system

Abstract

The system according to this embodiment aims to streamline meeting progress management and meeting minute creation. [Solution] The system according to the embodiment comprises a progress management unit, a summarization unit, an information provision unit, and a minutes creation unit. The progress management unit manages the progress of the meeting. The summarization unit summarizes the contents of the meeting managed by the progress management unit in real time. The information provision unit provides information based on the information summarized by the summarization unit. The minutes creation unit creates minutes based on the information provided by the information provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a lot of time and labor are required for managing the progress of a meeting and creating minutes.

[0005] The system according to the embodiment aims to improve the efficiency of managing the progress of a meeting and creating minutes.

Means for Solving the Problems

[0006] The system according to the embodiment includes a progress management unit, a summarization unit, an information providing unit, and a minutes creation unit. The progress management unit manages the progress of a meeting. The summarization unit summarizes the content of the meeting managed by the progress management unit in real time. The information providing unit provides information based on the information summarized by the summarization unit. The minutes creation unit creates minutes based on the information provided by the information providing unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline meeting progress management and meeting minute creation. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The meeting support system according to an embodiment of the present invention is a system that replaces the roles of meeting facilitator and minute-taker. This meeting support system manages the progress of the meeting and appropriately manages the way the meeting is conducted and the time allocation. Furthermore, it summarizes the content of the meeting in real time and organizes the discussion when it becomes complex or when the meeting ends. The meeting support system also gathers information on its own, disseminates necessary information during the meeting, and offers suggestions when the discussion reaches an impasse. After the meeting, it promptly creates and distributes the minutes. This system can realize these functions in real time based on recorded information. For example, when managing the progress of a meeting, the way the meeting is conducted and the time allocation are set in advance, the purpose and goals are confirmed at the start of the meeting, and the time allocation for each agenda item is set. During the meeting, the progress is monitored and adjustments are made to ensure that the discussion is completed within the allotted time. Next, when summarizing the content of the meeting in real time, the recorded information is analyzed and the discussion is organized when it becomes complex or when the meeting ends. For example, summarization is performed when the discussion is heated, and the key points of the discussion are organized. Also, at the end of the meeting, the results of the discussion are summarized and the next actions are proposed. Furthermore, when gathering and disseminating information, past discussions, related documents, and information from the internet are collected in real time and shared during the meeting. This allows discussions to proceed smoothly and necessary information to be obtained immediately. After the meeting, minutes are automatically generated based on the recorded information and distributed to meeting attendees. This eliminates the effort required to create minutes and allows for the rapid sharing of meeting content. As a result, the meeting support system is expected to improve the quality of meetings and reduce workload. By having AI take over meeting progress management, real-time summarization, information gathering and dissemination, and minute creation, meetings become more efficient. In addition, because the progress of the meeting and the content of the discussions can be grasped in real time, the quality of meetings improves and attendee satisfaction increases.

[0029] The meeting support system according to this embodiment comprises a progress management unit, a summarization unit, an information provision unit, and a meeting minutes creation unit. The progress management unit manages the progress of the meeting. The progress management unit pre-sets the meeting procedure and time allocation and manages it appropriately. For example, the progress management unit confirms the purpose and goals at the start of the meeting and sets the time allocation for each agenda item. The progress management unit monitors the progress during the meeting and adjusts it so that the discussion is completed within the allotted time. For example, the progress management unit monitors the progress of the meeting in real time and promotes the discussion if it is running behind schedule. The progress management unit can also allocate time for deeper discussion if the meeting is progressing too quickly. The summarization unit summarizes the content of the meeting in real time. The summarization unit analyzes the recorded information and organizes the discussion when it becomes complex or at the end of the meeting. For example, the summarization unit summarizes when the discussion is heated and organizes the key points of the discussion. The summarization unit also summarizes the results of the discussion at the end of the meeting and proposes the next actions. The summarization unit extracts important statements and decisions based on the audio recording and creates a summary. The summarization unit can also organize the flow of the discussion and clarify the next steps. The information provision unit collects past discussion content, related materials, and information from the internet in real time and disseminates it during the meeting. For example, the information provision unit searches the database for past discussion content and provides relevant information. It can also collect the latest information from the internet and disseminate it during the meeting. For example, the information provision unit automatically collects relevant materials and provides them in accordance with the progress of the meeting. It can also collect information from the internet in real time and disseminate it during the meeting. The minutes creation unit automatically generates minutes based on the audio recording and distributes them to meeting attendees. For example, the minutes creation unit analyzes the audio recording and automatically reflects statements and decisions in the minutes. It can also pre-set the format and content of the minutes and quickly create them after the meeting ends. The minutes creation department can, for example, automatically generate meeting minutes based on audio recordings and distribute them to meeting attendees via email. The minutes creation department can also save the minutes to the cloud, allowing attendees to access them at any time.As a result, the meeting support system according to this embodiment can efficiently manage the progress of meetings, summarize them in real time, provide information, and create meeting minutes.

[0030] The meeting management department oversees the progress of meetings. They pre-set and appropriately manage the meeting's format and time allocation. For example, at the start of a meeting, they confirm the purpose and goals and set time allocations for each agenda item. During the meeting, they monitor the progress and adjust the pace to ensure discussions are completed within the allotted time. For instance, they monitor the meeting's progress in real time and accelerate discussions if it's falling behind. They can also allocate time for deeper discussions if the meeting is progressing too quickly. The meeting management department can utilize AI to optimize meeting progress. For example, AI analyzes past meeting data and suggests points where discussions tend to stall and optimize time allocations. Furthermore, the meeting management department provides a dashboard to visualize the meeting's progress, allowing all participants to understand the current status. The dashboard displays the progress of each agenda item, remaining time, and the scheduled start time for the next item. This makes it easier for participants to adjust their speaking timing, improving meeting efficiency. Furthermore, the meeting management department can collect feedback on the progress of meetings and incorporate it into future meetings. For example, based on feedback from participants, they can review the order of agenda items and time allocation to achieve more effective meeting management. In addition, the meeting management department has an alert function that can send notifications to participants when time is running out or when the discussion is stalled. This allows the meeting management department to smoothly manage the progress of meetings and promote efficient discussions.

[0031] The summarization unit summarizes the meeting content in real time. It analyzes the recorded information and organizes the discussion at key points, such as when the discussion becomes complex or at the end of the meeting. For example, it summarizes the discussion when it is heated, highlighting the key points. It also summarizes the results of the discussion at the end of the meeting and proposes the next steps. For instance, it extracts and summarizes important statements and decisions based on the recorded information. Furthermore, it can organize the flow of the discussion and clarify the next steps. The summarization unit utilizes AI to analyze the recorded information and automatically extract important statements and keywords. The AI ​​uses natural language processing technology to understand the content of the statements and summarize the main points of the discussion. For example, the AI ​​analyzes the frequency and importance of statements to extract particularly important points from the discussion. The summarization unit can also update the summary in real time as the meeting progresses and provide it to participants. This makes it easier for participants to grasp the flow of the discussion and enables more efficient discussion. In addition, the summarization unit has a function to visually display the summary, using graphs and charts to clearly present the key points of the discussion. This makes it easier for participants to grasp the progress of the discussion based on visual information. Furthermore, the summarization function can save the summary to the cloud, allowing participants to access it at any time. This enables the summarization function to maximize the effectiveness of the meeting by efficiently summarizing the meeting content and providing it to participants.

