system

Abstract

The system according to the embodiment aims to efficiently support the process from preparation to feedback of a presentation. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, a practice unit, and a feedback unit. The receiving unit receives input of the theme or purpose of the presentation. The generating unit creates a presentation structure proposal based on the information received by the receiving unit. The practice unit supports presentation practice based on the structure proposal created by the generating unit. The feedback unit makes suggestions for improving the next presentation based on the feedback provided by the practice unit.

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 technologies do not efficiently handle the processes of presentation preparation, practice, and feedback, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently support the process from preparation to feedback of a presentation. [Means for solving the problem]

[0006] A system according to an embodiment includes a receiving unit, a generating unit, a practice unit, and a feedback unit. The receiving unit receives input of a theme or purpose of a presentation. The generating unit creates a proposed presentation structure based on the information received by the receiving unit. The practice unit supports practice of the presentation based on the proposed structure created by the generating unit. The feedback unit makes suggestions for improving the next presentation based on the feedback provided by the practice unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently support everything from preparation of a presentation to feedback. [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) In an embodiment of the present invention, a presentation support system uses a generation AI to provide full support, from pre-presentation preparation to post-presentation feedback. In this presentation support system, a user inputs the presentation theme and purpose, and the generation AI creates a presentation outline based on that information. The outline includes the content and order of slides, key points to be discussed, and other information. The generation AI then supports the user in practicing the presentation. For example, the generation AI evaluates what the user says in real time and provides feedback on areas for improvement. The generation AI also analyzes the user's speaking style, tone, and speed to provide advice for delivering a more effective presentation. After the presentation, the generation AI collects and analyzes feedback from participants. This feedback includes evaluations of the presentation content, delivery style, and slide design. Based on this feedback, the generation AI suggests specific improvements to improve the next presentation. For example, if a user is giving a presentation on the theme of "introducing a new product," the generation AI first creates an outline that includes the product's features and benefits, a comparison with competing products, and other information. Then, as the user practices the presentation based on the outline, the generation AI provides feedback on the content and delivery style of the presentation. After the presentation is over, the generating AI collects feedback from participants and suggests improvements for the next presentation. In this way, the generating AI provides full support from pre-presentation preparation to post-presentation feedback, allowing users to give more effective presentations and increasing their success rate. This allows the presentation support system to improve the quality of users' presentations and increase their success rate.

[0029] A presentation support system according to an embodiment includes a reception unit, a generation unit, a practice unit, and a feedback unit. The reception unit receives input from a user about the theme and purpose of a presentation. For example, the user can input the theme and purpose of a business presentation or academic presentation. The generation unit creates a presentation structure proposal based on the information received by the reception unit. For example, the generation unit generates the content and order of slides and key points to be discussed. The generation unit uses a generation AI to generate the content, order, and key points to be discussed based on the information entered by the user. For example, the generation AI automatically generates the slide content based on the information entered by the user. The generation unit can also determine the order of slides using the generation AI. For example, the generation AI arranges the slides in a logical order. Furthermore, the generation unit can extract key points to be discussed using the generation AI. For example, the generation AI extracts important points from the information entered by the user and generates key points to be discussed based on the extracted key points. The practice unit supports presentation practice based on the structure proposal created by the generation unit. For example, the practice unit evaluates the content and style of the user's speech in real time and provides feedback on areas for improvement. The practice unit uses the generation AI to evaluate the content and speaking style of the user in real time. For example, the generation AI analyzes the content of what the user says and evaluates the clarity of pronunciation and the flow of speech. The practice unit can also use the generation AI to analyze the user's speaking style. For example, the generation AI analyzes the tone and speed of the user's voice and provides feedback on areas for improvement. The practice unit can also use the generation AI to provide specific advice on the content and speaking style of the user. For example, the generation AI suggests specific areas for improvement on the content and speaking style of the user. The feedback unit makes suggestions to improve the next presentation based on the feedback provided by the practice unit. For example, the feedback unit collects and analyzes feedback from participants. The feedback unit uses the generation AI to collect feedback from participants. For example, the generation AI collects feedback using questionnaires and voice analysis.The feedback unit can also use a generation AI to analyze the collected feedback. For example, the generation AI can analyze the content of the feedback and make specific suggestions to improve the next presentation. This allows the presentation support system according to the embodiment to improve the quality of the user's presentation and increase the success rate.

[0030] The generation unit can generate the slide content, order, and talking points based on information input by the user. The generation unit, for example, generates the slide content based on information input by the user. For example, the generation unit automatically generates the slide content based on the information input by the user using a generation AI. The generation unit can also determine the order of the slides using the generation AI. For example, the generation unit logically arranges the order of the slides using the generation AI. The generation unit can also extract talking points using the generation AI. For example, the generation unit extracts important points from the information input by the user and generates talking points based on the extracted important points. This can improve the quality of the presentation by generating the slide content, order, and talking points based on the information input by the user. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the information input by the user to the generation AI and have the generation AI generate the slide content, order, and talking points.

[0031] The practice unit can evaluate the content or speaking style of the user in real time and provide feedback on areas for improvement. The practice unit, for example, evaluates the content of the user's speech in real time. For example, the practice unit uses a generation AI to analyze the content of the user's speech and evaluate the clarity of pronunciation and the flow of speech. The practice unit can also evaluate the user's speaking style in real time. For example, the practice unit uses a generation AI to analyze the tone and speed of the user's voice and provide feedback on areas for improvement. The practice unit can also provide specific advice on the content and speaking style of the user. For example, the practice unit uses a generation AI to suggest specific areas for improvement on the content and speaking style of the user. This allows the user to effectively practice presentations by evaluating the content and speaking style of the user in real time and providing feedback on areas for improvement. Some or all of the above-described processing in the practice unit may be performed using or without a generation AI. For example, the practice unit can input the content and speaking style of the user into the generation AI and have the generation AI perform real-time evaluation and feedback.

