system

Abstract

The system according to this embodiment aims to evaluate the content and flow of a presentation and provide specific advice. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit inputs the user's presentation content. The analysis unit analyzes the presentation content input by the reception unit. The evaluation unit evaluates the slide content and the flow of the talk based on the presentation content analyzed by the analysis unit. The provision unit provides specific advice based on the evaluation results obtained by the evaluation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to objectively evaluate the content of a presentation and the flow of conversation and provide specific advice.

[0005] The system according to the embodiment aims to evaluate the content of a presentation and the flow of conversation and provide specific advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit inputs the user's presentation content. The analysis unit analyzes the presentation content input by the reception unit. The evaluation unit evaluates the slide content and the flow of the talk based on the presentation content analyzed by the analysis unit. The provision unit provides specific advice based on the evaluation results obtained by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can evaluate the content and flow of a presentation and provide specific advice. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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 practice support system according to an embodiment of the present invention is a system in which an AI agent supports presentation practice and provides advice. In this presentation practice support system, the user inputs the content of the presentation into the AI ​​agent, the AI ​​agent analyzes the user's presentation content using natural language processing technology and transcribes the utterances made during practice into text using speech recognition technology. Furthermore, a large-scale language model (LLM) is used to evaluate the consistency of the slide content and the flow of speech, expressiveness, and impact on the audience, and the generating AI proposes specific advice and areas for improvement to the user. In addition, an interactive AI is used to simulate an actual presentation situation. First, the user inputs the content of the presentation into the AI ​​agent. At this time, the user only needs to input the content of the slides and the flow of speech. For example, the user inputs the theme of the presentation and the main points of each slide. Next, the AI ​​agent analyzes the input presentation content. The generating AI analyzes the presentation content using natural language processing technology and evaluates the consistency of the slide content and the flow of speech. For example, it evaluates whether the order and content of the slides are logical and whether the flow of speech is smooth. Furthermore, the AI ​​agent transcribes the utterances made during practice into text using speech recognition technology. When a user practices a presentation, the AI ​​agent transcribes the user's speech in real time and analyzes the content. For example, it evaluates the speed, intonation, and accuracy of pronunciation. The generative AI uses a large-scale language model (LLM) to evaluate the consistency, expressiveness, and impact on the audience of the slides and the flow of the speech. For example, it evaluates whether the slide design and structure of the speech are effective and what impression they give to the audience. Finally, the generative AI suggests specific advice and areas for improvement to the user. For example, it provides advice on how to improve the slide design and how to make the flow of the speech smoother. In addition, it uses conversational AI to simulate actual presentation situations and provides feedback to the user as they practice their presentation. This system allows users to practice their presentations at their own pace and receive objective advice.This allows for efficient and effective improvement of presentation skills. The presentation practice support system can efficiently analyze and evaluate the user's presentation content and provide specific advice.

[0029] The presentation practice support system according to this embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit inputs the user's presentation content. The user's presentation content includes, but is not limited to, business presentations, academic presentations, technical presentations, etc. The reception unit allows the user to input, for example, the theme of the presentation and the key points of each slide. The analysis unit analyzes the presentation content input by the reception unit using a generation AI. The analysis is performed using, for example, natural language processing technology or data mining technology, but is not limited to, such examples. For example, the analysis unit analyzes the presentation content using natural language processing technology and evaluates the consistency of the slide content and the flow of the speech. The evaluation unit uses a generation AI to evaluate the slide content and the flow of the speech based on the presentation content analyzed by the analysis unit. The evaluation is performed based on, for example, criteria such as slide content, flow of speech, expressiveness, and impact on the audience, but is not limited to, such examples. For example, the evaluation unit evaluates whether the order and content of the slides are logical and whether the flow of speech is smooth. The provision unit uses a generation AI to provide specific advice based on the evaluation results obtained by the evaluation unit. Specific advice may include, but is not limited to, suggestions for improving slide design or smoothing the flow of the presentation. For example, the provider may offer advice on improving slide design or smoothing the flow of the presentation. This allows the presentation practice support system according to the embodiment to efficiently analyze and evaluate the user's presentation content and provide specific advice.

[0030] The reception desk inputs the user's presentation content. This content includes, but is not limited to, business presentations, academic presentations, and technical presentations. For example, the reception desk allows users to input the presentation theme and key points for each slide. Specifically, users can upload the presentation title, slide headings, detailed descriptions, and media files such as images and graphs through a dedicated interface. Furthermore, the reception desk allows users to input information about the presentation's purpose and target audience. This enables the system to perform analysis and evaluation tailored to the user's needs. The reception desk centrally manages the information entered by users and stores it in a database for access by the analysis and evaluation departments. The user interface is designed to be intuitive and easy to use, allowing users to easily input presentation content and make necessary modifications or additions. The reception desk also features a voice input function, automatically converting spoken content into text and inputting it as presentation content. This allows users to efficiently input presentation content and begin using the system.

[0031] The analysis unit uses generative AI to analyze the presentation content entered by the reception unit. The analysis is performed using, for example, natural language processing and data mining techniques, but is not limited to these examples. Specifically, the analysis unit uses natural language processing to analyze the presentation content and evaluate the consistency of the slide content and the flow of the speech. The generative AI analyzes the input text data and detects grammatical errors and unnatural expressions. It also evaluates the logical connections between slides and the flow of the speech to determine whether the overall structure of the presentation is appropriate. Furthermore, the analysis unit uses data mining techniques to extract patterns and trends for optimizing the user's presentation content based on past presentation data and successful cases. For example, it can analyze effective presentation methods and slide design trends in specific industries or themes and reflect them in the user's presentation content. By utilizing these technologies, the analysis unit analyzes the user's presentation content from multiple angles and generates detailed data to provide to the evaluation unit. This allows the analysis unit to analyze the user's presentation content with high accuracy and provide a foundation for the evaluation unit to conduct appropriate evaluations.

[0032] The evaluation unit uses generative AI to evaluate the slide content and flow of the presentation based on the analysis performed by the analysis unit. The evaluation is based on criteria such as slide content, flow of speech, expressiveness, and impact on the audience, but is not limited to these examples. Specifically, the evaluation unit assesses whether the order and content of the slides are logical and whether the flow of speech is smooth. The generative AI analyzes each element of the presentation content in detail and evaluates whether the slide design and layout are visually effective and whether the information is conveyed appropriately. It also evaluates the quality of the overall storytelling in the presentation to determine whether the flow of speech is consistent and easy for the audience to understand. Furthermore, the evaluation unit uses voice analysis technology to analyze the user's speaking style, tone of voice, and pace in order to evaluate the expressiveness of the presentation and its impact on the audience. This allows for a comprehensive evaluation of the expressiveness and impact on the audience when the user gives a presentation. Based on these evaluation results, the evaluation unit clarifies the strengths and areas for improvement of the user's presentation content and provides feedback to the delivery unit. This allows the evaluation unit to evaluate the user's presentation content from multiple perspectives and clearly indicate specific areas for improvement.

