system

Abstract

The system according to the embodiment aims to provide first-time feedback as many times as necessary when practicing a presentation. [Solution] A system according to an embodiment includes a receiving unit, an analyzing unit, a providing unit, and a resetting unit. The receiving unit inputs the content of the presentation. The analyzing unit analyzes the content input by the receiving unit and generates feedback for the new user. The providing unit provides specific advice for improving the presentation based on the feedback generated by the analyzing unit. The resetting unit resets the history and generates feedback for the new user again.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to get feedback on a first-time presentation when practicing.

[0005] The system according to the embodiment aims to provide first-time feedback as many times as necessary when practicing a presentation. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a providing unit, and a resetting unit. The receiving unit inputs the content of the presentation. The analyzing unit analyzes the content input by the receiving unit and generates feedback for the new user. The providing unit provides specific advice for improving the presentation based on the feedback generated by the analyzing unit. The resetting unit resets the history and generates feedback for the new user again. [Effects of the Invention]

[0007] The system according to the embodiment allows users to get feedback on their first presentation as many times as they like when practicing their presentation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A presentation feedback system according to an embodiment of the present invention allows a user to receive first-time feedback as many times as they like during presentation practice or rehearsal. The system inputs the content of a presentation, analyzes it, and generates first-time feedback. The generated feedback includes specific advice for the user to improve their presentation. Furthermore, the system can reset the history and generate first-time feedback again. This allows a user to receive first-time feedback as many times as they like, thereby improving the quality of their presentation. For example, when a user inputs presentation slides into the system, the system analyzes the slides and generates first-time feedback. This feedback includes specific advice, such as whether the slide content is easy to understand, whether the information is sufficient, and whether the visual design is appropriate. The user can then revise the slides based on this feedback and re-enter them into the system to receive further improved feedback. This system allows a user to receive first-time feedback as many times as they like, significantly improving the quality of their presentation. Furthermore, the system can reset the history to constantly provide feedback from a new perspective. This allows a user to improve their presentation from multiple perspectives and achieve more effective presentations. This allows the presentation feedback system to provide specific advice to help users improve the quality of their presentations and allows users to receive first-time feedback any number of times.

[0029] A presentation feedback system according to an embodiment includes a receiving unit, an analyzing unit, a providing unit, and a resetting unit. The receiving unit is a unit through which a user inputs the content of a presentation. The presentation content input by the user includes, but is not limited to, slides, speeches, visual aids, and the like. The receiving unit can receive the content of the presentation via, for example, text input, voice input, or image input. The analyzing unit analyzes the content of the presentation input by the receiving unit and generates feedback for a new user. The analyzing unit can analyze the content of the presentation using, for example, natural language processing technology, and generate feedback for a first-time viewer. The analyzing unit can also analyze the content of the presentation and generate feedback using a generation AI (e.g., a text generation AI or a multimodal generation AI). The providing unit provides specific advice to help a user improve their presentation based on the feedback generated by the analyzing unit. The providing unit provides specific advice, such as suggestions for improving slides, speaking tips, and visual design suggestions. The resetting unit resets the system history and generates feedback for a new user again. The reset unit can, for example, reset the user's operation history and feedback history and return the system to its initial state, thereby generating feedback as if the user were a first-time viewer again. This allows the presentation feedback system according to the embodiment to provide specific advice to help the user improve the quality of their presentation and to receive first-time feedback as many times as the user desires. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model that receives feedback generated by the analyzing unit as input and outputs specific advice. This allows the presentation feedback system to provide specific advice to help the user improve the quality of their presentation and to receive first-time feedback as many times as the user desires.

[0030] The analysis unit can generate specific advice on whether the content of a slide is easy to understand, whether the information is included in the right amount, and whether the visual design is appropriate. For example, the analysis unit generates advice based on the degree of information organization and visual clarity to evaluate whether the content of a slide is easy to understand. For example, the analysis unit evaluates whether the information on a slide is appropriately organized and visually clear, and provides advice to improve understandability. Furthermore, the analysis unit generates advice based on the comprehensiveness of necessary information and the elimination of redundant information to evaluate whether the information included in a slide is appropriate. For example, the analysis unit evaluates whether all necessary information is included in a slide and whether unnecessary information is included, and provides advice to adjust whether the information is excessive or insufficient. Furthermore, the analysis unit generates advice based on the use of color, layout, font selection, etc. to evaluate whether the visual design of a slide is appropriate. For example, the analysis unit evaluates whether the use of color on a slide is visually appealing, whether the layout is well-organized, and whether the font selection is appropriate, and provides advice to improve the visual design. This allows the analysis unit to generate specific advice on whether the content of a slide is easy to understand, whether the information is included in the right amount, and whether the visual design is appropriate. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may generate advice using an AI model that inputs the content of a slide and outputs specific advice. This allows the analysis unit to generate specific advice such as whether the content of the slide is easy to understand, whether it contains just the right amount of information, and whether the visual design is appropriate.

[0031] The reset unit can reset the system history and generate feedback for a new user again. For example, the reset unit can delete the user's operation history and feedback history to reset the system history. For example, the reset unit can delete the history of operations previously performed by the user and the feedback received by the user, and return the system to its initial state. The reset unit can also reset specific data to reset the system history. For example, the reset unit can reset only the history related to a specific presentation and retain the history of other presentations. Furthermore, the reset unit can adjust the timing of the reset to reset the system history. For example, the reset unit can reset the history when a certain amount of time has elapsed after the user received feedback. This allows the reset unit to reset the system history and generate feedback for a new user again. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without AI. For example, the reset unit can adjust the timing of the reset using an AI model that receives the user's operation history and feedback history as input and outputs the timing of the reset. This allows the reset unit to reset the system history and generate feedback for a new user again.

[0032] The providing unit can provide specific advice to help the user improve their presentation. For example, the providing unit points out areas for improvement in slides in order to provide the user with specific advice to improve their presentation. For example, the providing unit suggests ways to organize information so that the content of slides is easier to understand. The providing unit can also teach speaking tips in order to provide the user with specific advice to improve their presentation. For example, the providing unit advises how to speak during a presentation so that the audience can easily understand what is being said. Furthermore, the providing unit can also make visual design suggestions in order to provide the user with specific advice to improve their presentation. For example, the providing unit provides advice to improve the visual design, such as the use of colors, layout, and font selection of slides. This allows the providing unit to provide the user with specific advice to improve their presentation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model that receives feedback generated by the analysis unit as input and outputs specific advice. This allows the providing unit to provide the user with specific advice to improve their presentation.

[0033] The reception unit can analyze the user's past presentation history and select an appropriate input method. For example, the reception unit refers to past slides and feedback history to analyze the user's past presentation history. For example, the reception unit selects the optimal input method based on slides the user has used in the past and feedback received. The reception unit can also refer to examples of successful past presentations to analyze the user's past presentation history. For example, the reception unit selects the optimal input method based on input methods used in presentations that were successful in the past. The reception unit can also refer to examples of unsuccessful past presentations to analyze the user's past presentation history. For example, the reception unit finds areas for improvement based on input methods used in presentations that were unsuccessful in the past and selects the optimal input method. In this way, the reception unit can analyze the user's past presentation history and select an appropriate input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can select an input method using an AI model that inputs the user's past presentation history and outputs the optimal input method. In this way, the reception unit can analyze the user's past presentation history and select an appropriate input method.

[0034] The reception unit may filter the presentation content based on the user's current project or area of ​​interest when inputting the presentation content. For example, the reception unit may filter based on the project's theme or progress status to prioritize input of content related to the user's current project. For example, the reception unit may extract keywords related to the user's current project and prioritize input of related information based on the keywords. The reception unit may also filter based on the user's past activity history or interests to filter based on the user's area of ​​interest. For example, the reception unit may prioritize input of related information based on topics or themes in which the user has previously shown interest. Furthermore, the reception unit may monitor the progress of the project to input appropriate content according to the progress of the user's project. For example, the reception unit may provide necessary information in a timely manner according to the project's progress. This allows the reception unit to filter based on the user's current project or area of ​​interest when inputting the presentation content. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may perform filtering using an AI model that inputs the user's project information or area of ​​interest and outputs filtered information. This allows the reception unit to filter presentation content based on the user's current project or area of ​​interest when the presentation content is input.

[0035] When inputting presentation content, the reception unit can prioritize inputting highly relevant content in consideration of the user's geographical location information. The reception unit, for example, uses GPS data to acquire the user's geographical location information. For example, the reception unit acquires the user's current location from the GPS data and prioritizes inputting information related to the location. The reception unit can also use an IP address to acquire the user's geographical location information. For example, the reception unit identifies a geographical location from the user's IP address and prioritizes inputting information related to the location. The reception unit can also use information input by the user to acquire the user's geographical location information. For example, the reception unit prioritizes inputting related information based on the address or area information input by the user. This allows the reception unit to prioritize inputting highly relevant content in consideration of the user's geographical location information when inputting presentation content. For example, the reception unit prioritizes inputting information related to the user's current location. The reception unit can also input content related to the area based on the user's geographical location information. The reception unit can also prioritize inputting information related to places the user plans to visit. As a result, the reception unit can input highly relevant content based on the geographical location information, thereby achieving a more effective presentation. Some or all of the above-described processing by the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input content using an AI model that inputs the user's geographical location information and outputs highly relevant content. As a result, when inputting presentation content, the reception unit can prioritize inputting highly relevant content in consideration of the user's geographical location information.

