearsal unit, and a simulation unit. The reception unit receives the content and slides of a presentation from the user. When uploading the content and slides of a presentation, the user inputs, for example, the text and slide data of the presentation into the system. For example, a business person can upload their own presentation materials. The reception unit receives the uploaded content and slides of the presentation and passes them on to the next process. The analysis unit automatically analyzes the uploaded content and slides of the presentation using a generation AI. The generation AI evaluates the structure and expression of the presentation, the visibility of the slide design, etc., and identifies areas for improvement. For example, the generation AI makes suggestions for improving the flow and expression of the speech, and for the layout and color scheme of the slides. The generation AI analyzes the content of the presentation and extracts specific areas for improvement. For example, the generation AI provides specific feedback such as, "Changing the expression in this part like this will make it more effective," or "Changing the background color of the slide will improve visibility." The provision unit provides advice to the user based on the analysis results obtained by the analysis unit. The service provider uses generative AI to provide users with specific advice. For example, the service provider provides specific feedback such as, "Changing the wording of this part like this will make it more effective," or "Changing the background color of the slide will improve visibility." The rehearsal service provider acts as a partner in presentation rehearsals, asking questions to the user. Using an AI agent, the rehearsal service provider simulates actual questions asked when the user gives a mock presentation, providing the user with a realistic rehearsal environment. For example, the rehearsal service provider can ask questions such as, "Please explain this point in more detail." The simulation service provider simulates the reactions of the target audience. Using generative AI, the simulation service provider simulates the reactions of a virtual audience and provides feedback to the user.For example, the simulation unit can simulate reactions such as, "Audience interest increases at this point." This allows the presentation feedback system according to the embodiment to improve the quality and effectiveness of the user's presentation.

[0030] The reception desk allows users to upload presentation content and slides. When uploading, users input presentation text and slide data into the system. Specifically, users can upload presentation files by dragging and dropping them through the system interface or by using a file selection dialog. The reception desk automatically recognizes the format of the uploaded files and extracts text and image data. For example, a business person can upload their own presentation materials. The reception desk receives the uploaded presentation content and slides and passes them on to the next processing stage. The reception desk checks the integrity of the files and performs file format conversion and data cleansing as needed. For example, it converts PDF slides to image formats or extracts text data and prepares it in a format that is easy to analyze. Furthermore, the reception desk automatically extracts metadata (title, author, creation date, etc.) from the user-uploaded presentation and saves it to a database. This allows users to easily search and manage their presentations later. The reception desk securely stores the uploaded data and implements security measures to prevent data leaks and unauthorized access. For example, data is encrypted and access permissions are strictly managed. This allows the reception department to provide an environment where users can upload presentation data with peace of mind.

[0031] The analysis department uses generative AI to automatically analyze the content and slides of uploaded presentations. The generative AI evaluates the structure, expression, and readability of the slide design of the presentation, and identifies areas for improvement. Specifically, the generative AI uses natural language processing technology to analyze the text of the presentation and evaluate its logical consistency and persuasiveness. It also uses image recognition technology to analyze the design and layout of the slides and evaluate their readability and design consistency. For example, the generative AI may suggest improvements to the flow and expression of the speech, as well as improvements to the layout and color scheme of the slides. The generative AI analyzes the content of the presentation and extracts specific areas for improvement. For example, the generative AI provides specific feedback such as, "Changing the expression in this section like this will make it more effective," or "Changing the background color of the slide will improve readability." Furthermore, the generative AI can learn from past presentation data and provide advice based on best practices. For example, it can learn the characteristics of successful presentations and evaluate and improve the user's presentation based on that. This allows the analysis department to provide specific and practical feedback to improve the quality of the user's presentations.

[0032] The service provider provides advice to users based on the analysis results obtained by the analysis department. The service provider uses generative AI to provide specific advice to users. Specifically, based on the analysis results, the service provider clearly presents areas for improvement and suggestions to users. For example, the service provider provides specific feedback such as, "Changing the wording in this section like this will make it more effective," or "Changing the background color of the slide will improve visibility." The service provider uses an interactive interface to provide feedback in a format that is easy for users to receive. For example, when a user clicks on feedback, a detailed explanation and specific improvement methods are displayed. The service provider also supports users in actually applying the feedback. For example, they provide templates and tools for changing the slide design, making it easy for users to make improvements. Furthermore, the service provider collects user responses to the feedback and continuously improves the accuracy and effectiveness of the feedback. This allows the service provider to provide users with specific and practical advice and improve the quality of their presentations.

[0033] The rehearsal department acts as a partner in presentation rehearsals, asking questions to the user. Using an AI agent, the rehearsal department simulates actual questions during the user's mock presentation, providing the user with a realistic rehearsal environment. Specifically, the rehearsal department generates relevant questions based on the user's presentation content and asks them to the user. For example, the rehearsal department can ask questions such as, "Please explain this point in more detail." The AI ​​agent analyzes the user's answers and provides appropriate feedback. For example, if the user's answer is insufficient, it will ask additional questions such as, "Please explain with a more specific example." The rehearsal department also evaluates the flow and timing of the user's presentation and points out areas for improvement. For example, it may provide advice such as, "Taking a short pause at this point would be effective." This allows the rehearsal department to enable the user to conduct effective rehearsals for the actual presentation. Furthermore, the rehearsal department can accumulate the user's rehearsal data and provide advice based on past rehearsal results. This allows the user to continuously improve their presentation skills.

[0034] The simulation unit simulates the reactions of the target audience. Using generative AI, the simulation unit simulates the reactions of a virtual audience and provides feedback to the user. Specifically, the simulation unit evaluates the level of interest and understanding of the virtual audience based on the presentation content and slides. For example, the simulation unit can simulate reactions such as, "The audience's interest increases at this point." The generative AI learns from past presentation data and models audience reaction patterns. This allows the simulation unit to predict audience reactions to the user's presentation with high accuracy. Furthermore, the simulation unit provides specific advice for the user to improve their presentation. For example, it may provide feedback such as, "Explaining this part in more detail will deepen the audience's understanding." The simulation unit provides specific guidance for the user to improve the quality of their presentation, supporting success in actual presentations. In this way, the simulation unit can predict audience reactions and provide support for delivering effective presentations.

[0035] The analysis department can evaluate the structure, expression, and slide design visibility of a presentation and identify areas for improvement. For example, the analysis department can evaluate the structure of the presentation, checking its logical flow and visual appeal. For example, the analysis department can evaluate the effectiveness of the introduction, the emphasis on main points, and the way the conclusion is summarized. The analysis department can also evaluate the expression of the presentation, checking the clarity of language and the ingenuity of expression. For example, the analysis department can identify areas for improvement in the flow and expression of the speech. Furthermore, the analysis department can evaluate the visibility of the slide design, checking font size, color contrast, and layout balance. For example, the analysis department can suggest slide layouts and color schemes. In this way, the quality of the presentation can be improved by evaluating the structure, expression, and slide design visibility and identifying areas for improvement. Some or all of the above processes in the analysis department may be performed using generative AI, or they may not be performed using generative AI. For example, the analysis department can input the content of the presentation into a generative AI, which can then automatically analyze and identify areas for improvement.

