system

Abstract

The system according to this embodiment aims to streamline the preparation and running of meetings and reduce the burden on the organizer. [Solution] The system according to this embodiment comprises a reception unit, an agenda creation unit, a document creation unit, a scheduling unit, a moderator unit, and a closing unit. The reception unit receives input of the purpose and goals of the meeting. The agenda creation unit creates an agenda based on the information received by the reception unit. The document creation unit creates meeting materials based on the agenda created by the agenda creation unit. The scheduling unit schedules the meeting based on the materials created by the document creation unit. The moderator unit conducts the meeting based on the schedule adjusted by the scheduling unit. The closing unit closes the meeting based on the content of the meeting conducted by the moderator 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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] Conventional technologies have a problem that it takes a lot of time and effort for meeting preparation and progress, and it is difficult to perform efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of meeting preparation and progress and reduce the burden on the organizer.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an agenda creation unit, a document creation unit, a scheduling unit, a moderation unit, and a closing unit. The reception unit receives input of the purpose and goals of the meeting. The agenda creation unit creates an agenda based on the information received by the reception unit. The document creation unit creates meeting materials based on the agenda created by the agenda creation unit. The scheduling unit schedules the meeting based on the materials created by the document creation unit. The moderation unit conducts the meeting based on the schedule adjusted by the scheduling unit. The closing unit closes the meeting based on the content of the meeting conducted by the moderation unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the preparation and running of meetings and reduce the burden on the organizer. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The meeting efficiency system according to an embodiment of the present invention is a system designed to solve various problems faced by meeting organizers and improve meeting efficiency. This system automates pre-meeting preparation, meeting facilitation, and meeting closing. When the meeting organizer inputs the "purpose and goals of the meeting," the system creates an agenda and, in conjunction with the company's internal drive, portal site, and calendar function, creates meeting materials, schedules the meeting, contacts participants, and registers them. It also inputs information (attributes and positions) and audio data of meeting participants and learns the characteristics of each participant. On the day of the meeting, the system is projected onto a display and speaks to facilitate the meeting. It supplements the content of speakers who are not clear enough and clarifies misunderstandings among participants. If someone is speaking for too long, it intervenes to stop them at an appropriate time. Furthermore, if the meeting's discussion expands, it quickly retrieves necessary numerical data from the company's internal drive to support decision-making. At the end of the meeting, the system controls the scope of the discussion, checks all statements, and provides an optimal summary. Furthermore, by organizing meeting tasks (assignments), assigning them to appropriate participants, and setting deadlines, the next actions become clear. This system frees meeting organizers from tedious tasks such as preparing materials, scheduling, moderating, and closing, creating an environment where they can focus on their core priorities. Thus, the meeting efficiency system frees meeting organizers from tedious tasks such as preparing materials, scheduling, moderating, and closing, creating an environment where they can focus on their core priorities.

[0029] The meeting efficiency system according to this embodiment comprises a reception unit, an agenda creation unit, a document creation unit, a scheduling unit, a moderator unit, and a closing unit. The reception unit receives input of the purpose and goals of the meeting. For example, the reception unit accepts the user entering the purpose and goals of the meeting into an input form. The reception unit can also accept the purpose and goals of the meeting using voice input. For example, it accepts the user dictating the purpose and goals of the meeting using a microphone. Furthermore, the reception unit can refer to past meeting data and automatically suggest the purpose and goals of similar meetings. For example, it can suggest the optimal purpose and goals based on data from similar meetings held in the past. The agenda creation unit creates an agenda based on the information received by the reception unit. For example, the agenda creation unit creates a list of topics based on the purpose and goals of the meeting. The agenda creation unit can also create an agenda in conjunction with the company's internal drive, portal site, and calendar function. For example, it can retrieve relevant materials from the company drive and reflect them in the agenda. The document creation department prepares meeting materials based on the agenda created by the agenda creation department. The document creation department inputs information (attributes and positions) and audio data of meeting participants and learns the characteristics of each participant. The document creation department can also refer to past meeting materials and automatically add relevant information. The scheduling department schedules the meeting based on the materials created by the document creation department. The scheduling department proposes the best date considering the schedules of the meeting participants. The scheduling department can also propose the best date considering the geographical location information of the meeting participants. The moderator department conducts the meeting based on the date adjusted by the scheduling department. The moderator department conducts the meeting by projecting information onto a display and speaking. The moderator department can also supplement the content of speakers who are not clear and clarify any misunderstandings among participants. The closing department closes the meeting based on the content of the meeting conducted by the moderator department. The closing section, for example, controls the scope of the discussion, reviews all comments, and provides an optimal summary.Furthermore, the closing section can organize meeting tasks (assignments), assign them to appropriate participants, and set deadlines. As a result, the meeting efficiency system according to this embodiment frees meeting organizers from cumbersome tasks such as preparing materials, scheduling, moderating, and closing, creating an environment where they can concentrate on tasks that should be prioritized.

[0030] The reception desk accepts input of the meeting's purpose and goals. For example, the reception desk accepts the user entering the meeting's purpose and goals into an input form. The reception desk can also accept the meeting's purpose and goals using voice input. For example, it accepts the user dictating the meeting's purpose and goals using a microphone. Furthermore, the reception desk can refer to past meeting data and automatically suggest similar meeting purposes and goals. For example, it can suggest optimal purposes and goals based on data from similar meetings held in the past. The reception desk can also analyze the information entered by the user in real time and provide appropriate feedback. For example, if the purpose entered by the user is ambiguous, the reception desk can provide specific examples to help the user set clearer purposes. In the case of voice input, speech recognition technology is used to convert the user's speech into text and correct or complete it as needed. Furthermore, the reception desk can learn the user's past input history and provide suggestions optimized for individual users. For example, it can learn the patterns of meetings that a particular user frequently holds and provide suggestions to that user based on past successes. This allows the reception desk to help users efficiently and effectively set the purpose and goals of the meeting, and to smoothly proceed with the meeting preparation phase.