[0036] The reception unit can analyze the user's social media activity and input related content when inputting presentation content. The reception unit, for example, analyzes social media postings to analyze the user's social media activity. For example, the reception unit analyzes content posted by the user on social media and inputs information related to the content. The reception unit can also analyze the number of likes and the number of followers to analyze the user's social media activity. For example, the reception unit inputs related information based on posts that the user has received many likes or posts with a large number of followers. The reception unit can also analyze social media trends to analyze the user's social media activity. For example, the reception unit inputs related information based on topics and hashtags that are trending on social media. In this way, the reception unit can analyze the user's social media activity and input related content when inputting presentation content. For example, the reception unit inputs related information based on the user's social media activity. The reception unit can also preferentially input content related to topics in which the user has shown interest on social media. The reception unit can also input related information based on the interests of the user's social media followers. As a result, the reception unit can input relevant content based on the user's social media activity, thereby realizing a presentation that meets the user's interests. Some or all of the above-described processing by the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input content using an AI model that inputs the user's social media data and outputs relevant content. As a result, the reception unit can analyze the user's social media activity and input relevant content when inputting presentation content.

[0037] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the presentation. For example, to evaluate the importance of a presentation, the analysis unit performs an evaluation based on the size of the audience and the purpose of the presentation. For example, the analysis unit performs a detailed analysis when the audience size is large or the purpose of the presentation is important. The analysis unit can also perform an evaluation based on the content or theme of the presentation to evaluate the importance of the presentation. For example, the analysis unit performs a detailed analysis when the content of the presentation is specialized or the theme is important. The analysis unit can also perform an evaluation based on the purpose and goal of the presentation to evaluate the importance of the presentation. For example, the analysis unit performs a detailed analysis when the purpose of the presentation involves an important decision or has a high goal. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the presentation. For example, the analysis unit performs a detailed analysis for an important presentation. On the other hand, the analysis unit can perform a brief analysis for a less important presentation. Furthermore, the analysis unit can adjust the level of detail of the analysis depending on the purpose of the presentation. This allows the analysis unit to provide more effective feedback by adjusting the level of detail of the analysis depending on the importance of the presentation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may adjust the level of detail using an AI model that inputs the importance of the presentation and outputs the level of detail of the analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the presentation during analysis.

[0038] The analysis unit can apply different analysis algorithms depending on the category of the presentation during analysis. The analysis unit, for example, analyzes the content or theme of the presentation to identify the category of the presentation. For example, if the content of the presentation is technical, the analysis unit can classify it into a technical category. Furthermore, if the content of the presentation is business-related, the analysis unit can classify it into a business category. Furthermore, if the content of the presentation is educational, the analysis unit can classify it into an educational category. This allows the analysis unit to apply different analysis algorithms depending on the category of the presentation. For example, the analysis unit can apply an analysis algorithm specialized for technical content to a technical presentation. Furthermore, the analysis unit can apply an algorithm that performs analysis from a business perspective to a business presentation. Furthermore, the analysis unit can apply an algorithm that performs analysis from an educational perspective to an educational presentation. This allows the analysis unit to provide more appropriate feedback by applying an analysis algorithm depending on the category of the presentation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can apply an analysis algorithm using an AI model that inputs the category of the presentation and outputs an appropriate analysis algorithm. This allows the analysis unit to apply different analysis algorithms depending on the category of the presentation during analysis.

[0039] The analysis unit can determine analysis priorities based on presentation submission dates during analysis. For example, the analysis unit evaluates presentation submission dates based on submission deadlines and submission timing. For example, the analysis unit prioritizes analysis of presentations with upcoming submission deadlines. The analysis unit can also postpone presentations with more distant submission dates. Furthermore, the analysis unit can dynamically adjust analysis priorities based on submission dates. For example, the analysis unit prioritizes analysis of presentations with upcoming submission deadlines and postpones presentations with more distant submission deadlines. This allows the analysis unit to provide more effective feedback by determining analysis priorities based on submission dates. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can determine priorities using an AI model that inputs presentation submission dates and outputs analysis priorities. This allows the analysis unit to determine analysis priorities based on presentation submission dates during analysis.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the presentations. For example, the analysis unit evaluates the relevance of the presentations based on the relevance of the topic or the audience's interest. For example, the analysis unit can prioritize analyzing presentations related to the user's current project. Furthermore, the analysis unit can prioritize analyzing presentations related to the user's field of interest. Furthermore, the analysis unit can prioritize analyzing presentations related to the user's work. This allows the analysis unit to adjust the order of analysis based on the relevance of the presentations. For example, the analysis unit can prioritize analyzing presentations related to the user's current project. Furthermore, the analysis unit can prioritize analyzing presentations related to the user's field of interest. Furthermore, the analysis unit can prioritize analyzing presentations related to the user's work. This allows the analysis unit to provide more effective feedback by adjusting the order of analysis based on relevance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can adjust the order using an AI model that inputs the relevance of the presentations and outputs the order of analysis. This allows the analyzer to adjust the order of analysis based on the relevance of the presentation during analysis.

[0041] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the presentation. For example, to evaluate the importance of the presentation, the providing unit performs an evaluation based on the size of the audience and the purpose of the presentation. For example, the providing unit provides detailed advice when the audience size is large or the purpose of the presentation is important. The providing unit can also perform an evaluation based on the content or theme of the presentation to evaluate the importance of the presentation. For example, the providing unit provides detailed advice when the content of the presentation is specialized or the theme is important. The providing unit can also perform an evaluation based on the purpose or goal of the presentation to evaluate the importance of the presentation. For example, the providing unit provides detailed advice when the purpose of the presentation involves an important decision or has a high goal. This allows the providing unit to adjust the level of detail of the advice based on the importance of the presentation. For example, the providing unit provides detailed advice for an important presentation. The providing unit can also provide concise advice for a less important presentation. The providing unit can also adjust the level of detail of the advice depending on the purpose of the presentation. As a result, the providing unit can provide more effective advice by adjusting the level of detail of the advice according to the importance of the presentation. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can adjust the level of detail using an AI model that inputs the importance of the presentation and outputs the level of detail of the advice. As a result, the providing unit can adjust the level of detail of the advice based on the importance of the presentation when providing the advice.

[0042] When providing advice, the providing unit can apply different advice algorithms depending on the category of the presentation. The providing unit, for example, analyzes the content or theme of the presentation to identify the category of the presentation. For example, if the content of the presentation is technical, the providing unit can classify it into a technical category. Furthermore, if the content of the presentation is business-related, the providing unit can classify it into a business category. Furthermore, if the content of the presentation is educational, the providing unit can classify it into an educational category. This allows the providing unit to apply different advice algorithms depending on the category of the presentation. For example, the providing unit can apply an advice algorithm specialized for technical content to a technical presentation. Furthermore, the providing unit can apply an algorithm that provides advice from a business perspective to a business presentation. Furthermore, the providing unit can apply an algorithm that provides advice from an educational perspective to an educational presentation. This allows the providing unit to provide more appropriate advice by applying an advice algorithm depending on the category of the presentation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can apply an advice algorithm using an AI model that inputs the category of the presentation and outputs an appropriate advice algorithm. This allows the providing unit to apply different advice algorithms depending on the category of the presentation when providing advice.

[0043] When providing advice, the providing unit can determine the priority of advice based on the submission date of the presentation. For example, to evaluate the submission date of the presentation, the providing unit performs an evaluation based on the submission deadline or the timing of submission. For example, the providing unit prioritizes advice for presentations with an upcoming submission deadline. The providing unit can also postpone presentations with a more distant submission date. Furthermore, the providing unit can dynamically adjust the priority of advice based on the submission date. For example, the providing unit prioritizes advice for presentations with an approaching submission deadline and postpones presentations with a more distant submission deadline. In this way, the providing unit can provide more effective advice by determining the priority of advice based on the submission date. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can determine the priority using an AI model that inputs the submission date of the presentation and outputs the priority of advice. In this way, the providing unit can determine the priority of advice based on the submission date of the presentation when providing advice.

[0044] When providing advice, the providing unit can adjust the order of advice based on the relevance of the presentation. For example, the providing unit evaluates the relevance of the presentation based on the relevance of the topic and the audience's interest. For example, the providing unit can prioritize advice on presentations related to the user's current project. Furthermore, the providing unit can prioritize advice on presentations related to the user's field of interest. Furthermore, the providing unit can prioritize advice on presentations related to the user's work. This allows the providing unit to adjust the order of advice based on the relevance of the presentation. For example, the providing unit can prioritize advice on presentations related to the user's current project. Furthermore, the providing unit can prioritize advice on presentations related to the user's field of interest. Furthermore, the providing unit can prioritize advice on presentations related to the user's work. This allows the providing unit to provide more effective advice by adjusting the order of advice based on relevance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the order using an AI model that inputs the relevance of the presentation and outputs the order of advice. This allows the providing unit to adjust the order of advice based on the relevance of the presentation when providing advice.