[0036] The service provider can offer suggestions for improving the flow and expression of a speech, as well as layout and color schemes for slides. For example, the service provider can provide specific advice on improving the flow of a speech. For instance, they can give specific feedback such as, "Changing the introduction like this would be more effective," or "Using this expression would be good for emphasizing the main point." The service provider can also offer suggestions for slide layouts and color schemes. For example, they can offer specific advice such as, "Changing the background color of the slides will improve visibility," or "Increasing the font size will make it easier to read." By offering suggestions for improving the flow and expression of a speech, as well as layout and color schemes for slides, the quality of the presentation can be improved. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can provide specific advice to the user based on the results of analysis by generative AI.

[0037] The rehearsal unit can simulate actual questions asked by users during mock presentations, providing users with a realistic rehearsal environment. For example, when a user is giving a mock presentation, the AI ​​agent in the rehearsal unit can ask actual questions. For instance, the rehearsal unit can ask questions such as, "Please explain this point in more detail." The rehearsal unit can also simulate the target audience's reactions when the user is giving a presentation. For example, the rehearsal unit can simulate reactions such as, "The audience's interest increases at this point." This allows users to effectively prepare for their presentations by simulating actual questions and providing a realistic rehearsal environment during mock presentations. Some or all of the above processes in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can have an AI agent analyze the user's presentation in real time and ask appropriate questions.

[0038] The simulation unit can simulate the reactions of a virtual audience and provide feedback to the user. For example, the simulation unit can simulate reactions such as, "The audience's interest increases at this point." The simulation unit can also simulate the reactions of a virtual audience in real time when the user is giving a presentation. For example, the simulation unit can simulate reactions such as, "The audience's interest increases at this point." This allows for the improvement of presentation quality by simulating the reactions of a virtual audience and providing feedback to the user. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can use AI to analyze the user's presentation in real time and simulate the reactions of a virtual audience.

[0039] The reception desk can analyze the user's past presentation history and select the optimal upload method. For example, the reception desk can prioritize suggesting upload methods (audio, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest upload methods to be used at specific times based on the user's past presentation history. Furthermore, the reception desk can suggest the optimal upload method based on the format of presentations the user has uploaded in the past. This allows for efficient presentation uploads by selecting the optimal upload method through analysis of the user's past presentation history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past presentation history data into a generating AI, which can then select the optimal upload method.

[0040] The reception system can filter presentations based on the user's current projects and areas of interest when they are uploaded. For example, the reception system can prioritize uploading presentations related to the user's current projects. It can also filter and upload highly relevant presentations based on the user's areas of interest. Furthermore, the reception system can select and upload the most suitable presentations according to the progress of the user's projects. This allows for the efficient uploading of highly relevant presentations 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 or not. For example, the reception system can input the user's project data into a generating AI, which can then filter highly relevant presentations.

[0041] The reception desk can prioritize uploading presentations that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize uploading presentations related to that region. The reception desk can also filter and upload presentations that are highly relevant based on the user's geographical location. Furthermore, if the user is on the move, the reception desk can prioritize uploading presentations related to the user's current location. This allows for efficient presentation uploads by prioritizing the upload of highly relevant presentations that take the user's geographical location into account. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into a generating AI, which can then filter presentations that are highly relevant.

[0042] The reception desk can analyze a user's social media activity when they upload a presentation and upload relevant presentations. For example, the reception desk can prioritize uploading relevant presentations based on the user's social media activity. It can also upload presentations related to topics the user has shown interest in on social media. Furthermore, the reception desk can upload relevant presentations based on the activity of the user's social media followers and friends. This allows for the efficient uploading of relevant presentations by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into a generating AI, which can then select relevant presentations.

[0043] The analysis unit can adjust the level of detail in its analysis based on the importance of the presentation. For example, for important presentations, the analysis unit can perform a detailed analysis and provide specific areas for improvement. For regular presentations, the analysis unit can also perform a concise analysis focusing on the key points. Furthermore, for urgent presentations, the analysis unit can perform a rapid analysis and provide major areas for improvement. This allows for efficient analysis by adjusting the level of detail based on the importance of the presentation. Some or all of the above processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input presentation importance data into a generative AI, which can then adjust the level of detail in its analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the presentation category during analysis. For example, in the case of a business presentation, the analysis unit can apply a business-oriented analysis algorithm. Similarly, in the case of an academic presentation, it can apply an academic-oriented analysis algorithm. Furthermore, in the case of an entertainment presentation, it can apply an entertainment-oriented analysis algorithm. This allows for the provision of appropriate analysis results by applying different analysis algorithms depending on the presentation category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input presentation category data into a generative AI, which can then apply an appropriate analysis algorithm.

[0045] The analysis department can prioritize analyses based on the submission dates of presentations. For example, in the case of urgent presentations, the analysis department can perform a rapid analysis and provide key areas for improvement. The analysis department can also prioritize the analysis of presentations with approaching deadlines. Furthermore, for presentations with later deadlines, the analysis department can perform a detailed analysis and provide detailed areas for improvement. This allows for efficient analysis by prioritizing analyses based on the submission dates of presentations. Some or all of the above processes in the analysis department may be performed using or without a generative AI. For example, the analysis department can input presentation submission date data into a generative AI, which can then determine the analysis priorities.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the presentations during the analysis. For example, the analysis unit may prioritize the analysis of presentations related to the user's current project. It can also prioritize the analysis of highly relevant presentations based on the user's areas of interest. Furthermore, the analysis unit can select and analyze the most suitable presentations according to the progress of the user's project. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the presentations. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input the user's project data into a generative AI, which can then select highly relevant presentations and adjust the order of analysis.

[0047] The service provider can adjust the level of detail in the advice given based on the importance of the presentation. For example, in the case of an important presentation, the service provider will provide detailed advice. In the case of a regular presentation, the service provider can also provide concise advice that gets straight to the point. Furthermore, in the case of an urgent presentation, the service provider can provide advice quickly. This allows for efficient advice delivery by adjusting the level of detail based on the importance of the presentation. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input presentation importance data into a generative AI, and the generative AI can adjust the level of detail in the advice.

[0048] The service provider can apply different advice algorithms depending on the presentation category when providing advice. For example, in the case of a business presentation, the service provider can apply a business-oriented advice algorithm. Similarly, in the case of an academic presentation, the service provider can apply an academic-oriented advice algorithm. Furthermore, in the case of an entertainment presentation, the service provider can apply an entertainment-oriented advice algorithm. This allows for the provision of appropriate advice by applying different advice algorithms depending on the presentation category. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input presentation category data into a generative AI, which can then apply an appropriate advice algorithm.

[0049] The service provider can prioritize advice based on the presentation submission deadline when providing advice. For example, in the case of an urgent presentation, the service provider will provide advice quickly. The service provider can also prioritize advice for presentations with approaching deadlines. Furthermore, the service provider can provide detailed advice for presentations with distant deadlines. This allows for efficient advice delivery by prioritizing advice based on the presentation submission deadline. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input presentation submission date data into a generative AI, which can then determine the priority of advice.

[0050] The service provider can adjust the order of advice based on the relevance of the presentations when providing advice. For example, the service provider may prioritize advice on presentations relevant to the user's current project. It can also prioritize advice on highly relevant presentations based on the user's areas of interest. Furthermore, the service provider may select and advise on the most suitable presentations according to the progress of the user's project. This allows for efficient advice delivery by adjusting the order of advice based on the relevance of the presentations. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input the user's project data into a generative AI, which can then select highly relevant presentations and adjust the order of advice.