[0032] The Information Provision Department collects past discussion content, related materials, and information from the internet in real time and disseminates it during meetings. For example, the Information Provision Department searches past discussion content from a database and provides relevant information. It can also collect the latest information from the internet and disseminate it during meetings. For example, the Information Provision Department automatically collects relevant materials and provides them in accordance with the progress of the meeting. It can also collect information from the internet in real time and disseminate it during meetings. The Information Provision Department utilizes AI to automatically collect relevant information and provide it in accordance with the progress of the meeting. For example, the AI ​​analyzes past discussion content and automatically searches for and provides relevant materials and information. The AI ​​can also collect the latest information from the internet in real time and disseminate it during meetings. This allows the Information Provision Department to provide the information necessary for the progress of the meeting quickly and accurately. Furthermore, the Information Provision Department has a function to evaluate the reliability of the information it provides, ensuring the accuracy of the information it provides. For example, the AI ​​evaluates the source and reliability of the information and provides only highly reliable information. Furthermore, the information provision department is equipped with a function to visually display the information it provides, allowing it to present information clearly using graphs and charts. This enables participants to efficiently advance discussions based on the information provided. In addition, the information provision department can store the provided information on the cloud, making it accessible to participants at any time. This allows the information provision department to efficiently provide the information necessary for the progress of the meeting, maximizing the effectiveness of the meeting.

[0033] The minutes creation department automatically generates meeting minutes based on the audio recording and distributes them to meeting attendees. For example, the minutes creation department analyzes the audio recording and automatically reflects the content of statements and decisions in the minutes. Furthermore, the minutes creation department can pre-set the format and content of the minutes and quickly create them after the meeting ends. For example, the minutes creation department automatically generates meeting minutes based on the audio recording and distributes them to meeting attendees via email. The minutes creation department can also save the minutes to the cloud, allowing attendees to access them at any time. The minutes creation department utilizes AI to analyze the audio recording and automatically reflects the content of statements and decisions in the minutes. The AI ​​uses natural language processing technology to understand the content of statements and extract important points and decisions. For example, the AI ​​analyzes the frequency and importance of statements and extracts particularly important points from the discussion. Furthermore, the minutes creation department can pre-set the format and content of the minutes and quickly create them after the meeting ends. This allows the meeting minutes department to accurately and quickly record the content of meetings and provide it to participants. Furthermore, the meeting minutes department has a function to visually display the content of the minutes, and can clearly present the points of discussion using graphs and charts. This allows participants to efficiently plan the next steps based on the meeting minutes. In addition, the meeting minutes department can store the minutes on the cloud, making them accessible to attendees at any time. This allows the meeting minutes department to maximize the effectiveness of meetings by efficiently recording the content and providing it to participants.

[0034] The meeting management department can pre-set and appropriately manage the meeting's procedure and time allocation. For example, the department can confirm the purpose and goals at the start of the meeting and set the time allocation for each agenda item. The department can monitor the progress during the meeting and adjust it to ensure that discussions are completed within the allotted time. For example, the department can monitor the meeting's progress in real time and expedite discussions if it is falling behind schedule. The department can also allocate time for deeper discussions if the meeting is progressing too quickly. This ensures that meetings are conducted in a planned manner. Some or all of the above processes performed by the meeting management department may be carried out using AI, or not. For example, the meeting management department can use an AI model to monitor the meeting's progress in real time and expedite discussions if it is falling behind schedule.

[0035] The summarization unit can analyze the recorded information and organize the discussion at times when it becomes complex or at the end of the meeting. For example, the summarization unit can summarize the discussion when it is heated and organize the key points. It can also summarize the results of the discussion at the end of the meeting and propose the next actions. For example, the summarization unit can extract and summarize important statements and decisions based on the recorded information. It can also organize the flow of the discussion and clarify the next steps. This organizes the main points of the discussion and makes them easier to understand. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not. For example, the summarization unit can use an AI model to extract important statements and decisions based on the recorded information.

[0036] The information provision department can collect past discussion content, related materials, or information from the internet in real time and disseminate it during meetings. For example, the information provision department can search a database for past discussion content and provide relevant information. It can also collect the latest information from the internet and disseminate it during meetings. For example, the information provision department can automatically collect relevant materials and provide them in accordance with the progress of the meeting. It can also collect information from the internet in real time and disseminate it during meetings. This ensures that necessary information is provided immediately, allowing discussions to proceed smoothly. Some or all of the above processing in the information provision department may be performed using AI, for example, or not. For example, the information provision department can use an AI model to search a database for past discussion content and provide relevant information.

[0037] The minutes creation unit can automatically generate meeting minutes based on audio recordings and distribute them to meeting attendees. For example, the minutes creation unit can analyze the audio recordings and automatically reflect the content of discussions and decisions in the minutes. Furthermore, the minutes creation unit can pre-set the format and content of the minutes and quickly create them after the meeting. For example, the minutes creation unit can automatically generate minutes based on audio recordings and distribute them to meeting attendees via email. The minutes creation unit can also save the minutes to the cloud, allowing attendees to access them at any time. This reduces the effort required to create minutes and allows for rapid sharing of meeting content. Some or all of the above processes in the minutes creation unit may be performed using AI, or not. For example, the minutes creation unit can use an AI model to automatically generate minutes based on audio recordings.

[0038] The progress management department can monitor the progress of the meeting and adjust it to ensure that the discussion is completed on time. For example, the progress management department can monitor the progress of the meeting in real time and accelerate the discussion if it is running behind schedule. The progress management department can also allocate time for deeper discussion if the meeting is running too fast. For example, if the progress is running behind schedule, the progress management department can propose actions to accelerate the discussion. The progress management department can also ask questions to deepen the discussion if it is running too fast. This ensures that the meeting proceeds efficiently and on time. Some or all of the above processes performed by the progress management department may be carried out using AI, for example, or not. For example, the progress management department can use an AI model to monitor the progress of the meeting in real time and accelerate the discussion if it is running behind schedule.

[0039] The meeting management department can analyze the frequency of participants' contributions during the meeting and make adjustments to ensure equal opportunities for participation. For example, the department can suggest when to encourage participants who speak less to speak. It can also encourage participants who speak frequently to listen to the opinions of other participants. The department can also balance the frequency of contributions to ensure that everyone has an equal opportunity to express their opinion. Some or all of the above processes performed by the meeting management department may be carried out using AI, for example, or not. For example, the meeting management department could use an AI model to analyze the frequency of participants' contributions and ensure equal opportunities for participation.

[0040] The meeting management unit can monitor participants' engagement levels in real time during the meeting and suggest breaks if engagement levels decline. For example, if participants' engagement levels decline, the management unit may suggest a short break. Alternatively, if participants' engagement levels remain high, the management unit may continue the meeting. For example, if participants' engagement levels decline, the management unit may suggest activities to refresh them. This ensures that appropriate breaks are suggested according to participants' engagement levels. Some or all of the above processes performed by the meeting management unit may be carried out using AI, for example, or not. For example, the meeting management unit could use an AI model to monitor participants' engagement levels in real time and suggest breaks if engagement levels decline.

[0041] The progress management unit can monitor the participants' physical condition (e.g., fatigue level) during the meeting and suggest breaks at appropriate times. For example, if a participant's fatigue level is high, the progress management unit may suggest a break. Alternatively, if a participant's fatigue level is low, the progress management unit may continue the meeting. For example, if a participant's fatigue level is moderate, the progress management unit may suggest a short break. This ensures that appropriate breaks are suggested according to the participants' physical condition. Some or all of the above processes performed by the progress management unit may be carried out using AI, for example, or not. For example, the progress management unit may use an AI model to monitor participants' physical condition and suggest breaks at appropriate times.

[0042] The meeting management department can refer to participants' past statements during the meeting and prioritize relevant topics. For example, the management department can prioritize relevant topics based on participants' past statements. The management department can also analyze participants' past statements to determine the priority of topics. The management department can also adjust the progress of the agenda by referring to participants' past statements. This ensures that relevant topics are prioritized based on past statements. Some or all of the above processes by the meeting management department may be performed using AI, or not. For example, the meeting management department can use an AI model to refer to participants' past statements and prioritize relevant topics.