[0045] The reset unit can optimize the reset algorithm by referring to the past feedback history at the time of reset. For example, the reset unit performs analysis based on the content and frequency of feedback by referring to the past feedback history. For example, the reset unit analyzes the content of past feedback and determines the optimal reset timing. Furthermore, the reset unit can perform analysis based on the effectiveness of feedback by referring to the past feedback history. For example, the reset unit evaluates how effective past feedback was and optimizes the reset algorithm. Furthermore, the reset unit can perform analysis based on the frequency of feedback by referring to the past feedback history. For example, the reset unit analyzes the frequency of past feedback and optimizes an algorithm for maximizing the effectiveness of the reset. Thus, the reset unit can optimize the reset algorithm by referring to the past feedback history at the time of reset. For example, the reset unit determines the optimal reset timing based on the past feedback history. Furthermore, the reset unit can analyze the past feedback history and optimize the reset algorithm. Furthermore, the reset unit can maximize the effectiveness of the reset by referring to the past feedback history. Thus, the reset unit can optimize the reset algorithm by referring to the past feedback history. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without using AI. For example, the reset unit can optimize the algorithm using an AI model that takes past feedback history as input and outputs a reset algorithm, which allows the reset unit to optimize the reset algorithm by referring to the past feedback history at the time of reset.

[0046] The reset unit can apply different reset methods depending on the category of the presentation when resetting. The reset unit, for example, analyzes the content or theme of the presentation to identify the category of the presentation. For example, if the content of the presentation is technical, the reset unit classifies it into a technical category. Furthermore, if the content of the presentation is business-related, the reset unit can classify it into a business category. Furthermore, if the content of the presentation is educational, the reset unit can classify it into an educational category. This allows the reset unit to apply different reset methods depending on the category of the presentation. For example, the reset unit can apply a reset method specialized for technical content to a technical presentation. Furthermore, the reset unit can apply a reset method from a business perspective to a business presentation. Furthermore, the reset unit can apply a reset method from an educational perspective to an educational presentation. This allows the reset unit to provide more appropriate feedback by applying a reset method depending on the category of the presentation. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without AI. For example, the reset unit can apply a reset method using an AI model that inputs the category of the presentation and outputs an appropriate reset method. This allows the reset unit to apply different reset methods depending on the category of the presentation at the time of reset.

[0047] The reset unit can adjust the reset frequency based on the presentation submission date when resetting. For example, the reset unit evaluates the presentation submission date based on the submission deadline or the timing of submission. For example, the reset unit can increase the reset frequency for presentations whose submission deadline is approaching. The reset unit can also decrease the reset frequency for presentations whose submission date is further away. Furthermore, the reset unit can dynamically adjust the reset frequency based on the submission date. For example, the reset unit can increase the reset frequency for presentations whose submission deadline is approaching and decrease the reset frequency for presentations whose submission deadline is further away. This allows the reset unit to provide more effective feedback by adjusting the reset frequency based on the submission date. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without AI. For example, the reset unit can adjust the frequency using an AI model that inputs the presentation submission date and outputs the reset frequency. This allows the reset unit to adjust the reset frequency based on the presentation submission date when resetting.

[0048] The reset unit may adjust the reset order based on the relevance of the presentations when resetting. The reset unit may, for example, evaluate the relevance of the presentations based on the relevance of the topic or the audience's interest. For example, the reset unit may prioritize resetting presentations related to the user's current project. The reset unit may also prioritize resetting presentations related to the user's field of interest. The reset unit may also prioritize resetting presentations related to the user's work. This allows the reset unit to adjust the reset order based on the relevance of the presentations. For example, the reset unit may prioritize resetting presentations related to the user's current project. The reset unit may also prioritize resetting presentations related to the user's field of interest. The reset unit may also prioritize resetting presentations related to the user's work. This allows the reset unit to provide more effective feedback by adjusting the reset order based on the relevance. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without using AI. For example, the reset unit may adjust the order using an AI model that receives the relevance of the presentations as input and outputs the reset order. This allows the reset unit to adjust the reset order based on presentation relevance at the time of reset.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The reception unit can analyze the user's past presentation history and select an appropriate input method. For example, the reception unit refers to past slides and feedback history to analyze the user's past presentation history. For example, the reception unit selects the optimal input method based on slides the user has used in the past and feedback received. The reception unit can also refer to successful examples of past presentations to analyze the user's past presentation history. For example, the reception unit selects the optimal input method based on input methods used in presentations that were successful in the past. The reception unit can also refer to unsuccessful examples of past presentations to analyze the user's past presentation history. For example, the reception unit finds areas for improvement based on input methods used in presentations that were unsuccessful in the past and selects the optimal input method. In this way, the reception unit can analyze the user's past presentation history and select an appropriate input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select an input method using an AI model that inputs the user's past presentation history and outputs the optimal input method. In this way, the reception unit can analyze the user's past presentation history and select an appropriate input method.

[0051] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the presentation. For example, to evaluate the importance of a presentation, the analysis unit performs an evaluation based on the size of the audience and the purpose of the presentation. For example, if the audience size is large or the purpose of the presentation is important, the analysis unit performs a detailed analysis. The analysis unit can also evaluate the importance of a presentation based on the content or theme of the presentation. For example, if the content of the presentation is specialized or the theme is important, the analysis unit performs a detailed analysis. The analysis unit can also evaluate the importance of a presentation based on the purpose and goal of the presentation. For example, if the purpose of the presentation involves an important decision or the goal is high, the analysis unit performs a detailed analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the presentation. For example, the analysis unit performs a detailed analysis for an important presentation. On the other hand, the analysis unit can perform a brief analysis for a less important presentation. The analysis unit can also adjust the level of detail of the analysis depending on the purpose of the presentation. This allows the analysis unit to provide more effective feedback by adjusting the level of detail of the analysis depending on the importance of the presentation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may adjust the level of detail using an AI model that inputs the importance of the presentation and outputs the level of detail of the analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the presentation during analysis.

[0052] The reset unit can optimize the reset algorithm by referring to the past feedback history at the time of reset. For example, the reset unit performs analysis based on the content and frequency of feedback by referring to the past feedback history. For example, the reset unit analyzes the content of past feedback and determines the optimal reset timing. Furthermore, the reset unit can perform analysis based on the effectiveness of feedback by referring to the past feedback history. For example, the reset unit evaluates how effective past feedback was and optimizes the reset algorithm. Furthermore, the reset unit can perform analysis based on the frequency of feedback by referring to the past feedback history. For example, the reset unit analyzes the frequency of past feedback and optimizes an algorithm for maximizing the effect of the reset. Thus, the reset unit can optimize the reset algorithm by referring to the past feedback history at the time of reset. For example, the reset unit determines the optimal reset timing based on the past feedback history. Furthermore, the reset unit can analyze the past feedback history and optimize the reset algorithm. Furthermore, the reset unit can maximize the effect of the reset by referring to the past feedback history. Thus, the reset unit can optimize the reset algorithm by referring to the past feedback history. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without using AI. For example, the reset unit can optimize the algorithm using an AI model that takes past feedback history as input and outputs a reset algorithm, which allows the reset unit to optimize the reset algorithm by referring to the past feedback history at the time of reset.

[0053] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the presentation. For example, the providing unit evaluates the importance of the presentation based on the size of the audience and the purpose of the presentation. For example, the providing unit provides detailed advice when the audience size is large or the purpose of the presentation is important. The providing unit can also evaluate the importance of the presentation based on the content or theme of the presentation. For example, the providing unit provides detailed advice when the content of the presentation is specialized or the theme is important. The providing unit can also evaluate the importance of the presentation based on the purpose and goal of the presentation. For example, the providing unit provides detailed advice when the purpose of the presentation involves an important decision or has a high goal. In this way, the providing unit can adjust the level of detail of the advice based on the importance of the presentation. For example, the providing unit provides detailed advice for an important presentation. In addition, the providing unit can provide concise advice for a less important presentation. The providing unit can also adjust the level of detail of the advice depending on the purpose of the presentation. In this way, the providing unit can provide more effective advice by adjusting the level of detail of the advice depending on the importance of the presentation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may adjust the level of detail of advice using an AI model that inputs the importance of the presentation and outputs the level of detail of advice. This allows the providing unit to adjust the level of detail of advice based on the importance of the presentation when providing the advice.

[0054] When providing advice, the providing unit can apply different advice algorithms depending on the category of the presentation. For example, the providing unit analyzes the content or theme of the presentation to identify the category of the presentation. For example, if the content of the presentation is technical, the providing unit can classify it into a technical category. Furthermore, if the content of the presentation is business-related, the providing unit can classify it into a business category. Furthermore, if the content of the presentation is educational, the providing unit can classify it into an educational category. This allows the providing unit to apply different advice algorithms depending on the category of the presentation. For example, the providing unit can apply an advice algorithm specialized for technical content to a technical presentation. Furthermore, the providing unit can apply an algorithm that provides advice from a business perspective to a business presentation. Furthermore, the providing unit can apply an algorithm that provides advice from an educational perspective to an educational presentation. This allows the providing unit to provide more appropriate advice by applying an advice algorithm depending on the category of the presentation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can apply an advice algorithm using an AI model that inputs the category of the presentation and outputs an appropriate advice algorithm. This allows the providing unit to apply different advice algorithms depending on the category of the presentation when providing advice.