[0031] The agenda creation department creates an agenda based on the information received by the reception department. For example, the agenda creation department creates a list of topics based on the purpose and goals of the meeting. The agenda creation department can also create agendas in conjunction with the company's internal drive, portal site, and calendar functions. For example, it can retrieve relevant documents from the internal drive and incorporate them into the agenda. The agenda creation department can use AI to analyze the received information and automatically generate optimal topics. For example, if the purpose of the meeting is "market launch of a new product," the AI ​​will refer to data from similar past meetings and suggest topics such as "market analysis," "competitor research," "marketing strategy," and "sales plan." The agenda creation department can also consider the roles and areas of expertise of the meeting participants and assign appropriate personnel to each topic. Furthermore, to optimize the meeting's progress, the agenda creation department automatically calculates the time required for each topic and creates an efficient agenda. For example, based on past meeting data, it can allocate appropriate time to specific topics and adjust the meeting to ensure it finishes on time. This allows the agenda creation team to support users in conducting meetings efficiently and effectively, and to smoothly proceed with the meeting preparation phase.

[0032] The document creation department prepares meeting materials based on the agenda created by the agenda creation department. For example, the document creation department inputs information (attributes and positions) and audio data of meeting participants to learn the characteristics of each participant. It can also refer to past meeting materials and automatically add relevant information. The document creation department uses AI to automatically generate meeting materials. For example, it collects the latest data and statistics related to the meeting agenda from the internet and incorporates them into the materials. It also analyzes the expertise and past statements of meeting participants to provide each participant with the most relevant information. Furthermore, the document creation department can automatically adjust the format and design of the materials according to the purpose of the meeting. For example, in the case of presentation materials, it automatically generates visually easy-to-understand graphs and charts to improve the quality of the materials. The document creation department can also update materials in real time as the meeting progresses. For example, if new information is provided during the meeting, the document creation department immediately incorporates that information into the materials and shares it with all participants. In this way, the document creation department helps users create meeting materials efficiently and effectively, allowing the meeting preparation phase to proceed smoothly.

[0033] The scheduling department schedules meetings based on materials created by the document creation department. For example, the scheduling department proposes the most suitable dates considering the schedules of meeting participants. It can also propose optimal dates considering the geographical location of participants. The scheduling department uses AI to analyze participants' schedules and automatically proposes the best dates. For example, it obtains each participant's calendar information in real time to identify dates and times when everyone can attend. By considering geographical location, it can also propose optimal dates for participants in remote locations. Furthermore, the scheduling department can prioritize meetings based on their importance and urgency. For example, for highly urgent meetings, it adjusts participants' schedules to hold the meeting as soon as possible. The scheduling department can also collect participant feedback to continuously improve the accuracy of scheduling. For example, it analyzes attendance rates and participant satisfaction from past meetings and incorporates this into scheduling future meetings. This allows the scheduling department to help users efficiently and effectively schedule meetings, ensuring a smooth meeting preparation process.

[0034] The moderator conducts the meeting based on the schedule set by the scheduling department. The moderator, for example, may project their instructions onto a display and speak to facilitate the meeting. The moderator can also supplement the content of speakers who are unclear and clarify any misunderstandings among participants. The moderator uses AI to automate the meeting process. For example, it manages the progress of each agenda item based on the meeting agenda and optimizes time allocation. It also analyzes speakers' statements in real time and highlights important points. Furthermore, the moderator can immediately respond to problems and questions that arise during the meeting. For example, it can receive questions from participants and have the AI ​​provide appropriate answers. It can also supplement speakers' statements and add explanations to ensure that all participants can understand. In addition, the moderator can monitor the progress of the meeting in real time and adjust the process as needed. For example, if an agenda item finishes earlier than planned, it can smoothly transition to the next item. If an agenda item runs longer than planned, it can make adjustments to end the meeting on time. This allows the moderator to support users in conducting meetings efficiently and effectively, and to ensure that the meeting progresses smoothly through each stage.

[0035] The closing team closes the meeting based on the content facilitated by the moderator. For example, the closing team controls the scope of the discussion, reviews all comments, and provides an optimal summary. The closing team can also organize meeting tasks, assign them to appropriate participants, and set deadlines. Using AI, the closing team analyzes the meeting content and automatically generates an optimal summary. For example, it extracts key points and decisions made during the meeting and compiles them into a summary. It also organizes issues and tasks that arose during the meeting and assigns them to appropriate personnel. Furthermore, the closing team can collect feedback from participants after the meeting and suggest improvements for the next meeting. For example, it collects participant satisfaction and opinions and incorporates them into the agenda and proceedings of the next meeting. The closing team can also automatically create meeting minutes after the meeting and share them with all participants. In this way, the closing team helps users close meetings efficiently and effectively, ensuring a smooth conclusion to the meeting.

[0036] The agenda creation unit can create agendas in conjunction with the company's internal drive, portal site, and calendar function. For example, the agenda creation unit can retrieve relevant documents from the internal drive and reflect them in the agenda. It can also retrieve meeting-related information from the portal site and reflect it in the agenda. Furthermore, the agenda creation unit can coordinate meeting schedules in conjunction with the calendar function and reflect them in the agenda. This allows for efficient agenda creation by integrating with various internal systems. Some or all of the above processes in the agenda creation unit may be performed using AI, for example, or not. For example, the agenda creation unit can input documents retrieved from the internal drive into a generation AI and have the generation AI create the agenda.

[0037] The document creation unit can input information and audio data of meeting participants and learn the characteristics of each participant. For example, the document creation unit can input information such as the names, positions, departments, and areas of expertise of meeting participants. It can also input recordings of past statements and audio memos of meeting participants. Furthermore, the document creation unit can learn the characteristics of each participant based on the input information and reflect this in the document creation process. This allows for the creation of more appropriate documents by learning the characteristics of meeting participants. Some or all of the above processing in the document creation unit may be performed using AI, for example, or without AI. For example, the document creation unit can input meeting participant information into a generating AI and have the generating AI learn the characteristics of each participant.