[0032] The feedback unit can collect and analyze feedback from participants. The feedback unit, for example, collects feedback from participants. For example, the feedback unit uses a generation AI to collect feedback using a questionnaire or voice analysis. The feedback unit can also analyze the collected feedback. For example, the feedback unit uses the generation AI to analyze the content of the feedback and make specific suggestions for improving the next presentation. In this way, by collecting and analyzing feedback from participants, it is possible to clarify areas for improvement in the next presentation. Some or all of the above-mentioned processing in the feedback unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input feedback from participants into the generation AI and have the generation AI collect and analyze the feedback.

[0033] The feedback unit can make suggestions to improve the next presentation. For example, the feedback unit makes suggestions to improve the next presentation based on feedback from participants. For example, the feedback unit uses a generation AI to analyze the collected feedback and make specific suggestions to improve the next presentation. By making specific suggestions to improve the next presentation, the quality of the presentation can be improved. Some or all of the above-described processing in the feedback unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the collected feedback into the generation AI and cause the generation AI to execute suggestions to improve the next presentation.

[0034] The presentation support system further includes a reception unit that analyzes the user's past presentation history and proposes an optimal input format. The reception unit, for example, automatically displays themes and purposes previously used by the user as candidates. The reception unit can also prioritize and propose input methods (voice, text, etc.) previously used by the user. The reception unit can also predict and propose themes and purposes to be used during a specific time period based on the user's past presentation history. This allows for the analysis of the user's past presentation history to propose an optimal input format and achieve efficient input. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past presentation history data into the generation AI and have the generation AI propose an optimal input format.

[0035] The presentation support system further includes a reception unit that filters the input content based on the user's current project or field of interest when the user inputs a theme or purpose. The reception unit, for example, prioritizes displaying themes and purposes related to the user's current project. The reception unit can also suggest related themes and purposes based on the user's field of interest. The reception unit can also filter and display appropriate themes and purposes based on fields in which the user has previously shown interest. This allows the user to efficiently input highly relevant information by filtering the input content based on the user's current project or field of interest. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit may input the user's project data and field of interest data into the generation AI and have the generation AI filter the input content.

[0036] Furthermore, the presentation support system includes a reception unit that, when inputting a theme or purpose, prioritizes input of highly relevant information based on the user's geographical location information. For example, if the user is in a specific area, the reception unit may prioritize displaying themes and purposes related to that area. Furthermore, if the user is traveling, the reception unit may also suggest themes and purposes related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit may prioritize displaying themes and purposes related to the event. This allows for efficient input of highly relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit may input the user's geographical location information to the generation AI and cause the generation AI to prioritize input of highly relevant information.

[0037] Furthermore, the presentation support system includes a reception unit that analyzes the user's social media activity and inputs related information when the user inputs a theme or purpose. For example, the reception unit automatically displays themes and purposes frequently mentioned by the user on social media as candidates. The reception unit can also suggest themes and purposes related to fields of interest based on the user's social media activity. Furthermore, the reception unit can analyze the content of posts from accounts the user follows on social media and display related themes and purposes. This allows the user to efficiently input related information by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit may input the user's social media data into the generation AI and have the generation AI input related information.

[0038] When generating slides, the generation unit can adjust the detailed content of the slides based on the importance of the presentation. For example, in the case of an important presentation, the generation unit generates slides containing detailed information. In addition, in the case of a brief report, the generation unit can generate concise slides that focus on the main points. Furthermore, in the case of a presentation of medium importance, the generation unit can generate slides with an appropriate level of detail. In this way, an effective presentation can be achieved by adjusting the level of detail of the slides based on the importance of the presentation. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input presentation importance data into the generation AI and cause the generation AI to adjust the level of detail of the slides.

[0039] When generating slides, the generation unit can apply a specific generation algorithm depending on the category of the presentation. For example, in the case of a technical presentation, the generation unit can apply a generation algorithm that emphasizes technical details. In addition, in the case of a marketing presentation, the generation unit can apply an algorithm that generates visually appealing slides. Furthermore, in the case of an educational presentation, the generation unit can apply a generation algorithm that emphasizes educational elements. In this way, by applying different generation algorithms depending on the category of the presentation, an effective presentation can be achieved. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input presentation category data into the generation AI and cause the generation AI to apply a specific generation algorithm.

[0040] When generating slides, the generation unit can determine the priority of slides based on the submission date of the presentation. For example, when the deadline is approaching, the generation unit prioritizes the generation of important slides. The generation unit can also generate detailed slides when there is ample time for submission. Furthermore, the generation unit can adjust the priority of slides according to the submission date and generate them efficiently. This enables efficient slide generation by determining the priority of slides based on the submission date of the presentation. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input presentation submission date data into the generation AI and have the generation AI determine the slide priority.

[0041] When generating slides, the generation unit can adjust the order of slides based on the relevance of the presentation. For example, the generation unit can place the slide most relevant to the theme of the presentation first. The generation unit can also arrange highly relevant slides in order according to the flow of the presentation. Furthermore, the generation unit can prioritize highly relevant slides according to the purpose of the presentation. In this way, by adjusting the order of slides based on the relevance of the presentation, an effective presentation can be achieved. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input presentation relevance data into the generation AI and have the generation AI adjust the order of the slides.

[0042] The practice unit can provide feedback by analyzing the user's past speaking style or voice tone during practice. The practice unit can provide optimal feedback based on, for example, the speaking style and voice tone used by the user in the past. The practice unit can also analyze the user's past speaking style and voice tone and specifically indicate areas for improvement. Furthermore, the practice unit can provide effective feedback by referring to the user's past speaking style and voice tone. In this way, by analyzing the user's past speaking style and voice tone, optimal feedback can be provided and effective practice can be achieved. Some or all of the above-mentioned processing in the practice unit can be performed using or without the generation AI. For example, the practice unit can input data on the user's past speaking style and voice tone into the generation AI and cause the generation AI to provide feedback.