[0051] The rehearsal unit can provide optimal questions during rehearsals by referring to the user's past rehearsal history. For example, the rehearsal unit can ask related questions based on questions the user has answered in the past. The rehearsal unit can also provide questions related to a specific theme based on the user's past rehearsal history. Furthermore, the rehearsal unit can analyze the user's past rehearsal history and provide the most effective questions. This allows for optimal questions to be provided and effective rehearsals to be conducted by referring to the user's past rehearsal history. Some or all of the above processes in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can input the user's past rehearsal history data into a generating AI, which can then select the optimal questions.

[0052] The rehearsal unit can apply different question algorithms during rehearsals depending on the category of the user's presentation. For example, in the case of a business presentation, the rehearsal unit can apply a business-oriented question algorithm. It can also apply an academic-oriented question algorithm for academic presentations, and an entertainment-oriented question algorithm for entertainment presentations. This allows for the provision of appropriate questions by applying different question algorithms depending on the presentation category. Some or all of the above processing in the rehearsal unit may be performed using AI, or not. For example, the rehearsal unit can input presentation category data into a generating AI, which can then apply an appropriate question algorithm.

[0053] The rehearsal unit can provide optimal questions during rehearsals, taking into account the user's geographical location. For example, if the user is in a specific region, the rehearsal unit will ask questions related to that region. The rehearsal unit can also provide highly relevant questions based on the user's geographical location. Furthermore, if the user is on the move, the rehearsal unit can ask questions related to their current location. This allows for effective rehearsals by providing optimal questions that take the user's geographical location into account. Some or all of the above processing in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can input the user's geographical location data into a generating AI, which can then select the optimal questions.

[0054] The rehearsal unit can analyze the user's social media activity during rehearsals and provide relevant questions. For example, the rehearsal unit can ask relevant questions based on the user's social media activity. It can also provide questions related to topics the user has shown interest in on social media. Furthermore, the rehearsal unit can provide relevant questions based on the activity of the user's followers and friends on social media. This allows for the provision of relevant questions and effective rehearsals by analyzing the user's social media activity. Some or all of the above processing in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can input the user's social media activity data into a generating AI, which can then select relevant questions.

[0055] The simulation unit can simulate the optimal response by referring to the user's past presentation history during the simulation. For example, the simulation unit can simulate relevant responses based on the responses to presentations the user has given in the past. The simulation unit can also simulate responses related to a specific theme from the user's past presentation history. Furthermore, the simulation unit can analyze the user's past presentation history and simulate the most effective response. This allows for the simulation of the optimal response and the provision of effective feedback by referring to the user's past presentation history. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input the user's past presentation history data into a generating AI, which can then simulate the optimal response.

[0056] The simulation unit can apply different response simulation algorithms during simulation depending on the category of the user's presentation. For example, in the case of a business presentation, the simulation unit applies a business-oriented response simulation algorithm. It can also apply an academic-oriented response simulation algorithm in the case of an academic presentation. Furthermore, it can apply an entertainment-oriented response simulation algorithm in the case of an entertainment presentation. This allows for the provision of appropriate feedback by applying different response simulation algorithms depending on the presentation category. Some or all of the above processing in the simulation unit may be performed using AI, or without AI. For example, the simulation unit can input presentation category data into a generating AI, which can then apply an appropriate response simulation algorithm.

[0057] The simulation unit can simulate the optimal response by considering the user's geographical location information during the simulation. For example, if the user is in a specific region, the simulation unit will simulate a response related to that region. The simulation unit can also simulate highly relevant responses based on the user's geographical location information. Furthermore, if the user is on the move, the simulation unit can simulate a response related to their current location. This allows for effective feedback by simulating the optimal response while considering the user's geographical location information. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input the user's geographical location information data into a generating AI, which can then simulate the optimal response.

[0058] The simulation unit can analyze the user's social media activity during a simulation and simulate relevant responses. For example, the simulation unit can simulate relevant responses based on the content of the user's social media activity. The simulation unit can also simulate responses related to topics the user has shown interest in on social media. Furthermore, the simulation unit can simulate relevant responses based on the activity of the user's followers and friends on social media. This allows for the simulation of relevant responses and the provision of effective feedback by analyzing the user's social media activity. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input the user's social media activity data into a generating AI, which can then simulate relevant responses.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] A presentation feedback system can provide feedback not only on the content and slide design of a user's presentation, but also on the tone of voice and speaking style. For example, if a user's speaking tone is monotonous, the system can offer specific advice such as, "Raising your voice tone in this section will make it more effective." If the user speaks too quickly, the system can provide feedback such as, "Speaking a little slower will make it easier for the audience to understand." Furthermore, if the user's voice volume is too low, the system can offer advice such as, "Raising your voice volume will make it easier for the audience to hear you." This allows users to improve not only the content and slide design of their presentations, but also their speaking style and tone of voice, leading to more effective presentations.

[0061] A presentation feedback system can provide feedback not only on the content and slide design of a user's presentation, but also on the user's gestures and body language. For example, if a user uses their hands too little during a presentation, the system can offer specific advice such as, "Using your hands to emphasize this part would be more effective." If a user has poor posture, the system can provide feedback such as, "Speaking with your back straight will make you appear more confident." Furthermore, if a user's gaze is fixed, the system can offer advice such as, "Making eye contact with the entire audience would be more effective." This allows users to improve not only the content and slide design of their presentations, but also their gestures and body language, resulting in more effective presentations.

[0062] A presentation feedback system can provide feedback not only on the content and slide design of a user's presentation, but also on the timing and pacing of their presentation. For example, if a user speaks too quickly during a presentation, the system can offer specific advice such as, "Slowing down a little in this section would be more effective." If a user is not emphasizing important points at the right time, the system can provide feedback such as, "Taking a breath here would be more effective." Furthermore, if a user is not managing their time effectively, the system can offer advice such as, "Spending a little more time on this section would be more effective." This allows users to improve not only the content and slide design, but also the timing and pacing of their presentations, leading to more effective presentations.

[0063] A presentation feedback system can provide feedback not only on the content and slide design of a user's presentation, but also on the storytelling techniques used. For example, if a user's presentation lacks clarity in its narrative flow, the system can offer specific advice such as, "Making the narrative flow clearer in this section would make it more effective." Similarly, if a user lacks the storytelling skills to highlight key points, the system can provide feedback such as, "Giving specific examples in this section would make it more effective." Furthermore, if a user lacks the storytelling skills to engage the audience, the system can offer advice such as, "Asking questions in this section would make it more effective." This allows users to improve not only their presentation content and slide design, but also their storytelling techniques, leading to more effective presentations.

[0064] A presentation feedback system can provide feedback not only on the content and slide design of a user's presentation, but also on the visual elements of the presentation. For example, if the images or graphs used by the user in their presentation are inappropriate, the system can provide specific advice such as, "Using a more appropriate image in this section would make it more effective." Similarly, if the user's font or color choices are inappropriate, the system can provide feedback such as, "Changing the font in this section would improve readability." Furthermore, if the user's slide layout is poorly designed, the system can offer advice such as, "Changing the layout in this section would make it more effective." This allows users to improve not only the content and slide design, but also the visual elements of their presentations, leading to more effective presentations.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives the user's presentation content and slides. For example, a business person can upload their presentation materials. The reception desk receives the uploaded presentation content and slides and passes them on to the next step. Step 2: The analysis department uses a generative AI to automatically analyze the content and slides of the uploaded presentation. The generative AI evaluates the presentation's structure, expression, and the readability of the slide design, and identifies areas for improvement. For example, the generative AI may suggest improvements to the flow and expression of the speech, as well as improvements to the slide layout and color scheme. Step 3: The service department provides advice to the user based on the analysis results obtained by the analysis department. The service department uses generative AI to provide specific advice to the user. For example, it provides specific feedback such as, "Changing the wording of this part like this will make it more effective" or "Changing the background color of the slide will improve visibility." Step 4: The rehearsal team acts as a partner in the presentation rehearsal, asking questions to the user. Using an AI agent, the rehearsal team simulates actual questions during the user's mock presentation, providing the user with a realistic rehearsal environment. For example, it can ask questions such as, "Could you explain this point in more detail?" Step 5: The simulation unit simulates the target audience's reaction. The simulation unit uses generative AI to simulate the reaction of a virtual audience and provides feedback to the user. For example, it can simulate reactions such as, "The audience's interest increases at this point."