[0038] The moderator unit can be projected onto a display and speak to conduct the proceedings. The moderator unit can be projected onto a display using, for example, a projector. It can also be projected onto a monitor or smartboard. Furthermore, the moderator unit can speak and conduct the proceedings using speech synthesis technology. For example, the moderator unit can conduct the proceedings by playing back recorded audio. This allows for visually easy-to-understand proceedings when projected onto a display. Some or all of the above processing in the moderator unit may be performed using, for example, AI, or not using AI. For example, the moderator unit can input the content projected onto the display into a generating AI and have the generating AI generate the spoken content.

[0039] The moderator can supplement the content of speakers who are unclear or lacking information, and resolve misunderstandings among participants. For example, the moderator can supplement the content of statements when they are unclear or lacking information. The moderator can also resolve disagreements or differences in understanding among participants. This allows the meeting to proceed smoothly by supplementing the content of statements and resolving misunderstandings. Some or all of the above processes performed by the moderator may be carried out using AI, for example, or not. For example, the moderator can input the content of the statements into a generating AI and have the generating AI generate supplementary content.

[0040] The moderator can interrupt someone who is speaking for too long at an appropriate time. For example, the moderator can set a limit on speaking time and interrupt if that limit is exceeded. Alternatively, the moderator can interrupt based on a comparison of speaking time with other participants. This allows the meeting to proceed more efficiently by interrupting speech. Some or all of the above processes by the moderator may be performed using AI, or not. For example, the moderator can input speaking time into a generating AI and have the generating AI execute the timing of speech interruption.

[0041] The moderator can quickly retrieve necessary numerical data from the company's internal drive if the meeting's topics expand, thereby supporting decision-making. The moderator expands the meeting's topics, for example, when new issues arise or when the discussion deepens. The moderator also quickly retrieves necessary numerical data such as sales data, cost data, and performance data from the company's internal drive. This allows for smoother decision-making through rapid data retrieval. Some or all of the above processes performed by the moderator may be carried out using AI, for example, or without AI. For example, the moderator can input the necessary numerical data into a generating AI and have the generating AI perform the data retrieval.

[0042] The closing unit can control the scope of the discussion and make an optimal summary after checking all comments. For example, the closing unit can set the agenda and restrict comments to control the scope of the discussion. The closing unit can also check all comments and make an optimal summary after reflecting all opinions. By controlling the scope of the discussion and making an optimal summary, the conclusions of the meeting become clear. Some or all of the above processes in the closing unit may be performed using AI, for example, or not using AI. For example, the closing unit can input the content of the comments into a generation AI and have the generation AI generate the summary.

[0043] The closing department can organize meeting tasks, assign them to appropriate participants, and set deadlines. For example, the closing department can organize tasks such as items to be researched or materials to prepare before the next meeting. The closing department can also assign tasks to participants or those in charge with expertise and set deadlines. This clarifies the next actions by organizing meeting tasks and assigning them to appropriate participants. Some or all of the above processes in the closing department may be performed using AI, for example, or not. For example, the closing department can input the content of the tasks into a generating AI and have the generating AI perform the assignment and deadline setting.

[0044] The reception desk can refer to past meeting data and automatically suggest objectives and goals for similar meetings. For example, the reception desk can suggest optimal objectives and goals based on data from similar meetings held in the past. It can also refer to successful examples of similar meetings and automatically suggest effective objectives and goals. Furthermore, the reception desk can analyze past meeting data and prioritize suggesting frequently used objectives and goals. This allows for efficient setting of objectives and goals by referring to past meeting data. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past meeting data into a generating AI and have the generating AI suggest objectives and goals.

[0045] The reception desk can suggest the optimal input timing based on the user's work situation when the purpose and goals of a meeting are entered. For example, the reception desk can refer to the user's calendar information to suggest the optimal input timing. The reception desk can also consider the user's workload and encourage input during less busy times. Furthermore, the reception desk can analyze the user's work progress and send reminders at appropriate times. This allows for efficient input work by suggesting input timings that are tailored to the user's work situation. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input user work situation data into a generating AI and have the generating AI generate input timing suggestions.

[0046] The reception desk can provide input assistance when the user enters the purpose and goals of a meeting by referring to the user's past input history. For example, the reception desk can automatically display purposes and goals that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest purposes and goals to be used at a specific time period based on the user's past input history. This allows for efficient input work by referring to past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past input history data into a generating AI and have the generating AI perform input assistance.

[0047] The reception desk can determine the priority of input when entering the purpose and goals of meetings, taking into account the user's work schedule. For example, the reception desk can refer to the user's calendar information and prioritize the input of the purpose and goals of important meetings. It can also analyze the user's work progress and prioritize the input of the purpose and goals of high-priority meetings. Furthermore, the reception desk can consider the user's workload and input the purpose and goals of important meetings during less busy times. This allows for efficient input work by determining the priority of input according to the user's work schedule. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's work schedule data into a generating AI and have the generating AI determine the input priority.

[0048] The agenda creation unit can automatically select the optimal agenda template by referring to past meeting agendas. For example, the agenda creation unit can select the optimal template based on the agendas of past successful meetings. It can also automatically select an effective template by referring to agendas of similar meetings. Furthermore, the agenda creation unit can analyze past meeting data and prioritize the selection of frequently used templates. This allows for efficient agenda creation by referring to past meeting agendas. Some or all of the above processes in the agenda creation unit may be performed using AI, for example, or without AI. For example, the agenda creation unit can input past meeting agenda data into a generation AI and have the generation AI perform the template selection.