[0055] The reset unit can apply different reset methods depending on the category of the presentation when resetting. For example, the reset unit analyzes the content or theme of the presentation to identify the category of the presentation. For example, if the content of the presentation is technical, the reset unit classifies it into a technical category. Furthermore, if the content of the presentation is business-related, the reset unit can classify it into a business category. Furthermore, if the content of the presentation is educational, the reset unit can classify it into an educational category. This allows the reset unit to apply different reset methods depending on the category of the presentation. For example, the reset unit can apply a reset method specialized for technical content to a technical presentation. Furthermore, the reset unit can apply a reset method from a business perspective to a business presentation. Furthermore, the reset unit can apply a reset method from an educational perspective to an educational presentation. This allows the reset unit to provide more appropriate feedback by applying a reset method depending on the category of the presentation. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without using AI. For example, the reset unit can apply a reset method using an AI model that inputs the category of the presentation and outputs an appropriate reset method. This allows the reset unit to apply different reset methods depending on the category of the presentation at the time of reset.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit is a section where the user inputs the content of the presentation. The presentation content input by the user may include, but is not limited to, slides, speeches, visual aids, etc. The reception unit can receive the content of the presentation by, for example, text input, voice input, image input, etc. Step 2: The analysis unit analyzes the content of the presentation input by the reception unit and generates feedback for a new user. The analysis unit analyzes the content of the presentation using, for example, natural language processing technology and generates feedback for a first-time user. The analysis unit can also analyze the content of the presentation and generate feedback using a generation AI (for example, a text generation AI or a multimodal generation AI). Step 3: The providing unit provides specific advice to help the user improve their presentation based on the feedback generated by the analyzing unit. For example, the providing unit provides specific advice such as slide improvements, speaking tips, and visual design suggestions. Step 4: The reset unit resets the system history and generates feedback for a new user again. The reset unit resets the user's operation history and feedback history, for example, and returns the system to its initial state, thereby generating feedback for a first-time user again.

[0058] (Example 2) A presentation feedback system according to an embodiment of the present invention allows a user to receive first-time feedback as many times as they like when practicing or rehearsing a presentation. The system allows a user to input the content of their presentation, analyze the content, and generate first-time feedback. The generated feedback includes specific advice for the user to improve their presentation. Furthermore, the system can reset the history and generate first-time feedback again. This allows a user to receive first-time feedback as many times as they like, thereby improving the quality of their presentation. For example, when a user inputs presentation slides into the system, the system analyzes the slides and generates first-time feedback. This feedback includes specific advice, such as whether the slide content is easy to understand, whether the information is sufficient, and whether the visual design is appropriate. The user can then revise the slides based on this feedback and input them into the system again to receive further improved feedback. This system allows a user to receive first-time feedback as many times as they like, thereby significantly improving the quality of their presentation. Furthermore, the system can reset the history to constantly provide feedback from a new perspective. This allows a user to improve their presentation from multiple perspectives and achieve more effective presentations. This allows the presentation feedback system to provide specific advice to help users improve the quality of their presentations and allows users to receive first-time feedback any number of times.

[0059] A presentation feedback system according to an embodiment includes a receiving unit, an analyzing unit, a providing unit, and a resetting unit. The receiving unit is a unit through which a user inputs the content of a presentation. The presentation content input by the user includes, but is not limited to, slides, speeches, visual aids, and the like. The receiving unit can receive the content of the presentation via, for example, text input, voice input, or image input. The analyzing unit analyzes the content of the presentation input by the receiving unit and generates feedback for a new user. The analyzing unit can analyze the content of the presentation using, for example, natural language processing technology, and generate feedback for a first-time viewer. The analyzing unit can also analyze the content of the presentation and generate feedback using a generation AI (e.g., a text generation AI or a multimodal generation AI). The providing unit provides specific advice to help a user improve their presentation based on the feedback generated by the analyzing unit. The providing unit provides specific advice, such as suggestions for improving slides, speaking tips, and visual design suggestions. The resetting unit resets the system history and generates feedback for a new user again. The reset unit can, for example, reset the user's operation history and feedback history and return the system to its initial state, thereby generating feedback as if the user were a first-time viewer again. This allows the presentation feedback system according to the embodiment to provide specific advice to help the user improve the quality of their presentation and to receive first-time feedback as many times as the user desires. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model that receives feedback generated by the analyzing unit as input and outputs specific advice. This allows the presentation feedback system to provide specific advice to help the user improve the quality of their presentation and to receive first-time feedback as many times as the user desires.

[0060] The analysis unit can generate specific advice on whether the content of a slide is easy to understand, whether the information is included in the right amount, and whether the visual design is appropriate. For example, the analysis unit generates advice based on the degree of information organization and visual clarity to evaluate whether the content of a slide is easy to understand. For example, the analysis unit evaluates whether the information on a slide is appropriately organized and visually clear, and provides advice to improve understandability. Furthermore, the analysis unit generates advice based on the comprehensiveness of necessary information and the elimination of redundant information to evaluate whether the information included in a slide is appropriate. For example, the analysis unit evaluates whether all necessary information is included in a slide and whether unnecessary information is included, and provides advice to adjust whether the information is excessive or insufficient. Furthermore, the analysis unit generates advice based on the use of color, layout, font selection, etc. to evaluate whether the visual design of a slide is appropriate. For example, the analysis unit evaluates whether the use of color on a slide is visually appealing, whether the layout is well-organized, and whether the font selection is appropriate, and provides advice to improve the visual design. This allows the analysis unit to generate specific advice on whether the content of a slide is easy to understand, whether the information is included in the right amount, and whether the visual design is appropriate. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may generate advice using an AI model that inputs the content of a slide and outputs specific advice. This allows the analysis unit to generate specific advice such as whether the content of the slide is easy to understand, whether it contains just the right amount of information, and whether the visual design is appropriate.

[0061] The reset unit can reset the system history and generate feedback for a new user again. For example, the reset unit can delete the user's operation history and feedback history to reset the system history. For example, the reset unit can delete the history of operations previously performed by the user and the feedback received by the user, and return the system to its initial state. The reset unit can also reset specific data to reset the system history. For example, the reset unit can reset only the history related to a specific presentation and retain the history of other presentations. Furthermore, the reset unit can adjust the timing of the reset to reset the system history. For example, the reset unit can reset the history when a certain amount of time has elapsed after the user received feedback. This allows the reset unit to reset the system history and generate feedback for a new user again. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without AI. For example, the reset unit can adjust the timing of the reset using an AI model that receives the user's operation history and feedback history as input and outputs the timing of the reset. This allows the reset unit to reset the system history and generate feedback for a new user again.

[0062] The providing unit can provide specific advice to help the user improve their presentation. For example, the providing unit points out areas for improvement in slides in order to provide the user with specific advice to improve their presentation. For example, the providing unit suggests ways to organize information so that the content of slides is easier to understand. The providing unit can also teach speaking tips in order to provide the user with specific advice to improve their presentation. For example, the providing unit advises how to speak during a presentation so that the audience can easily understand what is being said. Furthermore, the providing unit can also make visual design suggestions in order to provide the user with specific advice to improve their presentation. For example, the providing unit provides advice to improve the visual design, such as the use of colors, layout, and font selection of slides. This allows the providing unit to provide the user with specific advice to improve their presentation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model that receives feedback generated by the analysis unit as input and outputs specific advice. This allows the providing unit to provide the user with specific advice to improve their presentation.

[0063] The reception unit can analyze the user's emotions and adjust the timing of inputting presentation content based on the analyzed user's emotions. The reception unit, for example, uses facial expression analysis technology to analyze the user's emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotion using a facial expression analysis algorithm. The reception unit can also use voice analysis technology to analyze the user's emotions. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the emotion. The reception unit can also use survey results to analyze the user's emotions. For example, the reception unit conducts a survey of the user and estimates the emotion based on the results. In this way, the reception unit can analyze the user's emotions and adjust the timing of inputting presentation content based on the analyzed user's emotions. For example, if the user is nervous, the reception unit can delay the input timing to allow the user to relax. If the user is relaxed, the reception unit can speed up the input timing to allow the user to input smoothly. Furthermore, if the user is impatient, the reception unit can adjust the input timing to reduce the impatience. This allows the reception unit to adjust the input timing according to the user's emotions, thereby allowing the presentation content to be input at a more appropriate timing. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can adjust the input timing using an AI model that receives the user's facial expression data or voice data and outputs the input timing. This allows the reception unit to analyze the user's emotions and adjust the input timing of the presentation content based on the analyzed user's emotions.

[0064] The reception unit can analyze the user's past presentation history and select an appropriate input method. For example, the reception unit refers to past slides and feedback history to analyze the user's past presentation history. For example, the reception unit selects the optimal input method based on slides the user has used in the past and feedback received. The reception unit can also refer to examples of successful past presentations to analyze the user's past presentation history. For example, the reception unit selects the optimal input method based on input methods used in presentations that were successful in the past. The reception unit can also refer to examples of unsuccessful past presentations to analyze the user's past presentation history. For example, the reception unit finds areas for improvement based on input methods used in presentations that were unsuccessful in the past and selects the optimal input method. In this way, the reception unit can analyze the user's past presentation history and select an appropriate input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can select an input method using an AI model that inputs the user's past presentation history and outputs the optimal input method. In this way, the reception unit can analyze the user's past presentation history and select an appropriate input method.