[0067] (Example of form 2) The presentation feedback system according to an embodiment of the present invention is a system that uses generative AI to provide feedback on areas for improvement in the content and slide design of a user's presentation. This presentation feedback system allows the user to upload the content and slides of their presentation, and the generative AI automatically analyzes them and provides advice to make the user's presentation more engaging and effective. It also provides a function where an AI agent autonomously asks questions as a presentation rehearsal partner and simulates the reactions of a target audience. For example, the user uploads the content and slides of their presentation. In this process, the user inputs the text and slide data of the presentation into the system. For example, a business person can upload their own presentation materials. Next, the generative AI automatically analyzes the uploaded presentation content and slides. The generative AI evaluates the structure and expression of the presentation, the visibility of the slide design, etc., and identifies areas for improvement. For example, this includes suggestions for improving the flow and expression of the speech, and suggestions for slide layout and color scheme. Furthermore, the generative AI provides specific advice to the user. For example, it provides specific feedback such as, "Changing the expression in this section like this would make it more effective," or "Changing the background color of the slide will improve visibility." Furthermore, the system provides a feature where an AI agent autonomously asks questions as a partner in presentation rehearsals. When a user gives a mock presentation, the AI ​​agent simulates actual questions, providing the user with a realistic rehearsal environment. For example, it can ask questions such as, "Please explain this point in more detail." In addition, it provides a function to simulate the reactions of a target audience. The generative AI simulates the reactions of a virtual audience and provides feedback to the user. For example, it can simulate reactions such as, "The audience's interest increases at this point."This system allows users to receive professional feedback, improving the quality and effectiveness of their presentations. Furthermore, by providing a rehearsal environment, it enables effective preparation for the actual presentation. In this way, the presentation feedback system can improve the quality and effectiveness of users' presentations.

[0068] The presentation feedback system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a rehearsal unit, and a simulation unit. The reception unit receives the content and slides of a presentation from the user. When uploading the content and slides of a presentation, the user inputs, for example, the text and slide data of the presentation into the system. For example, a business person can upload their own presentation materials. The reception unit receives the uploaded content and slides of the presentation and passes them on to the next process. The analysis unit automatically analyzes the uploaded content and slides of the presentation using a generation AI. The generation AI evaluates the structure and expression of the presentation, the visibility of the slide design, etc., and identifies areas for improvement. For example, the generation AI makes suggestions for improving the flow and expression of the speech, and for the layout and color scheme of the slides. The generation AI analyzes the content of the presentation and extracts specific areas for improvement. For example, the generation AI provides specific feedback such as, "Changing the expression in this part like this will make it more effective," or "Changing the background color of the slide will improve visibility." The provision unit provides advice to the user based on the analysis results obtained by the analysis unit. The service provider uses generative AI to provide users with specific advice. For example, the service provider provides specific feedback such as, "Changing the wording of this part like this will make it more effective," or "Changing the background color of the slide will improve visibility." The rehearsal service provider acts as a partner in presentation rehearsals, asking questions to the user. Using an AI agent, the rehearsal service provider simulates actual questions asked when the user gives a mock presentation, providing the user with a realistic rehearsal environment. For example, the rehearsal service provider can ask questions such as, "Please explain this point in more detail." The simulation service provider simulates the reactions of the target audience. Using generative AI, the simulation service provider simulates the reactions of a virtual audience and provides feedback to the user.For example, the simulation unit can simulate reactions such as, "Audience interest increases at this point." This allows the presentation feedback system according to the embodiment to improve the quality and effectiveness of the user's presentation.

[0069] The reception desk allows users to upload presentation content and slides. When uploading, users input presentation text and slide data into the system. Specifically, users can upload presentation files by dragging and dropping them through the system interface or by using a file selection dialog. The reception desk automatically recognizes the format of the uploaded files and extracts text and image data. For example, a business person can upload their own presentation materials. The reception desk receives the uploaded presentation content and slides and passes them on to the next processing stage. The reception desk checks the integrity of the files and performs file format conversion and data cleansing as needed. For example, it converts PDF slides to image formats or extracts text data and prepares it in a format that is easy to analyze. Furthermore, the reception desk automatically extracts metadata (title, author, creation date, etc.) from the user-uploaded presentation and saves it to a database. This allows users to easily search and manage their presentations later. The reception desk securely stores the uploaded data and implements security measures to prevent data leaks and unauthorized access. For example, data is encrypted and access permissions are strictly managed. This allows the reception department to provide an environment where users can upload presentation data with peace of mind.

[0070] The analysis department uses generative AI to automatically analyze the content and slides of uploaded presentations. The generative AI evaluates the structure, expression, and readability of the slide design of the presentation, and identifies areas for improvement. Specifically, the generative AI uses natural language processing technology to analyze the text of the presentation and evaluate its logical consistency and persuasiveness. It also uses image recognition technology to analyze the design and layout of the slides and evaluate their readability and design consistency. For example, the generative AI may suggest improvements to the flow and expression of the speech, as well as improvements to the layout and color scheme of the slides. The generative AI analyzes the content of the presentation and extracts specific areas for improvement. For example, the generative AI provides specific feedback such as, "Changing the expression in this section like this will make it more effective," or "Changing the background color of the slide will improve readability." Furthermore, the generative AI can learn from past presentation data and provide advice based on best practices. For example, it can learn the characteristics of successful presentations and evaluate and improve the user's presentation based on that. This allows the analysis department to provide specific and practical feedback to improve the quality of the user's presentations.

[0071] The service provider provides advice to users based on the analysis results obtained by the analysis department. The service provider uses generative AI to provide specific advice to users. Specifically, based on the analysis results, the service provider clearly presents areas for improvement and suggestions to users. For example, the service provider provides specific feedback such as, "Changing the wording in this section like this will make it more effective," or "Changing the background color of the slide will improve visibility." The service provider uses an interactive interface to provide feedback in a format that is easy for users to receive. For example, when a user clicks on feedback, a detailed explanation and specific improvement methods are displayed. The service provider also supports users in actually applying the feedback. For example, they provide templates and tools for changing the slide design, making it easy for users to make improvements. Furthermore, the service provider collects user responses to the feedback and continuously improves the accuracy and effectiveness of the feedback. This allows the service provider to provide users with specific and practical advice and improve the quality of their presentations.

[0072] The rehearsal department acts as a partner in presentation rehearsals, asking questions to the user. Using an AI agent, the rehearsal department simulates actual questions during the user's mock presentation, providing the user with a realistic rehearsal environment. Specifically, the rehearsal department generates relevant questions based on the user's presentation content and asks them to the user. For example, the rehearsal department can ask questions such as, "Please explain this point in more detail." The AI ​​agent analyzes the user's answers and provides appropriate feedback. For example, if the user's answer is insufficient, it will ask additional questions such as, "Please explain with a more specific example." The rehearsal department also evaluates the flow and timing of the user's presentation and points out areas for improvement. For example, it may provide advice such as, "Taking a short pause at this point would be effective." This allows the rehearsal department to enable the user to conduct effective rehearsals for the actual presentation. Furthermore, the rehearsal department can accumulate the user's rehearsal data and provide advice based on past rehearsal results. This allows the user to continuously improve their presentation skills.