[0049] The agenda creation unit can adjust the level of detail of the agenda based on the purpose and goals of the meeting. For example, the agenda creation unit can provide a detailed agenda for important meetings. It can also provide a concise agenda that gets straight to the point for simpler meetings. Furthermore, the agenda creation unit can provide an agenda with an appropriate level of detail depending on the purpose and goals of the meeting. This allows for efficient agenda creation by adjusting the level of detail according to the purpose and goals of the meeting. Some or all of the above processes in the agenda creation unit may be performed using AI, for example, or not. For example, the agenda creation unit can input meeting purpose and goal data into a generating AI and have the generating AI perform the adjustment of the level of detail of the agenda.

[0050] The agenda creation unit can create an optimal agenda by considering the schedules of meeting participants. For example, the agenda creation unit can refer to the calendar information of meeting participants to create an optimal agenda. The agenda creation unit can also consider the workload of meeting participants and set the agenda for times when they have free time. Furthermore, the agenda creation unit can analyze the schedules of meeting participants and create an agenda that everyone can attend. In this way, by considering the schedules of meeting participants, an agenda that everyone can attend can be created. Some or all of the above processes in the agenda creation unit may be performed using AI, for example, or not using AI. For example, the agenda creation unit can input the schedule data of meeting participants into a generation AI and have the generation AI create the agenda.

[0051] The agenda creation unit can automatically add relevant topics by referring to past meeting minutes when creating an agenda. For example, the agenda creation unit can automatically add relevant topics based on past meeting minutes. It can also refer to the minutes of similar meetings and automatically add effective topics. Furthermore, the agenda creation unit can analyze past meeting data and prioritize adding frequently discussed topics. This allows for the efficient addition of relevant topics by referring to past meeting minutes. Some or all of the above processes in the agenda creation unit may be performed using AI, for example, or without AI. For example, the agenda creation unit can input past meeting minutes data into a generating AI and have the generating AI add topics.

[0052] The document creation unit can automatically generate optimal document content by referring to past statements from meeting participants. For example, the document creation unit can automatically generate relevant documents based on past statements from meeting participants. It can also automatically generate effective documents by referring to statements from similar meetings. Furthermore, the document creation unit can analyze past statements and prioritize reflecting frequently discussed topics in the documents. This allows for efficient document creation by referring to past statements. Some or all of the above processes in the document creation unit may be performed using AI, for example, or without AI. For example, the document creation unit can input past statements into a generation AI and have the generation AI generate the document content.

[0053] The document creation department can adjust the level of detail in documents based on the purpose and goals of the meeting. For example, the document creation department can provide detailed documents for important meetings. It can also provide concise documents that highlight the key points for simpler meetings. Furthermore, the document creation department can provide documents with an appropriate level of detail depending on the purpose and goals of the meeting. This allows for efficient document creation by adjusting the level of detail according to the purpose and goals of the meeting. Some or all of the above processes in the document creation department may be performed using AI, for example, or not. For example, the document creation department can input meeting purpose and goal data into a generating AI and have the generating AI adjust the level of detail of the documents.

[0054] The document creation department can create optimal documents by considering the roles and attributes of meeting participants. For example, the document creation department can provide documents with an appropriate level of detail according to the roles of the meeting participants. The document creation department can also consider the attributes of the meeting participants (such as their areas of expertise) and reflect relevant information in the documents. Furthermore, the document creation department can create optimal documents based on the past statements of the meeting participants. In this way, appropriate documents can be created by considering the roles and attributes of the meeting participants. Some or all of the above processes in the document creation department may be performed using AI, for example, or not using AI. For example, the document creation department can input the roles and attribute data of the meeting participants into a generating AI and have the generating AI create the documents.

[0055] The document creation unit can automatically add relevant information by referring to past meeting materials when creating documents. For example, the document creation unit can automatically add relevant information based on past meeting materials. It can also refer to materials from similar meetings and automatically add effective information. Furthermore, the document creation unit can analyze past meeting data and prioritize adding frequently used information. This allows for the efficient addition of relevant information by referring to past materials. Some or all of the above processes in the document creation unit may be performed using AI, for example, or without AI. For example, the document creation unit can input past document data into a generating AI and have the generating AI perform the information addition.

[0056] The scheduling unit can automatically suggest the optimal date by referring to the past attendance history of meeting participants. For example, the scheduling unit can suggest the optimal date based on the attendance history of similar meetings held in the past. It can also automatically suggest effective dates by referring to the attendance history of similar meetings. Furthermore, the scheduling unit can analyze past attendance history and prioritize suggesting dates that are frequently attended. This allows for efficient scheduling by referring to past attendance history. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input past attendance history data into a generating AI and have the generating AI execute date suggestions.

[0057] The scheduling unit can determine the priority of dates based on the purpose and goals of the meeting during the scheduling process. For example, the scheduling unit will prioritize important meetings. It can also propose a concise schedule for simpler meetings. Furthermore, the scheduling unit can adjust dates with appropriate priorities according to the purpose and goals of the meeting. This allows for efficient scheduling by determining the priority of dates according to the purpose and goals of the meeting. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input meeting purpose and goal data into a generating AI and have the generating AI determine the priority of dates.

[0058] The scheduling unit can propose the optimal date when scheduling a meeting, taking into account the geographical location information of the meeting participants. For example, the scheduling unit can propose the optimal date based on the current location of the meeting participants. It can also propose an efficient date by considering the travel time of the meeting participants. Furthermore, the scheduling unit can analyze the geographical location information of the meeting participants and propose a date that is easy for everyone to attend. In this way, scheduling can be done efficiently by taking into account the geographical location information of the meeting participants. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit can input the geographical location data of the meeting participants into a generating AI and have the generating AI execute the date proposal.

[0059] The scheduling unit can automatically select the optimal date by referring to the work schedules of meeting participants during scheduling. For example, the scheduling unit can refer to the calendar information of meeting participants and automatically select the optimal date. The scheduling unit can also consider the workload of meeting participants and set the date during a time when they have more free time. Furthermore, the scheduling unit can analyze the schedules of meeting participants and automatically select a date that everyone can attend. This allows for efficient scheduling by referring to the work schedules of meeting participants. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input the work schedule data of meeting participants into a generating AI and have the generating AI perform the date selection.