[0033] The service provider uses a generative AI to provide specific advice based on the evaluation results obtained by the evaluation department. This specific advice includes, but is not limited to, suggestions for improving slide design or streamlining the flow of the presentation. Specifically, the service provider will provide advice on improving slide design and streamlining the flow of the presentation. The generative AI generates specific improvement suggestions for the user's presentation content based on the evaluation results. For example, regarding slide design, it will offer specific suggestions on font selection, color usage, and layout optimization. To streamline the flow of the presentation, it will suggest rearranging the order of slides or methods for emphasizing important points. Furthermore, the service provider can also suggest training plans and practice methods to improve the user's presentation skills. For example, it will provide specific advice on improving presentation performance, such as vocal exercises, gesture usage, and eye contact. The service provider presents this advice to the user in an easy-to-understand and practical format. This allows the service provider to provide specific support to users in effectively improving their presentation content and enhancing the quality of their presentations.

[0034] The simulation unit can simulate actual presentation situations using conversational AI. For example, the simulation unit provides feedback to users when they practice their presentations using conversational AI. For instance, the simulation unit provides real-time feedback to users as they practice their presentations. The simulation unit can also evaluate the content and flow of a presentation and provide specific advice. For example, the simulation unit's conversational AI analyzes the user's speech and evaluates factors such as speaking speed, intonation, and pronunciation accuracy. This allows users to practice more practically by simulating actual presentation situations.

[0035] The simulation unit can provide feedback to users as they practice their presentations. For example, the simulation unit's conversational AI can provide real-time feedback when a user practices their presentation. For example, the simulation unit's conversational AI can evaluate the content and flow of a presentation and provide specific advice. For example, the simulation unit's conversational AI can analyze what the user says and evaluate the speed, intonation, and accuracy of pronunciation. In this way, it helps improve presentation skills by providing feedback to users as they practice their presentations.

[0036] The analysis unit can analyze the presentation content using natural language processing technology. For example, the analysis unit uses natural language processing technology to analyze the presentation content and evaluate the consistency of the slide content and the flow of the speech. For example, the analysis unit uses natural language processing technology to analyze the presentation content and evaluate whether the order and content of the slides are logical and whether the flow of the speech is smooth. In this way, the accuracy of the presentation content analysis is improved by using natural language processing technology.

[0037] The evaluation unit can assess the consistency of the slide content and the flow of the presentation, expressiveness, and impact on the audience. For example, the evaluation unit assesses the consistency of the slide content and the flow of the presentation. For instance, it evaluates whether the order and content of the slides are logical and whether the flow of the presentation is smooth. The evaluation unit can also assess expressiveness. For example, it evaluates word choice, tone of voice, and gestures. Furthermore, the evaluation unit can assess the impact on the audience. For example, it evaluates how well the presentation evokes emotions and whether it is memorable. By evaluating the consistency of the slide content and the flow of the presentation, expressiveness, and impact on the audience, the quality of the presentation can be improved.

[0038] The service provider can offer specific advice and suggestions for improvement to users. For example, they can provide advice on how to improve slide design and how to make the flow of the presentation smoother. Furthermore, the service provider can also suggest specific improvements based on the user's presentation content. For example, they can provide advice on how to improve slide design and how to make the flow of the presentation smoother. This allows the service provider to improve the quality of presentations by offering specific advice and suggestions for improvement to users.

[0039] The reception desk can analyze the user's past presentation content and select the optimal input method. For example, the reception desk can automatically suggest a format similar to the user's past presentations. The reception desk can also select the most effective input method from the user's past presentations. The reception desk can also analyze the user's past presentations and suggest an input method that reflects areas for improvement. By analyzing the user's past presentations, the reception desk can select the optimal input method and enable efficient presentation preparation. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past presentation content into a generating AI and have the generating AI select the optimal input method.

[0040] The reception desk can filter the input of presentation content based on the user's current projects and areas of interest. For example, the reception desk can automatically extract keywords related to the user's current projects and filter the input content. The reception desk can also prioritize input of relevant information based on the user's areas of interest. The reception desk can also suggest appropriate input content according to the progress of the user's current projects. This allows for the input of highly relevant presentation content by filtering based on the user's current projects and areas of interest. 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 data about the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The reception desk can prioritize inputting highly relevant content when the user inputs presentation content, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize inputting information related to that region. The reception desk can also input relevant case studies and data based on the user's current location. The reception desk can also suggest optimal presentation content, taking into account the user's geographical location. This allows for the preparation of more effective presentations by prioritizing the input of highly relevant content, taking into account the user's geographical location. 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 information into a generating AI and have the generating AI select highly relevant content.

[0042] The reception desk can analyze the user's social media activity and input relevant content when inputting presentation content. For example, the reception desk can analyze the user's social media posts and suggest relevant presentation content. The reception desk can also extract topics of high interest from the user's social media activity and reflect them in the input content. The reception desk can also analyze the reactions of the user's social media followers and suggest effective presentation content. This allows for the input of relevant content by analyzing the user's social media activity, enabling the preparation of more effective presentations. 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 data on the user's social media activity into a generating AI and have the generating AI select relevant content.

[0043] The analysis unit can adjust the level of detail of its analysis based on the importance of the presentation content. For example, the analysis unit can perform a detailed analysis on important slides. The analysis unit can also perform a concise analysis on slides of lower importance. The analysis unit can also adjust the level of detail of its analysis based on the overall importance of the presentation. By adjusting the level of detail of the analysis based on the importance of the presentation content, a more effective presentation analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the importance of the presentation content into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the presentation content during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical presentation content. The analysis unit can also apply a business-oriented analysis algorithm to business presentation content. The analysis unit can also apply an educational analysis algorithm to educational presentation content. By applying different analysis algorithms depending on the category of the presentation content, more effective presentation analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the category of the presentation content into a generating AI and have the generating AI select an analysis algorithm.

