System

Abstract

An object of the system according to the embodiment is to automate the progress of a conference, the arrangement of agenda, and the adjustment of statements, and to improve the productivity and quality of the conference.SOLUTION: A system according to an embodiment includes a progress management unit, an agenda organization unit, a speech adjustment unit, and a minutes creation unit. The progress management unit manages the progress of the conference. The agenda organization unit organizes the agenda of the conference managed by the progress management unit. The statement adjusting unit adjusts a statement based on the agenda organized by the agenda organizing unit. The minutes creating unit records the statement contents adjusted by the statement adjusting unit and creates minutes.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology relies on humans to guide meetings, organize agendas, and coordinate comments, which limits the ability to improve meeting productivity and quality.

[0005] The system according to the embodiment aims to improve the productivity and quality of meetings by automating the progress of meetings, organizing agenda items, and adjusting comments. [Means for solving the problem]

[0006] The system according to the embodiment includes a progress management unit, an agenda organization unit, a speech adjustment unit, and a minutes creation unit. The progress management unit manages the progress of the meeting. The agenda organization unit organizes the agenda of the meeting managed by the progress management unit. The speech adjustment unit adjusts the speech based on the agenda organized by the agenda organization unit. The minutes creation unit records the speech content adjusted by the speech adjustment unit and creates minutes. [Effects of the Invention]

[0007] The system according to the embodiment automates the progress of a meeting, the organization of an agenda, and the adjustment of remarks, thereby improving the productivity and quality of the meeting. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A conference facilitation system according to an embodiment of the present invention is a system that manages the progress of a conference, organizes the agenda, coordinates comments, and creates minutes of the conference. As a result, the conference facilitation system can improve the productivity of the conference, clarify the goals, and improve the quality of the conference.

[0029] A conference facilitation system according to an embodiment includes a progress management unit, an agenda organization unit, a speech adjustment unit, and a minutes creation unit. The progress management unit manages the progress of the conference. For example, the progress management unit checks the agenda at the start of the conference and clarifies the purpose and goals of the conference. The progress management unit monitors the progress of the conference in real time and allocates appropriate time for each agenda item. For example, the progress management unit presents the purpose of the conference and encourages participants to move on to the next agenda item. The agenda organization unit organizes the agenda items of the conference managed by the progress management unit. For example, the agenda organization unit prioritizes the agenda items and evaluates their relevance. If a participant goes off track, the agenda organization unit encourages participants to return to the agenda item. The speech adjustment unit adjusts the speech based on the agenda items organized by the agenda organization unit. For example, when a participant wishes to speak, the speech adjustment unit manages the order in which participants speak and encourages them to speak at an appropriate time. The speech adjustment unit can also instruct the order of speeches and limit speaking time. The minutes-taking unit records the remarks adjusted by the remarks adjustment unit and creates minutes. For example, the minutes-taking unit converts the remarks of participants into text and compiles them as minutes. The minutes-taking unit automatically generates minutes after the meeting and shares them with participants. In this way, the meeting facilitation system can improve meeting productivity, clarify goals, and improve the quality of meetings.

[0030] The progress management unit can maximize the efficiency of meetings by learning from past meeting data and proposing an optimal progress pattern. The progress management unit, for example, analyzes past meeting data and proposes an optimal progress pattern. For example, it adjusts the progress of the current meeting based on progress methods that were successful in past meetings. The progress management unit also learns from past meeting data and proposes the optimal time allocation for each agenda item. For example, it allocates appropriate time to each agenda item based on past data. The progress management unit also analyzes past meeting data and learns the speaking patterns of participants. For example, it limits the speaking time of participants who speak a lot and encourages participants who speak less to speak. In this way, it is possible to maximize the efficiency of meetings by learning from past meeting data and proposing an optimal progress pattern.

[0031] The progress management unit can monitor the health status of participants and suggest break times when their heart rates and stress levels increase. The progress management unit, for example, monitors participants' heart rates and suggests breaks when their stress levels increase. For example, it encourages participants to take a break when their heart rates exceed a certain threshold. The progress management unit also monitors participants' stress levels and suggests appropriate break times. For example, it provides refreshment time when stress levels increase. The progress management unit also monitors participants' health status and suggests appropriate break times. For example, it encourages participants to take a break when their heart rates or stress levels increase. In this way, the quality of the meeting can be improved by monitoring participants' health status and suggesting appropriate break times.

[0032] The progress management unit can select the most appropriate speaker based on the participants' expertise and past speech history to deepen the discussion. The progress management unit, for example, analyzes the participants' expertise and selects the most appropriate speaker. For example, it encourages participants with expertise on a specific topic to speak. The progress management unit also analyzes the participants' past speech history to select the most appropriate speaker. For example, it encourages participants who have spoken on related topics in the past to speak. The progress management unit also selects the most appropriate speaker based on the participants' expertise and past speech history to deepen the discussion. For example, it encourages participants with expertise to speak to deepen the discussion. In this way, the quality of the meeting can be improved by selecting the most appropriate speaker based on the participants' expertise and past speech history to deepen the discussion.