[0043] The summarization unit can adjust the level of detail in the summary based on the importance of the discussion during summary generation. For example, the summarization unit can provide a detailed summary for important discussions. It can also provide a concise summary for less important discussions. For example, it can provide a summary with a moderate level of detail for discussions of moderate importance. This ensures that the summary is provided with a level of detail appropriate to the importance of the discussion. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can use an AI model to evaluate the importance of discussions and adjust the level of detail in the summary based on that importance.

[0044] The summarization unit can apply different summarization algorithms to each agenda item when generating summaries. For example, the summarization unit can apply a technical summarization algorithm to technical agenda items. It can also apply a management-oriented summarization algorithm to management agenda items. For example, it can apply a personnel-oriented summarization algorithm to personnel agenda items. This ensures that summaries are generated using a summarization algorithm appropriate to the agenda item. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can use an AI model to apply different summarization algorithms to each agenda item.

[0045] The summarization unit can determine the priority of summaries based on the progress of the discussion when generating summaries. For example, if the discussion is ongoing, the summarization unit will prioritize summarizing the ongoing topics. It can also prioritize summarizing the completed topics if the discussion has concluded. Furthermore, if the discussion has been interrupted, the summarization unit can prioritize summarizing the interrupted topics. This ensures that summaries are provided with priorities corresponding to the progress of the discussion. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit may use an AI model to evaluate the progress of the discussion and determine the priority of summaries based on that progress.

[0046] The summarization unit can adjust the order of summaries based on the relevance of the arguments during summary generation. For example, the summarization unit may prioritize summarizing highly relevant arguments. It can also postpone summarizing less relevant arguments. For example, the summarization unit may summarize arguments of moderate relevance in an appropriate order. This ensures that summaries are provided in an order that reflects the relevance of the arguments. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not. For example, the summarization unit may use an AI model to evaluate the relevance of arguments and adjust the order of summaries based on relevance.

[0047] The information provision unit can, when providing information, prioritize the provision of relevant information by referring to past discussion content. For example, the information provision unit prioritizes the provision of relevant information based on past discussion content. The information provision unit can also analyze past discussion content and provide highly relevant information. For example, the information provision unit can provide relevant information by referring to past discussion content. This ensures that relevant information is prioritized based on past discussion content. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can use an AI model to refer to past discussion content and prioritize the provision of relevant information.

[0048] The information provision department can apply different information gathering algorithms to each agenda item when providing information. For example, the information provision department can apply a technical information gathering algorithm to technical agenda items. It can also apply a management-related information gathering algorithm to management agenda items. For example, it can apply a personnel-related information gathering algorithm to personnel agenda items. This ensures that information is provided using an information gathering algorithm appropriate to the agenda item. Some or all of the above processing in the information provision department may be performed using AI, for example, or without AI. For example, the information provision department can use an AI model to apply different information gathering algorithms to each agenda item.

[0049] The information provision department can collect and provide the latest information from the internet in real time when providing information. For example, the information provision department can collect and provide the latest news from the internet in real time. The information provision department can also collect and provide the latest technological information from the internet in real time. For example, the information provision department can also collect and provide the latest market information from the internet in real time. This ensures that the latest information from the internet is provided in real time. Some or all of the above processing in the information provision department may be performed using AI, for example, or without AI. For example, the information provision department can use an AI model to collect and provide the latest information from the internet in real time.

[0050] The information provider can adjust the level of detail of information based on the participant's expertise when providing information. For example, if the participant has expertise, the information provider can provide detailed information. Conversely, if the participant does not have expertise, the information provider can provide concise information. The information provider can also adjust the level of detail of information according to the participant's level of expertise, for example. This ensures that information is provided with a level of detail appropriate to the participant's expertise. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can use an AI model to evaluate the participant's expertise and adjust the level of detail of information based on that expertise.

[0051] The minutes creation unit can adjust the level of detail in the minutes based on the importance of the discussions. For example, the minutes creation unit can provide detailed minutes for important discussions. It can also provide concise minutes for less important discussions. For example, it can provide minutes with a moderate level of detail for discussions of moderate importance. This ensures that minutes are provided with a level of detail appropriate to the importance of the discussions. Some or all of the above processing in the minutes creation unit may be performed using AI, for example, or not. For example, the minutes creation unit can use an AI model to evaluate the importance of discussions and adjust the level of detail in the minutes based on that importance.

[0052] The minutes creation unit can apply different minutes creation algorithms to each agenda item when creating minutes. For example, the minutes creation unit can apply a technical minutes creation algorithm to technical agenda items. It can also apply a management-oriented minutes creation algorithm to management agenda items. For example, it can apply a personnel-oriented minutes creation algorithm to personnel agenda items. This ensures that minutes are provided using an algorithm appropriate to the agenda item. Some or all of the above processing in the minutes creation unit may be performed using AI, for example, or without AI. For example, the minutes creation unit can use an AI model to apply different minutes creation algorithms to each agenda item.

[0053] The minutes creation unit can determine the priority of meeting minutes based on the progress of the discussion. For example, if a discussion is ongoing, the minutes creation unit will prioritize the ongoing agenda items when creating the minutes. The minutes creation unit can also prioritize the completed agenda items when a discussion has concluded. Furthermore, if a discussion is interrupted, the minutes creation unit can prioritize the interrupted agenda items when creating the minutes. This ensures that the minutes are provided with priorities corresponding to the progress of the discussion. Some or all of the above processes in the minutes creation unit may be performed using AI, for example, or not. For example, the minutes creation unit could use an AI model to evaluate the progress of the discussion and determine the priority of the minutes based on that progress.

[0054] The minutes creation unit can adjust the order of the minutes based on the relevance of the discussions during the minutes creation process. For example, the minutes creation unit can prioritize creating minutes for highly relevant discussions. It can also postpone creating minutes for less relevant discussions. For example, the minutes creation unit can create minutes for discussions of moderate relevance in an appropriate order. This ensures that the minutes are provided in an order that reflects the relevance of the discussions. Some or all of the above processing in the minutes creation unit may be performed using AI, for example, or not. For example, the minutes creation unit can use an AI model to evaluate the relevance of discussions and adjust the order of the minutes based on that relevance.

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

[0056] The meeting management team can analyze participants' comments in real time during the meeting and adjust the direction of the discussion. For example, if the discussion goes off track, the management team can prompt participants to return to the original topic. They can also stimulate the discussion by offering new perspectives or questions when it reaches an impasse. Furthermore, based on participants' comments, the management team can propose actions to smooth the discussion. This ensures that the meeting proceeds efficiently and the discussion progresses effectively.

[0057] The summarization function can analyze participants' statements in real time during a meeting, extract key points, and summarize them. For example, it can extract and summarize important statements and decisions made during a discussion in real time. It can also organize the flow of the discussion and provide a summary to clarify the next steps. Furthermore, based on participants' statements, the summarization function can visually organize the key points of the discussion in an easy-to-understand way. This makes the key points of the discussion clearer and easier to understand.

[0058] The information provision department can analyze participants' comments in real time during the meeting and provide relevant information. For example, based on participants' comments, the information provision department can provide relevant materials and data in real time. Furthermore, the information provision department can collect the latest information from the internet and disseminate it during the meeting. In addition, based on participants' comments, the information provision department can provide necessary information in accordance with the progress of the discussion. This ensures that necessary information is provided immediately, allowing the discussion to proceed smoothly.

[0059] The meeting minutes creation system can analyze participants' statements in real time during a meeting and automatically generate meeting minutes. For example, it can reflect important statements and decisions in the minutes in real time based on participants' comments. Furthermore, the system can pre-set the format and content of the minutes, allowing for quick creation after the meeting. It can also visually organize the minutes based on participants' comments, making them easy to understand. This reduces the effort required for creating meeting minutes and allows for rapid sharing of meeting content.