[0043] The practice unit can adjust the practice content based on the user's current presentation skills during practice. For example, the practice unit evaluates the user's current presentation skills and suggests appropriate practice content. The practice unit can also gradually customize the practice content according to the user's skill level. Furthermore, the practice unit can also adjust the practice content as the user's skills improve. This allows for effective practice by customizing the practice content based on the user's current presentation skills. Some or all of the above-described processing in the practice unit may be performed using or without the generation AI. For example, the practice unit can input the user's current presentation skill data into the generation AI and have the generation AI adjust the practice content.

[0044] The practice unit can select a practice method based on the user's geographical location information during practice. For example, if the user is in a specific area, the practice unit can suggest a practice method related to that area. Furthermore, if the user is traveling, the practice unit can suggest a practice method related to the travel destination. Furthermore, if the user is participating in a specific event, the practice unit can suggest a practice method related to the event. This allows the optimal practice method to be selected by taking the user's geographical location information into consideration, enabling effective practice. Some or all of the above-described processing in the practice unit may be performed using or without the generation AI. For example, the practice unit can input the user's geographical location information into the generation AI and have the generation AI select a practice method.

[0045] During practice, the practice unit can analyze the user's social media activity to suggest practice content. For example, the practice unit can suggest practice content related to topics frequently mentioned by the user on social media. The practice unit can also suggest practice content related to areas of interest from the user's social media activity. Furthermore, the practice unit can analyze the content posted by accounts the user follows on social media to suggest related practice content. In this way, by analyzing the user's social media activity, relevant practice content can be suggested, enabling effective practice. Some or all of the above-described processing in the practice unit may be performed using or without the generation AI. For example, the practice unit can input the user's social media data into the generation AI and have the generation AI execute the practice content suggestions.

[0046] When collecting feedback, the feedback unit can adjust the current feedback by referring to past feedback data. For example, the feedback unit can optimize the current feedback based on feedback the user has received in the past. The feedback unit can also analyze past feedback data, extract common improvements, and reflect them in the current feedback. Furthermore, the feedback unit can provide effective feedback by referring to the user's past feedback history. In this way, by referring to the past feedback data, the current feedback can be optimized and effective improvements can be provided. Some or all of the above-mentioned processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input past feedback data into the generation AI and cause the generation AI to adjust the current feedback.

[0047] When collecting feedback, the feedback unit can apply a specific feedback analysis method to each presentation category. For example, in the case of a technical presentation, the feedback unit can apply a feedback analysis method that emphasizes technical details. In the case of a marketing presentation, the feedback unit can also apply a feedback analysis method that emphasizes visual elements. In addition, in the case of an educational presentation, the feedback unit can also apply a feedback analysis method that emphasizes educational elements. In this way, by applying different feedback analysis methods to each presentation category, effective feedback can be provided. Some or all of the above-mentioned processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input presentation category data into the generation AI and cause the generation AI to apply a specific feedback analysis method.

[0048] When collecting feedback, the feedback unit can analyze changes in feedback based on the presentation submission time. For example, when the submission time is approaching, the feedback unit prioritizes collecting important feedback. The feedback unit can also collect detailed feedback when there is ample time left until the submission time. Furthermore, the feedback unit can analyze changes in feedback depending on the submission time and extract effective improvements. In this way, by analyzing changes in feedback based on the presentation submission time, effective improvements can be provided. Some or all of the above-mentioned processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input presentation submission time data into the generation AI and have the generation AI perform an analysis of feedback changes.

[0049] When collecting feedback, the feedback unit can analyze the feedback by referring to market data for the presentation. For example, the feedback unit can evaluate the content of the presentation based on related market data. The feedback unit can also compare the presentation with competing presentations by referring to the market data. Furthermore, the feedback unit can specifically indicate areas for improvement in the presentation based on the market data. This makes it possible to provide effective feedback by referring to the market data for the presentation. Some or all of the above-mentioned processing in the feedback unit can be performed using or without the generation AI. For example, the feedback unit can input related market data into the generation AI and have the generation AI analyze the feedback.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The presentation support system may further include a practice unit that analyzes the user's past presentation data and proposes an optimal practice schedule. For example, the practice unit may propose optimal practice time and frequency based on data from the user's past presentations. The practice unit may also analyze the success rate and feedback of the user's past presentations and propose effective practice methods. Furthermore, the practice unit may provide a practice plan focused on improving specific skills based on the user's past presentation data. This allows for more effective practice by utilizing the user's past data.

[0052] The generator can also generate slides in different styles depending on the purpose of the user's presentation. For example, for a business presentation, it can generate slides with a professional and simple design. For an academic presentation, it can generate slides containing detailed data and graphs. Furthermore, for an entertainment presentation, it can generate visually appealing and interactive slides. This allows the user to provide optimal slides depending on the purpose of the presentation, resulting in an effective presentation.

[0053] The practice module can also provide specific practice scenarios based on the content of the user's presentation. For example, for a technical presentation, a practice scenario focusing on technical terms and technical details can be provided. For a marketing presentation, a practice scenario focusing on persuasive speaking and visual elements can be provided. For an educational presentation, a practice scenario focusing on educational and interactive elements can be provided. This allows for effective practice tailored to the content of the presentation.

[0054] The feedback unit can also customize feedback based on the audience of the user's presentation. For example, if the audience is business partners, feedback from a business perspective can be provided. If the audience is an academic audience, feedback from an academic perspective can be provided. Furthermore, if the audience is a general audience, feedback focused on clarity and entertainment value can be provided. This allows for effective feedback to be provided according to the audience of the presentation.

[0055] The feedback unit can also adjust the feedback based on the time of day the user is giving their presentation. For example, for a morning presentation, the feedback unit can provide energetic and positive feedback. For a midday presentation, the feedback unit can provide specific points for improvement to help the user maintain focus. For an evening presentation, the feedback unit can provide feedback in a more relaxed tone. This allows the user to receive effective feedback depending on the time of day the presentation is given.