[0033] The agenda organizing unit can automatically collect relevant external data and provide it as reference material for discussion. The agenda organizing unit, for example, automatically collects the latest market trends and uses them to organize the agenda. For example, it provides reference material for discussion based on the latest market reports and competitive information. The agenda organizing unit also automatically collects relevant external data and uses them to organize the agenda. For example, it provides reference material for discussion based on the latest technology trends and industry news. The agenda organizing unit also automatically collects competitive information and uses them to organize the agenda. For example, it provides reference material for discussion based on the strategies and market shares of competing companies. In this way, the quality of meetings can be improved by automatically collecting relevant external data and providing it as reference material for discussion.

[0034] The speech adjustment unit can determine the optimal speaking order based on the participants' expertise and past speech content. The speech adjustment unit, for example, analyzes the participants' expertise and determines the optimal speaking order. For example, it gives priority to encouraging participants with expertise on a specific topic to speak. The speech adjustment unit also analyzes the participants' past speech content and determines the optimal speaking order. For example, it gives priority to encouraging participants who have spoken on related topics in the past to speak. The speech adjustment unit also determines the optimal speaking order based on the participants' expertise and past speech content. For example, it gives priority to encouraging participants with expertise to speak, deepening the discussion. In this way, the quality of the meeting can be improved by determining the optimal speaking order based on the participants' expertise and past speech content.

[0035] The agenda organizing unit can automatically collect opinions from experts in different industries and fields to provide a multifaceted perspective for the discussion. The agenda organizing unit, for example, automatically collects opinions from experts in different industries to help organize the agenda. For example, reference materials for the discussion are provided based on the opinions of experts in technology, design, and marketing. The agenda organizing unit also automatically collects opinions from experts in different fields to help organize the agenda. For example, reference materials for the discussion are provided based on the opinions of experts in fields such as medicine, education, and energy. The agenda organizing unit also automatically collects opinions from experts in different industries and fields to help organize the agenda. For example, a multifaceted perspective for the discussion is provided based on opinions from different perspectives. This makes it possible to improve the quality of meetings by automatically collecting opinions from experts in different industries and fields and providing a multifaceted perspective for the discussion.

[0036] The speech adjustment unit translates the content of participants' remarks in real time, enabling smooth communication even in international conferences. The speech adjustment unit, for example, translates the content of participants' remarks in real time, enabling smooth communication even in international conferences. For example, translation into multiple languages ​​such as English, Japanese, and Chinese. The speech adjustment unit also translates the content of participants' remarks in real time, supporting communication between participants who speak different languages. For example, converting the content of remarks into text, translating it, and displaying it. The speech adjustment unit also translates the content of participants' remarks in real time, enabling smooth communication even in international conferences. For example, translating the content of remarks into audio and providing it to the participants. In this way, the quality of the conference can be improved by translating the content of participants' remarks in real time and enabling smooth communication even in international conferences.

[0037] The minutes-taking unit can generate minutes that are easy to read by summarizing what is said and emphasizing important points. The minutes-taking unit can generate minutes that are easy to read by, for example, summarizing what is said and emphasizing important points. For example, the unit can summarize the main points of each statement in bullet points. The minutes-taking unit can also generate minutes that are easy to read by summarizing what is said and emphasizing important points. For example, the unit can highlight important statements and decisions in bold or in color. The minutes-taking unit can also generate minutes that are easy to read by summarizing what is said and emphasizing important points. For example, the unit can summarize the main points for each agenda item and add headings. In this way, by summarizing what is said and emphasizing important points, easy-to-read minutes can be generated, improving the quality of meetings.

[0038] The minutes-taking unit can automatically tag the content of statements to make them easier to search later. The minutes-taking unit, for example, automatically tags the content of statements to make them easier to search later. For example, it assigns keywords related to each statement as a tag. The minutes-taking unit also automatically tags the content of statements to make them easier to search later. For example, it assigns tags to each agenda item and groups related statements together. The minutes-taking unit also automatically tags the content of statements to make them easier to search later. For example, it assigns specific tags to important statements and decisions to make them easier to search. In this way, the quality of meetings can be improved by automatically tagging the content of statements to make them easier to search later.

[0039] The minutes-taking unit can also record the remarks as audio or video clips to provide minutes in multimedia format. The minutes-taking unit, for example, records the remarks as audio clips to provide minutes in multimedia format. For example, important remarks are saved as audio files. The minutes-taking unit can also record the remarks as video clips to provide minutes in multimedia format. For example, highlights of the meeting are saved as video clips. The minutes-taking unit can also record the remarks as audio or video clips to provide minutes in multimedia format. For example, the main points of the remarks can be played back as audio or video. In this way, the quality of the meeting can be improved by recording the remarks as audio or video clips and providing minutes in multimedia format.