[0060] The moderator can automatically select the most suitable moderation method by referring to past statements from meeting participants. For example, the moderator can automatically select a relevant moderation method based on past statements from meeting participants. The moderator can also automatically select an effective moderation method by referring to statements from similar meetings. Furthermore, the moderator can analyze past statements and prioritize frequently used moderation methods. This allows for efficient moderation by referring to past statements. Some or all of the above processing in the moderator may be performed using AI, for example, or without AI. For example, the moderator can input past statements into a generating AI and have the generating AI select the moderation method.

[0061] The moderator can adjust the level of detail in the meeting's proceedings based on its purpose and goals. For example, in the case of an important meeting, the moderator can provide a detailed procedure. In the case of a simple meeting, the moderator can also provide a concise procedure that gets straight to the point. Furthermore, the moderator can provide a procedure with an appropriate level of detail depending on the purpose and goals of the meeting. This allows for efficient moderation by adjusting the level of detail according to the purpose and goals of the meeting. Some or all of the above processes in the moderator may be performed using AI, for example, or not. For example, the moderator can input meeting purpose and goal data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proceedings.

[0062] The moderator can select the most appropriate moderation method during the moderation process, taking into account the roles and attributes of the meeting participants. For example, the moderator can provide a moderation method with an appropriate level of detail depending on the role of the meeting participants. The moderator can also consider the attributes of the meeting participants (such as their areas of expertise) and reflect relevant information in the moderation. Furthermore, the moderator can select the most appropriate moderation method based on the past statements of the meeting participants. In this way, an appropriate moderation method can be selected by considering the roles and attributes of the meeting participants. Some or all of the above processing in the moderator may be performed using AI, for example, or not using AI. For example, the moderator can input the roles and attribute data of the meeting participants into a generating AI and have the generating AI perform the selection of the moderation method.

[0063] The moderator can automatically add relevant agenda items by referring to past meeting minutes during the moderation process. For example, the moderator can automatically add relevant agenda items based on past minutes. The moderator can also refer to minutes of similar meetings and automatically add effective agenda items. Furthermore, the moderator can analyze past meeting data and prioritize adding frequently discussed agenda items. This allows for the efficient addition of relevant agenda items by referring to past minutes. Some or all of the above processes in the moderator may be performed using AI, for example, or not. For example, the moderator can input past meeting minutes data into a generating AI and have the generating AI perform the addition of agenda items.

[0064] The closing unit can automatically select the optimal closing method by referring to past statements from meeting participants. For example, the closing unit can automatically select a relevant closing method based on past statements from meeting participants. The closing unit can also automatically select an effective closing method by referring to statements from similar meetings. Furthermore, the closing unit can analyze past statements and prioritize the selection of frequently used closing methods. This allows for efficient closing by referring to past statements. Some or all of the above processing in the closing unit may be performed using AI, for example, or without AI. For example, the closing unit can input past statements into a generating AI and have the generating AI select a closing method.

[0065] The closing unit can adjust the level of detail in the closing based on the purpose and goals of the meeting. For example, in the case of an important meeting, the closing unit can provide a detailed closing method. In the case of a simple meeting, the closing unit can also provide a concise closing method that gets straight to the point. Furthermore, the closing unit can provide a closing method with an appropriate level of detail depending on the purpose and goals of the meeting. This allows for efficient closing by adjusting the level of detail in the closing according to the purpose and goals of the meeting. Some or all of the above processing in the closing unit may be performed using AI, for example, or not using AI. For example, the closing unit can input meeting purpose and goal data into a generating AI and have the generating AI perform the adjustment of the level of detail in the closing.

[0066] The closing unit can select the optimal closing method at the time of closing, taking into account the roles and attributes of the meeting participants. For example, the closing unit can provide a closing method with an appropriate level of detail depending on the role of the meeting participants. The closing unit can also consider the attributes of the meeting participants (such as their areas of expertise) and reflect relevant information in the closing. Furthermore, the closing unit can select the optimal closing method based on the past statements of the meeting participants. This allows for the selection of an appropriate closing method by considering the roles and attributes of the meeting participants. Some or all of the above processing in the closing unit may be performed using AI, for example, or without AI. For example, the closing unit can input the roles and attribute data of the meeting participants into a generating AI and have the generating AI select the closing method.

[0067] The closing unit can automatically add relevant information by referring to past meeting minutes during the closing process. For example, the closing unit can automatically add relevant information based on past meeting minutes. It can also refer to minutes from similar meetings and automatically add effective information. Furthermore, the closing unit can analyze past meeting data and prioritize adding frequently used information. This allows for the efficient addition of relevant information by referring to past meeting minutes. Some or all of the above processing in the closing unit may be performed using AI, for example, or without AI. For example, the closing unit can input past meeting minutes data into a generating AI and have the generating AI perform the information addition.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The meeting efficiency system can also be equipped with a real-time translation unit. This unit can translate what is said during a meeting in real time and provide it immediately to participants who speak different languages. For example, it can translate what is said in English into Japanese and display it to Japanese-speaking participants. Furthermore, the real-time translation unit can also speak the translated content using speech synthesis technology. This facilitates smoother communication between participants who speak different languages ​​and improves the efficiency of international meetings.

[0070] The meeting efficiency system can also be equipped with a concentration monitoring unit that monitors the level of concentration of participants. The concentration monitoring unit uses cameras and sensors to analyze participants' gaze and posture and evaluate their concentration level in real time. For example, if a participant's gaze is diverted from the display or their posture is poor, it will be determined that their concentration level is low. The concentration monitoring unit can also issue an alert and notify the facilitator if the concentration level drops. This helps maintain participants' concentration throughout the meeting and allows for efficient discussion.

[0071] The meeting efficiency system can also include a summarization unit that automatically summarizes participants' remarks. The summarization unit analyzes what is said during the meeting in real time, extracting and summarizing the key points. For example, it can shorten long statements and clarify the main points. Furthermore, the summarization unit can display the summarized content on a screen and share it with all participants. This ensures smoother meeting progress and that important information is shared without being overlooked.