[0065] The reception unit may filter the presentation content based on the user's current project or area of ​​interest when inputting the presentation content. For example, the reception unit may filter based on the project's theme or progress status to prioritize input of content related to the user's current project. For example, the reception unit may extract keywords related to the user's current project and prioritize input of related information based on the keywords. The reception unit may also filter based on the user's past activity history or interests to filter based on the user's area of ​​interest. For example, the reception unit may prioritize input of related information based on topics or themes in which the user has previously shown interest. Furthermore, the reception unit may monitor the progress of the project to input appropriate content according to the progress of the user's project. For example, the reception unit may provide necessary information in a timely manner according to the project's progress. This allows the reception unit to filter based on the user's current project or area of ​​interest when inputting the presentation content. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may perform filtering using an AI model that inputs the user's project information or area of ​​interest and outputs filtered information. This allows the reception unit to filter presentation content based on the user's current project or area of ​​interest when the presentation content is input.

[0066] The reception unit can analyze the user's emotions and determine the priority of presentation content to be input based on the analyzed user's emotions. The reception unit, for example, uses facial expression analysis technology to analyze the user's emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using a facial expression analysis algorithm. The reception unit can also use voice analysis technology to analyze the user's emotions. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the emotion. The reception unit can also use survey results to analyze the user's emotions. For example, the reception unit conducts a survey of the user and estimates the emotion based on the results. In this way, the reception unit can analyze the user's emotions and determine the priority of presentation content to be input based on the analyzed user's emotions. For example, if the user is nervous, the reception unit puts important content off and inputs relaxing content first. Furthermore, if the user is relaxed, the reception unit can prioritize input of important content. Furthermore, if the user is impatient, the reception unit can input simple content first to reduce the user's impatience. This allows the reception unit to determine the priority of content according to the user's emotions, thereby enabling more effective presentation content to be input. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can determine the priority using an AI model that receives the user's facial expression data and voice data and outputs the priority of content. This allows the reception unit to analyze the user's emotions and determine the priority of presentation content to be input based on the analyzed user's emotions.

[0067] When inputting presentation content, the reception unit can prioritize inputting highly relevant content in consideration of the user's geographical location information. The reception unit, for example, uses GPS data to acquire the user's geographical location information. For example, the reception unit acquires the user's current location from the GPS data and prioritizes inputting information related to the location. The reception unit can also use an IP address to acquire the user's geographical location information. For example, the reception unit identifies a geographical location from the user's IP address and prioritizes inputting information related to the location. The reception unit can also use information input by the user to acquire the user's geographical location information. For example, the reception unit prioritizes inputting related information based on the address or area information input by the user. This allows the reception unit to prioritize inputting highly relevant content in consideration of the user's geographical location information when inputting presentation content. For example, the reception unit prioritizes inputting information related to the user's current location. The reception unit can also input content related to the area based on the user's geographical location information. The reception unit can also prioritize inputting information related to places the user plans to visit. As a result, the reception unit can input highly relevant content based on the geographical location information, thereby achieving a more effective presentation. Some or all of the above-described processing by the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input content using an AI model that inputs the user's geographical location information and outputs highly relevant content. As a result, when inputting presentation content, the reception unit can prioritize inputting highly relevant content in consideration of the user's geographical location information.

[0068] The reception unit can analyze the user's social media activity and input related content when inputting presentation content. The reception unit, for example, analyzes social media postings to analyze the user's social media activity. For example, the reception unit analyzes content posted by the user on social media and inputs information related to the content. The reception unit can also analyze the number of likes and the number of followers to analyze the user's social media activity. For example, the reception unit inputs related information based on posts that the user has received many likes or posts with a large number of followers. The reception unit can also analyze social media trends to analyze the user's social media activity. For example, the reception unit inputs related information based on topics and hashtags that are trending on social media. In this way, the reception unit can analyze the user's social media activity and input related content when inputting presentation content. For example, the reception unit inputs related information based on the user's social media activity. The reception unit can also preferentially input content related to topics in which the user has shown interest on social media. The reception unit can also input related information based on the interests of the user's social media followers. As a result, the reception unit can input relevant content based on the user's social media activity, thereby realizing a presentation that meets the user's interests. Some or all of the above-described processing by the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input content using an AI model that inputs the user's social media data and outputs relevant content. As a result, the reception unit can analyze the user's social media activity and input relevant content when inputting presentation content.

[0069] The analysis unit can analyze the user's emotions and adjust the way feedback is expressed based on the analyzed user's emotions. The analysis unit, for example, uses facial expression analysis technology to analyze the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression analysis algorithm. The analysis unit can also use voice analysis technology to analyze the user's emotions. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The analysis unit can also use survey results to analyze the user's emotions. For example, the analysis unit conducts a survey of the user and estimates the emotions based on the results. This allows the analysis unit to analyze the user's emotions and adjust the way feedback is expressed based on the analyzed user's emotions. For example, the analysis unit can provide gentle feedback when the user is nervous. The analysis unit can provide detailed feedback when the user is relaxed. The analysis unit can provide concise and to-the-point feedback when the user is impatient. This allows the analysis unit to provide more appropriate feedback by adjusting the feedback expression method according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the expression method using an AI model that receives the user's facial expression data and voice data and outputs the feedback expression method. This allows the analysis unit to analyze the user's emotions and adjust the feedback expression method based on the analyzed user's emotions.

[0070] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the presentation. For example, to evaluate the importance of a presentation, the analysis unit performs an evaluation based on the size of the audience and the purpose of the presentation. For example, the analysis unit performs a detailed analysis when the audience size is large or the purpose of the presentation is important. The analysis unit can also perform an evaluation based on the content or theme of the presentation to evaluate the importance of the presentation. For example, the analysis unit performs a detailed analysis when the content of the presentation is specialized or the theme is important. The analysis unit can also perform an evaluation based on the purpose and goal of the presentation to evaluate the importance of the presentation. For example, the analysis unit performs a detailed analysis when the purpose of the presentation involves an important decision or has a high goal. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the presentation. For example, the analysis unit performs a detailed analysis for an important presentation. On the other hand, the analysis unit can perform a brief analysis for a less important presentation. Furthermore, the analysis unit can adjust the level of detail of the analysis depending on the purpose of the presentation. This allows the analysis unit to provide more effective feedback by adjusting the level of detail of the analysis depending on the importance of the presentation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may adjust the level of detail using an AI model that inputs the importance of the presentation and outputs the level of detail of the analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the presentation during analysis.

[0071] The analysis unit can apply different analysis algorithms depending on the category of the presentation during analysis. The analysis unit, for example, analyzes the content or theme of the presentation to identify the category of the presentation. For example, if the content of the presentation is technical, the analysis unit can classify it into a technical category. Furthermore, if the content of the presentation is business-related, the analysis unit can classify it into a business category. Furthermore, if the content of the presentation is educational, the analysis unit can classify it into an educational category. This allows the analysis unit to apply different analysis algorithms depending on the category of the presentation. For example, the analysis unit can apply an analysis algorithm specialized for technical content to a technical presentation. Furthermore, the analysis unit can apply an algorithm that performs analysis from a business perspective to a business presentation. Furthermore, the analysis unit can apply an algorithm that performs analysis from an educational perspective to an educational presentation. This allows the analysis unit to provide more appropriate feedback by applying an analysis algorithm depending on the category of the presentation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can apply an analysis algorithm using an AI model that inputs the category of the presentation and outputs an appropriate analysis algorithm. This allows the analysis unit to apply different analysis algorithms depending on the category of the presentation during analysis.

[0072] The analysis unit can analyze the user's emotions and adjust the length of the feedback based on the analyzed user's emotions. The analysis unit, for example, uses facial expression analysis technology to analyze the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression analysis algorithm. The analysis unit can also use voice analysis technology to analyze the user's emotions. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The analysis unit can also use survey results to analyze the user's emotions. For example, the analysis unit conducts a survey of the user and estimates the emotions based on the results. This allows the analysis unit to analyze the user's emotions and adjust the length of the feedback based on the analyzed user's emotions. For example, the analysis unit can provide short and to-the-point feedback when the user is nervous. Furthermore, the analysis unit can provide detailed feedback when the user is relaxed. Furthermore, the analysis unit can provide concise and quick feedback when the user is impatient. This allows the analysis unit to provide more appropriate feedback by adjusting the length of the feedback according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may adjust the length using an AI model that receives the user's facial expression data and voice data and outputs the length of the feedback. This allows the analysis unit to analyze the user's emotions and adjust the length of the feedback based on the analyzed user emotions.

[0073] The analysis unit can determine analysis priorities based on presentation submission dates during analysis. For example, the analysis unit evaluates presentation submission dates based on submission deadlines and submission timing. For example, the analysis unit prioritizes analysis of presentations with upcoming submission deadlines. The analysis unit can also postpone presentations with more distant submission dates. Furthermore, the analysis unit can dynamically adjust analysis priorities based on submission dates. For example, the analysis unit prioritizes analysis of presentations with upcoming submission deadlines and postpones presentations with more distant submission deadlines. This allows the analysis unit to provide more effective feedback by determining analysis priorities based on submission dates. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can determine priorities using an AI model that inputs presentation submission dates and outputs analysis priorities. This allows the analysis unit to determine analysis priorities based on presentation submission dates during analysis.