[0045] The evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of the presentation content during the evaluation process. For example, the evaluation unit can evaluate the logical connections between slides. The evaluation unit can also evaluate whether the flow of the presentation is smooth. The evaluation unit can also evaluate the overall consistency of the presentation. By improving the accuracy of the evaluation by considering the interrelationships of the presentation content, it becomes possible to evaluate presentations more effectively. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on the interrelationships of the presentation content into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluation.

[0046] The evaluation unit can consider the attribute information of the presenter of the presentation content when making its evaluation. For example, the evaluation unit can consider the presenter's expertise when making its evaluation. For example, the evaluation unit can consider the presenter's years of experience when making its evaluation. For example, the evaluation unit can consider the presenter's years of experience when making its evaluation. For example, the evaluation unit can consider the presenter's past presentation record when making its evaluation. For example, the evaluation unit can consider the presenter's past presentation record when making its evaluation. By considering the attribute information of the presenter of the presentation content when making its evaluation, it becomes possible to evaluate presentations more effectively. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input data on the presenter's attribute information into a generating AI and have the generating AI perform the evaluation.

[0047] The service provider can adjust the level of detail in the advice based on the importance of the presentation content. For example, it can provide detailed advice for important slides. It can also provide concise advice for less important slides. Furthermore, the service provider can adjust the level of detail in the advice based on the overall importance of the presentation. By adjusting the level of detail in the advice based on the importance of the presentation content, more effective advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the importance of the presentation content into a generating AI and have the generating AI adjust the level of detail in the advice.

[0048] The service provider can apply different advice algorithms depending on the category of the presentation content when providing advice. For example, the service provider can apply a specialized advice algorithm to technical presentations. For example, the service provider can apply a business-oriented advice algorithm to business presentations. For example, the service provider can apply an educational advice algorithm to educational presentations. By applying different advice algorithms depending on the category of the presentation content, more effective advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the category of the presentation content into a generating AI and have the generating AI select an advice algorithm.

[0049] The service provider can prioritize advice based on the submission deadline of the presentation content. For example, the service provider will prioritize advice for presentations with approaching deadlines. The service provider can also postpone advice for presentations with distant deadlines. The service provider can also dynamically adjust the priority of advice according to the submission deadline. This allows for more effective advice to be provided by prioritizing advice based on the submission deadline of the presentation content. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input data on the submission deadline of the presentation content into a generating AI and have the generating AI determine the priority of advice.

[0050] The advice provider can adjust the order of advice based on the relevance of the presentation content when providing advice. For example, the provider will prioritize providing advice to slides that are highly relevant. The provider can also postpone providing advice to slides that are less relevant. The provider can also dynamically adjust the order of advice based on the relevance of the entire presentation. By adjusting the order of advice based on the relevance of the presentation content, more effective advice can be provided. Some or all of the above processing in the advice provider may be performed using AI, for example, or not using AI. For example, the provider can input data on the relevance of the presentation content into a generating AI and have the generating AI adjust the order of advice.

[0051] The simulation unit can select the optimal simulation method by referring to the user's past presentation history during the simulation. For example, the simulation unit can propose the optimal simulation method based on the user's past presentation history. The simulation unit can also select an effective simulation method from the user's past presentation history. For example, the simulation unit can select an effective simulation method from the user's past presentation history. The simulation unit can also analyze the user's past presentation history and propose a simulation method that reflects areas for improvement. For example, the simulation unit can analyze the user's past presentation history and propose a simulation method that reflects areas for improvement. This allows for the selection of the optimal simulation method by referring to the user's past presentation history, enabling more effective presentation practice. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input data on the user's past presentation history into a generating AI and have the generating AI select the optimal simulation method.

[0052] The simulation unit can customize the simulation methods based on the user's current living situation during the simulation. For example, the simulation unit can suggest the optimal simulation method according to the user's current living situation. The simulation unit can also adjust the timing of the simulation to match the user's daily rhythm. The simulation unit can also customize the content of the simulation based on the user's living environment. For example, the simulation unit customizes the content of the simulation based on the user's living environment. This allows for more effective presentation practice by customizing the simulation methods based on the user's current living situation. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input data about the user's current living situation into a generating AI and have the generating AI perform the customization of the simulation methods.

[0053] The simulation unit can select the optimal simulation method during simulation, taking into account the user's geographical location information. For example, the simulation unit can propose the optimal simulation method based on the user's current location. The simulation unit can also provide relevant simulation content, taking into account the user's geographical location information. The simulation unit can also adjust the timing of the simulation based on the user's geographical location information. By selecting the optimal simulation method while considering the user's geographical location information, more effective presentation practice becomes possible. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input data on the user's geographical location information into a generating AI and have the generating AI select the optimal simulation method.

[0054] The simulation unit can analyze the user's social media activity during a simulation and propose simulation methods. For example, the simulation unit can analyze the user's social media posts and propose relevant simulation methods. The simulation unit can also extract topics of high interest from the user's social media activity and reflect them in the simulation content. The simulation unit can also analyze the reactions of the user's social media followers and propose effective simulation methods. By analyzing the user's social media activity, relevant simulation methods can be proposed, enabling more effective presentation practice. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input data on the user's social media activity into a generating AI and have the generating AI execute the proposal of simulation methods.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The presentation practice support system can also include a feedback function. This feedback function can record the user's presentation practice progress and evaluate it by comparing it to past practice data. For example, it can save data from past presentations and compare it to current practice data to identify areas for improvement. It can also record the user's practice frequency and duration and suggest an effective practice schedule. Furthermore, the feedback function can visualize the user's practice results in graphs and charts, allowing for a quick overview of progress. This enables users to objectively assess their presentation skill improvement and practice more effectively.

[0057] The presentation practice support system can also include a customization feature. This customization feature allows users to tailor practice content and advice to their presentation style and preferences. For example, if a user prefers visually-driven presentations, the customization feature can enhance slide design advice. If a user prioritizes storytelling, it can provide advice on speech structure and flow. Furthermore, the customization feature can continuously improve practice content and advice based on user feedback. This allows users to improve their presentation skills using the practice method best suited to them.

[0058] The presentation practice support system can also include a collaboration section. This collaboration section helps multiple users practice presentations together. For example, it allows users to share real-time feedback when practicing presentations with team members. It can also allow users to refer to other members' practice data and advice, enabling mutual learning. Furthermore, the collaboration section can manage the overall presentation progress of the team and suggest an effective practice schedule. This allows users to improve their team's presentation skills.