[0060] The meeting management team can analyze participants' comments in real time during the meeting and adjust the flow of the discussion. For example, if the discussion goes off track, the management team can prompt participants to return to the original topic. They can also stimulate the discussion by offering new perspectives or questions when it reaches an impasse. Furthermore, based on participants' comments, the management team can propose actions to smooth the discussion. This ensures that the meeting is conducted efficiently and the discussion progresses effectively.

[0061] The summarization function can analyze participants' statements in real time during a meeting, extract key points, and summarize them. For example, it can extract and summarize important statements and decisions made during a discussion in real time. It can also organize the flow of the discussion and provide a summary to clarify the next steps. Furthermore, based on participants' statements, the summarization function can visually organize the key points of the discussion in an easy-to-understand way. This makes the key points of the discussion clearer and easier to understand.

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

[0063] Step 1: The meeting management team manages the progress of the meeting. The meeting management team sets the meeting procedures and time allocation in advance and manages them appropriately. For example, they confirm the purpose and goals at the start of the meeting and set the time allocation for each agenda item. They monitor the progress during the meeting and make adjustments to ensure that the discussion is completed within the allotted time. If the progress is slow, they can speed up the discussion, and if it is progressing too quickly, they can allocate time to deepen the discussion. Step 2: The summarization section summarizes the meeting content in real time. It analyzes the recorded information and organizes the discussion at times when it becomes complex or at the end of the meeting. For example, it summarizes when the discussion is heated to organize the key points. At the end of the meeting, it summarizes the results of the discussion and proposes the next actions. It extracts important statements and decisions based on the recorded information and summarizes them. It can also organize the flow of the discussion and clarify the next steps. Step 3: The information provision department collects past discussion content, related materials, and information from the internet in real time and disseminates it during the meeting. For example, it searches past discussion content from a database and provides relevant information. It can also collect the latest information from the internet and disseminate it during the meeting. It can automatically collect relevant materials and provide them in accordance with the progress of the meeting. Step 4: The minutes creation department automatically generates meeting minutes based on the audio recording and distributes them to meeting attendees. The system analyzes the audio recording and automatically reflects the content of discussions and decisions in the minutes. The format and content of the minutes can be set in advance, allowing for quick creation of the minutes after the meeting. The minutes can be automatically generated based on the audio recording and distributed to meeting attendees via email. The minutes can also be saved to the cloud, allowing attendees to access them at any time.

[0064] (Example of form 2) The meeting support system according to an embodiment of the present invention is a system that replaces the roles of meeting facilitator and minute-taker. This meeting support system manages the progress of the meeting and appropriately manages the way the meeting is conducted and the time allocation. Furthermore, it summarizes the content of the meeting in real time and organizes the discussion when it becomes complex or when the meeting ends. The meeting support system also gathers information on its own, disseminates necessary information during the meeting, and offers suggestions when the discussion reaches an impasse. After the meeting, it promptly creates and distributes the minutes. This system can realize these functions in real time based on recorded information. For example, when managing the progress of a meeting, the way the meeting is conducted and the time allocation are set in advance, the purpose and goals are confirmed at the start of the meeting, and the time allocation for each agenda item is set. During the meeting, the progress is monitored and adjustments are made to ensure that the discussion is completed within the allotted time. Next, when summarizing the content of the meeting in real time, the recorded information is analyzed and the discussion is organized when it becomes complex or when the meeting ends. For example, summarization is performed when the discussion is heated, and the key points of the discussion are organized. Also, at the end of the meeting, the results of the discussion are summarized and the next actions are proposed. Furthermore, when gathering and disseminating information, past discussions, related documents, and information from the internet are collected in real time and shared during the meeting. This allows discussions to proceed smoothly and necessary information to be obtained immediately. After the meeting, minutes are automatically generated based on the recorded information and distributed to meeting attendees. This eliminates the effort required to create minutes and allows for the rapid sharing of meeting content. As a result, the meeting support system is expected to improve the quality of meetings and reduce workload. By having AI take over meeting progress management, real-time summarization, information gathering and dissemination, and minute creation, meetings become more efficient. In addition, because the progress of the meeting and the content of the discussions can be grasped in real time, the quality of meetings improves and attendee satisfaction increases.

[0065] The meeting support system according to this embodiment comprises a progress management unit, a summarization unit, an information provision unit, and a meeting minutes creation unit. The progress management unit manages the progress of the meeting. The progress management unit pre-sets the meeting procedure and time allocation and manages it appropriately. For example, the progress management unit confirms the purpose and goals at the start of the meeting and sets the time allocation for each agenda item. The progress management unit monitors the progress during the meeting and adjusts it so that the discussion is completed within the allotted time. For example, the progress management unit monitors the progress of the meeting in real time and promotes the discussion if it is running behind schedule. The progress management unit can also allocate time for deeper discussion if the meeting is progressing too quickly. The summarization unit summarizes the content of the meeting in real time. The summarization unit analyzes the recorded information and organizes the discussion when it becomes complex or at the end of the meeting. For example, the summarization unit summarizes when the discussion is heated and organizes the key points of the discussion. The summarization unit also summarizes the results of the discussion at the end of the meeting and proposes the next actions. The summarization unit extracts important statements and decisions based on the audio recording and creates a summary. The summarization unit can also organize the flow of the discussion and clarify the next steps. The information provision unit collects past discussion content, related materials, and information from the internet in real time and disseminates it during the meeting. For example, the information provision unit searches the database for past discussion content and provides relevant information. It can also collect the latest information from the internet and disseminate it during the meeting. For example, the information provision unit automatically collects relevant materials and provides them in accordance with the progress of the meeting. It can also collect information from the internet in real time and disseminate it during the meeting. The minutes creation unit automatically generates minutes based on the audio recording and distributes them to meeting attendees. For example, the minutes creation unit analyzes the audio recording and automatically reflects statements and decisions in the minutes. It can also pre-set the format and content of the minutes and quickly create them after the meeting ends. The minutes creation department can, for example, automatically generate meeting minutes based on audio recordings and distribute them to meeting attendees via email. The minutes creation department can also save the minutes to the cloud, allowing attendees to access them at any time.As a result, the meeting support system according to this embodiment can efficiently manage the progress of meetings, summarize them in real time, provide information, and create meeting minutes.

[0066] The meeting management department oversees the progress of meetings. They pre-set and appropriately manage the meeting's format and time allocation. For example, at the start of a meeting, they confirm the purpose and goals and set time allocations for each agenda item. During the meeting, they monitor the progress and adjust the pace to ensure discussions are completed within the allotted time. For instance, they monitor the meeting's progress in real time and accelerate discussions if it's falling behind. They can also allocate time for deeper discussions if the meeting is progressing too quickly. The meeting management department can utilize AI to optimize meeting progress. For example, AI analyzes past meeting data and suggests points where discussions tend to stall and optimize time allocations. Furthermore, the meeting management department provides a dashboard to visualize the meeting's progress, allowing all participants to understand the current status. The dashboard displays the progress of each agenda item, remaining time, and the scheduled start time for the next item. This makes it easier for participants to adjust their speaking timing, improving meeting efficiency. Furthermore, the meeting management department can collect feedback on the progress of meetings and incorporate it into future meetings. For example, based on feedback from participants, they can review the order of agenda items and time allocation to achieve more effective meeting management. In addition, the meeting management department has an alert function that can send notifications to participants when time is running out or when the discussion is stalled. This allows the meeting management department to smoothly manage the progress of meetings and promote efficient discussions.