[0056] The reception unit can also analyze the user's past presentation history and propose an optimal presentation structure. For example, it can propose a new structure based on the user's past successful presentation structures. It can also provide advice to help the user avoid presentation structures that failed in the past. Furthermore, it can extract specific patterns and trends from the user's past presentation history and propose an optimal structure based on them. This makes it possible to utilize the user's past data to achieve more effective presentations.

[0057] The reception unit can also automatically update the theme and objective of the presentation based on the user's current project or area of ​​interest. For example, if the user starts a new project, the reception unit can automatically suggest themes and objectives related to that project. Also, if the user begins to have a new area of ​​interest, the reception unit can suggest themes and objectives related to that area. Furthermore, the reception unit can dynamically update the theme and objective according to the progress of the user's project or area of ​​interest. This allows the reception unit to provide the most appropriate theme and objective according to the user's current situation, thereby improving the effectiveness of the presentation.

[0058] The reception unit can also customize the theme and purpose of the presentation based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can suggest a theme and purpose related to that area. If the user is traveling, the reception unit can suggest a theme and purpose related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can suggest a theme and purpose related to the event. This allows the reception unit to provide the optimal theme and purpose taking into account the user's geographical location information, thereby improving the effectiveness of the presentation.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives input from the user about the theme and purpose of the presentation. For example, the user can input the theme and purpose of a business presentation or academic presentation. Step 2: The generation unit creates a draft presentation structure based on the information received by the reception unit. For example, the generation unit generates the content and order of slides and the main points to be discussed. Using the generation AI, the generation unit generates the content and order of slides and the main points to be discussed based on the information entered by the user. For example, the generation AI automatically generates the content of the slides based on the information entered by the user, arranges the order of the slides logically, extracts important points, and generates the main points to be discussed. Step 3: The practice unit supports presentation practice based on the proposed structure created by the generation unit. For example, the practice unit evaluates the user's speech content and speaking style in real time and provides feedback on areas for improvement. Using the generation AI, the practice unit evaluates the user's speech content and speaking style in real time, analyzing the clarity of pronunciation, flow of speech, tone and speed of voice, and suggests specific areas for improvement. Step 4: The feedback department makes suggestions to improve the next presentation based on the feedback provided by the practice department. For example, the feedback department collects and analyzes feedback from participants. Using generative AI, the feedback is collected using questionnaires and voice analysis, and the collected feedback is analyzed to make specific suggestions to improve the next presentation.

[0061] (Example 2) In an embodiment of the present invention, a presentation support system uses a generation AI to provide full support, from pre-presentation preparation to post-presentation feedback. In this presentation support system, a user inputs the presentation theme and purpose, and the generation AI creates a presentation outline based on that information. The outline includes the content and order of slides, key points to be discussed, and other information. The generation AI then supports the user in practicing the presentation. For example, the generation AI evaluates what the user says in real time and provides feedback on areas for improvement. The generation AI also analyzes the user's speaking style, tone, and speed to provide advice for delivering a more effective presentation. After the presentation, the generation AI collects and analyzes feedback from participants. This feedback includes evaluations of the presentation content, delivery style, and slide design. Based on this feedback, the generation AI suggests specific improvements to improve the next presentation. For example, if a user is giving a presentation on the theme of "introducing a new product," the generation AI first creates an outline that includes the product's features and benefits, a comparison with competing products, and other information. Then, as the user practices the presentation based on the outline, the generation AI provides feedback on the content and delivery style of the presentation. After the presentation is over, the generating AI collects feedback from participants and suggests improvements for the next presentation. In this way, the generating AI provides full support from pre-presentation preparation to post-presentation feedback, allowing users to give more effective presentations and increasing their success rate. This allows the presentation support system to improve the quality of users' presentations and increase their success rate.

[0062] A presentation support system according to an embodiment includes a reception unit, a generation unit, a practice unit, and a feedback unit. The reception unit receives input from a user about the theme and purpose of a presentation. For example, the user can input the theme and purpose of a business presentation or academic presentation. The generation unit creates a presentation structure proposal based on the information received by the reception unit. For example, the generation unit generates the content and order of slides and key points to be discussed. The generation unit uses a generation AI to generate the content, order, and key points to be discussed based on the information entered by the user. For example, the generation AI automatically generates the slide content based on the information entered by the user. The generation unit can also determine the order of slides using the generation AI. For example, the generation AI arranges the slides in a logical order. Furthermore, the generation unit can extract key points to be discussed using the generation AI. For example, the generation AI extracts important points from the information entered by the user and generates key points to be discussed based on the extracted key points. The practice unit supports presentation practice based on the structure proposal created by the generation unit. For example, the practice unit evaluates the content and style of the user's speech in real time and provides feedback on areas for improvement. The practice unit uses the generation AI to evaluate the content and speaking style of the user in real time. For example, the generation AI analyzes the content of what the user says and evaluates the clarity of pronunciation and the flow of speech. The practice unit can also use the generation AI to analyze the user's speaking style. For example, the generation AI analyzes the tone and speed of the user's voice and provides feedback on areas for improvement. The practice unit can also use the generation AI to provide specific advice on the content and speaking style of the user. For example, the generation AI suggests specific areas for improvement on the content and speaking style of the user. The feedback unit makes suggestions to improve the next presentation based on the feedback provided by the practice unit. For example, the feedback unit collects and analyzes feedback from participants. The feedback unit uses the generation AI to collect feedback from participants. For example, the generation AI collects feedback using questionnaires and voice analysis.The feedback unit can also use a generation AI to analyze the collected feedback. For example, the generation AI can analyze the content of the feedback and make specific suggestions to improve the next presentation. This allows the presentation support system according to the embodiment to improve the quality of the user's presentation and increase the success rate.