[0059] The presentation practice support system can also include a reminder function. This reminder function manages the user's practice schedule and notifies them of practice times. For example, it can notify the user of the start time of practice based on the practice schedule they have set. It can also send periodic reminders to help the user remember to practice. Furthermore, it can suggest effective practice times based on the user's practice data. This allows users to practice their presentations systematically and improve their skills.

[0060] The presentation practice support system can also include a scenario generation unit. This unit can automatically generate practice scenarios based on the user's presentation content. For example, it can create a practice scenario based on the presentation theme and key points entered by the user. Furthermore, it can suggest different scenarios depending on the user's presentation style and objectives. In addition, the scenario generation unit can continuously improve scenarios based on user feedback. This allows users to practice their presentations using effective scenarios and improve their skills.

[0061] The presentation practice support system can also be equipped with a virtual reality (VR) component. The VR component helps users practice presentations in a virtual environment. For example, the VR component provides a simulation of the user giving a presentation in a virtual conference room or auditorium. The VR component can also place a virtual audience and recreate a realistic presentation environment. Furthermore, the VR component can analyze the user's movements and voice tone and provide real-time feedback. This allows users to practice in an environment close to an actual presentation and improve their skills.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk inputs the user's presentation content. This content can include business presentations, academic presentations, technical presentations, etc. The reception desk allows the user to input the presentation theme and key points for each slide. Step 2: The analysis unit uses a generation AI to analyze the presentation content entered by the reception unit. The analysis is performed using natural language processing and data mining techniques. For example, the analysis unit uses natural language processing to analyze the presentation content and evaluate the consistency of the slide content and the flow of the speech. Step 3: The evaluation unit uses a generation AI to evaluate the slide content and flow of the presentation based on the analysis performed by the analysis unit. The evaluation is based on criteria such as slide content, flow of the presentation, expressiveness, and impact on the audience. For example, the evaluation unit assesses whether the order and content of the slides are logical and whether the flow of the presentation is smooth. Step 4: The provisioning department uses the generation AI to provide specific advice based on the evaluation results obtained by the evaluation department. This specific advice includes suggestions for improving slide design and streamlining the presentation flow. For example, the provisioning department provides advice on how to improve slide design and streamline the presentation flow.

[0064] (Example of form 2) The presentation practice support system according to an embodiment of the present invention is a system in which an AI agent supports presentation practice and provides advice. In this presentation practice support system, the user inputs the content of the presentation into the AI ​​agent, the AI ​​agent analyzes the user's presentation content using natural language processing technology and transcribes the utterances made during practice into text using speech recognition technology. Furthermore, a large-scale language model (LLM) is used to evaluate the consistency of the slide content and the flow of speech, expressiveness, and impact on the audience, and the generating AI proposes specific advice and areas for improvement to the user. In addition, an interactive AI is used to simulate an actual presentation situation. First, the user inputs the content of the presentation into the AI ​​agent. At this time, the user only needs to input the content of the slides and the flow of speech. For example, the user inputs the theme of the presentation and the main points of each slide. Next, the AI ​​agent analyzes the input presentation content. The generating AI analyzes the presentation content using natural language processing technology and evaluates the consistency of the slide content and the flow of speech. For example, it evaluates whether the order and content of the slides are logical and whether the flow of speech is smooth. Furthermore, the AI ​​agent transcribes the utterances made during practice into text using speech recognition technology. When a user practices a presentation, the AI ​​agent transcribes the user's speech in real time and analyzes the content. For example, it evaluates the speed, intonation, and accuracy of pronunciation. The generative AI uses a large-scale language model (LLM) to evaluate the consistency, expressiveness, and impact on the audience of the slides and the flow of the speech. For example, it evaluates whether the slide design and structure of the speech are effective and what impression they give to the audience. Finally, the generative AI suggests specific advice and areas for improvement to the user. For example, it provides advice on how to improve the slide design and how to make the flow of the speech smoother. In addition, it uses conversational AI to simulate actual presentation situations and provides feedback to the user as they practice their presentation. This system allows users to practice their presentations at their own pace and receive objective advice.This allows for efficient and effective improvement of presentation skills. The presentation practice support system can efficiently analyze and evaluate the user's presentation content and provide specific advice.

[0065] The presentation practice support system according to this embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit inputs the user's presentation content. The user's presentation content includes, but is not limited to, business presentations, academic presentations, technical presentations, etc. The reception unit allows the user to input, for example, the theme of the presentation and the key points of each slide. The analysis unit analyzes the presentation content input by the reception unit using a generation AI. The analysis is performed using, for example, natural language processing technology or data mining technology, but is not limited to, such examples. For example, the analysis unit analyzes the presentation content using natural language processing technology and evaluates the consistency of the slide content and the flow of the speech. The evaluation unit uses a generation AI to evaluate the slide content and the flow of the speech based on the presentation content analyzed by the analysis unit. The evaluation is performed based on, for example, criteria such as slide content, flow of speech, expressiveness, and impact on the audience, but is not limited to, such examples. For example, the evaluation unit evaluates whether the order and content of the slides are logical and whether the flow of speech is smooth. The provision unit uses a generation AI to provide specific advice based on the evaluation results obtained by the evaluation unit. Specific advice may include, but is not limited to, suggestions for improving slide design or smoothing the flow of the presentation. For example, the provider may offer advice on improving slide design or smoothing the flow of the presentation. This allows the presentation practice support system according to the embodiment to efficiently analyze and evaluate the user's presentation content and provide specific advice.

[0066] The reception desk inputs the user's presentation content. This content includes, but is not limited to, business presentations, academic presentations, and technical presentations. For example, the reception desk allows users to input the presentation theme and key points for each slide. Specifically, users can upload the presentation title, slide headings, detailed descriptions, and media files such as images and graphs through a dedicated interface. Furthermore, the reception desk allows users to input information about the presentation's purpose and target audience. This enables the system to perform analysis and evaluation tailored to the user's needs. The reception desk centrally manages the information entered by users and stores it in a database for access by the analysis and evaluation departments. The user interface is designed to be intuitive and easy to use, allowing users to easily input presentation content and make necessary modifications or additions. The reception desk also features a voice input function, automatically converting spoken content into text and inputting it as presentation content. This allows users to efficiently input presentation content and begin using the system.