[0040] The minutes-taking unit can automatically translate the content of remarks into different languages ​​and generate minutes that can be used by international teams. The minutes-taking unit, for example, automatically translates the content of remarks and generates minutes in different languages. For example, it translates into multiple languages ​​such as English, Japanese, and Chinese. The minutes-taking unit also automatically translates the content of remarks and generates minutes that can be used by international teams. For example, it translates the content of remarks in real time and reflects it in the minutes. The minutes-taking unit also automatically translates the content of remarks and generates minutes in different languages. For example, it converts the content of remarks into text, translates it, and reflects it in the minutes. In this way, the quality of meetings can be improved by automatically translating the content of remarks into different languages ​​and generating minutes that can be used by international teams.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The progress management department manages the progress of the meeting. For example, the progress management department checks the agenda at the start of the meeting and clarifies the purpose and goals of the meeting. The progress management department monitors the progress of the meeting in real time and allocates appropriate time for each agenda item. For example, the progress management department presents the purpose of the meeting and encourages participants to move on to the next agenda item. The agenda organization department organizes the agenda items of the meeting managed by the progress management department. For example, the agenda organization department prioritizes the agenda items and evaluates their relevance. If the agenda item goes off track, the agenda organization department encourages participants to return to the agenda item. The speech adjustment department adjusts comments based on the agenda items organized by the agenda organization department. For example, when a participant wishes to speak, the speech adjustment department manages the order and encourages them to speak at an appropriate time. The speech adjustment department can also instruct the order of comments and limit the speaking time. The minutes creation department records the comments adjusted by the speech adjustment department and creates minutes. For example, the minutes creation department converts the participants' comments into text and compiles them as minutes. The minutes-taking module automatically generates minutes after the meeting and shares them with participants. This allows the meeting facilitation system to improve meeting productivity, clarify goals, and improve the quality of meetings.

[0043] The progress management unit can maximize meeting efficiency by learning from past meeting data and proposing the optimal progress pattern. For example, it can analyze past meeting data and propose the optimal progress pattern. For example, it can adjust the progress of the current meeting based on the progress methods that were successful in past meetings. The progress management unit also learns from past meeting data and proposes the optimal time allocation for each agenda item. For example, it can allocate appropriate time to each agenda item based on past data. The progress management unit also analyzes past meeting data and learns the speaking patterns of participants. For example, it can limit the speaking time of participants who speak a lot and encourage participants who speak less to speak. In this way, it can maximize meeting efficiency by learning from past meeting data and proposing the optimal progress pattern.

[0044] The progress management unit can monitor the health status of participants and suggest break times when their heart rates and stress levels increase. For example, it can monitor participants' heart rates and suggest breaks when their stress levels increase. For example, it can encourage participants to take a break when their heart rates exceed a certain threshold. The progress management unit can also monitor participants' stress levels and suggest appropriate break times. For example, it can provide refreshment time when stress levels increase. The progress management unit can also monitor participants' health status and suggest appropriate break times. For example, it can encourage participants to take a break when their heart rates or stress levels increase. In this way, the quality of meetings can be improved by monitoring participants' health status and suggesting appropriate break times.

[0045] The progress management unit can select the most appropriate speaker based on the participants' expertise and past speech history to deepen the discussion. For example, it can analyze the participants' expertise and select the most appropriate speaker. For example, it can encourage participants with expertise on a specific topic to speak. The progress management unit can also analyze the participants' past speech history to select the most appropriate speaker. For example, it can encourage participants who have spoken on related topics in the past to speak. The progress management unit can also select the most appropriate speaker based on the participants' expertise and past speech history to deepen the discussion. For example, it can encourage participants with expertise to speak to deepen the discussion. In this way, the quality of the meeting can be improved by selecting the most appropriate speaker based on the participants' expertise and past speech history to deepen the discussion.

[0046] The agenda organizing unit can automatically collect relevant external data and provide it as reference material for discussions. For example, it can automatically collect the latest market trends and use them to organize the agenda. For example, it can provide reference material for discussions based on the latest market reports and competitive information. The agenda organizing unit can also automatically collect relevant external data and use them to organize the agenda. For example, it can provide reference material for discussions based on the latest technology trends and industry news. The agenda organizing unit can also automatically collect competitive information and use them to organize the agenda. For example, it can provide reference material for discussions based on the strategies and market shares of competing companies. In this way, the quality of meetings can be improved by automatically collecting relevant external data and providing it as reference material for discussions.