[0072] The meeting efficiency system can also include a recording unit that automatically records participants' remarks and saves them in a searchable format for later use. The recording unit records what is said during the meeting in text format and organizes it by speaker. For example, it can make it possible to search for remarks by keyword, allowing for quick retrieval of comments on specific topics. The recording unit can also save audio recordings, allowing for review in both text and audio formats. This is helpful when reviewing meeting content later, preventing information omissions and misunderstandings.

[0073] The meeting efficiency system can also include an information provision unit that analyzes participants' statements in real time and automatically provides relevant external information. The information provision unit analyzes the statements and retrieves relevant data and materials from the internet or internal databases. For example, it can instantly provide specific market data or technical information to support discussions. Furthermore, the information provision unit can display the retrieved information on a screen and share it with all participants. This ensures that necessary information is quickly provided during the meeting, facilitating smoother decision-making.

[0074] The meeting efficiency system can also include an evaluation unit that automatically assesses participants' contributions and provides feedback. The evaluation unit analyzes the content of the contributions and evaluates their logic and persuasiveness. For example, it evaluates whether the contribution is logical and based on specific data, and provides feedback. The evaluation unit can also point out areas for improvement and provide advice to help with future contributions. This improves participants' communication skills and enhances the quality of meetings.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The reception desk accepts input of the meeting's purpose and goals. For example, it accepts the user entering the meeting's purpose and goals into an input form. It can also accept the meeting's purpose and goals using voice input. Furthermore, it can refer to past meeting data and automatically suggest similar meeting purposes and goals. Step 2: The agenda creation team creates the agenda based on the information received by the reception team. For example, they create a list of topics based on the meeting's purpose and goals. They can also create the agenda in conjunction with the company's internal drive, portal site, and calendar functions. Step 3: The document creation team creates meeting materials based on the agenda created by the agenda creation team. For example, they input information (attributes and positions) and audio data of meeting participants to learn the characteristics of each participant. They can also refer to past meeting materials and automatically add relevant information. Step 4: The scheduling department schedules the meeting based on the materials prepared by the document creation department. For example, they propose the best date considering the schedules of the meeting participants. They can also propose the best date considering the geographical location of the meeting participants. Step 5: The moderator conducts the meeting based on the schedule set by the scheduling team. For example, they may project their information onto a screen and speak to facilitate the meeting. They can also supplement the content of speakers who are unclear and clarify any misunderstandings among participants. Step 6: The closing team closes the meeting based on the content facilitated by the moderator. For example, they control the scope of the discussion, review all comments, and provide an optimal summary. They can also organize the meeting's tasks (assignments), assign them to appropriate participants, and set deadlines.

[0077] (Example of form 2) The meeting efficiency system according to an embodiment of the present invention is a system designed to solve various problems faced by meeting organizers and improve meeting efficiency. This system automates pre-meeting preparation, meeting facilitation, and meeting closing. When the meeting organizer inputs the "purpose and goals of the meeting," the system creates an agenda and, in conjunction with the company's internal drive, portal site, and calendar function, creates meeting materials, schedules the meeting, contacts participants, and registers them. It also inputs information (attributes and positions) and audio data of meeting participants and learns the characteristics of each participant. On the day of the meeting, the system is projected onto a display and speaks to facilitate the meeting. It supplements the content of speakers who are not clear enough and clarifies misunderstandings among participants. If someone is speaking for too long, it intervenes to stop them at an appropriate time. Furthermore, if the meeting's discussion expands, it quickly retrieves necessary numerical data from the company's internal drive to support decision-making. At the end of the meeting, the system controls the scope of the discussion, checks all statements, and provides an optimal summary. Furthermore, by organizing meeting tasks (assignments), assigning them to appropriate participants, and setting deadlines, the next actions become clear. This system frees meeting organizers from tedious tasks such as preparing materials, scheduling, moderating, and closing, creating an environment where they can focus on their core priorities. Thus, the meeting efficiency system frees meeting organizers from tedious tasks such as preparing materials, scheduling, moderating, and closing, creating an environment where they can focus on their core priorities.

[0078] The meeting efficiency system according to this embodiment comprises a reception unit, an agenda creation unit, a document creation unit, a scheduling unit, a moderator unit, and a closing unit. The reception unit receives input of the purpose and goals of the meeting. For example, the reception unit accepts the user entering the purpose and goals of the meeting into an input form. The reception unit can also accept the purpose and goals of the meeting using voice input. For example, it accepts the user dictating the purpose and goals of the meeting using a microphone. Furthermore, the reception unit can refer to past meeting data and automatically suggest the purpose and goals of similar meetings. For example, it can suggest the optimal purpose and goals based on data from similar meetings held in the past. The agenda creation unit creates an agenda based on the information received by the reception unit. For example, the agenda creation unit creates a list of topics based on the purpose and goals of the meeting. The agenda creation unit can also create an agenda in conjunction with the company's internal drive, portal site, and calendar function. For example, it can retrieve relevant materials from the company drive and reflect them in the agenda. The document creation department prepares meeting materials based on the agenda created by the agenda creation department. The document creation department inputs information (attributes and positions) and audio data of meeting participants and learns the characteristics of each participant. The document creation department can also refer to past meeting materials and automatically add relevant information. The scheduling department schedules the meeting based on the materials created by the document creation department. The scheduling department proposes the best date considering the schedules of the meeting participants. The scheduling department can also propose the best date considering the geographical location information of the meeting participants. The moderator department conducts the meeting based on the date adjusted by the scheduling department. The moderator department conducts the meeting by projecting information onto a display and speaking. The moderator department can also supplement the content of speakers who are not clear and clarify any misunderstandings among participants. The closing department closes the meeting based on the content of the meeting conducted by the moderator department. The closing section, for example, controls the scope of the discussion, reviews all comments, and provides an optimal summary.Furthermore, the closing section can organize meeting tasks (assignments), assign them to appropriate participants, and set deadlines. As a result, the meeting efficiency system according to this embodiment frees meeting organizers from cumbersome tasks such as preparing materials, scheduling, moderating, and closing, creating an environment where they can concentrate on tasks that should be prioritized.