[0067] The analysis unit uses generative AI to analyze the presentation content entered by the reception unit. The analysis is performed using, for example, natural language processing and data mining techniques, but is not limited to these examples. Specifically, the analysis unit uses natural language processing to analyze the presentation content and evaluate the consistency of the slide content and the flow of the speech. The generative AI analyzes the input text data and detects grammatical errors and unnatural expressions. It also evaluates the logical connections between slides and the flow of the speech to determine whether the overall structure of the presentation is appropriate. Furthermore, the analysis unit uses data mining techniques to extract patterns and trends for optimizing the user's presentation content based on past presentation data and successful cases. For example, it can analyze effective presentation methods and slide design trends in specific industries or themes and reflect them in the user's presentation content. By utilizing these technologies, the analysis unit analyzes the user's presentation content from multiple angles and generates detailed data to provide to the evaluation unit. This allows the analysis unit to analyze the user's presentation content with high accuracy and provide a foundation for the evaluation unit to conduct appropriate evaluations.

[0068] The evaluation unit uses generative AI to evaluate the slide content and flow of the presentation based on the analysis performed by the analysis unit. The evaluation is based on criteria such as slide content, flow of speech, expressiveness, and impact on the audience, but is not limited to these examples. Specifically, the evaluation unit assesses whether the order and content of the slides are logical and whether the flow of speech is smooth. The generative AI analyzes each element of the presentation content in detail and evaluates whether the slide design and layout are visually effective and whether the information is conveyed appropriately. It also evaluates the quality of the overall storytelling in the presentation to determine whether the flow of speech is consistent and easy for the audience to understand. Furthermore, the evaluation unit uses voice analysis technology to analyze the user's speaking style, tone of voice, and pace in order to evaluate the expressiveness of the presentation and its impact on the audience. This allows for a comprehensive evaluation of the expressiveness and impact on the audience when the user gives a presentation. Based on these evaluation results, the evaluation unit clarifies the strengths and areas for improvement of the user's presentation content and provides feedback to the delivery unit. This allows the evaluation unit to evaluate the user's presentation content from multiple perspectives and clearly indicate specific areas for improvement.

[0069] The service provider uses a generative AI to provide specific advice based on the evaluation results obtained by the evaluation department. This specific advice includes, but is not limited to, suggestions for improving slide design or streamlining the flow of the presentation. Specifically, the service provider will provide advice on improving slide design and streamlining the flow of the presentation. The generative AI generates specific improvement suggestions for the user's presentation content based on the evaluation results. For example, regarding slide design, it will offer specific suggestions on font selection, color usage, and layout optimization. To streamline the flow of the presentation, it will suggest rearranging the order of slides or methods for emphasizing important points. Furthermore, the service provider can also suggest training plans and practice methods to improve the user's presentation skills. For example, it will provide specific advice on improving presentation performance, such as vocal exercises, gesture usage, and eye contact. The service provider presents this advice to the user in an easy-to-understand and practical format. This allows the service provider to provide specific support to users in effectively improving their presentation content and enhancing the quality of their presentations.

[0070] The simulation unit can simulate actual presentation situations using conversational AI. For example, the simulation unit provides feedback to users when they practice their presentations using conversational AI. For instance, the simulation unit provides real-time feedback to users as they practice their presentations. The simulation unit can also evaluate the content and flow of a presentation and provide specific advice. For example, the simulation unit's conversational AI analyzes the user's speech and evaluates factors such as speaking speed, intonation, and pronunciation accuracy. This allows users to practice more practically by simulating actual presentation situations.

[0071] The simulation unit can provide feedback to users as they practice their presentations. For example, the simulation unit's conversational AI can provide real-time feedback when a user practices their presentation. For example, the simulation unit's conversational AI can evaluate the content and flow of a presentation and provide specific advice. For example, the simulation unit's conversational AI can analyze what the user says and evaluate the speed, intonation, and accuracy of pronunciation. In this way, it helps improve presentation skills by providing feedback to users as they practice their presentations.

[0072] The analysis unit can analyze the presentation content using natural language processing technology. For example, the analysis unit uses natural language processing technology to analyze the presentation content and evaluate the consistency of the slide content and the flow of the speech. For example, the analysis unit uses natural language processing technology to analyze the presentation content and evaluate whether the order and content of the slides are logical and whether the flow of the speech is smooth. In this way, the accuracy of the presentation content analysis is improved by using natural language processing technology.

[0073] The evaluation unit can assess the consistency of the slide content and the flow of the presentation, expressiveness, and impact on the audience. For example, the evaluation unit assesses the consistency of the slide content and the flow of the presentation. For instance, it evaluates whether the order and content of the slides are logical and whether the flow of the presentation is smooth. The evaluation unit can also assess expressiveness. For example, it evaluates word choice, tone of voice, and gestures. Furthermore, the evaluation unit can assess the impact on the audience. For example, it evaluates how well the presentation evokes emotions and whether it is memorable. By evaluating the consistency of the slide content and the flow of the presentation, expressiveness, and impact on the audience, the quality of the presentation can be improved.

[0074] The service provider can offer specific advice and suggestions for improvement to users. For example, they can provide advice on how to improve slide design and how to make the flow of the presentation smoother. Furthermore, the service provider can also suggest specific improvements based on the user's presentation content. For example, they can provide advice on how to improve slide design and how to make the flow of the presentation smoother. This allows the service provider to improve the quality of presentations by offering specific advice and suggestions for improvement to users.

[0075] The reception desk can estimate the user's emotions and adjust the timing of input for the presentation content based on the estimated emotions. For example, if the user is nervous, the reception desk can delay the input timing to help them relax. The reception desk can also expedite input if the user is focused. The reception desk can also allow the user to take breaks while inputting if they are tired. By adjusting the timing of input for the presentation content according to the user's emotions, it becomes possible to prepare a more effective presentation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The reception desk can analyze the user's past presentation content and select the optimal input method. For example, the reception desk can automatically suggest a format similar to the user's past presentations. The reception desk can also select the most effective input method from the user's past presentations. The reception desk can also analyze the user's past presentations and suggest an input method that reflects areas for improvement. By analyzing the user's past presentations, the reception desk can select the optimal input method and enable efficient presentation preparation. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past presentation content into a generating AI and have the generating AI select the optimal input method.