[0067] The summarization unit summarizes the meeting content in real time. It analyzes the recorded information and organizes the discussion at key points, such as when the discussion becomes complex or at the end of the meeting. For example, it summarizes the discussion when it is heated, highlighting the key points. It also summarizes the results of the discussion at the end of the meeting and proposes the next steps. For instance, it extracts and summarizes important statements and decisions based on the recorded information. Furthermore, it can organize the flow of the discussion and clarify the next steps. The summarization unit utilizes AI to analyze the recorded information and automatically extract important statements and keywords. The AI ​​uses natural language processing technology to understand the content of the statements and summarize the main points of the discussion. For example, the AI ​​analyzes the frequency and importance of statements to extract particularly important points from the discussion. The summarization unit can also update the summary in real time as the meeting progresses and provide it to participants. This makes it easier for participants to grasp the flow of the discussion and enables more efficient discussion. In addition, the summarization unit has a function to visually display the summary, using graphs and charts to clearly present the key points of the discussion. This makes it easier for participants to grasp the progress of the discussion based on visual information. Furthermore, the summarization function can save the summary to the cloud, allowing participants to access it at any time. This enables the summarization function to maximize the effectiveness of the meeting by efficiently summarizing the meeting content and providing it to participants.

[0068] The Information Provision Department collects past discussion content, related materials, and information from the internet in real time and disseminates it during meetings. For example, the Information Provision Department searches past discussion content from a database and provides relevant information. It can also collect the latest information from the internet and disseminate it during meetings. For example, the Information Provision Department automatically collects relevant materials and provides them in accordance with the progress of the meeting. It can also collect information from the internet in real time and disseminate it during meetings. The Information Provision Department utilizes AI to automatically collect relevant information and provide it in accordance with the progress of the meeting. For example, the AI ​​analyzes past discussion content and automatically searches for and provides relevant materials and information. The AI ​​can also collect the latest information from the internet in real time and disseminate it during meetings. This allows the Information Provision Department to provide the information necessary for the progress of the meeting quickly and accurately. Furthermore, the Information Provision Department has a function to evaluate the reliability of the information it provides, ensuring the accuracy of the information it provides. For example, the AI ​​evaluates the source and reliability of the information and provides only highly reliable information. Furthermore, the information provision department is equipped with a function to visually display the information it provides, allowing it to present information clearly using graphs and charts. This enables participants to efficiently advance discussions based on the information provided. In addition, the information provision department can store the provided information on the cloud, making it accessible to participants at any time. This allows the information provision department to efficiently provide the information necessary for the progress of the meeting, maximizing the effectiveness of the meeting.

[0069] The minutes creation department automatically generates meeting minutes based on the audio recording and distributes them to meeting attendees. For example, the minutes creation department analyzes the audio recording and automatically reflects the content of statements and decisions in the minutes. Furthermore, the minutes creation department can pre-set the format and content of the minutes and quickly create them after the meeting ends. For example, the minutes creation department automatically generates meeting minutes based on the audio recording and distributes them to meeting attendees via email. The minutes creation department can also save the minutes to the cloud, allowing attendees to access them at any time. The minutes creation department utilizes AI to analyze the audio recording and automatically reflects the content of statements and decisions in the minutes. The AI ​​uses natural language processing technology to understand the content of statements and extract important points and decisions. For example, the AI ​​analyzes the frequency and importance of statements and extracts particularly important points from the discussion. Furthermore, the minutes creation department can pre-set the format and content of the minutes and quickly create them after the meeting ends. This allows the meeting minutes department to accurately and quickly record the content of meetings and provide it to participants. Furthermore, the meeting minutes department has a function to visually display the content of the minutes, and can clearly present the points of discussion using graphs and charts. This allows participants to efficiently plan the next steps based on the meeting minutes. In addition, the meeting minutes department can store the minutes on the cloud, making them accessible to attendees at any time. This allows the meeting minutes department to maximize the effectiveness of meetings by efficiently recording the content and providing it to participants.

[0070] The meeting management department can pre-set and appropriately manage the meeting's procedure and time allocation. For example, the department can confirm the purpose and goals at the start of the meeting and set the time allocation for each agenda item. The department can monitor the progress during the meeting and adjust it to ensure that discussions are completed within the allotted time. For example, the department can monitor the meeting's progress in real time and expedite discussions if it is falling behind schedule. The department can also allocate time for deeper discussions if the meeting is progressing too quickly. This ensures that meetings are conducted in a planned manner. Some or all of the above processes performed by the meeting management department may be carried out using AI, or not. For example, the meeting management department can use an AI model to monitor the meeting's progress in real time and expedite discussions if it is falling behind schedule.

[0071] The summarization unit can analyze the recorded information and organize the discussion at times when it becomes complex or at the end of the meeting. For example, the summarization unit can summarize the discussion when it is heated and organize the key points. It can also summarize the results of the discussion at the end of the meeting and propose the next actions. For example, the summarization unit can extract and summarize important statements and decisions based on the recorded information. It can also organize the flow of the discussion and clarify the next steps. This organizes the main points of the discussion and makes them easier to understand. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not. For example, the summarization unit can use an AI model to extract important statements and decisions based on the recorded information.

[0072] The information provision department can collect past discussion content, related materials, or information from the internet in real time and disseminate it during meetings. For example, the information provision department can search a database for past discussion content and provide relevant information. It can also collect the latest information from the internet and disseminate it during meetings. For example, the information provision department can automatically collect relevant materials and provide them in accordance with the progress of the meeting. It can also collect information from the internet in real time and disseminate it during meetings. This ensures that necessary information is provided immediately, allowing discussions to proceed smoothly. Some or all of the above processing in the information provision department may be performed using AI, for example, or not. For example, the information provision department can use an AI model to search a database for past discussion content and provide relevant information.

[0073] The minutes creation unit can automatically generate meeting minutes based on audio recordings and distribute them to meeting attendees. For example, the minutes creation unit can analyze the audio recordings and automatically reflect the content of discussions and decisions in the minutes. Furthermore, the minutes creation unit can pre-set the format and content of the minutes and quickly create them after the meeting. For example, the minutes creation unit can automatically generate minutes based on audio recordings and distribute them to meeting attendees via email. The minutes creation unit can also save the minutes to the cloud, allowing attendees to access them at any time. This reduces the effort required to create minutes and allows for rapid sharing of meeting content. Some or all of the above processes in the minutes creation unit may be performed using AI, or not. For example, the minutes creation unit can use an AI model to automatically generate minutes based on audio recordings.

[0074] The progress management department can monitor the progress of the meeting and adjust it to ensure that the discussion is completed on time. For example, the progress management department can monitor the progress of the meeting in real time and accelerate the discussion if it is running behind schedule. The progress management department can also allocate time for deeper discussion if the meeting is running too fast. For example, if the progress is running behind schedule, the progress management department can propose actions to accelerate the discussion. The progress management department can also ask questions to deepen the discussion if it is running too fast. This ensures that the meeting proceeds efficiently and on time. Some or all of the above processes performed by the progress management department may be carried out using AI, for example, or not. For example, the progress management department can use an AI model to monitor the progress of the meeting in real time and accelerate the discussion if it is running behind schedule.

[0075] The meeting management unit can estimate the user's emotions and adjust the meeting's pace based on those emotions. For example, if a user is anxious, the unit can slow down the meeting to allow time for deeper understanding. Conversely, if a user is relaxed, the unit can maintain a normal pace to ensure smooth progress. If a user is tired, the unit can also slow down the meeting and suggest a break. This ensures the meeting progresses at a pace appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the meeting management unit may be performed using AI or not. For example, the meeting management unit can use an AI model to estimate the user's emotions and adjust the meeting's pace based on those emotions.