[0073] The simulation unit simulates the reactions of the target audience. Using generative AI, the simulation unit simulates the reactions of a virtual audience and provides feedback to the user. Specifically, the simulation unit evaluates the level of interest and understanding of the virtual audience based on the presentation content and slides. For example, the simulation unit can simulate reactions such as, "The audience's interest increases at this point." The generative AI learns from past presentation data and models audience reaction patterns. This allows the simulation unit to predict audience reactions to the user's presentation with high accuracy. Furthermore, the simulation unit provides specific advice for the user to improve their presentation. For example, it may provide feedback such as, "Explaining this part in more detail will deepen the audience's understanding." The simulation unit provides specific guidance for the user to improve the quality of their presentation, supporting success in actual presentations. In this way, the simulation unit can predict audience reactions and provide support for delivering effective presentations.

[0074] The analysis department can evaluate the structure, expression, and slide design visibility of a presentation and identify areas for improvement. For example, the analysis department can evaluate the structure of the presentation, checking its logical flow and visual appeal. For example, the analysis department can evaluate the effectiveness of the introduction, the emphasis on main points, and the way the conclusion is summarized. The analysis department can also evaluate the expression of the presentation, checking the clarity of language and the ingenuity of expression. For example, the analysis department can identify areas for improvement in the flow and expression of the speech. Furthermore, the analysis department can evaluate the visibility of the slide design, checking font size, color contrast, and layout balance. For example, the analysis department can suggest slide layouts and color schemes. In this way, the quality of the presentation can be improved by evaluating the structure, expression, and slide design visibility and identifying areas for improvement. Some or all of the above processes in the analysis department may be performed using generative AI, or they may not be performed using generative AI. For example, the analysis department can input the content of the presentation into a generative AI, which can then automatically analyze and identify areas for improvement.

[0075] The service provider can offer suggestions for improving the flow and expression of a speech, as well as layout and color schemes for slides. For example, the service provider can provide specific advice on improving the flow of a speech. For instance, they can give specific feedback such as, "Changing the introduction like this would be more effective," or "Using this expression would be good for emphasizing the main point." The service provider can also offer suggestions for slide layouts and color schemes. For example, they can offer specific advice such as, "Changing the background color of the slides will improve visibility," or "Increasing the font size will make it easier to read." By offering suggestions for improving the flow and expression of a speech, as well as layout and color schemes for slides, the quality of the presentation can be improved. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can provide specific advice to the user based on the results of analysis by generative AI.

[0076] The rehearsal unit can simulate actual questions asked by users during mock presentations, providing users with a realistic rehearsal environment. For example, when a user is giving a mock presentation, the AI ​​agent in the rehearsal unit can ask actual questions. For instance, the rehearsal unit can ask questions such as, "Please explain this point in more detail." The rehearsal unit can also simulate the target audience's reactions when the user is giving a presentation. For example, the rehearsal unit can simulate reactions such as, "The audience's interest increases at this point." This allows users to effectively prepare for their presentations by simulating actual questions and providing a realistic rehearsal environment during mock presentations. Some or all of the above processes in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can have an AI agent analyze the user's presentation in real time and ask appropriate questions.

[0077] The simulation unit can simulate the reactions of a virtual audience and provide feedback to the user. For example, the simulation unit can simulate reactions such as, "The audience's interest increases at this point." The simulation unit can also simulate the reactions of a virtual audience in real time when the user is giving a presentation. For example, the simulation unit can simulate reactions such as, "The audience's interest increases at this point." This allows for the improvement of presentation quality by simulating the reactions of a virtual audience and providing feedback to the user. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can use AI to analyze the user's presentation in real time and simulate the reactions of a virtual audience.

[0078] The reception desk can estimate the user's emotions and adjust the timing of the presentation upload based on the estimated emotions. For example, if the user is stressed, the reception desk can simplify the upload process to allow for a quicker upload. If the user is relaxed, the reception desk can also provide detailed upload options and suggest a customizable upload method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for a quicker presentation upload. This reduces user stress and allows for efficient presentation uploads by adjusting the upload timing 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. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the upload timing.

[0079] The reception desk can analyze the user's past presentation history and select the optimal upload method. For example, the reception desk can prioritize suggesting upload methods (audio, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest upload methods to be used at specific times based on the user's past presentation history. Furthermore, the reception desk can suggest the optimal upload method based on the format of presentations the user has uploaded in the past. This allows for efficient presentation uploads by selecting the optimal upload method through analysis of the user's past presentation history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past presentation history data into a generating AI, which can then select the optimal upload method.

[0080] The reception system can filter presentations based on the user's current projects and areas of interest when they are uploaded. For example, the reception system can prioritize uploading presentations related to the user's current projects. It can also filter and upload highly relevant presentations based on the user's areas of interest. Furthermore, the reception system can select and upload the most suitable presentations according to the progress of the user's projects. This allows for the efficient uploading of highly relevant presentations 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 or not. For example, the reception system can input the user's project data into a generating AI, which can then filter highly relevant presentations.

[0081] The reception desk can estimate the user's emotions and determine the priority of presentations to upload based on the estimated emotions. For example, if the user is nervous, the reception desk will prioritize uploading important presentations. Conversely, if the user is relaxed, the reception desk can prioritize uploading normal presentations. Furthermore, if the user is in a hurry, the reception desk can quickly upload the most important presentations. This ensures that important presentations are prioritized by determining the priority of presentations to upload 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can estimate the user's emotions and determine the priority of presentations to upload.

[0082] The reception desk can prioritize uploading presentations that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize uploading presentations related to that region. The reception desk can also filter and upload presentations that are highly relevant based on the user's geographical location. Furthermore, if the user is on the move, the reception desk can prioritize uploading presentations related to the user's current location. This allows for efficient presentation uploads by prioritizing the upload of highly relevant presentations that take the user's geographical location into account. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into a generating AI, which can then filter presentations that are highly relevant.

[0083] The reception desk can analyze a user's social media activity when they upload a presentation and upload relevant presentations. For example, the reception desk can prioritize uploading relevant presentations based on the user's social media activity. It can also upload presentations related to topics the user has shown interest in on social media. Furthermore, the reception desk can upload relevant presentations based on the activity of the user's social media followers and friends. This allows for the efficient uploading of relevant presentations by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into a generating AI, which can then select relevant presentations.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide specific areas for improvement. If the user is in a hurry, the analysis unit can perform a concise analysis that gets straight to the point. Furthermore, if the user is nervous, the analysis unit can provide a simple and easy-to-understand analysis result. In this way, by adjusting the analysis method based on the user's emotions, the analysis results can be tailored to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user facial expression data into the generative AI, which can estimate the user's emotions and adjust the analysis method.