[0077] The reception desk can filter the input of presentation content based on the user's current projects and areas of interest. For example, the reception desk can automatically extract keywords related to the user's current projects and filter the input content. The reception desk can also prioritize input of relevant information based on the user's areas of interest. The reception desk can also suggest appropriate input content according to the progress of the user's current projects. This allows for the input of highly relevant presentation content by filtering based on the user's current projects and areas of interest. 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 data about the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0078] The reception desk can estimate the user's emotions and determine the priority of the presentation content to be entered based on those emotions. For example, if the user is nervous, the reception desk will enter important information first. For example, if the user is nervous, the reception desk will enter important information first. For example, if the user is relaxed, the reception desk will postpone detailed information. For example, if the user is relaxed, the reception desk will postpone detailed information. For example, if the user is in a hurry, the reception desk will prioritize entering key points. For example, if the user is in a hurry, the reception desk will prioritize entering key points. This allows for the preparation of more effective presentations by prioritizing the presentation content according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The reception desk can prioritize inputting highly relevant content when the user inputs presentation content, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize inputting information related to that region. The reception desk can also input relevant case studies and data based on the user's current location. The reception desk can also suggest optimal presentation content, taking into account the user's geographical location. This allows for the preparation of more effective presentations by prioritizing the input of highly relevant content, taking into account the user's geographical location. 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 information into a generating AI and have the generating AI select highly relevant content.

[0080] The reception desk can analyze the user's social media activity and input relevant content when inputting presentation content. For example, the reception desk can analyze the user's social media posts and suggest relevant presentation content. The reception desk can also extract topics of high interest from the user's social media activity and reflect them in the input content. The reception desk can also analyze the reactions of the user's social media followers and suggest effective presentation content. This allows for the input of relevant content by analyzing the user's social media activity, enabling the preparation of more effective presentations. 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 data on the user's social media activity into a generating AI and have the generating AI select relevant content.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis method of the presentation content based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a concise and easy-to-understand analysis method. For example, if the user is nervous, the analysis unit can provide a concise and easy-to-understand analysis method. The analysis unit can also perform a detailed analysis if the user is relaxed. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, if the user is in a hurry, the analysis unit can perform a concise and easy-to-understand analysis. For example, if the user is in a hurry, the analysis unit can perform a concise and easy-to-understand analysis method. This allows for more effective presentation analysis by adjusting the analysis method of the presentation content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The analysis unit can adjust the level of detail of its analysis based on the importance of the presentation content. For example, the analysis unit can perform a detailed analysis on important slides. The analysis unit can also perform a concise analysis on slides of lower importance. The analysis unit can also adjust the level of detail of its analysis based on the overall importance of the presentation. By adjusting the level of detail of the analysis based on the importance of the presentation content, a more effective presentation analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the importance of the presentation content into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the presentation content during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical presentation content. The analysis unit can also apply a business-oriented analysis algorithm to business presentation content. The analysis unit can also apply an educational analysis algorithm to educational presentation content. By applying different analysis algorithms depending on the category of the presentation content, more effective presentation analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the category of the presentation content into a generating AI and have the generating AI select an analysis algorithm.

[0084] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is nervous, the evaluation unit may relax strict evaluation criteria. For example, if the user is relaxed, the evaluation unit may apply detailed evaluation criteria. For example, if the user is relaxed, the evaluation unit may apply detailed evaluation criteria. For example, if the user is in a hurry, the evaluation unit may apply concise evaluation criteria. For example, if the user is in a hurry, the evaluation unit may apply concise evaluation criteria. This allows for more effective evaluation of presentations by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of the presentation content during the evaluation process. For example, the evaluation unit can evaluate the logical connections between slides. The evaluation unit can also evaluate whether the flow of the presentation is smooth. The evaluation unit can also evaluate the overall consistency of the presentation. By improving the accuracy of the evaluation by considering the interrelationships of the presentation content, it becomes possible to evaluate presentations more effectively. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on the interrelationships of the presentation content into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluation.

[0086] The evaluation unit can consider the attribute information of the presenter of the presentation content when making its evaluation. For example, the evaluation unit can consider the presenter's expertise when making its evaluation. For example, the evaluation unit can consider the presenter's years of experience when making its evaluation. For example, the evaluation unit can consider the presenter's years of experience when making its evaluation. For example, the evaluation unit can consider the presenter's past presentation record when making its evaluation. For example, the evaluation unit can consider the presenter's past presentation record when making its evaluation. By considering the attribute information of the presenter of the presentation content when making its evaluation, it becomes possible to evaluate presentations more effectively. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input data on the presenter's attribute information into a generating AI and have the generating AI perform the evaluation.

[0087] The service provider can estimate the user's emotions and adjust the way advice is expressed based on those emotions. For example, if the user is nervous, the service provider can offer advice in gentle language. The service provider can also offer detailed advice if the user is relaxed. Furthermore, if the user is in a hurry, the service provider can offer concise advice. By adjusting the way advice is expressed according to the user's emotions, more effective advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The service provider can adjust the level of detail in the advice based on the importance of the presentation content. For example, it can provide detailed advice for important slides. It can also provide concise advice for less important slides. Furthermore, the service provider can adjust the level of detail in the advice based on the overall importance of the presentation. By adjusting the level of detail in the advice based on the importance of the presentation content, more effective advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the importance of the presentation content into a generating AI and have the generating AI adjust the level of detail in the advice.

[0089] The service provider can apply different advice algorithms depending on the category of the presentation content when providing advice. For example, the service provider can apply a specialized advice algorithm to technical presentations. For example, the service provider can apply a business-oriented advice algorithm to business presentations. For example, the service provider can apply an educational advice algorithm to educational presentations. By applying different advice algorithms depending on the category of the presentation content, more effective advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the category of the presentation content into a generating AI and have the generating AI select an advice algorithm.

[0090] 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 nervous, the service provider can provide short, to-the-point advice. For example, if the user is nervous, the service provider can provide short, to-the-point advice. The service provider can also provide detailed advice if the user is relaxed. For example, if the user is relaxed, the service provider can provide detailed advice. The service provider can also provide concise advice if the user is in a hurry. For example, if the user is in a hurry, the service provider can provide concise advice. By adjusting the length of the advice according to the user's emotions, more effective advice can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The service provider can prioritize advice based on the submission deadline of the presentation content. For example, the service provider will prioritize advice for presentations with approaching deadlines. The service provider can also postpone advice for presentations with distant deadlines. The service provider can also dynamically adjust the priority of advice according to the submission deadline. This allows for more effective advice to be provided by prioritizing advice based on the submission deadline of the presentation content. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input data on the submission deadline of the presentation content into a generating AI and have the generating AI determine the priority of advice.

[0092] The advice provider can adjust the order of advice based on the relevance of the presentation content when providing advice. For example, the provider will prioritize providing advice to slides that are highly relevant. The provider can also postpone providing advice to slides that are less relevant. The provider can also dynamically adjust the order of advice based on the relevance of the entire presentation. By adjusting the order of advice based on the relevance of the presentation content, more effective advice can be provided. Some or all of the above processing in the advice provider may be performed using AI, for example, or not using AI. For example, the provider can input data on the relevance of the presentation content into a generating AI and have the generating AI adjust the order of advice.