[0074] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the presentations. For example, the analysis unit evaluates the relevance of the presentations based on the relevance of the topic or the audience's interest. For example, the analysis unit can prioritize analyzing presentations related to the user's current project. Furthermore, the analysis unit can prioritize analyzing presentations related to the user's field of interest. Furthermore, the analysis unit can prioritize analyzing presentations related to the user's work. This allows the analysis unit to adjust the order of analysis based on the relevance of the presentations. For example, the analysis unit can prioritize analyzing presentations related to the user's current project. Furthermore, the analysis unit can prioritize analyzing presentations related to the user's field of interest. Furthermore, the analysis unit can prioritize analyzing presentations related to the user's work. This allows the analysis unit to provide more effective feedback by adjusting the order of analysis based on relevance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can adjust the order using an AI model that inputs the relevance of the presentations and outputs the order of analysis. This allows the analyzer to adjust the order of analysis based on the relevance of the presentation during analysis.

[0075] The providing unit can analyze the user's emotions and adjust the way the advice is presented based on the analyzed user's emotions. The providing unit, for example, uses facial expression analysis technology to analyze the user's emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression analysis algorithm. The providing unit can also use voice analysis technology to analyze the user's emotions. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotions. The providing unit can also use survey results to analyze the user's emotions. For example, the providing unit conducts a survey of the user and estimates the emotions based on the results. This allows the providing unit to analyze the user's emotions and adjust the way the advice is presented based on the analyzed user's emotions. For example, the providing unit can provide gentle advice when the user is nervous. The providing unit can provide detailed advice when the user is relaxed. The providing unit can provide concise and to-the-point advice when the user is impatient. As a result, the providing unit can provide more appropriate advice by adjusting the way the advice is expressed in accordance with the user's emotions. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can adjust the way the advice is expressed using an AI model that receives the user's facial expression data and voice data as input and outputs the way the advice is expressed. As a result, the providing unit can analyze the user's emotions and adjust the way the advice is expressed based on the analyzed user's emotions.

[0076] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the presentation. For example, to evaluate the importance of the presentation, the providing unit performs an evaluation based on the size of the audience and the purpose of the presentation. For example, the providing unit provides detailed advice when the audience size is large or the purpose of the presentation is important. The providing unit can also perform an evaluation based on the content or theme of the presentation to evaluate the importance of the presentation. For example, the providing unit provides detailed advice when the content of the presentation is specialized or the theme is important. The providing unit can also perform an evaluation based on the purpose or goal of the presentation to evaluate the importance of the presentation. For example, the providing unit provides detailed advice when the purpose of the presentation involves an important decision or has a high goal. This allows the providing unit to adjust the level of detail of the advice based on the importance of the presentation. For example, the providing unit provides detailed advice for an important presentation. The providing unit can also provide concise advice for a less important presentation. The providing unit can also adjust the level of detail of the advice depending on the purpose of the presentation. As a result, the providing unit can provide more effective advice by adjusting the level of detail of the advice according to the importance of the presentation. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can adjust the level of detail using an AI model that inputs the importance of the presentation and outputs the level of detail of the advice. As a result, the providing unit can adjust the level of detail of the advice based on the importance of the presentation when providing the advice.

[0077] When providing advice, the providing unit can apply different advice algorithms depending on the category of the presentation. The providing unit, for example, analyzes the content or theme of the presentation to identify the category of the presentation. For example, if the content of the presentation is technical, the providing unit can classify it into a technical category. Furthermore, if the content of the presentation is business-related, the providing unit can classify it into a business category. Furthermore, if the content of the presentation is educational, the providing unit can classify it into an educational category. This allows the providing unit to apply different advice algorithms depending on the category of the presentation. For example, the providing unit can apply an advice algorithm specialized for technical content to a technical presentation. Furthermore, the providing unit can apply an algorithm that provides advice from a business perspective to a business presentation. Furthermore, the providing unit can apply an algorithm that provides advice from an educational perspective to an educational presentation. This allows the providing unit to provide more appropriate advice by applying an advice algorithm depending on the category of the presentation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can apply an advice algorithm using an AI model that inputs the category of the presentation and outputs an appropriate advice algorithm. This allows the providing unit to apply different advice algorithms depending on the category of the presentation when providing advice.

[0078] The providing unit can analyze the user's emotions and adjust the length of the advice based on the analyzed user's emotions. The providing unit, for example, uses facial expression analysis technology to analyze the user's emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression analysis algorithm. The providing unit can also use voice analysis technology to analyze the user's emotions. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the providing unit can use survey results to analyze the user's emotions. For example, the providing unit conducts a survey of the user and estimates the emotions based on the results. In this way, the providing unit can analyze the user's emotions and adjust the length of the advice based on the analyzed user's emotions. For example, the providing unit can provide short and to-the-point advice when the user is nervous. In addition, the providing unit can provide detailed advice when the user is relaxed. In addition, the providing unit can provide concise and quick advice when the user is impatient. In this way, the providing unit can provide more appropriate advice by adjusting the length of the advice according to the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may adjust the length of advice using an AI model that receives the user's facial expression data and voice data and outputs the length of advice. This allows the providing unit to analyze the user's emotions and adjust the length of advice based on the analyzed user emotions.

[0079] When providing advice, the providing unit can determine the priority of advice based on the submission date of the presentation. For example, to evaluate the submission date of the presentation, the providing unit performs an evaluation based on the submission deadline or the timing of submission. For example, the providing unit prioritizes advice for presentations with an upcoming submission deadline. The providing unit can also postpone presentations with a more distant submission date. Furthermore, the providing unit can dynamically adjust the priority of advice based on the submission date. For example, the providing unit prioritizes advice for presentations with an approaching submission deadline and postpones presentations with a more distant submission deadline. In this way, the providing unit can provide more effective advice by determining the priority of advice based on the submission date. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can determine the priority using an AI model that inputs the submission date of the presentation and outputs the priority of advice. In this way, the providing unit can determine the priority of advice based on the submission date of the presentation when providing advice.

[0080] When providing advice, the providing unit can adjust the order of advice based on the relevance of the presentation. For example, the providing unit evaluates the relevance of the presentation based on the relevance of the topic and the audience's interest. For example, the providing unit can prioritize advice on presentations related to the user's current project. Furthermore, the providing unit can prioritize advice on presentations related to the user's field of interest. Furthermore, the providing unit can prioritize advice on presentations related to the user's work. This allows the providing unit to adjust the order of advice based on the relevance of the presentation. For example, the providing unit can prioritize advice on presentations related to the user's current project. Furthermore, the providing unit can prioritize advice on presentations related to the user's field of interest. Furthermore, the providing unit can prioritize advice on presentations related to the user's work. This allows the providing unit to provide more effective advice by adjusting the order of advice based on relevance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the order using an AI model that inputs the relevance of the presentation and outputs the order of advice. This allows the providing unit to adjust the order of advice based on the relevance of the presentation when providing advice.

[0081] The reset unit can analyze the user's emotions and adjust the timing of the reset based on the analyzed user's emotions. The reset unit, for example, uses facial expression analysis technology to analyze the user's emotions. For example, the reset unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression analysis algorithm. The reset unit can also use voice analysis technology to analyze the user's emotions. For example, the reset unit analyzes the tone and speed of the user's voice to estimate the emotions. The reset unit can also use survey results to analyze the user's emotions. For example, the reset unit conducts a survey of the user and estimates the emotions based on the results. This allows the reset unit to analyze the user's emotions and adjust the timing of the reset based on the analyzed user's emotions. For example, if the user is nervous, the reset unit delays the timing of the reset to relax the user. Also, if the user is relaxed, the reset unit can advance the timing of the reset to provide efficient feedback. Furthermore, if the user is impatient, the reset unit can adjust the timing of the reset to reduce the impatience. This allows the reset unit to provide more appropriate feedback by adjusting the timing of the reset according to the user's emotions. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without using AI. For example, the reset unit can adjust the timing using an AI model that receives the user's facial expression data and voice data and outputs the timing of the reset. This allows the reset unit to analyze the user's emotions and adjust the timing of the reset based on the analyzed user's emotions.

[0082] The reset unit can optimize the reset algorithm by referring to the past feedback history at the time of reset. For example, the reset unit performs analysis based on the content and frequency of feedback by referring to the past feedback history. For example, the reset unit analyzes the content of past feedback and determines the optimal reset timing. Furthermore, the reset unit can perform analysis based on the effectiveness of feedback by referring to the past feedback history. For example, the reset unit evaluates how effective past feedback was and optimizes the reset algorithm. Furthermore, the reset unit can perform analysis based on the frequency of feedback by referring to the past feedback history. For example, the reset unit analyzes the frequency of past feedback and optimizes an algorithm for maximizing the effectiveness of the reset. Thus, the reset unit can optimize the reset algorithm by referring to the past feedback history at the time of reset. For example, the reset unit determines the optimal reset timing based on the past feedback history. Furthermore, the reset unit can analyze the past feedback history and optimize the reset algorithm. Furthermore, the reset unit can maximize the effectiveness of the reset by referring to the past feedback history. Thus, the reset unit can optimize the reset algorithm by referring to the past feedback history. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without using AI. For example, the reset unit can optimize the algorithm using an AI model that takes past feedback history as input and outputs a reset algorithm, which allows the reset unit to optimize the reset algorithm by referring to the past feedback history at the time of reset.