[0085] The analysis unit can adjust the level of detail in its analysis based on the importance of the presentation. For example, for important presentations, the analysis unit can perform a detailed analysis and provide specific areas for improvement. For regular presentations, the analysis unit can also perform a concise analysis focusing on the key points. Furthermore, for urgent presentations, the analysis unit can perform a rapid analysis and provide major areas for improvement. This allows for efficient analysis by adjusting the level of detail based on the importance of the presentation. Some or all of the above processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input presentation importance data into a generative AI, which can then adjust the level of detail in its analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the presentation category during analysis. For example, in the case of a business presentation, the analysis unit can apply a business-oriented analysis algorithm. Similarly, in the case of an academic presentation, it can apply an academic-oriented analysis algorithm. Furthermore, in the case of an entertainment presentation, it can apply an entertainment-oriented analysis algorithm. This allows for the provision of appropriate analysis results by applying different analysis algorithms depending on the presentation category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input presentation category data into a generative AI, which can then apply an appropriate analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit will prioritize analyzing important presentations. Conversely, if the user is relaxed, the analysis unit can prioritize analyzing normal presentations. Furthermore, if the user is in a hurry, the analysis unit can quickly analyze the most important presentations. This allows for prioritizing the analysis of important presentations 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using or without generative AI. For example, the analysis unit can input user facial expression data into a generative AI, which can then estimate the user's emotions and determine the priority of analysis.

[0088] The analysis department can prioritize analyses based on the submission dates of presentations. For example, in the case of urgent presentations, the analysis department can perform a rapid analysis and provide key areas for improvement. The analysis department can also prioritize the analysis of presentations with approaching deadlines. Furthermore, for presentations with later deadlines, the analysis department can perform a detailed analysis and provide detailed areas for improvement. This allows for efficient analysis by prioritizing analyses based on the submission dates of presentations. Some or all of the above processes in the analysis department may be performed using or without a generative AI. For example, the analysis department can input presentation submission date data into a generative AI, which can then determine the analysis priorities.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the presentations during the analysis. For example, the analysis unit may prioritize the analysis of presentations related to the user's current project. It can also prioritize the analysis of highly relevant presentations based on the user's areas of interest. Furthermore, the analysis unit can select and analyze the most suitable presentations according to the progress of the user's project. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the presentations. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input the user's project data into a generative AI, which can then select highly relevant presentations and adjust the order of analysis.

[0090] The service provider can estimate the user's emotions and adjust the way advice is presented based on the estimated emotions. For example, if the user is nervous, the service provider can provide simple and easily understandable advice. If the user is relaxed, the service provider can also provide advice that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide concise advice that gets straight to the point. In this way, by adjusting the way advice is presented based on the user's emotions, the service provider can provide advice that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the way advice is presented.

[0091] The service provider can adjust the level of detail in the advice given based on the importance of the presentation. For example, in the case of an important presentation, the service provider will provide detailed advice. In the case of a regular presentation, the service provider can also provide concise advice that gets straight to the point. Furthermore, in the case of an urgent presentation, the service provider can provide advice quickly. This allows for efficient advice delivery by adjusting the level of detail based on the importance of the presentation. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input presentation importance data into a generative AI, and the generative AI can adjust the level of detail in the advice.

[0092] The service provider can apply different advice algorithms depending on the presentation category when providing advice. For example, in the case of a business presentation, the service provider can apply a business-oriented advice algorithm. Similarly, in the case of an academic presentation, the service provider can apply an academic-oriented advice algorithm. Furthermore, in the case of an entertainment presentation, the service provider can apply an entertainment-oriented advice algorithm. This allows for the provision of appropriate advice by applying different advice algorithms depending on the presentation category. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input presentation category data into a generative AI, which can then apply an appropriate advice algorithm.

[0093] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise advice. If the user is relaxed, the service provider can also provide longer advice with detailed explanations. Furthermore, if the user is excited, the service provider can provide advice with visually stimulating effects. This allows the service provider to provide advice that is appropriate for the user by adjusting the length of the advice 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. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the length of the advice.

[0094] The service provider can prioritize advice based on the presentation submission deadline when providing advice. For example, in the case of an urgent presentation, the service provider will provide advice quickly. The service provider can also prioritize advice for presentations with approaching deadlines. Furthermore, the service provider can provide detailed advice for presentations with distant deadlines. This allows for efficient advice delivery by prioritizing advice based on the presentation submission deadline. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input presentation submission date data into a generative AI, which can then determine the priority of advice.

[0095] The service provider can adjust the order of advice based on the relevance of the presentations when providing advice. For example, the service provider may prioritize advice on presentations relevant to the user's current project. It can also prioritize advice on highly relevant presentations based on the user's areas of interest. Furthermore, the service provider may select and advise on the most suitable presentations according to the progress of the user's project. This allows for efficient advice delivery by adjusting the order of advice based on the relevance of the presentations. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input the user's project data into a generative AI, which can then select highly relevant presentations and adjust the order of advice.

[0096] The rehearsal unit can estimate the user's emotions and adjust the rehearsal questions based on those emotions. For example, if the user is nervous, the rehearsal unit can ask simple and easy-to-answer questions. If the user is relaxed, the rehearsal unit can ask questions that require more detailed explanations. Furthermore, if the user is in a hurry, the rehearsal unit can ask concise questions that get straight to the point. By adjusting the rehearsal questions based on the user's emotions, a rehearsal environment suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the rehearsal questions accordingly.

[0097] The rehearsal unit can provide optimal questions during rehearsals by referring to the user's past rehearsal history. For example, the rehearsal unit can ask related questions based on questions the user has answered in the past. The rehearsal unit can also provide questions related to a specific theme based on the user's past rehearsal history. Furthermore, the rehearsal unit can analyze the user's past rehearsal history and provide the most effective questions. This allows for optimal questions to be provided and effective rehearsals to be conducted by referring to the user's past rehearsal history. Some or all of the above processes in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can input the user's past rehearsal history data into a generating AI, which can then select the optimal questions.

[0098] The rehearsal unit can apply different question algorithms during rehearsals depending on the category of the user's presentation. For example, in the case of a business presentation, the rehearsal unit can apply a business-oriented question algorithm. It can also apply an academic-oriented question algorithm for academic presentations, and an entertainment-oriented question algorithm for entertainment presentations. This allows for the provision of appropriate questions by applying different question algorithms depending on the presentation category. Some or all of the above processing in the rehearsal unit may be performed using AI, or not. For example, the rehearsal unit can input presentation category data into a generating AI, which can then apply an appropriate question algorithm.

[0099] The rehearsal unit can estimate the user's emotions and determine the priority of rehearsals based on the estimated emotions. For example, if the user is nervous, the rehearsal unit can prioritize important rehearsals. It can also prioritize normal rehearsals if the user is relaxed. Furthermore, if the user is in a hurry, the rehearsal unit can quickly perform the most important rehearsals. This allows important rehearsals to be prioritized by determining the priority of rehearsals 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can input user facial expression data into a generative AI, which can estimate the user's emotions and determine the priority of rehearsals.

[0100] The rehearsal unit can provide optimal questions during rehearsals, taking into account the user's geographical location. For example, if the user is in a specific region, the rehearsal unit will ask questions related to that region. The rehearsal unit can also provide highly relevant questions based on the user's geographical location. Furthermore, if the user is on the move, the rehearsal unit can ask questions related to their current location. This allows for effective rehearsals by providing optimal questions that take the user's geographical location into account. Some or all of the above processing in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can input the user's geographical location data into a generating AI, which can then select the optimal questions.

[0101] The rehearsal unit can analyze the user's social media activity during rehearsals and provide relevant questions. For example, the rehearsal unit can ask relevant questions based on the user's social media activity. It can also provide questions related to topics the user has shown interest in on social media. Furthermore, the rehearsal unit can provide relevant questions based on the activity of the user's followers and friends on social media. This allows for the provision of relevant questions and effective rehearsals by analyzing the user's social media activity. Some or all of the above processing in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can input the user's social media activity data into a generating AI, which can then select relevant questions.