[0079] The reception desk accepts input of the meeting's purpose and goals. For example, the reception desk accepts the user entering the meeting's purpose and goals into an input form. The reception desk can also accept the meeting's purpose and goals using voice input. For example, it accepts the user dictating the meeting's purpose and goals using a microphone. Furthermore, the reception desk can refer to past meeting data and automatically suggest similar meeting purposes and goals. For example, it can suggest optimal purposes and goals based on data from similar meetings held in the past. The reception desk can also analyze the information entered by the user in real time and provide appropriate feedback. For example, if the purpose entered by the user is ambiguous, the reception desk can provide specific examples to help the user set clearer purposes. In the case of voice input, speech recognition technology is used to convert the user's speech into text and correct or complete it as needed. Furthermore, the reception desk can learn the user's past input history and provide suggestions optimized for individual users. For example, it can learn the patterns of meetings that a particular user frequently holds and provide suggestions to that user based on past successes. This allows the reception desk to help users efficiently and effectively set the purpose and goals of the meeting, and to smoothly proceed with the meeting preparation phase.

[0080] The agenda creation department creates an agenda based on the information received by the reception department. For example, the agenda creation department creates a list of topics based on the purpose and goals of the meeting. The agenda creation department can also create agendas in conjunction with the company's internal drive, portal site, and calendar functions. For example, it can retrieve relevant documents from the internal drive and incorporate them into the agenda. The agenda creation department can use AI to analyze the received information and automatically generate optimal topics. For example, if the purpose of the meeting is "market launch of a new product," the AI ​​will refer to data from similar past meetings and suggest topics such as "market analysis," "competitor research," "marketing strategy," and "sales plan." The agenda creation department can also consider the roles and areas of expertise of the meeting participants and assign appropriate personnel to each topic. Furthermore, to optimize the meeting's progress, the agenda creation department automatically calculates the time required for each topic and creates an efficient agenda. For example, based on past meeting data, it can allocate appropriate time to specific topics and adjust the meeting to ensure it finishes on time. This allows the agenda creation team to support users in conducting meetings efficiently and effectively, and to smoothly proceed with the meeting preparation phase.

[0081] The document creation department prepares meeting materials based on the agenda created by the agenda creation department. For example, the document creation department inputs information (attributes and positions) and audio data of meeting participants to learn the characteristics of each participant. It can also refer to past meeting materials and automatically add relevant information. The document creation department uses AI to automatically generate meeting materials. For example, it collects the latest data and statistics related to the meeting agenda from the internet and incorporates them into the materials. It also analyzes the expertise and past statements of meeting participants to provide each participant with the most relevant information. Furthermore, the document creation department can automatically adjust the format and design of the materials according to the purpose of the meeting. For example, in the case of presentation materials, it automatically generates visually easy-to-understand graphs and charts to improve the quality of the materials. The document creation department can also update materials in real time as the meeting progresses. For example, if new information is provided during the meeting, the document creation department immediately incorporates that information into the materials and shares it with all participants. In this way, the document creation department helps users create meeting materials efficiently and effectively, allowing the meeting preparation phase to proceed smoothly.

[0082] The scheduling department schedules meetings based on materials created by the document creation department. For example, the scheduling department proposes the most suitable dates considering the schedules of meeting participants. It can also propose optimal dates considering the geographical location of participants. The scheduling department uses AI to analyze participants' schedules and automatically proposes the best dates. For example, it obtains each participant's calendar information in real time to identify dates and times when everyone can attend. By considering geographical location, it can also propose optimal dates for participants in remote locations. Furthermore, the scheduling department can prioritize meetings based on their importance and urgency. For example, for highly urgent meetings, it adjusts participants' schedules to hold the meeting as soon as possible. The scheduling department can also collect participant feedback to continuously improve the accuracy of scheduling. For example, it analyzes attendance rates and participant satisfaction from past meetings and incorporates this into scheduling future meetings. This allows the scheduling department to help users efficiently and effectively schedule meetings, ensuring a smooth meeting preparation process.

[0083] The moderator conducts the meeting based on the schedule set by the scheduling department. The moderator, for example, may project their instructions onto a display and speak to facilitate the meeting. The moderator can also supplement the content of speakers who are unclear and clarify any misunderstandings among participants. The moderator uses AI to automate the meeting process. For example, it manages the progress of each agenda item based on the meeting agenda and optimizes time allocation. It also analyzes speakers' statements in real time and highlights important points. Furthermore, the moderator can immediately respond to problems and questions that arise during the meeting. For example, it can receive questions from participants and have the AI ​​provide appropriate answers. It can also supplement speakers' statements and add explanations to ensure that all participants can understand. In addition, the moderator can monitor the progress of the meeting in real time and adjust the process as needed. For example, if an agenda item finishes earlier than planned, it can smoothly transition to the next item. If an agenda item runs longer than planned, it can make adjustments to end the meeting on time. This allows the moderator to support users in conducting meetings efficiently and effectively, and to ensure that the meeting progresses smoothly through each stage.

[0084] The closing team closes the meeting based on the content facilitated by the moderator. For example, the closing team controls the scope of the discussion, reviews all comments, and provides an optimal summary. The closing team can also organize meeting tasks, assign them to appropriate participants, and set deadlines. Using AI, the closing team analyzes the meeting content and automatically generates an optimal summary. For example, it extracts key points and decisions made during the meeting and compiles them into a summary. It also organizes issues and tasks that arose during the meeting and assigns them to appropriate personnel. Furthermore, the closing team can collect feedback from participants after the meeting and suggest improvements for the next meeting. For example, it collects participant satisfaction and opinions and incorporates them into the agenda and proceedings of the next meeting. The closing team can also automatically create meeting minutes after the meeting and share them with all participants. In this way, the closing team helps users close meetings efficiently and effectively, ensuring a smooth conclusion to the meeting.