[0047] The speech adjustment unit can determine the optimal speaking order based on the participants' expertise and past speech content. For example, it can analyze the participants' expertise to determine the optimal speaking order. For example, it can prioritize speaking for participants with expertise on a specific topic. The speech adjustment unit can also analyze the participants' past speech content to determine the optimal speaking order. For example, it can prioritize speaking for participants who have previously spoken on related topics. The speech adjustment unit can also determine the optimal speaking order based on the participants' expertise and past speech content. For example, it can prioritize speaking for participants with expertise, deepening the discussion. In this way, the quality of the meeting can be improved by determining the optimal speaking order based on the participants' expertise and past speech content.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The progress management department manages the progress of the meeting. For example, the progress management department reviews the agenda at the start of the meeting and clarifies the purpose and goals of the meeting. The progress management department also monitors the progress of the meeting in real time and allocates appropriate time for each agenda item. For example, the progress management department presents the purpose of the meeting and encourages participants to move on to the next agenda item. Step 2: The agenda organizer organizes the meeting agenda managed by the progress manager. For example, the agenda organizer prioritizes the agenda items, evaluates their relevance, and encourages participants to return to the agenda if they go off track. Step 3: The speech moderator adjusts the speeches based on the agenda organized by the agenda organizer. For example, when a participant wishes to speak, the speech moderator manages the order in which they speak and encourages them to speak at the appropriate time. The speech moderator can also instruct the order in which participants speak and limit the speaking time. Step 4: The minutes-taking unit records the remarks adjusted by the speech adjustment unit and creates minutes. For example, the minutes-taking unit converts the remarks of the participants into text and compiles them as minutes. After the meeting, the minutes-taking unit automatically generates the minutes and shares them with the participants.

[0050] (Example 2) A conference facilitation system according to an embodiment of the present invention is a system that manages the progress of a conference, organizes the agenda, coordinates comments, and creates minutes of the conference. As a result, the conference facilitation system can improve the productivity of the conference, clarify the goals, and improve the quality of the conference.

[0051] A conference facilitation system according to an embodiment includes a progress management unit, an agenda organization unit, a speech adjustment unit, and a minutes creation unit. The progress management unit manages the progress of the conference. For example, the progress management unit checks the agenda at the start of the conference and clarifies the purpose and goals of the conference. The progress management unit monitors the progress of the conference in real time and allocates appropriate time for each agenda item. For example, the progress management unit presents the purpose of the conference and encourages participants to move on to the next agenda item. The agenda organization unit organizes the agenda items of the conference managed by the progress management unit. For example, the agenda organization unit prioritizes the agenda items and evaluates their relevance. If a participant goes off track, the agenda organization unit encourages participants to return to the agenda item. The speech adjustment unit adjusts the speech based on the agenda items organized by the agenda organization unit. For example, when a participant wishes to speak, the speech adjustment unit manages the order in which participants speak and encourages them to speak at an appropriate time. The speech adjustment unit can also instruct the order of speeches and limit speaking time. The minutes-taking unit records the remarks adjusted by the remarks adjustment unit and creates minutes. For example, the minutes-taking unit converts the remarks of participants into text and compiles them as minutes. The minutes-taking unit automatically generates minutes after the meeting and shares them with participants. In this way, the meeting facilitation system can improve meeting productivity, clarify goals, and improve the quality of meetings.

[0052] The progress management unit can analyze participants' facial expressions and tones of voice to evaluate the atmosphere of the meeting in real time and adjust the progress as necessary. For example, the progress management unit uses a camera and microphone during the meeting to analyze participants' facial expressions and tones of voice to evaluate the atmosphere of the meeting in real time. For example, if a participant appears tired, the progress management unit suggests a break. The progress management unit can also analyze participants' tones of voice and suggest light topics to help participants relax if tension is building. For example, the progress management unit can temporarily halt the meeting and provide a break. The progress management unit can also analyze participants' facial expressions to adjust the progress of the meeting if negative emotions are building. For example, the progress management unit can change the agenda or temporarily halt the discussion to suggest a break. In this way, the quality of the meeting can be improved by evaluating the atmosphere of the meeting in real time and adjusting the progress.

[0053] The progress management unit can maximize the efficiency of meetings by learning from past meeting data and proposing an optimal progress pattern. The progress management unit, for example, analyzes past meeting data and proposes an optimal progress pattern. For example, it adjusts the progress of the current meeting based on progress methods that were successful in past meetings. The progress management unit also learns from past meeting data and proposes the optimal time allocation for each agenda item. For example, it allocates appropriate time to each agenda item based on past data. The progress management unit also analyzes past meeting data and learns the speaking patterns of participants. For example, it limits the speaking time of participants who speak a lot and encourages participants who speak less to speak. In this way, it is possible to maximize the efficiency of meetings by learning from past meeting data and proposing an optimal progress pattern.

[0054] The progress management unit can use the emotion estimation function to analyze the emotions of participants in real time and suggest refreshment time if negative emotions become intense. The progress management unit, for example, analyzes the facial expressions and tone of voice of participants and suggests refreshment time if negative emotions become intense. For example, it may temporarily halt the meeting to provide refreshment time. The progress management unit also analyzes the emotions of participants in real time and suggests refreshment time if stress becomes intense. For example, it may suggest light exercise or relaxing music. The progress management unit also analyzes the emotions of participants and suggests changing the agenda or temporarily halting the discussion to provide refreshment time if negative emotions become intense. In this way, the quality of the meeting can be improved by analyzing the emotions of participants in real time and suggesting refreshment time if negative emotions become intense.