[0076] The meeting management department can analyze the frequency of participants' contributions during the meeting and make adjustments to ensure equal opportunities for participation. For example, the department can suggest when to encourage participants who speak less to speak. It can also encourage participants who speak frequently to listen to the opinions of other participants. The department can also balance the frequency of contributions to ensure that everyone has an equal opportunity to express their opinion. Some or all of the above processes performed by the meeting management department may be carried out using AI, for example, or not. For example, the meeting management department could use an AI model to analyze the frequency of participants' contributions and ensure equal opportunities for participation.

[0077] The meeting management unit can monitor participants' engagement levels in real time during the meeting and suggest breaks if engagement levels decline. For example, if participants' engagement levels decline, the management unit may suggest a short break. Alternatively, if participants' engagement levels remain high, the management unit may continue the meeting. For example, if participants' engagement levels decline, the management unit may suggest activities to refresh them. This ensures that appropriate breaks are suggested according to participants' engagement levels. Some or all of the above processes performed by the meeting management unit may be carried out using AI, for example, or not. For example, the meeting management unit could use an AI model to monitor participants' engagement levels in real time and suggest breaks if engagement levels decline.

[0078] The meeting management unit can estimate the user's emotions and modify the meeting's progress based on those emotions. For example, if the user is nervous, the management unit can suggest a more relaxing approach. If the user is relaxed, the management unit can maintain the normal approach. If the user is anxious, the management unit can slow down the pace to allow for better understanding. This ensures the meeting progresses in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the meeting management unit may be performed using AI or not. For example, the meeting management unit can use an AI model to estimate the user's emotions and modify the meeting's progress based on those emotions.

[0079] The progress management unit can monitor the participants' physical condition (e.g., fatigue level) during the meeting and suggest breaks at appropriate times. For example, if a participant's fatigue level is high, the progress management unit may suggest a break. Alternatively, if a participant's fatigue level is low, the progress management unit may continue the meeting. For example, if a participant's fatigue level is moderate, the progress management unit may suggest a short break. This ensures that appropriate breaks are suggested according to the participants' physical condition. Some or all of the above processes performed by the progress management unit may be carried out using AI, for example, or not. For example, the progress management unit may use an AI model to monitor participants' physical condition and suggest breaks at appropriate times.

[0080] The meeting management department can refer to participants' past statements during the meeting and prioritize relevant topics. For example, the management department can prioritize relevant topics based on participants' past statements. The management department can also analyze participants' past statements to determine the priority of topics. The management department can also adjust the progress of the agenda by referring to participants' past statements. This ensures that relevant topics are prioritized based on past statements. Some or all of the above processes by the meeting management department may be performed using AI, or not. For example, the meeting management department can use an AI model to refer to participants' past statements and prioritize relevant topics.

[0081] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is relaxed, the summarization unit can provide a detailed summary. If the user is in a hurry, the summarization unit can also provide a concise summary. If the user is excited, the summarization unit can also provide a visually easy-to-understand summary. This ensures that the summary is presented in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can use an AI model to estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions.

[0082] The summarization unit can adjust the level of detail in the summary based on the importance of the discussion during summary generation. For example, the summarization unit can provide a detailed summary for important discussions. It can also provide a concise summary for less important discussions. For example, it can provide a summary with a moderate level of detail for discussions of moderate importance. This ensures that the summary is provided with a level of detail appropriate to the importance of the discussion. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can use an AI model to evaluate the importance of discussions and adjust the level of detail in the summary based on that importance.

[0083] The summarization unit can apply different summarization algorithms to each agenda item when generating summaries. For example, the summarization unit can apply a technical summarization algorithm to technical agenda items. It can also apply a management-oriented summarization algorithm to management agenda items. For example, it can apply a personnel-oriented summarization algorithm to personnel agenda items. This ensures that summaries are generated using a summarization algorithm appropriate to the agenda item. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can use an AI model to apply different summarization algorithms to each agenda item.

[0084] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is in a hurry, the summarization unit can provide a short summary. Alternatively, if the user is relaxed, it can provide a longer summary. For example, if the user is excited, it can provide a visually easy-to-understand summary. This ensures that the summary is provided at a length appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not. For example, the summarization unit can use an AI model to estimate the user's emotions and adjust the length of the summary based on the estimated emotions.

[0085] The summarization unit can determine the priority of summaries based on the progress of the discussion when generating summaries. For example, if the discussion is ongoing, the summarization unit will prioritize summarizing the ongoing topics. It can also prioritize summarizing the completed topics if the discussion has concluded. Furthermore, if the discussion has been interrupted, the summarization unit can prioritize summarizing the interrupted topics. This ensures that summaries are provided with priorities corresponding to the progress of the discussion. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit may use an AI model to evaluate the progress of the discussion and determine the priority of summaries based on that progress.

[0086] The summarization unit can adjust the order of summaries based on the relevance of the arguments during summary generation. For example, the summarization unit may prioritize summarizing highly relevant arguments. It can also postpone summarizing less relevant arguments. For example, the summarization unit may summarize arguments of moderate relevance in an appropriate order. This ensures that summaries are provided in an order that reflects the relevance of the arguments. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not. For example, the summarization unit may use an AI model to evaluate the relevance of arguments and adjust the order of summaries based on relevance.

[0087] The information provider can estimate the user's emotions and adjust the type of information provided based on the estimated emotions. For example, if the user is relaxed, the information provider can provide detailed information. Alternatively, if the user is in a hurry, it can provide concise information. For example, if the user is excited, it can provide visually easy-to-understand information. This ensures that the type of information provided is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the information provider may be performed using AI, or not. For example, the information provider can use an AI model to estimate the user's emotions and adjust the type of information provided based on the estimated emotions.

[0088] The information provision unit can, when providing information, prioritize the provision of relevant information by referring to past discussion content. For example, the information provision unit prioritizes the provision of relevant information based on past discussion content. The information provision unit can also analyze past discussion content and provide highly relevant information. For example, the information provision unit can provide relevant information by referring to past discussion content. This ensures that relevant information is prioritized based on past discussion content. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can use an AI model to refer to past discussion content and prioritize the provision of relevant information.

[0089] The information provision department can apply different information gathering algorithms to each agenda item when providing information. For example, the information provision department can apply a technical information gathering algorithm to technical agenda items. It can also apply a management-related information gathering algorithm to management agenda items. For example, it can apply a personnel-related information gathering algorithm to personnel agenda items. This ensures that information is provided using an information gathering algorithm appropriate to the agenda item. Some or all of the above processing in the information provision department may be performed using AI, for example, or without AI. For example, the information provision department can use an AI model to apply different information gathering algorithms to each agenda item.

[0090] The information provider can estimate the user's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the user is nervous, the information provider can provide a simple and highly visible display method. Alternatively, if the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can provide a concise display method. This ensures that information is provided in a display method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the information provider may be performed using AI, or not. For example, the information provider can use an AI model to estimate the user's emotions and adjust the way information is displayed based on the estimated emotions.

[0091] The information provision department can collect and provide the latest information from the internet in real time when providing information. For example, the information provision department can collect and provide the latest news from the internet in real time. The information provision department can also collect and provide the latest technological information from the internet in real time. For example, the information provision department can also collect and provide the latest market information from the internet in real time. This ensures that the latest information from the internet is provided in real time. Some or all of the above processing in the information provision department may be performed using AI, for example, or without AI. For example, the information provision department can use an AI model to collect and provide the latest information from the internet in real time.

[0092] The information provider can adjust the level of detail of information based on the participant's expertise when providing information. For example, if the participant has expertise, the information provider can provide detailed information. Conversely, if the participant does not have expertise, the information provider can provide concise information. The information provider can also adjust the level of detail of information according to the participant's level of expertise, for example. This ensures that information is provided with a level of detail appropriate to the participant's expertise. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can use an AI model to evaluate the participant's expertise and adjust the level of detail of information based on that expertise.