[0093] The simulation unit can estimate the user's emotions and adjust the simulation method based on the estimated emotions. For example, if the user is nervous, the simulation unit can provide a simulation method that helps them relax. The simulation unit can also perform a detailed simulation if the user is relaxed. The simulation unit can also perform a concise simulation if the user is in a hurry. By adjusting the simulation method according to the user's emotions, more effective presentation practice becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The simulation unit can select the optimal simulation method by referring to the user's past presentation history during the simulation. For example, the simulation unit can propose the optimal simulation method based on the user's past presentation history. The simulation unit can also select an effective simulation method from the user's past presentation history. For example, the simulation unit can select an effective simulation method from the user's past presentation history. The simulation unit can also analyze the user's past presentation history and propose a simulation method that reflects areas for improvement. For example, the simulation unit can analyze the user's past presentation history and propose a simulation method that reflects areas for improvement. This allows for the selection of the optimal simulation method by referring to the user's past presentation history, enabling more effective presentation practice. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input data on the user's past presentation history into a generating AI and have the generating AI select the optimal simulation method.

[0095] The simulation unit can customize the simulation methods based on the user's current living situation during the simulation. For example, the simulation unit can suggest the optimal simulation method according to the user's current living situation. The simulation unit can also adjust the timing of the simulation to match the user's daily rhythm. The simulation unit can also customize the content of the simulation based on the user's living environment. For example, the simulation unit customizes the content of the simulation based on the user's living environment. This allows for more effective presentation practice by customizing the simulation methods based on the user's current living situation. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input data about the user's current living situation into a generating AI and have the generating AI perform the customization of the simulation methods.

[0096] The simulation unit can estimate the user's emotions and determine the priority of the simulation based on the estimated emotions. For example, if the user is nervous, the simulation unit will prioritize simulating the important parts. For example, if the user is relaxed, the simulation unit will simulate the entire presentation evenly. For example, if the user is relaxed, the simulation unit will simulate the entire presentation evenly. For example, if the user is in a hurry, the simulation unit will prioritize simulating the key points. For example, if the user is in a hurry, the simulation unit will prioritize simulating the key points. This allows for more effective presentation practice by determining the priority of the simulation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The simulation unit can select the optimal simulation method during simulation, taking into account the user's geographical location information. For example, the simulation unit can propose the optimal simulation method based on the user's current location. The simulation unit can also provide relevant simulation content, taking into account the user's geographical location information. The simulation unit can also adjust the timing of the simulation based on the user's geographical location information. By selecting the optimal simulation method while considering the user's geographical location information, more effective presentation practice becomes possible. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input data on the user's geographical location information into a generating AI and have the generating AI select the optimal simulation method.

[0098] The simulation unit can analyze the user's social media activity during a simulation and propose simulation methods. For example, the simulation unit can analyze the user's social media posts and propose relevant simulation methods. The simulation unit can also extract topics of high interest from the user's social media activity and reflect them in the simulation content. The simulation unit can also analyze the reactions of the user's social media followers and propose effective simulation methods. By analyzing the user's social media activity, relevant simulation methods can be proposed, enabling more effective presentation practice. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input data on the user's social media activity into a generating AI and have the generating AI execute the proposal of simulation methods.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The presentation practice support system can also include a feedback function. This feedback function can record the user's presentation practice progress and evaluate it by comparing it to past practice data. For example, it can save data from past presentations and compare it to current practice data to identify areas for improvement. It can also record the user's practice frequency and duration and suggest an effective practice schedule. Furthermore, the feedback function can visualize the user's practice results in graphs and charts, allowing for a quick overview of progress. This enables users to objectively assess their presentation skill improvement and practice more effectively.

[0101] The presentation practice support system can also include a motivation component. This component can estimate the user's emotions and provide messages and rewards to boost motivation based on those estimates. For example, if the user is feeling tired during practice, the motivation component can display encouraging messages. It can also award badges or points when the user achieves their goals. Furthermore, the motivation component can evaluate the user's achievements and progress based on their practice data and set new goals. This allows users to maintain motivation for practice and continuously improve their presentation skills.

[0102] The presentation practice support system can also include a customization feature. This customization feature allows users to tailor practice content and advice to their presentation style and preferences. For example, if a user prefers visually-driven presentations, the customization feature can enhance slide design advice. If a user prioritizes storytelling, it can provide advice on speech structure and flow. Furthermore, the customization feature can continuously improve practice content and advice based on user feedback. This allows users to improve their presentation skills using the practice method best suited to them.

[0103] The presentation practice support system can also include a collaboration section. This collaboration section helps multiple users practice presentations together. For example, it allows users to share real-time feedback when practicing presentations with team members. It can also allow users to refer to other members' practice data and advice, enabling mutual learning. Furthermore, the collaboration section can manage the overall presentation progress of the team and suggest an effective practice schedule. This allows users to improve their team's presentation skills.

[0104] The presentation practice support system can also include a reminder function. This reminder function manages the user's practice schedule and notifies them of practice times. For example, it can notify the user of the start time of practice based on the practice schedule they have set. It can also send periodic reminders to help the user remember to practice. Furthermore, it can suggest effective practice times based on the user's practice data. This allows users to practice their presentations systematically and improve their skills.

[0105] The presentation practice support system can also be equipped with an emotion analysis unit. This unit can analyze the user's facial expressions and tone of voice to estimate their emotions during practice. For example, if the user is nervous, the emotion analysis unit can provide advice to alleviate that nervousness. It can also provide feedback to further enhance the user's confidence if they are feeling confident. Furthermore, the emotion analysis unit can record changes in the user's emotions and evaluate them in relation to the progress of the practice. This allows the user to understand their own emotional state and practice their presentations effectively.

[0106] The presentation practice support system can also include a scenario generation unit. This unit can automatically generate practice scenarios based on the user's presentation content. For example, it can create a practice scenario based on the presentation theme and key points entered by the user. Furthermore, it can suggest different scenarios depending on the user's presentation style and objectives. In addition, the scenario generation unit can continuously improve scenarios based on user feedback. This allows users to practice their presentations using effective scenarios and improve their skills.