[0055] The progress management unit can monitor the health status of participants and suggest break times when their heart rates and stress levels increase. The progress management unit, for example, monitors participants' heart rates and suggests breaks when their stress levels increase. For example, it encourages participants to take a break when their heart rates exceed a certain threshold. The progress management unit also monitors participants' stress levels and suggests appropriate break times. For example, it provides refreshment time when stress levels increase. The progress management unit also monitors participants' health status and suggests appropriate break times. For example, it encourages participants to take a break when their heart rates or stress levels increase. In this way, the quality of the meeting can be improved by monitoring participants' health status and suggesting appropriate break times.

[0056] The progress management unit can select the most appropriate speaker based on the participants' expertise and past speech history to deepen the discussion. The progress management unit, for example, analyzes the participants' expertise and selects the most appropriate speaker. For example, it encourages participants with expertise on a specific topic to speak. The progress management unit also analyzes the participants' past speech history to select the most appropriate speaker. For example, it encourages participants who have spoken on related topics in the past to speak. The progress management unit also selects the most appropriate speaker based on the participants' expertise and past speech history to deepen the discussion. For example, it encourages participants with expertise to speak to deepen the discussion. In this way, the quality of the meeting can be improved by selecting the most appropriate speaker based on the participants' expertise and past speech history to deepen the discussion.

[0057] The progress management unit can use the emotion estimation function to analyze the emotions of participants and suggest interactive games and activities to elicit positive emotions. The progress management unit, for example, analyzes the emotions of participants and suggests interactive games to elicit positive emotions. For example, it suggests simple quizzes or team building games. The progress management unit also analyzes the emotions of participants and suggests activities to elicit positive emotions. For example, it suggests stretching or light exercise to relax. The progress management unit also analyzes the emotions of participants and suggests interactive games and activities to elicit positive emotions. For example, it suggests games that promote communication between participants. In this way, the quality of the meeting can be improved by analyzing the emotions of participants and suggesting interactive games and activities to elicit positive emotions.

[0058] The agenda organizing unit can automatically collect relevant external data and provide it as reference material for discussion. The agenda organizing unit, for example, automatically collects the latest market trends and uses them to organize the agenda. For example, it provides reference material for discussion based on the latest market reports and competitive information. The agenda organizing unit also automatically collects relevant external data and uses them to organize the agenda. For example, it provides reference material for discussion based on the latest technology trends and industry news. The agenda organizing unit also automatically collects competitive information and uses them to organize the agenda. For example, it provides reference material for discussion based on the strategies and market shares of competing companies. In this way, the quality of meetings can be improved by automatically collecting relevant external data and providing it as reference material for discussion.

[0059] The speech adjustment unit can determine the optimal speaking order based on the participants' expertise and past speech content. The speech adjustment unit, for example, analyzes the participants' expertise and determines the optimal speaking order. For example, it gives priority to encouraging participants with expertise on a specific topic to speak. The speech adjustment unit also analyzes the participants' past speech content and determines the optimal speaking order. For example, it gives priority to encouraging participants who have spoken on related topics in the past to speak. The speech adjustment unit also determines the optimal speaking order based on the participants' expertise and past speech content. For example, it gives priority to encouraging participants with expertise to speak, deepening the discussion. In this way, the quality of the meeting can be improved by determining the optimal speaking order based on the participants' expertise and past speech content.

[0060] The speech adjustment unit can use the emotion estimation function to analyze the emotions of the participants and make suggestions for cooling down if the discussion gets too heated. The speech adjustment unit, for example, analyzes the emotions of the participants and makes suggestions for cooling down if the discussion gets too heated. For example, by temporarily pausing the discussion and setting aside a refresh time. The speech adjustment unit also analyzes the emotions of the participants and makes suggestions for cooling down if stress is building up. For example, by suggesting light exercise or relaxing music. The speech adjustment unit also analyzes the emotions of the participants and makes suggestions for cooling down if the discussion gets too heated. For example, by changing the agenda or temporarily pausing the discussion and setting aside a refresh time. In this way, the quality of the meeting can be improved by analyzing the emotions of the participants and making suggestions for cooling down if the discussion gets too heated.

[0061] The agenda organizing unit can automatically collect opinions from experts in different industries and fields to provide a multifaceted perspective for the discussion. The agenda organizing unit, for example, automatically collects opinions from experts in different industries to help organize the agenda. For example, reference materials for the discussion are provided based on the opinions of experts in technology, design, and marketing. The agenda organizing unit also automatically collects opinions from experts in different fields to help organize the agenda. For example, reference materials for the discussion are provided based on the opinions of experts in fields such as medicine, education, and energy. The agenda organizing unit also automatically collects opinions from experts in different industries and fields to help organize the agenda. For example, a multifaceted perspective for the discussion is provided based on opinions from different perspectives. This makes it possible to improve the quality of meetings by automatically collecting opinions from experts in different industries and fields and providing a multifaceted perspective for the discussion.