[0063] The generation unit can generate the slide content, order, and talking points based on information input by the user. The generation unit, for example, generates the slide content based on information input by the user. For example, the generation unit automatically generates the slide content based on the information input by the user using a generation AI. The generation unit can also determine the order of the slides using the generation AI. For example, the generation unit logically arranges the order of the slides using the generation AI. The generation unit can also extract talking points using the generation AI. For example, the generation unit extracts important points from the information input by the user and generates talking points based on the extracted important points. This can improve the quality of the presentation by generating the slide content, order, and talking points based on the information input by the user. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the information input by the user to the generation AI and have the generation AI generate the slide content, order, and talking points.

[0064] The practice unit can evaluate the content or speaking style of the user in real time and provide feedback on areas for improvement. The practice unit, for example, evaluates the content of the user's speech in real time. For example, the practice unit uses a generation AI to analyze the content of the user's speech and evaluate the clarity of pronunciation and the flow of speech. The practice unit can also evaluate the user's speaking style in real time. For example, the practice unit uses a generation AI to analyze the tone and speed of the user's voice and provide feedback on areas for improvement. The practice unit can also provide specific advice on the content and speaking style of the user. For example, the practice unit uses a generation AI to suggest specific areas for improvement on the content and speaking style of the user. This allows the user to effectively practice presentations by evaluating the content and speaking style of the user in real time and providing feedback on areas for improvement. Some or all of the above-described processing in the practice unit may be performed using or without a generation AI. For example, the practice unit can input the content and speaking style of the user into the generation AI and have the generation AI perform real-time evaluation and feedback.

[0065] The feedback unit can collect and analyze feedback from participants. The feedback unit, for example, collects feedback from participants. For example, the feedback unit uses a generation AI to collect feedback using a questionnaire or voice analysis. The feedback unit can also analyze the collected feedback. For example, the feedback unit uses the generation AI to analyze the content of the feedback and make specific suggestions for improving the next presentation. In this way, by collecting and analyzing feedback from participants, it is possible to clarify areas for improvement in the next presentation. Some or all of the above-mentioned processing in the feedback unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input feedback from participants into the generation AI and have the generation AI collect and analyze the feedback.

[0066] The feedback unit can make suggestions to improve the next presentation. For example, the feedback unit makes suggestions to improve the next presentation based on feedback from participants. For example, the feedback unit uses a generation AI to analyze the collected feedback and make specific suggestions to improve the next presentation. By making specific suggestions to improve the next presentation, the quality of the presentation can be improved. Some or all of the above-described processing in the feedback unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the collected feedback into the generation AI and cause the generation AI to execute suggestions to improve the next presentation.

[0067] The presentation support system further includes a reception unit that estimates a user's emotions and adjusts the input method for the theme or purpose based on the estimated user emotions. For example, if the user is nervous, the reception unit provides a simple and intuitive interface and minimizes input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input the theme or purpose. This reduces the user's burden by adjusting the input method for the theme or purpose based on the user's emotions, thereby enabling efficient input. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or without the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the input method for the theme or purpose.

[0068] The presentation support system further includes a reception unit that analyzes the user's past presentation history and proposes an optimal input format. The reception unit, for example, automatically displays themes and purposes previously used by the user as candidates. The reception unit can also prioritize and propose input methods (voice, text, etc.) previously used by the user. The reception unit can also predict and propose themes and purposes to be used during a specific time period based on the user's past presentation history. This allows for the analysis of the user's past presentation history to propose an optimal input format and achieve efficient input. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past presentation history data into the generation AI and have the generation AI propose an optimal input format.

[0069] The presentation support system further includes a reception unit that filters the input content based on the user's current project or field of interest when the user inputs a theme or purpose. The reception unit, for example, prioritizes displaying themes and purposes related to the user's current project. The reception unit can also suggest related themes and purposes based on the user's field of interest. The reception unit can also filter and display appropriate themes and purposes based on fields in which the user has previously shown interest. This allows the user to efficiently input highly relevant information by filtering the input content based on the user's current project or field of interest. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit may input the user's project data and field of interest data into the generation AI and have the generation AI filter the input content.

[0070] The presentation support system further includes a reception unit that estimates a user's emotions and prioritizes input content based on the estimated user emotions. For example, when the user is stressed, the reception unit prioritizes displaying important input items and postpones other items. Furthermore, when the user is relaxed, the reception unit can sequentially display detailed input items to facilitate overall input. Furthermore, when the user is in a hurry, the reception unit can display the most important input items first to quickly complete input. This allows efficient input by prioritizing input content according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without the generation AI. For example, the reception unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of the input content.

[0071] Furthermore, the presentation support system includes a reception unit that, when inputting a theme or purpose, prioritizes input of highly relevant information based on the user's geographical location information. For example, if the user is in a specific area, the reception unit may prioritize displaying themes and purposes related to that area. Furthermore, if the user is traveling, the reception unit may also suggest themes and purposes related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit may prioritize displaying themes and purposes related to the event. This allows for efficient input of highly relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit may input the user's geographical location information to the generation AI and cause the generation AI to prioritize input of highly relevant information.

[0072] Furthermore, the presentation support system includes a reception unit that analyzes the user's social media activity and inputs related information when the user inputs a theme or purpose. For example, the reception unit automatically displays themes and purposes frequently mentioned by the user on social media as candidates. The reception unit can also suggest themes and purposes related to fields of interest based on the user's social media activity. Furthermore, the reception unit can analyze the content of posts from accounts the user follows on social media and display related themes and purposes. This allows the user to efficiently input related information by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit may input the user's social media data into the generation AI and have the generation AI input related information.

[0073] The presentation support system further includes a generation unit that estimates a user's emotions and adjusts the design and content of slides based on the estimated user emotions. For example, if the user is nervous, the generation unit generates a simple, highly visible slide design. Furthermore, if the user is relaxed, the generation unit can generate a slide design that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can generate a concise slide design that focuses on the main points. This allows for an effective presentation by adjusting the design and content of slides according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without the generation AI. For example, the generation unit may input user emotion data into the generation AI and have the generation AI adjust the design and content of the slides.