[0102] The simulation unit can estimate the user's emotions and adjust the audience response simulation based on the estimated user emotions. For example, if the user is nervous, the simulation unit can simulate a positive response. It can also simulate a realistic response if the user is relaxed. Furthermore, if the user is in a hurry, the simulation unit can simulate a concise response. This allows for the provision of appropriate feedback to the user by adjusting the audience response simulation 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the audience response simulation.

[0103] The simulation unit can simulate the optimal response by referring to the user's past presentation history during the simulation. For example, the simulation unit can simulate relevant responses based on the responses to presentations the user has given in the past. The simulation unit can also simulate responses related to a specific theme from the user's past presentation history. Furthermore, the simulation unit can analyze the user's past presentation history and simulate the most effective response. This allows for the simulation of the optimal response and the provision of effective feedback by referring to the user's past presentation history. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input the user's past presentation history data into a generating AI, which can then simulate the optimal response.

[0104] The simulation unit can apply different response simulation algorithms during simulation depending on the category of the user's presentation. For example, in the case of a business presentation, the simulation unit applies a business-oriented response simulation algorithm. It can also apply an academic-oriented response simulation algorithm in the case of an academic presentation. Furthermore, it can apply an entertainment-oriented response simulation algorithm in the case of an entertainment presentation. This allows for the provision of appropriate feedback by applying different response simulation algorithms depending on the presentation category. Some or all of the above processing in the simulation unit may be performed using AI, or without AI. For example, the simulation unit can input presentation category data into a generating AI, which can then apply an appropriate response simulation algorithm.

[0105] The simulation unit can estimate the user's emotions and determine the priority of simulations based on the estimated emotions. For example, if the user is tense, the simulation unit will prioritize important simulations. It can also prioritize normal simulations if the user is relaxed. Furthermore, if the user is in a hurry, the simulation unit can quickly perform the most important simulations. This allows for prioritizing important simulations 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input user facial expression data into a generative AI, which can estimate the user's emotions and determine the priority of simulations.

[0106] The simulation unit can simulate the optimal response by considering the user's geographical location information during the simulation. For example, if the user is in a specific region, the simulation unit will simulate a response related to that region. The simulation unit can also simulate highly relevant responses based on the user's geographical location information. Furthermore, if the user is on the move, the simulation unit can simulate a response related to their current location. This allows for effective feedback by simulating the optimal response while considering the user's geographical location information. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input the user's geographical location information data into a generating AI, which can then simulate the optimal response.

[0107] The simulation unit can analyze the user's social media activity during a simulation and simulate relevant responses. For example, the simulation unit can simulate relevant responses based on the content of the user's social media activity. The simulation unit can also simulate responses related to topics the user has shown interest in on social media. Furthermore, the simulation unit can simulate relevant responses based on the activity of the user's followers and friends on social media. This allows for the simulation of relevant responses and the provision of effective feedback by analyzing the user's social media activity. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input the user's social media activity data into a generating AI, which can then simulate relevant responses.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] A presentation feedback system can provide feedback not only on the content and slide design of a user's presentation, but also on the tone of voice and speaking style. For example, if a user's speaking tone is monotonous, the system can offer specific advice such as, "Raising your voice tone in this section will make it more effective." If the user speaks too quickly, the system can provide feedback such as, "Speaking a little slower will make it easier for the audience to understand." Furthermore, if the user's voice volume is too low, the system can offer advice such as, "Raising your voice volume will make it easier for the audience to hear you." This allows users to improve not only the content and slide design of their presentations, but also their speaking style and tone of voice, leading to more effective presentations.

[0110] A presentation feedback system can provide feedback not only on the content and slide design of a user's presentation, but also on the user's gestures and body language. For example, if a user uses their hands too little during a presentation, the system can offer specific advice such as, "Using your hands to emphasize this part would be more effective." If a user has poor posture, the system can provide feedback such as, "Speaking with your back straight will make you appear more confident." Furthermore, if a user's gaze is fixed, the system can offer advice such as, "Making eye contact with the entire audience would be more effective." This allows users to improve not only the content and slide design of their presentations, but also their gestures and body language, resulting in more effective presentations.

[0111] A presentation feedback system can provide feedback not only on the content and slide design of a user's presentation, but also on the timing and pacing of their presentation. For example, if a user speaks too quickly during a presentation, the system can offer specific advice such as, "Slowing down a little in this section would be more effective." If a user is not emphasizing important points at the right time, the system can provide feedback such as, "Taking a breath here would be more effective." Furthermore, if a user is not managing their time effectively, the system can offer advice such as, "Spending a little more time on this section would be more effective." This allows users to improve not only the content and slide design, but also the timing and pacing of their presentations, leading to more effective presentations.

[0112] A presentation feedback system can provide feedback not only on the content and slide design of a user's presentation, but also on the storytelling techniques used. For example, if a user's presentation lacks clarity in its narrative flow, the system can offer specific advice such as, "Making the narrative flow clearer in this section would make it more effective." Similarly, if a user lacks the storytelling skills to highlight key points, the system can provide feedback such as, "Giving specific examples in this section would make it more effective." Furthermore, if a user lacks the storytelling skills to engage the audience, the system can offer advice such as, "Asking questions in this section would make it more effective." This allows users to improve not only their presentation content and slide design, but also their storytelling techniques, leading to more effective presentations.

[0113] A presentation feedback system can provide feedback not only on the content and slide design of a user's presentation, but also on the visual elements of the presentation. For example, if the images or graphs used by the user in their presentation are inappropriate, the system can provide specific advice such as, "Using a more appropriate image in this section would make it more effective." Similarly, if the user's font or color choices are inappropriate, the system can provide feedback such as, "Changing the font in this section would improve readability." Furthermore, if the user's slide layout is poorly designed, the system can offer advice such as, "Changing the layout in this section would make it more effective." This allows users to improve not only the content and slide design, but also the visual elements of their presentations, leading to more effective presentations.

[0114] A presentation feedback system can estimate a user's emotions and provide feedback on how to improve the presentation content and slide design based on those emotions. For example, if a user is nervous, the system can offer specific advice such as, "It would be more effective if you spoke more relaxed in this section." If a user is confident, it can provide feedback such as, "It would be more effective if you spoke more confidently in this section." Furthermore, if a user is anxious, it can offer advice such as, "It would be more effective if you took a breath here." This allows users to receive feedback not only on the content and slide design of their presentation, but also on their emotions, enabling them to deliver more effective presentations.

[0115] A presentation feedback system can estimate a user's emotions and adjust the questions asked during presentation rehearsals based on those emotions. For example, if a user is nervous, the system can provide specific advice such as, "Asking simple questions in this section would be more effective." If a user is relaxed, it can provide feedback such as, "Asking more detailed questions in this section would be more effective." Furthermore, if a user is in a hurry, it can provide advice such as, "Asking concise questions in this section would be more effective." This allows users to adjust the questions asked during presentation rehearsals based on their emotions, leading to more effective rehearsals.

[0116] A presentation feedback system can estimate a user's emotions and adjust the audience reaction simulation of the presentation based on those emotions. For example, if the user is nervous, the system can provide specific advice such as, "It would be more effective to simulate a positive reaction in this section." If the user is relaxed, it can provide feedback such as, "It would be more effective to simulate a realistic reaction in this section." Furthermore, if the user is in a hurry, it can provide advice such as, "It would be more effective to simulate a concise reaction in this section." This allows users to adjust the audience reaction simulation of their presentation based on their emotions, resulting in more effective feedback.