[0062] The speech adjustment unit translates the content of participants' remarks in real time, enabling smooth communication even in international conferences. The speech adjustment unit, for example, translates the content of participants' remarks in real time, enabling smooth communication even in international conferences. For example, translation into multiple languages ​​such as English, Japanese, and Chinese. The speech adjustment unit also translates the content of participants' remarks in real time, supporting communication between participants who speak different languages. For example, converting the content of remarks into text, translating it, and displaying it. The speech adjustment unit also translates the content of participants' remarks in real time, enabling smooth communication even in international conferences. For example, translating the content of remarks into audio and providing it to the participants. In this way, the quality of the conference can be improved by translating the content of participants' remarks in real time and enabling smooth communication even in international conferences.

[0063] The speech adjustment unit can use the emotion estimation function to analyze the emotions of participants and propose incentives to increase motivation when the discussion stagnates. The speech adjustment unit, for example, analyzes the emotions of participants and proposes incentives to increase motivation when the discussion stagnates. For example, it awards points and provides rewards according to the progress of the discussion. The speech adjustment unit also analyzes the emotions of participants and proposes incentives to increase motivation when the discussion stagnates. For example, it awards badges and titles according to the progress of the discussion. The speech adjustment unit also analyzes the emotions of participants and proposes incentives to increase motivation when the discussion stagnates. For example, it provides benefits and rewards according to the progress of the discussion. In this way, by analyzing the emotions of participants and proposing incentives to increase motivation when the discussion stagnates, the quality of the meeting can be improved.

[0064] The minutes-taking unit can generate minutes that are easy to read by summarizing what is said and emphasizing important points. The minutes-taking unit can generate minutes that are easy to read by, for example, summarizing what is said and emphasizing important points. For example, the unit can summarize the main points of each statement in bullet points. The minutes-taking unit can also generate minutes that are easy to read by summarizing what is said and emphasizing important points. For example, the unit can highlight important statements and decisions in bold or in color. The minutes-taking unit can also generate minutes that are easy to read by summarizing what is said and emphasizing important points. For example, the unit can summarize the main points for each agenda item and add headings. In this way, by summarizing what is said and emphasizing important points, easy-to-read minutes can be generated, improving the quality of meetings.

[0065] The minutes-taking unit can automatically tag the content of statements to make them easier to search later. The minutes-taking unit, for example, automatically tags the content of statements to make them easier to search later. For example, it assigns keywords related to each statement as a tag. The minutes-taking unit also automatically tags the content of statements to make them easier to search later. For example, it assigns tags to each agenda item and groups related statements together. The minutes-taking unit also automatically tags the content of statements to make them easier to search later. For example, it assigns specific tags to important statements and decisions to make them easier to search. In this way, the quality of meetings can be improved by automatically tagging the content of statements to make them easier to search later.

[0066] The minutes-taking unit uses the emotion estimation function to reflect the emotional nuances of the remarks in the minutes, allowing the atmosphere of the meeting to be accurately conveyed. The minutes-taking unit, for example, analyzes the emotional nuances of the remarks and reflects them in the minutes. For example, it adds an icon to indicate whether the remarks are positive or negative. The minutes-taking unit also analyzes the emotional nuances of the remarks and reflects them in the minutes. For example, it indicates the intensity of the emotion with a color or a graph. The minutes-taking unit also analyzes the emotional nuances of the remarks and reflects them in the minutes. For example, it explains the tone of the remarks and facial expressions in text. In this way, the emotional nuances of the remarks can be reflected in the minutes, accurately conveying the atmosphere of the meeting and improving the quality of the meeting.

[0067] The minutes-taking unit can also record the remarks as audio or video clips to provide minutes in multimedia format. The minutes-taking unit, for example, records the remarks as audio clips to provide minutes in multimedia format. For example, important remarks are saved as audio files. The minutes-taking unit can also record the remarks as video clips to provide minutes in multimedia format. For example, highlights of the meeting are saved as video clips. The minutes-taking unit can also record the remarks as audio or video clips to provide minutes in multimedia format. For example, the main points of the remarks can be played back as audio or video. In this way, the quality of the meeting can be improved by recording the remarks as audio or video clips and providing minutes in multimedia format.

[0068] The minutes-taking unit can automatically translate the content of remarks into different languages ​​and generate minutes that can be used by international teams. The minutes-taking unit, for example, automatically translates the content of remarks and generates minutes in different languages. For example, it translates into multiple languages ​​such as English, Japanese, and Chinese. The minutes-taking unit also automatically translates the content of remarks and generates minutes that can be used by international teams. For example, it translates the content of remarks in real time and reflects it in the minutes. The minutes-taking unit also automatically translates the content of remarks and generates minutes in different languages. For example, it converts the content of remarks into text, translates it, and reflects it in the minutes. In this way, the quality of meetings can be improved by automatically translating the content of remarks into different languages ​​and generating minutes that can be used by international teams.