[0074] When generating slides, the generation unit can adjust the detailed content of the slides based on the importance of the presentation. For example, in the case of an important presentation, the generation unit generates slides containing detailed information. In addition, in the case of a brief report, the generation unit can generate concise slides that focus on the main points. Furthermore, in the case of a presentation of medium importance, the generation unit can generate slides with an appropriate level of detail. In this way, an effective presentation can be achieved by adjusting the level of detail of the slides based on the importance of the presentation. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input presentation importance data into the generation AI and cause the generation AI to adjust the level of detail of the slides.

[0075] When generating slides, the generation unit can apply a specific generation algorithm depending on the category of the presentation. For example, in the case of a technical presentation, the generation unit can apply a generation algorithm that emphasizes technical details. In addition, in the case of a marketing presentation, the generation unit can apply an algorithm that generates visually appealing slides. Furthermore, in the case of an educational presentation, the generation unit can apply a generation algorithm that emphasizes educational elements. In this way, by applying different generation algorithms depending on the category of the presentation, an effective presentation can be achieved. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input presentation category data into the generation AI and cause the generation AI to apply a specific generation algorithm.

[0076] The presentation support system further includes a generation unit that estimates a user's emotions and adjusts the order of slides based on the estimated user emotions. For example, if the user is nervous, the generation unit places important slides first, creating a sense of security. Furthermore, if the user is relaxed, the generation unit can freely adjust the order of slides to create a natural flow. Furthermore, if the user is in a hurry, the generation unit can place slides that highlight key points first, allowing the presentation to proceed quickly. This allows for an effective presentation by adjusting the order of slides according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without the generation AI. For example, the generation unit may input user emotion data into the generation AI and have the generation AI adjust the order of the slides.

[0077] When generating slides, the generation unit can determine the priority of slides based on the submission date of the presentation. For example, when the deadline is approaching, the generation unit prioritizes the generation of important slides. The generation unit can also generate detailed slides when there is ample time for submission. Furthermore, the generation unit can adjust the priority of slides according to the submission date and generate them efficiently. This enables efficient slide generation by determining the priority of slides based on the submission date of the presentation. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input presentation submission date data into the generation AI and have the generation AI determine the slide priority.

[0078] When generating slides, the generation unit can adjust the order of slides based on the relevance of the presentation. For example, the generation unit can place the slide most relevant to the theme of the presentation first. The generation unit can also arrange highly relevant slides in order according to the flow of the presentation. Furthermore, the generation unit can prioritize highly relevant slides according to the purpose of the presentation. In this way, by adjusting the order of slides based on the relevance of the presentation, an effective presentation can be achieved. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input presentation relevance data into the generation AI and have the generation AI adjust the order of the slides.

[0079] The presentation support system further includes a practice unit that estimates the user's emotions and adjusts the practice feedback method based on the estimated user emotions. For example, if the user is nervous, the practice unit provides feedback in a gentle tone, reassuring the user. Furthermore, if the user is relaxed, the practice unit can provide detailed feedback and specifically indicate areas for improvement. Furthermore, if the user is in a hurry, the practice unit can provide concise feedback that focuses on the key points and quickly inform the user of areas for improvement. This allows for effective practice by adjusting the practice feedback method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the practice unit may be performed using the generation AI, or may be performed without the generation AI. For example, the practice unit can input the user's emotion data into the generation AI and have the generation AI adjust the feedback method.

[0080] The practice unit can provide feedback by analyzing the user's past speaking style or voice tone during practice. The practice unit can provide optimal feedback based on, for example, the speaking style and voice tone used by the user in the past. The practice unit can also analyze the user's past speaking style and voice tone and specifically indicate areas for improvement. Furthermore, the practice unit can provide effective feedback by referring to the user's past speaking style and voice tone. In this way, by analyzing the user's past speaking style and voice tone, optimal feedback can be provided and effective practice can be achieved. Some or all of the above-mentioned processing in the practice unit can be performed using or without the generation AI. For example, the practice unit can input data on the user's past speaking style and voice tone into the generation AI and cause the generation AI to provide feedback.

[0081] The practice unit can adjust the practice content based on the user's current presentation skills during practice. For example, the practice unit evaluates the user's current presentation skills and suggests appropriate practice content. The practice unit can also gradually customize the practice content according to the user's skill level. Furthermore, the practice unit can also adjust the practice content as the user's skills improve. This allows for effective practice by customizing the practice content based on the user's current presentation skills. Some or all of the above-described processing in the practice unit may be performed using or without the generation AI. For example, the practice unit can input the user's current presentation skill data into the generation AI and have the generation AI adjust the practice content.

[0082] The presentation support system further includes a practice unit that estimates the user's emotions and determines the priority of practice sessions based on the estimated user emotions. For example, if the user is feeling stressed, the practice unit prioritizes important practice sessions. Furthermore, if the user is relaxed, the practice unit can sequentially perform detailed practice sessions. Furthermore, if the user is in a hurry, the practice unit can perform the most important practice session first. This allows for efficient practice sessions by determining the priority of practice sessions according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the practice unit may be performed using the generation AI, or may be performed without the generation AI. For example, the practice unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of practice sessions.

[0083] The practice unit can select a practice method based on the user's geographical location information during practice. For example, if the user is in a specific area, the practice unit can suggest a practice method related to that area. Furthermore, if the user is traveling, the practice unit can suggest a practice method related to the travel destination. Furthermore, if the user is participating in a specific event, the practice unit can suggest a practice method related to the event. This allows the optimal practice method to be selected by taking the user's geographical location information into consideration, enabling effective practice. Some or all of the above-described processing in the practice unit may be performed using or without the generation AI. For example, the practice unit can input the user's geographical location information into the generation AI and have the generation AI select a practice method.