[0107] The presentation practice support system can also be equipped with a stress management unit. This unit can estimate the user's emotions and provide advice to reduce stress based on those estimates. For example, if the user is feeling tense, the stress management unit can suggest breathing exercises or stretching techniques to help them relax. It can also encourage the user to take a break if they are feeling tired. Furthermore, the stress management unit can record the user's stress level and evaluate it in relation to their practice progress. This allows the user to practice their presentation effectively while managing their stress levels.

[0108] The presentation practice support system can also be equipped with a virtual reality (VR) component. The VR component helps users practice presentations in a virtual environment. For example, the VR component provides a simulation of the user giving a presentation in a virtual conference room or auditorium. The VR component can also place a virtual audience and recreate a realistic presentation environment. Furthermore, the VR component can analyze the user's movements and voice tone and provide real-time feedback. This allows users to practice in an environment close to an actual presentation and improve their skills.

[0109] The presentation practice support system can also be equipped with an emotional feedback unit. This unit can estimate the user's emotions and provide feedback based on those estimates. For example, if the user is nervous, it can offer advice to alleviate that nervousness. It can also provide feedback to further enhance the user's confidence if they are feeling confident. Furthermore, the emotional feedback unit can record changes in the user's emotions and evaluate them in relation to the progress of the practice. This allows the user to understand their own emotional state and practice their presentation effectively.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk inputs the user's presentation content. This content can include business presentations, academic presentations, technical presentations, etc. The reception desk allows the user to input the presentation theme and key points for each slide. Step 2: The analysis unit uses a generation AI to analyze the presentation content entered by the reception unit. The analysis is performed using natural language processing and data mining techniques. For example, the analysis unit uses natural language processing to analyze the presentation content and evaluate the consistency of the slide content and the flow of the speech. Step 3: The evaluation unit uses a generation AI to evaluate the slide content and flow of the presentation based on the analysis performed by the analysis unit. The evaluation is based on criteria such as slide content, flow of the presentation, expressiveness, and impact on the audience. For example, the evaluation unit assesses whether the order and content of the slides are logical and whether the flow of the presentation is smooth. Step 4: The provisioning department uses the generation AI to provide specific advice based on the evaluation results obtained by the evaluation department. This specific advice includes suggestions for improving slide design and streamlining the presentation flow. For example, the provisioning department provides advice on how to improve slide design and streamline the presentation flow.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, provision unit, and simulation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing the user to input the presentation theme and key points of each slide. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the presentation content using a generation AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, which evaluates the slide content and the flow of the talk based on the analyzed presentation content. The provision unit is implemented by the control unit 46A of the smart device 14, which provides specific advice based on the evaluation results. The simulation unit is implemented by the control unit 46A of the smart device 14, which simulates the actual presentation situation using an interactive AI and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, provision unit, and simulation unit, is implemented in 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, allowing the user to input the presentation theme and key points of each slide. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the presentation content using a generating AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, which evaluates the slide content and the flow of the talk based on the analyzed presentation content. The provision unit is implemented by the control unit 46A of the smart glasses 214, which provides specific advice based on the evaluation results. The simulation unit is implemented by the control unit 46A of the smart glasses 214, which simulates the actual presentation situation using an interactive AI and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, provision 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, allowing the user to input the presentation theme and key points of each slide. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the presentation content using a generation AI. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates the slide content and the flow of the talk based on the analyzed presentation content. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides specific advice based on the evaluation results. The simulation unit is implemented by, for example, the control unit 46A of the headset terminal 314, which simulates the actual presentation situation using an interactive AI and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, provision unit, and simulation unit, is implemented in at least one of the following: 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, allowing the user to input the presentation theme and key points of each slide. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the presentation content using a generating AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, which evaluates the slide content and the flow of the talk based on the analyzed presentation content. The provision unit is implemented by the control unit 46A of the robot 414, which provides specific advice based on the evaluation results. The simulation unit is implemented by the control unit 46A of the robot 414, which simulates the actual presentation situation using an interactive AI and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A reception area where users input their presentation content, An analysis unit analyzes the presentation content entered by the reception unit, Based on the presentation content analyzed by the aforementioned analysis unit, an evaluation unit evaluates the content of the slides and the flow of the talk. The system includes a provisioning unit that provides specific advice based on the evaluation results obtained by the evaluation unit. A system characterized by the following features. (Note 2) It includes a simulation unit that uses conversational AI to simulate actual presentation situations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned simulation unit, Provides feedback to users as they practice their presentations. The system described in Appendix 2, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyze the presentation content using natural language processing technology. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, The evaluation assesses the consistency of the slide content and flow of the presentation, the expressiveness, and the impact on the audience. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Providing users with specific advice and suggestions for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of input for presentation content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past presentations and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering presentation content, 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 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the presentation content to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering presentation content, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering presentation content, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the presentation content analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the presentation content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the presentation content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, When evaluating presentations, consider the interrelationships between the presentation content to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, During the evaluation process, the attribute information of the presenter of the presentation will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the presentation content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the category of the presentation content. The system described in Appendix 1, characterized by the features described herein. (Note 22) 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 23) The aforementioned supply unit is, When providing advice, we prioritize the advice based on the submission deadline for the presentation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing advice, adjust the order of advice based on the relevance of the presentation content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned simulation unit, During the simulation, the system selects the optimal simulation method by referring to the user's past presentation history. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned simulation unit, During the simulation, the simulation method is customized based on the user's current living situation. The system described in Appendix 2, characterized by the features described herein. (Note 28) 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 2, characterized by the features described herein. (Note 29) The aforementioned simulation unit, During the simulation, the optimal simulation method is selected by considering the user's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned simulation unit, During the simulation, we analyze the user's social media activity and propose simulation methods. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input their presentation content, An analysis unit analyzes the presentation content entered by the reception unit, Based on the presentation content analyzed by the aforementioned analysis unit, an evaluation unit evaluates the content of the slides and the flow of the talk. The system includes a provisioning unit that provides specific advice based on the evaluation results obtained by the evaluation unit. A system characterized by the following features.

2. It includes a simulation unit that uses conversational AI to simulate actual presentation situations. The system according to feature 1.

3. The aforementioned simulation unit, Provides feedback to users as they practice their presentations. The system according to feature 2.

4. The aforementioned analysis unit, Analyze the presentation content using natural language processing technology. The system according to feature 1.

5. The evaluation unit, The evaluation assesses the consistency of the slide content and flow of the presentation, the expressiveness, and the impact on the audience. The system according to feature 1.

6. The aforementioned supply unit is, Providing users with specific advice and suggestions for improvement. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of input for presentation content based on the estimated user emotions. The system according to feature 1.

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

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