[0069] The minutes-taking unit can use the emotion estimation function to collect participants' emotional reactions to the contents of the minutes and propose improvements for the next meeting. The minutes-taking unit, for example, collects participants' emotional reactions to the contents of the minutes and proposes improvements for the next meeting. For example, it identifies improvements based on feedback on the contents of the minutes. The minutes-taking unit also uses the emotion estimation function to collect participants' emotional reactions to the contents of the minutes and proposes improvements for the next meeting. For example, it prioritizes topics that receive many positive reactions. The minutes-taking unit also collects participants' emotional reactions to the contents of the minutes and proposes improvements for the next meeting. For example, it makes suggestions to improve topics that receive many negative reactions. In this way, by collecting participants' emotional reactions to the contents of the minutes and proposing improvements for the next meeting, the quality of the meeting can be improved.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The progress management department manages the progress of the meeting. For example, the progress management department checks the agenda at the start of the meeting and clarifies the purpose and goals of the meeting. The progress management department monitors the progress of the meeting in real time and allocates appropriate time for each agenda item. For example, the progress management department presents the purpose of the meeting and encourages participants to move on to the next agenda item. The agenda organization department organizes the agenda items of the meeting managed by the progress management department. For example, the agenda organization department prioritizes the agenda items and evaluates their relevance. If the agenda item goes off track, the agenda organization department encourages participants to return to the agenda item. The speech adjustment department adjusts comments based on the agenda items organized by the agenda organization department. For example, when a participant wishes to speak, the speech adjustment department manages the order and encourages them to speak at an appropriate time. The speech adjustment department can also instruct the order of comments and limit the speaking time. The minutes creation department records the comments adjusted by the speech adjustment department and creates minutes. For example, the minutes creation department converts the participants' comments into text and compiles them as minutes. The minutes-taking module automatically generates minutes after the meeting and shares them with participants. This allows the meeting facilitation system to improve meeting productivity, clarify goals, and improve the quality of meetings.

[0072] The progress management unit can analyze participants' facial expressions and tone of voice to assess the atmosphere of the meeting in real time and adjust the progress as necessary. For example, a camera and microphone can be used during a meeting to analyze participants' facial expressions and tone of voice to assess the atmosphere of the meeting in real time. For example, if participants appear tired, the progress management unit can suggest a break. The progress management unit can also analyze participants' tone of voice and suggest light topics to help them relax if tension is building. For example, it can temporarily halt the meeting and provide a break. The progress management unit can also analyze participants' facial expressions to adjust the progress of the meeting if negative emotions are building. For example, it can change the agenda or temporarily halt the discussion to suggest a break. In this way, the quality of meetings can be improved by assessing the atmosphere of the meeting in real time and adjusting the progress.

[0073] The progress management unit can maximize meeting efficiency by learning from past meeting data and proposing the optimal progress pattern. For example, it can analyze past meeting data and propose the optimal progress pattern. For example, it can adjust the progress of the current meeting based on the progress methods that were successful in past meetings. The progress management unit also learns from past meeting data and proposes the optimal time allocation for each agenda item. For example, it can allocate appropriate time to each agenda item based on past data. The progress management unit also analyzes past meeting data and learns the speaking patterns of participants. For example, it can limit the speaking time of participants who speak a lot and encourage participants who speak less to speak. In this way, it can maximize meeting efficiency by learning from past meeting data and proposing the optimal progress pattern.

[0074] The progress management unit can use the emotion estimation function to analyze participants' emotions in real time and suggest refreshment time if negative emotions rise. For example, it can analyze participants' facial expressions and tone of voice and suggest refreshment time if negative emotions rise. For example, it can temporarily halt the meeting to provide refreshment time. The progress management unit can also analyze participants' emotions in real time and suggest refreshment time if stress is rising. For example, it can suggest light exercise or relaxing music. The progress management unit can also analyze participants' emotions and suggest changing the agenda or temporarily halting the discussion to provide refreshment time if negative emotions rise. In this way, the quality of meetings can be improved by analyzing participants' emotions in real time and suggesting refreshment time if negative emotions rise.

[0075] The progress management unit can monitor the health status of participants and suggest break times when their heart rates and stress levels increase. For example, it can monitor participants' heart rates and suggest breaks when their stress levels increase. For example, it can encourage participants to take a break when their heart rates exceed a certain threshold. The progress management unit can also monitor participants' stress levels and suggest appropriate break times. For example, it can provide refreshment time when stress levels increase. The progress management unit can also monitor participants' health status and suggest appropriate break times. For example, it can encourage participants to take a break when their heart rates or stress levels increase. In this way, the quality of meetings can be improved by monitoring participants' health status and suggesting appropriate break times.

[0076] The progress management unit can select the most appropriate speaker based on the participants' expertise and past speech history to deepen the discussion. For example, it can analyze the participants' expertise and select the most appropriate speaker. For example, it can encourage participants with expertise on a specific topic to speak. The progress management unit can also analyze the participants' past speech history to select the most appropriate speaker. For example, it can encourage participants who have spoken on related topics in the past to speak. The progress management unit can also select the most appropriate speaker based on the participants' expertise and past speech history to deepen the discussion. For example, it can encourage participants with expertise to speak to deepen the discussion. In this way, the quality of the meeting can be improved by selecting the most appropriate speaker based on the participants' expertise and past speech history to deepen the discussion.