[0083] The reset unit can apply different reset methods depending on the category of the presentation when resetting. The reset unit, for example, analyzes the content or theme of the presentation to identify the category of the presentation. For example, if the content of the presentation is technical, the reset unit classifies it into a technical category. Furthermore, if the content of the presentation is business-related, the reset unit can classify it into a business category. Furthermore, if the content of the presentation is educational, the reset unit can classify it into an educational category. This allows the reset unit to apply different reset methods depending on the category of the presentation. For example, the reset unit can apply a reset method specialized for technical content to a technical presentation. Furthermore, the reset unit can apply a reset method from a business perspective to a business presentation. Furthermore, the reset unit can apply a reset method from an educational perspective to an educational presentation. This allows the reset unit to provide more appropriate feedback by applying a reset method depending on the category of the presentation. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without AI. For example, the reset unit can apply a reset method using an AI model that inputs the category of the presentation and outputs an appropriate reset method. This allows the reset unit to apply different reset methods depending on the category of the presentation at the time of reset.

[0084] The reset unit can analyze the user's emotions and determine a reset priority based on the analyzed user's emotions. The reset unit, for example, uses facial expression analysis technology to analyze the user's emotions. For example, the reset unit captures the user's facial expressions with a camera and estimates the emotion using a facial expression analysis algorithm. The reset unit can also use voice analysis technology to analyze the user's emotions. For example, the reset unit analyzes the tone and speed of the user's voice to estimate the emotion. The reset unit can also use survey results to analyze the user's emotions. For example, the reset unit conducts a survey of the user and estimates the emotion based on the results. This allows the reset unit to analyze the user's emotions and determine a reset priority based on the analyzed user's emotions. For example, if the user is nervous, the reset unit can lower the reset priority to relax the user. Furthermore, if the user is relaxed, the reset unit can increase the reset priority to provide efficient feedback. Furthermore, if the user is impatient, the reset unit can adjust the reset priority to reduce the user's impatience. This allows the reset unit to provide more appropriate feedback by determining the reset priority according to the user's emotions. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without using AI. For example, the reset unit can determine the priority using an AI model that receives the user's facial expression data and voice data and outputs the reset priority. This allows the reset unit to analyze the user's emotions and determine the reset priority based on the analyzed user's emotions.

[0085] The reset unit can adjust the reset frequency based on the presentation submission date when resetting. For example, the reset unit evaluates the presentation submission date based on the submission deadline or the timing of submission. For example, the reset unit can increase the reset frequency for presentations whose submission deadline is approaching. The reset unit can also decrease the reset frequency for presentations whose submission date is further away. Furthermore, the reset unit can dynamically adjust the reset frequency based on the submission date. For example, the reset unit can increase the reset frequency for presentations whose submission deadline is approaching and decrease the reset frequency for presentations whose submission deadline is further away. This allows the reset unit to provide more effective feedback by adjusting the reset frequency based on the submission date. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without AI. For example, the reset unit can adjust the frequency using an AI model that inputs the presentation submission date and outputs the reset frequency. This allows the reset unit to adjust the reset frequency based on the presentation submission date when resetting.

[0086] The reset unit may adjust the reset order based on the relevance of the presentations when resetting. The reset unit may, for example, evaluate the relevance of the presentations based on the relevance of the topic or the audience's interest. For example, the reset unit may prioritize resetting presentations related to the user's current project. The reset unit may also prioritize resetting presentations related to the user's field of interest. The reset unit may also prioritize resetting presentations related to the user's work. This allows the reset unit to adjust the reset order based on the relevance of the presentations. For example, the reset unit may prioritize resetting presentations related to the user's current project. The reset unit may also prioritize resetting presentations related to the user's field of interest. The reset unit may also prioritize resetting presentations related to the user's work. This allows the reset unit to provide more effective feedback by adjusting the reset order based on the relevance. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without using AI. For example, the reset unit may adjust the order using an AI model that receives the relevance of the presentations as input and outputs the reset order. This allows the reset unit to adjust the reset order based on presentation relevance at the time of reset. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, provision unit, and reset unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface for a user to input the content of the presentation. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input content of the presentation and generates feedback as if the user were a first-time viewer. The provision unit is realized by the control unit 46A of the smart device 14 and provides specific advice to the user based on the feedback generated by the analysis unit. The reset unit is realized by the specific processing unit 290 of the data processing device 12 and resets the system history and generates feedback as if the user were a first-time viewer again. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, provision unit, and reset unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for a user to input the content of the presentation. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input content of the presentation and generates feedback as if the user were a first-time viewer. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides specific advice to the user based on the feedback generated by the analysis unit. The reset unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and resets the system history and generates feedback as if the user were a first-time viewer again. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and reset unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface for the user to input the content of the presentation. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input content of the presentation and generates feedback as if the user were a first-time viewer. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides specific advice to the user based on the feedback generated by the analysis unit. The reset unit is realized by the specific processing unit 290 of the data processing device 12 and resets the system history and generates feedback as if the user were a first-time viewer again. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and reset unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for a user to input the content of the presentation. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input content of the presentation and generates feedback as if the user were a first-time viewer. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides specific advice to the user based on the feedback generated by the analysis unit. The reset unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and resets the system history and generates feedback as if the user were a first-time viewer again.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The reception unit can analyze the user's past presentation history and select an appropriate input method. For example, the reception unit refers to past slides and feedback history to analyze the user's past presentation history. For example, the reception unit selects the optimal input method based on slides the user has used in the past and feedback received. The reception unit can also refer to successful examples of past presentations to analyze the user's past presentation history. For example, the reception unit selects the optimal input method based on input methods used in presentations that were successful in the past. The reception unit can also refer to unsuccessful examples of past presentations to analyze the user's past presentation history. For example, the reception unit finds areas for improvement based on input methods used in presentations that were unsuccessful in the past and selects the optimal input method. In this way, the reception unit can analyze the user's past presentation history and select an appropriate input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select an input method using an AI model that inputs the user's past presentation history and outputs the optimal input method. In this way, the reception unit can analyze the user's past presentation history and select an appropriate input method.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the presentation. For example, to evaluate the importance of a presentation, the analysis unit performs an evaluation based on the size of the audience and the purpose of the presentation. For example, if the audience size is large or the purpose of the presentation is important, the analysis unit performs a detailed analysis. The analysis unit can also evaluate the importance of a presentation based on the content or theme of the presentation. For example, if the content of the presentation is specialized or the theme is important, the analysis unit performs a detailed analysis. The analysis unit can also evaluate the importance of a presentation based on the purpose and goal of the presentation. For example, if the purpose of the presentation involves an important decision or the goal is high, the analysis unit performs a detailed analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the presentation. For example, the analysis unit performs a detailed analysis for an important presentation. On the other hand, the analysis unit can perform a brief analysis for a less important presentation. The analysis unit can also adjust the level of detail of the analysis depending on the purpose of the presentation. This allows the analysis unit to provide more effective feedback by adjusting the level of detail of the analysis depending on the importance of the presentation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may adjust the level of detail using an AI model that inputs the importance of the presentation and outputs the level of detail of the analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the presentation during analysis.

[0090] The reset unit can optimize the reset algorithm by referring to the past feedback history at the time of reset. For example, the reset unit performs analysis based on the content and frequency of feedback by referring to the past feedback history. For example, the reset unit analyzes the content of past feedback and determines the optimal reset timing. Furthermore, the reset unit can perform analysis based on the effectiveness of feedback by referring to the past feedback history. For example, the reset unit evaluates how effective past feedback was and optimizes the reset algorithm. Furthermore, the reset unit can perform analysis based on the frequency of feedback by referring to the past feedback history. For example, the reset unit analyzes the frequency of past feedback and optimizes an algorithm for maximizing the effect of the reset. Thus, the reset unit can optimize the reset algorithm by referring to the past feedback history at the time of reset. For example, the reset unit determines the optimal reset timing based on the past feedback history. Furthermore, the reset unit can analyze the past feedback history and optimize the reset algorithm. Furthermore, the reset unit can maximize the effect of the reset by referring to the past feedback history. Thus, the reset unit can optimize the reset algorithm by referring to the past feedback history. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without using AI. For example, the reset unit can optimize the algorithm using an AI model that takes past feedback history as input and outputs a reset algorithm, which allows the reset unit to optimize the reset algorithm by referring to the past feedback history at the time of reset.

[0091] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the presentation. For example, the providing unit evaluates the importance of the presentation based on the size of the audience and the purpose of the presentation. For example, the providing unit provides detailed advice when the audience size is large or the purpose of the presentation is important. The providing unit can also evaluate the importance of the presentation based on the content or theme of the presentation. For example, the providing unit provides detailed advice when the content of the presentation is specialized or the theme is important. The providing unit can also evaluate the importance of the presentation based on the purpose and goal of the presentation. For example, the providing unit provides detailed advice when the purpose of the presentation involves an important decision or has a high goal. In this way, the providing unit can adjust the level of detail of the advice based on the importance of the presentation. For example, the providing unit provides detailed advice for an important presentation. In addition, the providing unit can provide concise advice for a less important presentation. The providing unit can also adjust the level of detail of the advice depending on the purpose of the presentation. In this way, the providing unit can provide more effective advice by adjusting the level of detail of the advice depending on the importance of the presentation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may adjust the level of detail of advice using an AI model that inputs the importance of the presentation and outputs the level of detail of advice. This allows the providing unit to adjust the level of detail of advice based on the importance of the presentation when providing the advice.