[0117] A presentation feedback system can estimate a user's emotions and adjust the way presentation advice is presented based on those emotions. For example, if a user is nervous, the system can provide specific advice such as, "In this section, providing simple and highly visible advice will be more effective." If a user is relaxed, the system can provide feedback such as, "In this section, providing advice that includes detailed information will be more effective." Furthermore, if a user is in a hurry, the system can provide advice such as, "In this section, providing concise advice that gets straight to the point will be more effective." This allows users to receive more effective advice by adjusting the way presentation advice is presented based on their emotions.

[0118] A presentation feedback system can estimate a user's emotions and adjust the length of the advice given in the presentation based on those emotions. For example, if the user is in a hurry, the system can provide specific advice such as, "In this section, it would be more effective to provide short, to-the-point advice." If the user is relaxed, the system can provide feedback such as, "In this section, it would be more effective to provide longer advice that includes a detailed explanation." Furthermore, if the user is excited, the system can provide advice such as, "In this section, it would be more effective to provide advice that incorporates visually stimulating effects." This allows users to adjust the length of the advice in their presentation based on their emotions, resulting in more effective advice.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk receives the user's presentation content and slides. For example, a business person can upload their presentation materials. The reception desk receives the uploaded presentation content and slides and passes them on to the next step. Step 2: The analysis department uses a generative AI to automatically analyze the content and slides of the uploaded presentation. The generative AI evaluates the presentation's structure, expression, and the readability of the slide design, and identifies areas for improvement. For example, the generative AI may suggest improvements to the flow and expression of the speech, as well as improvements to the slide layout and color scheme. Step 3: The service department provides advice to the user based on the analysis results obtained by the analysis department. The service department uses generative AI to provide specific advice to the user. For example, it provides specific feedback such as, "Changing the wording of this part like this will make it more effective" or "Changing the background color of the slide will improve visibility." Step 4: The rehearsal team acts as a partner in the presentation rehearsal, asking questions to the user. Using an AI agent, the rehearsal team simulates actual questions during the user's mock presentation, providing the user with a realistic rehearsal environment. For example, it can ask questions such as, "Could you explain this point in more detail?" Step 5: The simulation unit simulates the target audience's reaction. The simulation unit uses generative AI to simulate the reaction of a virtual audience and provides feedback to the user. For example, it can simulate reactions such as, "The audience's interest increases at this point."

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, rehearsal unit, and simulation 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 inputs the presentation text and slide data into the system when a user uploads the content and slides of a presentation. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically analyzes the content and slides of the uploaded presentation using a generating AI. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides specific advice to the user based on the analysis results. The rehearsal unit is implemented by, for example, the control unit 46A of the smart device 14, which asks questions to the user using an AI agent. The simulation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which simulates the reactions of a virtual audience. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, rehearsal unit, and simulation 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 inputs the presentation text and slide data into the system when the user uploads the presentation content and slides. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically analyzes the uploaded presentation content and slides using a generating AI. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides specific advice to the user based on the analysis results. The rehearsal unit is implemented by, for example, the control unit 46A of the smart glasses 214, which asks the user questions using an AI agent. The simulation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which simulates the reactions of a virtual audience. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, rehearsal unit, and simulation 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 inputs the presentation text and slide data into the system when a user uploads the content and slides of a presentation. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically analyzes the content and slides of the uploaded presentation using a generating AI. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides specific advice to the user based on the analysis results. The rehearsal unit is implemented by, for example, the control unit 46A of the headset terminal 314, which asks questions to the user using an AI agent. The simulation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which simulates the reactions of a virtual audience. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, rehearsal unit, and simulation 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 inputs the presentation text and slide data into the system when a user uploads the content and slides of a presentation. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically analyzes the content and slides of the uploaded presentation using a generating AI. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides specific advice to the user based on the analysis results. The rehearsal unit is implemented by, for example, the control unit 46A of the robot 414, which asks questions to the user using an AI agent. The simulation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which simulates the reactions of a virtual audience. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The reception area is where you upload the content and slides of your presentation, An analysis unit analyzes the content uploaded by the reception unit, A provision unit that provides advice based on the analysis results obtained by the aforementioned analysis unit, The rehearsal team asks questions as partners in presentation rehearsals, It includes a simulation unit that simulates the reactions of the target audience. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Evaluate the structure, expression, and slide design of the presentation to identify areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We provide suggestions for improving the flow and expression of your speech, as well as layout and color scheme for your slides. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned rehearsal section, When a user gives a mock presentation, the system simulates actual questions and provides the user with a realistic rehearsal environment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned simulation unit, Simulate the reactions of a virtual audience and provide feedback to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of presentation uploads based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past presentation history and select the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When uploading presentations, 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 9) The aforementioned reception unit is It estimates user sentiment and prioritizes presentations to upload based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When uploading presentations, the system prioritizes uploading presentations that are more relevant to the user, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a presentation is uploaded, the system analyzes the user's social media activity and uploads relevant presentations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the presentation analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During the analysis, 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 14) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the presentation category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, prioritize the analysis based on the presentation submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of the presentations. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, 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 20) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the presentation category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, prioritize the advice based on the presentation submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, adjust the order of advice based on the relevance of the presentation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned rehearsal section, The system estimates the user's emotions and adjusts the rehearsal questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned rehearsal section, During rehearsals, refer to the user's past rehearsal history to provide the most appropriate questions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned rehearsal section, During rehearsals, different question algorithms are applied depending on the category of the user's presentation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned rehearsal section, The system estimates the user's emotions and prioritizes rehearsals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned rehearsal section, During rehearsals, provide optimal questions that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned rehearsal section, During rehearsals, analyze users' social media activity and provide relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned simulation unit, It estimates user emotions and adjusts audience response simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned simulation unit, During the simulation, the system references the user's past presentation history to simulate the optimal response. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned simulation unit, During the simulation, different reaction simulation algorithms are applied depending on the category of the user's presentation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned simulation unit, It estimates the user's emotions and determines the priority of simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned simulation unit, During the simulation, the system considers the user's geographical location to simulate the optimal response. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned simulation unit, During the simulation, the system analyzes users' social media activity and simulates relevant responses. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 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 area is where you upload the content and slides of your presentation, An analysis unit analyzes the content uploaded by the reception unit, A provision unit that provides advice based on the analysis results obtained by the aforementioned analysis unit, The rehearsal team asks questions as partners in presentation rehearsals, It includes a simulation unit that simulates the reactions of the target audience. A system characterized by the following features.

2. The aforementioned analysis unit is Evaluate the structure, expression, and slide design of the presentation to identify areas for improvement. The system according to feature 1.

3. The aforementioned supply unit is, We provide suggestions for improving the flow and expression of your speech, as well as layout and color scheme for your slides. The system according to feature 1.

4. The aforementioned rehearsal section, When a user gives a mock presentation, the system simulates actual questions and provides the user with a realistic rehearsal environment. The system according to feature 1.

5. The aforementioned simulation unit, Simulate the reactions of a virtual audience and provide feedback to the user. The system according to feature 1.

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

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

8. The aforementioned reception unit is When uploading presentations, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates user sentiment and prioritizes presentations to upload based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned reception unit is When uploading presentations, the system prioritizes uploading presentations that are more relevant to the user, taking into account their geographical location. The system according to feature 1.

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