[0084] During practice, the practice unit can analyze the user's social media activity to suggest practice content. For example, the practice unit can suggest practice content related to topics frequently mentioned by the user on social media. The practice unit can also suggest practice content related to areas of interest from the user's social media activity. Furthermore, the practice unit can analyze the content posted by accounts the user follows on social media to suggest related practice content. In this way, by analyzing the user's social media activity, relevant practice content can be suggested, enabling effective practice. Some or all of the above-described processing in the practice unit may be performed using or without the generation AI. For example, the practice unit can input the user's social media data into the generation AI and have the generation AI execute the practice content suggestions.

[0085] The presentation support system further includes a feedback unit that estimates the user's emotions and adjusts the feedback display method based on the estimated user emotions. For example, if the user is nervous, the feedback unit displays the feedback in a gentle tone, providing a sense of security. Furthermore, if the user is relaxed, the feedback unit can display detailed feedback and specifically indicate areas for improvement. Furthermore, if the user is in a hurry, the feedback unit can display concise feedback that focuses on the key points and quickly communicate areas for improvement. This allows for effective feedback to be provided by adjusting the feedback display method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using the generation AI, or may be performed without the generation AI. For example, the feedback unit may input the user's emotion data into the generation AI and have the generation AI adjust the feedback display method.

[0086] When collecting feedback, the feedback unit can adjust the current feedback by referring to past feedback data. For example, the feedback unit can optimize the current feedback based on feedback the user has received in the past. The feedback unit can also analyze past feedback data, extract common improvements, and reflect them in the current feedback. Furthermore, the feedback unit can provide effective feedback by referring to the user's past feedback history. In this way, by referring to the past feedback data, the current feedback can be optimized and effective improvements can be provided. Some or all of the above-mentioned processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input past feedback data into the generation AI and cause the generation AI to adjust the current feedback.

[0087] When collecting feedback, the feedback unit can apply a specific feedback analysis method to each presentation category. For example, in the case of a technical presentation, the feedback unit can apply a feedback analysis method that emphasizes technical details. In the case of a marketing presentation, the feedback unit can also apply a feedback analysis method that emphasizes visual elements. In addition, in the case of an educational presentation, the feedback unit can also apply a feedback analysis method that emphasizes educational elements. In this way, by applying different feedback analysis methods to each presentation category, effective feedback can be provided. Some or all of the above-mentioned processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input presentation category data into the generation AI and cause the generation AI to apply a specific feedback analysis method.

[0088] The presentation support system further includes a feedback unit that estimates the user's emotions and adjusts the importance of feedback based on the estimated user emotions. For example, if the user is nervous, the feedback unit prioritizes displaying important feedback to provide a sense of security. Furthermore, if the user is relaxed, the feedback unit can sequentially display detailed feedback to promote overall improvement. Furthermore, if the user is in a hurry, the feedback unit can display the most important feedback first to quickly inform the user of areas for improvement. This allows for effective feedback by adjusting the importance of feedback according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using the generation AI, or may be performed without the generation AI. For example, the feedback unit may input the user's emotion data into the generation AI and have the generation AI adjust the importance of the feedback.

[0089] When collecting feedback, the feedback unit can analyze changes in feedback based on the presentation submission time. For example, when the submission time is approaching, the feedback unit prioritizes collecting important feedback. The feedback unit can also collect detailed feedback when there is ample time left until the submission time. Furthermore, the feedback unit can analyze changes in feedback depending on the submission time and extract effective improvements. In this way, by analyzing changes in feedback based on the presentation submission time, effective improvements can be provided. Some or all of the above-mentioned processing in the feedback unit may be performed using or without the generation AI. For example, the feedback unit can input presentation submission time data into the generation AI and have the generation AI perform an analysis of feedback changes.

[0090] When collecting feedback, the feedback unit can analyze the feedback by referring to market data for the presentation. For example, the feedback unit can evaluate the content of the presentation based on related market data. The feedback unit can also compare the presentation with competing presentations by referring to the market data. Furthermore, the feedback unit can specifically indicate areas for improvement in the presentation based on the market data. This makes it possible to provide effective feedback by referring to the market data for the presentation. Some or all of the above-mentioned processing in the feedback unit can be performed using or without the generation AI. For example, the feedback unit can input related market data into the generation AI and have the generation AI analyze the feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, practice unit, and feedback unit, described above, 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 the user inputs the theme and purpose of the presentation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a proposed presentation structure using a generation AI. The practice unit is realized, for example, by the control unit 46A of the smart device 14, and evaluates the content and style of the user's speech in real time and provides feedback on areas for improvement. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects feedback from participants and makes suggestions for improving the next presentation. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, practice unit, and feedback unit, described above, 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 the user inputs the theme and purpose of the presentation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a proposed presentation structure using a generation AI. The practice unit is realized, for example, by the control unit 46A of the smart glasses 214, and evaluates the content and style of the user's speech in real time and provides feedback on areas for improvement. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects feedback from participants and makes suggestions for improving the next presentation. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, practice unit, and feedback 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 the user inputs the theme and purpose of the presentation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a proposed presentation structure using a generation AI. The practice unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and evaluates the content and style of speech of the user in real time and provides feedback on areas for improvement. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects feedback from participants and makes suggestions for improving the next presentation. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, practice unit, and feedback 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 the user inputs the theme and purpose of the presentation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a presentation structure proposal using a generative AI. The practice unit is realized, for example, by the control unit 46A of the robot 414, and evaluates the content and speaking style of the user in real time and provides feedback on areas for improvement. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects feedback from participants and makes suggestions for improving the next presentation.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The presentation support system may further include a practice unit that analyzes the user's past presentation data and proposes an optimal practice schedule. For example, the practice unit may propose optimal practice time and frequency based on data from the user's past presentations. The practice unit may also analyze the success rate and feedback of the user's past presentations and propose effective practice methods. Furthermore, the practice unit may provide a practice plan focused on improving specific skills based on the user's past presentation data. This allows for more effective practice by utilizing the user's past data.