[0093] The minutes creation unit can estimate the user's emotions and adjust the presentation of the minutes based on the estimated emotions. For example, if the user is relaxed, the minutes creation unit can provide detailed minutes. Alternatively, if the user is in a hurry, it can provide concise minutes. If the user is excited, for example, the minutes creation unit can provide visually clear minutes. This ensures that the minutes are presented in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the minutes creation unit may be performed using AI, or not. For example, the minutes creation unit can use an AI model to estimate the user's emotions and adjust the presentation of the minutes based on the estimated emotions.

[0094] The minutes creation unit can adjust the level of detail in the minutes based on the importance of the discussions. For example, the minutes creation unit can provide detailed minutes for important discussions. It can also provide concise minutes for less important discussions. For example, it can provide minutes with a moderate level of detail for discussions of moderate importance. This ensures that minutes are provided with a level of detail appropriate to the importance of the discussions. Some or all of the above processing in the minutes creation unit may be performed using AI, for example, or not. For example, the minutes creation unit can use an AI model to evaluate the importance of discussions and adjust the level of detail in the minutes based on that importance.

[0095] The minutes creation unit can apply different minutes creation algorithms to each agenda item when creating minutes. For example, the minutes creation unit can apply a technical minutes creation algorithm to technical agenda items. It can also apply a management-oriented minutes creation algorithm to management agenda items. For example, it can apply a personnel-oriented minutes creation algorithm to personnel agenda items. This ensures that minutes are provided using an algorithm appropriate to the agenda item. Some or all of the above processing in the minutes creation unit may be performed using AI, for example, or without AI. For example, the minutes creation unit can use an AI model to apply different minutes creation algorithms to each agenda item.

[0096] The minutes creation unit can estimate the user's emotions and adjust the length of the minutes based on the estimated emotions. For example, if the user is in a hurry, the minutes creation unit can provide short minutes. Conversely, if the user is relaxed, it can provide long minutes. For example, if the user is excited, the minutes creation unit can provide visually easy-to-understand minutes. This ensures that minutes are provided at a length appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the minutes creation unit may be performed using AI or not. For example, the minutes creation unit can use an AI model to estimate the user's emotions and adjust the length of the minutes based on the estimated emotions.

[0097] The minutes creation unit can determine the priority of meeting minutes based on the progress of the discussion. For example, if a discussion is ongoing, the minutes creation unit will prioritize the ongoing agenda items when creating the minutes. The minutes creation unit can also prioritize the completed agenda items when a discussion has concluded. Furthermore, if a discussion is interrupted, the minutes creation unit can prioritize the interrupted agenda items when creating the minutes. This ensures that the minutes are provided with priorities corresponding to the progress of the discussion. Some or all of the above processes in the minutes creation unit may be performed using AI, for example, or not. For example, the minutes creation unit could use an AI model to evaluate the progress of the discussion and determine the priority of the minutes based on that progress.

[0098] The minutes creation unit can adjust the order of the minutes based on the relevance of the discussions during the minutes creation process. For example, the minutes creation unit can prioritize creating minutes for highly relevant discussions. It can also postpone creating minutes for less relevant discussions. For example, the minutes creation unit can create minutes for discussions of moderate relevance in an appropriate order. This ensures that the minutes are provided in an order that reflects the relevance of the discussions. Some or all of the above processing in the minutes creation unit may be performed using AI, for example, or not. For example, the minutes creation unit can use an AI model to evaluate the relevance of discussions and adjust the order of the minutes based on that relevance.

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

[0100] The meeting management team can analyze participants' comments in real time during the meeting and adjust the direction of the discussion. For example, if the discussion goes off track, the management team can prompt participants to return to the original topic. They can also stimulate the discussion by offering new perspectives or questions when it reaches an impasse. Furthermore, based on participants' comments, the management team can propose actions to smooth the discussion. This ensures that the meeting proceeds efficiently and the discussion progresses effectively.

[0101] The summarization function can analyze participants' statements in real time during a meeting, extract key points, and summarize them. For example, it can extract and summarize important statements and decisions made during a discussion in real time. It can also organize the flow of the discussion and provide a summary to clarify the next steps. Furthermore, based on participants' statements, the summarization function can visually organize the key points of the discussion in an easy-to-understand way. This makes the key points of the discussion clearer and easier to understand.

[0102] The information provision department can analyze participants' comments in real time during the meeting and provide relevant information. For example, based on participants' comments, the information provision department can provide relevant materials and data in real time. Furthermore, the information provision department can collect the latest information from the internet and disseminate it during the meeting. In addition, based on participants' comments, the information provision department can provide necessary information in accordance with the progress of the discussion. This ensures that necessary information is provided immediately, allowing the discussion to proceed smoothly.

[0103] The meeting minutes creation system can analyze participants' statements in real time during a meeting and automatically generate meeting minutes. For example, it can reflect important statements and decisions in the minutes in real time based on participants' comments. Furthermore, the system can pre-set the format and content of the minutes, allowing for quick creation after the meeting. It can also visually organize the minutes based on participants' comments, making them easy to understand. This reduces the effort required for creating meeting minutes and allows for rapid sharing of meeting content.

[0104] The meeting management team can estimate the participants' emotions during the meeting and adjust the pace of the discussion based on those estimates. For example, if participants are tense, the team can suggest a more relaxing approach. If participants are relaxed, the team can maintain a normal pace. Furthermore, if participants are anxious, the team can slow down the pace to allow for better understanding. This ensures that the meeting progresses in a way that is appropriate to the participants' emotions.

[0105] The summarization function can estimate the participants' emotions during the meeting and adjust the way the summary is presented based on those estimates. For example, if participants are relaxed, the summarization function can provide a detailed summary. If participants are in a hurry, it can provide a concise summary. Furthermore, if participants are excited, it can provide a visually easy-to-understand summary. This ensures that the summary is presented in a way that is appropriate to the participants' emotions.

[0106] The information provider can estimate the participants' emotions during the meeting and adjust the type of information provided based on those estimates. For example, if participants are relaxed, the information provider can provide detailed information. If participants are in a hurry, the information provider can provide concise information. Furthermore, if participants are excited, the information provider can provide visually easy-to-understand information. This ensures that the type of information provided is tailored to the participants' emotions.

[0107] The minutes-taking department can estimate the participants' emotions during the meeting and adjust the way the minutes are presented based on those estimates. For example, if participants are relaxed, the minutes-taking department can provide detailed minutes. If participants are in a hurry, the minutes-taking department can provide concise minutes. Furthermore, if participants are excited, the minutes-taking department can provide visually clear minutes. This ensures that the minutes are presented in a way that reflects the participants' emotions.

[0108] The meeting management team can analyze participants' comments in real time during the meeting and adjust the flow of the discussion. For example, if the discussion goes off track, the management team can prompt participants to return to the original topic. They can also stimulate the discussion by offering new perspectives or questions when it reaches an impasse. Furthermore, based on participants' comments, the management team can propose actions to smooth the discussion. This ensures that the meeting is conducted efficiently and the discussion progresses effectively.

[0109] The summarization function can analyze participants' statements in real time during a meeting, extract key points, and summarize them. For example, it can extract and summarize important statements and decisions made during a discussion in real time. It can also organize the flow of the discussion and provide a summary to clarify the next steps. Furthermore, based on participants' statements, the summarization function can visually organize the key points of the discussion in an easy-to-understand way. This makes the key points of the discussion clearer and easier to understand.