[0077] The progress management unit can use the emotion estimation function to analyze the emotions of participants and suggest interactive games and activities to elicit positive emotions. For example, it can analyze the emotions of participants and suggest interactive games to elicit positive emotions. For example, it can suggest simple quizzes or team building games. The progress management unit can also analyze the emotions of participants and suggest activities to elicit positive emotions. For example, it can suggest stretching or light exercise to relax. The progress management unit can also analyze the emotions of participants and suggest interactive games and activities to elicit positive emotions. For example, it can suggest games that promote communication between participants. In this way, the quality of the meeting can be improved by analyzing the emotions of participants and suggesting interactive games and activities to elicit positive emotions.

[0078] The agenda organizing unit can automatically collect relevant external data and provide it as reference material for discussions. For example, it can automatically collect the latest market trends and use them to organize the agenda. For example, it can provide reference material for discussions based on the latest market reports and competitive information. The agenda organizing unit can also automatically collect relevant external data and use them to organize the agenda. For example, it can provide reference material for discussions based on the latest technology trends and industry news. The agenda organizing unit can also automatically collect competitive information and use them to organize the agenda. For example, it can provide reference material for discussions based on the strategies and market shares of competing companies. In this way, the quality of meetings can be improved by automatically collecting relevant external data and providing it as reference material for discussions.

[0079] The speech adjustment unit can determine the optimal speaking order based on the participants' expertise and past speech content. For example, it can analyze the participants' expertise to determine the optimal speaking order. For example, it can prioritize speaking for participants with expertise on a specific topic. The speech adjustment unit can also analyze the participants' past speech content to determine the optimal speaking order. For example, it can prioritize speaking for participants who have previously spoken on related topics. The speech adjustment unit can also determine the optimal speaking order based on the participants' expertise and past speech content. For example, it can prioritize speaking for participants with expertise, deepening the discussion. In this way, the quality of the meeting can be improved by determining the optimal speaking order based on the participants' expertise and past speech content.

[0080] The speech moderator can use the emotion estimation function to analyze the emotions of the participants and make suggestions for cooling down if the discussion gets too heated. For example, it can analyze the emotions of the participants and make suggestions for cooling down if the discussion gets too heated. For example, it can temporarily halt the discussion and provide a refreshment period. The speech moderator can also analyze the emotions of the participants and make suggestions for cooling down if stress is building up. For example, it can suggest light exercise or relaxing music. The speech moderator can also analyze the emotions of the participants and make suggestions for cooling down if the discussion gets too heated. For example, it can change the agenda or temporarily halt the discussion and provide a refreshment period. In this way, the quality of the meeting can be improved by analyzing the emotions of the participants and making suggestions for cooling down if the discussion gets too heated.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The progress management department manages the progress of the meeting. For example, the progress management department reviews the agenda at the start of the meeting and clarifies the purpose and goals of the meeting. The progress management department also monitors the progress of the meeting in real time and allocates appropriate time for each agenda item. For example, the progress management department presents the purpose of the meeting and encourages participants to move on to the next agenda item. Step 2: The agenda organizer organizes the meeting agenda managed by the progress manager. For example, the agenda organizer prioritizes the agenda items, evaluates their relevance, and encourages participants to return to the agenda if they go off track. Step 3: The speech moderator adjusts the speeches based on the agenda organized by the agenda organizer. For example, when a participant wishes to speak, the speech moderator manages the order in which they speak and encourages them to speak at the appropriate time. The speech moderator can also instruct the order in which participants speak and limit the speaking time. Step 4: The minutes-taking unit records the remarks adjusted by the speech adjustment unit and creates minutes. For example, the minutes-taking unit converts the remarks of the participants into text and compiles them as minutes. After the meeting, the minutes-taking unit automatically generates the minutes and shares them with the participants.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the robot 414, 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 robot 414 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.

[0128] 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.

[0129] 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.

[0130] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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. [Explanation of symbols]

[0150] 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. The progress management department manages the progress of the meeting; an agenda organizing unit that organizes the agenda of the meeting managed by the progress management unit; a speech adjustment unit that adjusts speeches based on the agenda organized by the agenda organization unit; a minutes-taking section for recording the speech content adjusted by the speech adjustment section and taking minutes of the meeting. A system characterized by:

2. The progress management unit Analyze participants' facial expressions and tone of voice to assess the atmosphere of the meeting in real time and adjust the progress as needed.

2. The system of claim 1.

3. The progress management unit Maximize meeting efficiency by learning from past meeting data and proposing optimal progress patterns 2. The system of claim 1.

4. The progress management unit Analyze participants' emotions in real time and suggest refreshment time if negative emotions increase 2. The system of claim 1.

5. The progress management unit Monitors participants' health and suggests breaks when heart rate and stress levels increase 2. The system of claim 1.

6. The progress management unit Select the most suitable speaker based on the participants' expertise and past speaking history to deepen the discussion 2. The system of claim 1.

Images