[0093] The generator can also generate slides in different styles depending on the purpose of the user's presentation. For example, for a business presentation, it can generate slides with a professional and simple design. For an academic presentation, it can generate slides containing detailed data and graphs. Furthermore, for an entertainment presentation, it can generate visually appealing and interactive slides. This allows the user to provide optimal slides depending on the purpose of the presentation, resulting in an effective presentation.

[0094] The practice module can also provide specific practice scenarios based on the content of the user's presentation. For example, for a technical presentation, a practice scenario focusing on technical terms and technical details can be provided. For a marketing presentation, a practice scenario focusing on persuasive speaking and visual elements can be provided. For an educational presentation, a practice scenario focusing on educational and interactive elements can be provided. This allows for effective practice tailored to the content of the presentation.

[0095] The feedback unit can also customize feedback based on the audience of the user's presentation. For example, if the audience is business partners, feedback from a business perspective can be provided. If the audience is an academic audience, feedback from an academic perspective can be provided. Furthermore, if the audience is a general audience, feedback focused on clarity and entertainment value can be provided. This allows for effective feedback to be provided according to the audience of the presentation.

[0096] The feedback unit can also adjust the feedback based on the time of day the user is giving their presentation. For example, for a morning presentation, the feedback unit can provide energetic and positive feedback. For a midday presentation, the feedback unit can provide specific points for improvement to help the user maintain focus. For an evening presentation, the feedback unit can provide feedback in a more relaxed tone. This allows the user to receive effective feedback depending on the time of day the presentation is given.

[0097] The reception unit can also estimate the user's emotions and suggest presentation themes and objectives based on the estimated user emotions. For example, if the user is excited, a challenging and new theme can be suggested. If the user is calm, a theme based on detailed analysis and data can be suggested. Furthermore, if the user is anxious, a simple and easy-to-understand theme can be suggested. This makes it possible to suggest the most appropriate theme and objective according to the user's emotions and increase the success rate of presentations.

[0098] The reception unit can also analyze the user's past presentation history and propose an optimal presentation structure. For example, it can propose a new structure based on the user's past successful presentation structures. It can also provide advice to help the user avoid presentation structures that failed in the past. Furthermore, it can extract specific patterns and trends from the user's past presentation history and propose an optimal structure based on them. This makes it possible to utilize the user's past data to achieve more effective presentations.

[0099] The reception unit can also automatically update the theme and objective of the presentation based on the user's current project or area of ​​interest. For example, if the user starts a new project, the reception unit can automatically suggest themes and objectives related to that project. Also, if the user begins to have a new area of ​​interest, the reception unit can suggest themes and objectives related to that area. Furthermore, the reception unit can dynamically update the theme and objective according to the progress of the user's project or area of ​​interest. This allows the reception unit to provide the most appropriate theme and objective according to the user's current situation, thereby improving the effectiveness of the presentation.

[0100] The reception unit can also estimate the user's emotions and adjust the presentation practice method based on the estimated user emotions. For example, if the user is nervous, the reception unit can suggest a practice method to relax the user. If the user is relaxed, the reception unit can also suggest a challenging practice method. Furthermore, if the user is in a hurry, the reception unit can suggest a short and effective practice method. This allows the user to be provided with the optimal practice method according to their emotions and to prepare for the presentation efficiently.

[0101] The reception unit can also customize the theme and purpose of the presentation based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can suggest a theme and purpose related to that area. If the user is traveling, the reception unit can suggest a theme and purpose related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can suggest a theme and purpose related to the event. This allows the reception unit to provide the optimal theme and purpose taking into account the user's geographical location information, thereby improving the effectiveness of the presentation.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit receives input from the user about the theme and purpose of the presentation. For example, the user can input the theme and purpose of a business presentation or academic presentation. Step 2: The generation unit creates a draft presentation structure based on the information received by the reception unit. For example, the generation unit generates the content and order of slides and the main points to be discussed. Using the generation AI, the generation unit generates the content and order of slides and the main points to be discussed based on the information entered by the user. For example, the generation AI automatically generates the content of the slides based on the information entered by the user, arranges the order of the slides logically, extracts important points, and generates the main points to be discussed. Step 3: The practice unit supports presentation practice based on the proposed structure created by the generation unit. For example, the practice unit evaluates the user's speech content and speaking style in real time and provides feedback on areas for improvement. Using the generation AI, the practice unit evaluates the user's speech content and speaking style in real time, analyzing the clarity of pronunciation, flow of speech, tone and speed of voice, and suggests specific areas for improvement. Step 4: The feedback department makes suggestions to improve the next presentation based on the feedback provided by the practice department. For example, the feedback department collects and analyzes feedback from participants. Using generative AI, the feedback is collected using questionnaires and voice analysis, and the collected feedback is analyzed to make specific suggestions to improve the next presentation.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving input of a theme or purpose of a presentation; a generation unit that generates a presentation configuration plan based on the information received by the reception unit; a practice unit that supports practice of a presentation based on the composition plan created by the generation unit; a feedback section that makes suggestions to improve the next presentation based on the feedback provided by the practice section; Equipped with A system characterized by:

2. The generation unit Generate slide content, order, and talking points based on user-entered information The system of claim 1 .

3. The practice section: Evaluate what users say or how they say it in real time and provide feedback on how to improve The system of claim 1 .

4. The feedback unit Collect and analyze participant feedback The system of claim 1 .

5. The feedback unit Make suggestions to improve your next presentation The system of claim 1 .

6. The reception unit Inferring user emotions and adjusting the input method for a theme or purpose based on the estimated user emotions The system of claim 1 .

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

8. The reception unit When entering a subject or purpose, filter your input based on your current projects or areas of interest The system of claim 1 .

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