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

[0111] Step 1: The meeting management team manages the progress of the meeting. The meeting management team sets the meeting procedures and time allocation in advance and manages them appropriately. For example, they confirm the purpose and goals at the start of the meeting and set the time allocation for each agenda item. They monitor the progress during the meeting and make adjustments to ensure that the discussion is completed within the allotted time. If the progress is slow, they can speed up the discussion, and if it is progressing too quickly, they can allocate time to deepen the discussion. Step 2: The summarization section summarizes the meeting content in real time. It analyzes the recorded information and organizes the discussion at times when it becomes complex or at the end of the meeting. For example, it summarizes when the discussion is heated to organize the key points. At the end of the meeting, it summarizes the results of the discussion and proposes the next actions. It extracts important statements and decisions based on the recorded information and summarizes them. It can also organize the flow of the discussion and clarify the next steps. Step 3: The information provision department collects past discussion content, related materials, and information from the internet in real time and disseminates it during the meeting. For example, it searches past discussion content from a database and provides relevant information. It can also collect the latest information from the internet and disseminate it during the meeting. It can automatically collect relevant materials and provide them in accordance with the progress of the meeting. Step 4: The minutes creation department automatically generates meeting minutes based on the audio recording and distributes them to meeting attendees. The system analyzes the audio recording and automatically reflects the content of discussions and decisions in the minutes. The format and content of the minutes can be set in advance, allowing for quick creation of the minutes after the meeting. The minutes can be automatically generated based on the audio recording and distributed to meeting attendees via email. The minutes can also be saved to the cloud, allowing attendees to access them at any time.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the progress management unit, summarization unit, information provision unit, and minutes creation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the progress management unit is implemented by the control unit 46A of the smart device 14, which monitors the progress of the meeting in real time and facilitates the discussion if it is falling behind. The summarization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the recorded information and organizes the discussion when it becomes complex or at the end of the meeting. The information provision unit is implemented by, for example, the control unit 46A of the smart device 14, which collects past discussion content, related materials, and information from the internet in real time and transmits it at the meeting. The minutes creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates minutes based on the recorded information and distributes them to the meeting attendees. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the progress management unit, summarization unit, information provision unit, and minutes creation unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the progress management unit is implemented by the control unit 46A of the smart glasses 214, which monitors the progress of the meeting in real time and facilitates the discussion if it is falling behind. The summarization unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the recorded information and organizes the discussion when it becomes complex or at the end of the meeting. The information provision unit is implemented by the control unit 46A of the smart glasses 214, which collects past discussion content, related materials, and information from the internet in real time and transmits it at the meeting. The minutes creation unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically generates minutes based on the recorded information and distributes them to the meeting attendees. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the progress management unit, summarization unit, information provision unit, and minutes creation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the progress management unit is implemented by the control unit 46A of the headset terminal 314, which monitors the progress of the meeting in real time and facilitates the discussion if it is falling behind. The summarization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the recorded information and organizes the discussion when it becomes complex or at the end of the meeting. The information provision unit is implemented by, for example, the control unit 46A of the headset terminal 314, which collects past discussion content, related materials, and information from the internet in real time and transmits it at the meeting. The minutes creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates minutes based on the recorded information and distributes them to the meeting attendees. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the progress management unit, summarization unit, information provision unit, and minutes creation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the progress management unit is implemented by the control unit 46A of the robot 414, which monitors the progress of the meeting in real time and facilitates the discussion if it is falling behind. The summarization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the recorded information and organizes the discussion when it becomes complex or at the end of the meeting. The information provision unit is implemented by, for example, the control unit 46A of the robot 414, which collects past discussion content, related materials, and information from the internet in real time and transmits it at the meeting. The minutes creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates minutes based on the recorded information and distributes them to the meeting attendees. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) The meeting management department, which manages the progress of the meeting, A summarization unit that summarizes the contents of the meeting managed by the aforementioned progress management unit in real time, An information providing unit provides information based on the information summarized by the summarizing unit, The system includes a minutes creation unit that creates minutes based on the information provided by the aforementioned information provision unit. A system characterized by the following features. (Note 2) The aforementioned progress management department, Set the meeting procedures and time allocation in advance and manage them appropriately. The system described in Appendix 1, characterized by the features described herein. (Note 3) The summary section above is, The audio recordings are analyzed, and the discussion is summarized at points when it becomes complex or at the end of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned information provision unit, Collect past discussions, related materials, or information from the internet in real time and disseminate it during the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned minutes preparation department, The system automatically generates meeting minutes based on the audio recording and distributes them to meeting attendees. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned progress management department, Monitor the progress of the meeting and adjust the schedule to ensure that discussions are completed within the allotted time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned progress management department, It estimates the user's emotions and adjusts the pace of the meeting based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned progress management department, During the meeting, analyze the frequency of participants' contributions and make adjustments to ensure equal opportunities for them to speak. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned progress management department, During the meeting, monitor participants' engagement levels in real time and suggest breaks if their engagement declines. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned progress management department, Estimate user emotions and modify the meeting's progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned progress management department, During the meeting, monitor participants' physical condition and suggest breaks at appropriate times. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned progress management department, During the meeting, refer to past comments made by participants and prioritize addressing relevant topics. The system described in Appendix 1, characterized by the features described herein. (Note 13) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The summary section above is, When generating a summary, adjust the level of detail in the summary based on the importance of the discussion. The system described in Appendix 1, characterized by the features described herein. (Note 15) The summary section above is, When generating summaries, a different summarization algorithm is applied for each agenda item. The system described in Appendix 1, characterized by the features described herein. (Note 16) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The summary section above is, When generating summaries, the priority of summaries is determined based on the progress of the discussion. The system described in Appendix 1, characterized by the features described herein. (Note 18) The summary section above is, When generating summaries, the order of the summaries is adjusted based on the relevance of the discussion. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned information provision unit, It estimates the user's emotions and adjusts the type of information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned information provision unit, When providing information, we will prioritize providing relevant information by referring to past discussions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned information provision unit, When providing information, a different information gathering algorithm will be applied for each agenda item. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned information provision unit, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned information provision unit, When providing information, we collect and provide the latest information from the internet in real time. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned information provision unit, When providing information, adjust the level of detail based on the participant's expertise. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned minutes preparation department, The system estimates the user's emotions and adjusts the way the meeting minutes are written based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned minutes preparation department, When creating meeting minutes, adjust the level of detail in the minutes based on the importance of the discussion. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned minutes preparation department, When creating meeting minutes, a different minutes creation algorithm is applied for each agenda item. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned minutes preparation department, It estimates the user's emotions and adjusts the length of the meeting minutes based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned minutes preparation department, When creating meeting minutes, prioritize the minutes based on the progress of the discussion. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned minutes preparation department, When creating meeting minutes, adjust the order of the minutes based on the relevance of the discussion. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. The meeting management department, which manages the progress of the meeting, A summarization unit that summarizes the contents of the meeting managed by the aforementioned progress management unit in real time, An information providing unit provides information based on the information summarized by the summarizing unit, The system includes a minutes creation unit that creates minutes based on the information provided by the aforementioned information provision unit. A system characterized by the following features.

2. The aforementioned progress management department, Set the meeting procedures and time allocation in advance and manage them appropriately. The system according to feature 1.

3. The summary section above is, The audio recordings are analyzed, and the discussion is summarized at points when it becomes complex or at the end of the meeting. The system according to feature 1.

4. The aforementioned information provision unit, Collect past discussions, related materials, or information from the internet in real time and disseminate it during the meeting. The system according to feature 1.

5. The aforementioned minutes preparation department, The system automatically generates meeting minutes based on the audio recording and distributes them to meeting attendees. The system according to feature 1.

6. The aforementioned progress management department, Monitor the progress of the meeting and adjust the schedule to ensure that discussions are completed within the allotted time. The system according to feature 1.

7. The aforementioned progress management department, It estimates the user's emotions and adjusts the pace of the meeting based on those emotions. The system according to feature 1.

8. The aforementioned progress management department, During the meeting, analyze the frequency of participants' contributions and make adjustments to ensure equal opportunities for them to speak. The system according to feature 1.

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