[0092] The reception unit can analyze the user's emotions and adjust the timing of inputting presentation content based on the analyzed user's emotions. For example, the reception unit uses facial expression analysis technology to analyze the user's emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotion using a facial expression analysis algorithm. The reception unit can also use voice analysis technology to analyze the user's emotions. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the emotion. The reception unit can also use survey results to analyze the user's emotions. For example, the reception unit conducts a survey of the user and estimates the emotion based on the results. In this way, the reception unit can analyze the user's emotions and adjust the timing of inputting presentation content based on the analyzed user's emotions. For example, if the user is nervous, the reception unit can delay the input timing to allow the user to relax. If the user is relaxed, the reception unit can speed up the input timing to allow the user to input smoothly. Furthermore, if the user is impatient, the reception unit can adjust the input timing to reduce the impatience. This allows the reception unit to adjust the input timing according to the user's emotions, thereby allowing the presentation content to be input at a more appropriate timing. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can adjust the input timing using an AI model that receives the user's facial expression data or voice data and outputs the input timing. This allows the reception unit to analyze the user's emotions and adjust the input timing of the presentation content based on the analyzed user's emotions.

[0093] The analysis unit can analyze the user's emotions and adjust the feedback expression method based on the analyzed user's emotions. For example, the analysis unit uses facial expression analysis technology to analyze the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression analysis algorithm. The analysis unit can also use voice analysis technology to analyze the user's emotions. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the analysis unit can use survey results to analyze the user's emotions. For example, the analysis unit conducts a survey of the user and estimates the emotions based on the results. This allows the analysis unit to analyze the user's emotions and adjust the feedback expression method based on the analyzed user's emotions. For example, the analysis unit can provide gentle feedback when the user is nervous. Furthermore, the analysis unit can provide detailed feedback when the user is relaxed. Furthermore, the analysis unit can provide concise and to-the-point feedback when the user is impatient. This allows the analysis unit to provide more appropriate feedback by adjusting the feedback expression method according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may adjust the feedback expression method using an AI model that receives the user's facial expression data and voice data and outputs the feedback expression method. This allows the analysis unit to analyze the user's emotions and adjust the feedback expression method based on the analyzed user emotions.

[0094] The providing unit can analyze the user's emotion and adjust the way the advice is presented based on the analyzed user's emotion. For example, the providing unit can use facial expression analysis technology to analyze the user's emotion. For example, the providing unit may capture the user's facial expression with a camera and estimate the user's emotion using a facial expression analysis algorithm. The providing unit may also use voice analysis technology to analyze the user's emotion. For example, the providing unit may analyze the user's tone and speed of voice to estimate the user's emotion. The providing unit may also use survey results to analyze the user's emotion. For example, the providing unit may conduct a survey of the user and estimate the user's emotion based on the results. This allows the providing unit to analyze the user's emotion and adjust the way advice is presented based on the analyzed user's emotion. For example, if the user is nervous, the providing unit may provide gentle advice. Furthermore, if the user is relaxed, the providing unit may provide detailed advice. Furthermore, if the user is impatient, the providing unit may provide concise, to-the-point advice. This allows the providing unit to provide more appropriate advice by adjusting the way advice is presented depending on the user's emotion. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the way of expressing advice using an AI model that receives the user's facial expression data and voice data and outputs the way of expressing advice, thereby enabling the providing unit to analyze the user's emotions and adjust the way of expressing advice based on the analyzed user's emotions.

[0095] When providing advice, the providing unit can apply different advice algorithms depending on the category of the presentation. For example, the providing unit analyzes the content or theme of the presentation to identify the category of the presentation. For example, if the content of the presentation is technical, the providing unit can classify it into a technical category. Furthermore, if the content of the presentation is business-related, the providing unit can classify it into a business category. Furthermore, if the content of the presentation is educational, the providing unit can classify it into an educational category. This allows the providing unit to apply different advice algorithms depending on the category of the presentation. For example, the providing unit can apply an advice algorithm specialized for technical content to a technical presentation. Furthermore, the providing unit can apply an algorithm that provides advice from a business perspective to a business presentation. Furthermore, the providing unit can apply an algorithm that provides advice from an educational perspective to an educational presentation. This allows the providing unit to provide more appropriate advice by applying an advice algorithm depending on the category of the presentation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can apply an advice algorithm using an AI model that inputs the category of the presentation and outputs an appropriate advice algorithm. This allows the providing unit to apply different advice algorithms depending on the category of the presentation when providing advice.

[0096] The reset unit can analyze the user's emotions and adjust the timing of the reset based on the analyzed user's emotions. For example, the reset unit uses facial expression analysis technology to analyze the user's emotions. For example, the reset unit captures the user's facial expressions with a camera and estimates the emotion using a facial expression analysis algorithm. The reset unit can also use voice analysis technology to analyze the user's emotions. For example, the reset unit analyzes the tone and speed of the user's voice to estimate the emotion. The reset unit can also use survey results to analyze the user's emotions. For example, the reset unit conducts a survey of the user and estimates the emotion based on the results. This allows the reset unit to analyze the user's emotions and adjust the timing of the reset based on the analyzed user's emotions. For example, if the user is nervous, the reset unit delays the timing of the reset to relax the user. Also, if the user is relaxed, the reset unit can advance the timing of the reset to provide efficient feedback. Furthermore, if the user is impatient, the reset unit can adjust the timing of the reset to reduce the user's impatience. This allows the reset unit to provide more appropriate feedback by adjusting the timing of the reset according to the user's emotions. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without using AI. For example, the reset unit can adjust the timing using an AI model that receives the user's facial expression data and voice data and outputs the timing of the reset. This allows the reset unit to analyze the user's emotions and adjust the timing of the reset based on the analyzed user's emotions.

[0097] The reset unit can apply different reset methods depending on the category of the presentation when resetting. For example, the reset unit analyzes the content or theme of the presentation to identify the category of the presentation. For example, if the content of the presentation is technical, the reset unit classifies it into a technical category. Furthermore, if the content of the presentation is business-related, the reset unit can classify it into a business category. Furthermore, if the content of the presentation is educational, the reset unit can classify it into an educational category. This allows the reset unit to apply different reset methods depending on the category of the presentation. For example, the reset unit can apply a reset method specialized for technical content to a technical presentation. Furthermore, the reset unit can apply a reset method from a business perspective to a business presentation. Furthermore, the reset unit can apply a reset method from an educational perspective to an educational presentation. This allows the reset unit to provide more appropriate feedback by applying a reset method depending on the category of the presentation. Some or all of the above-described processing in the reset unit may be performed using, for example, AI, or may be performed without using AI. For example, the reset unit can apply a reset method using an AI model that inputs the category of the presentation and outputs an appropriate reset method. This allows the reset unit to apply different reset methods depending on the category of the presentation at the time of reset.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The reception unit is a section where the user inputs the content of the presentation. The presentation content input by the user may include, but is not limited to, slides, speeches, visual aids, etc. The reception unit can receive the content of the presentation by, for example, text input, voice input, image input, etc. Step 2: The analysis unit analyzes the content of the presentation input by the reception unit and generates feedback for a new user. The analysis unit analyzes the content of the presentation using, for example, natural language processing technology and generates feedback for a first-time user. The analysis unit can also analyze the content of the presentation and generate feedback using a generation AI (for example, a text generation AI or a multimodal generation AI). Step 3: The providing unit provides specific advice to help the user improve their presentation based on the feedback generated by the analyzing unit. For example, the providing unit provides specific advice such as slide improvements, speaking tips, and visual design suggestions. Step 4: The reset unit resets the system history and generates feedback for a new user again. The reset unit resets the user's operation history and feedback history, for example, and returns the system to its initial state, thereby generating feedback for a first-time user again.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0147] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0171] [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for inputting the contents of the presentation; an analysis unit that analyzes the content input by the reception unit and generates feedback as a new user; a providing unit that provides specific advice for improving the presentation based on the feedback generated by the analyzing unit; A reset unit that resets the history and generates feedback as a new user again. A system characterized by:

2. The analysis unit Is the slide content easy to understand? Does it contain just the right amount of information? Does the visual design generate specific advice? 2. The system of claim 1.

3. The reset unit Reset the system history and generate feedback again as a new user 2. The system of claim 1.

4. The providing unit Providing specific advice to help users improve their presentations 2. The system of claim 1.

5. The reception unit Analyzes user emotions and adjusts the timing of presentation input based on the analyzed user emotions 2. The system of claim 1.

6. The reception unit Analyze the user's past presentation history and select the appropriate input method 2. The system of claim 1.

7. The reception unit Filter presentation content based on your current projects and interests 2. The system of claim 1.

8. The reception unit Analyze user emotions and prioritize presentation content based on the analyzed user emotions.

2. The system of claim 1.

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