[0085] The agenda creation unit can create agendas in conjunction with the company's internal drive, portal site, and calendar function. For example, the agenda creation unit can retrieve relevant documents from the internal drive and reflect them in the agenda. It can also retrieve meeting-related information from the portal site and reflect it in the agenda. Furthermore, the agenda creation unit can coordinate meeting schedules in conjunction with the calendar function and reflect them in the agenda. This allows for efficient agenda creation by integrating with various internal systems. Some or all of the above processes in the agenda creation unit may be performed using AI, for example, or not. For example, the agenda creation unit can input documents retrieved from the internal drive into a generation AI and have the generation AI create the agenda.

[0086] The document creation unit can input information and audio data of meeting participants and learn the characteristics of each participant. For example, the document creation unit can input information such as the names, positions, departments, and areas of expertise of meeting participants. It can also input recordings of past statements and audio memos of meeting participants. Furthermore, the document creation unit can learn the characteristics of each participant based on the input information and reflect this in the document creation process. This allows for the creation of more appropriate documents by learning the characteristics of meeting participants. Some or all of the above processing in the document creation unit may be performed using AI, for example, or without AI. For example, the document creation unit can input meeting participant information into a generating AI and have the generating AI learn the characteristics of each participant.

[0087] The moderator unit can be projected onto a display and speak to conduct the proceedings. The moderator unit can be projected onto a display using, for example, a projector. It can also be projected onto a monitor or smartboard. Furthermore, the moderator unit can speak and conduct the proceedings using speech synthesis technology. For example, the moderator unit can conduct the proceedings by playing back recorded audio. This allows for visually easy-to-understand proceedings when projected onto a display. Some or all of the above processing in the moderator unit may be performed using, for example, AI, or not using AI. For example, the moderator unit can input the content projected onto the display into a generating AI and have the generating AI generate the spoken content.

[0088] The moderator can supplement the content of speakers who are unclear or lacking information, and resolve misunderstandings among participants. For example, the moderator can supplement the content of statements when they are unclear or lacking information. The moderator can also resolve disagreements or differences in understanding among participants. This allows the meeting to proceed smoothly by supplementing the content of statements and resolving misunderstandings. Some or all of the above processes performed by the moderator may be carried out using AI, for example, or not. For example, the moderator can input the content of the statements into a generating AI and have the generating AI generate supplementary content.

[0089] The moderator can interrupt someone who is speaking for too long at an appropriate time. For example, the moderator can set a limit on speaking time and interrupt if that limit is exceeded. Alternatively, the moderator can interrupt based on a comparison of speaking time with other participants. This allows the meeting to proceed more efficiently by interrupting speech. Some or all of the above processes by the moderator may be performed using AI, or not. For example, the moderator can input speaking time into a generating AI and have the generating AI execute the timing of speech interruption.

[0090] The moderator can quickly retrieve necessary numerical data from the company's internal drive if the meeting's topics expand, thereby supporting decision-making. The moderator expands the meeting's topics, for example, when new issues arise or when the discussion deepens. The moderator also quickly retrieves necessary numerical data such as sales data, cost data, and performance data from the company's internal drive. This allows for smoother decision-making through rapid data retrieval. Some or all of the above processes performed by the moderator may be carried out using AI, for example, or without AI. For example, the moderator can input the necessary numerical data into a generating AI and have the generating AI perform the data retrieval.

[0091] The closing unit can control the scope of the discussion and make an optimal summary after checking all comments. For example, the closing unit can set the agenda and restrict comments to control the scope of the discussion. The closing unit can also check all comments and make an optimal summary after reflecting all opinions. By controlling the scope of the discussion and making an optimal summary, the conclusions of the meeting become clear. Some or all of the above processes in the closing unit may be performed using AI, for example, or not using AI. For example, the closing unit can input the content of the comments into a generation AI and have the generation AI generate the summary.

[0092] The closing department can organize meeting tasks, assign them to appropriate participants, and set deadlines. For example, the closing department can organize tasks such as items to be researched or materials to prepare before the next meeting. The closing department can also assign tasks to participants or those in charge with expertise and set deadlines. This clarifies the next actions by organizing meeting tasks and assigning them to appropriate participants. Some or all of the above processes in the closing department may be performed using AI, for example, or not. For example, the closing department can input the content of the tasks into a generating AI and have the generating AI perform the assignment and deadline setting.

[0093] The reception desk can estimate the user's emotions and adjust the input method for meeting objectives and goals based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of meeting objectives and goals. This makes the input process smoother by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI adjust the input method.

[0094] The reception desk can refer to past meeting data and automatically suggest objectives and goals for similar meetings. For example, the reception desk can suggest optimal objectives and goals based on data from similar meetings held in the past. It can also refer to successful examples of similar meetings and automatically suggest effective objectives and goals. Furthermore, the reception desk can analyze past meeting data and prioritize suggesting frequently used objectives and goals. This allows for efficient setting of objectives and goals by referring to past meeting data. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past meeting data into a generating AI and have the generating AI suggest objectives and goals.

[0095] The reception desk can suggest the optimal input timing based on the user's work situation when the purpose and goals of a meeting are entered. For example, the reception desk can refer to the user's calendar information to suggest the optimal input timing. The reception desk can also consider the user's workload and encourage input during less busy times. Furthermore, the reception desk can analyze the user's work progress and send reminders at appropriate times. This allows for efficient input work by suggesting input timings that are tailored to the user's work situation. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input user work situation data into a generating AI and have the generating AI generate input timing suggestions.

[0096] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. This makes the input process more comfortable through interface design adjustments based on 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 reception unit may be performed using AI, or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the interface design.

[0097] The reception desk can provide input assistance when the user enters the purpose and goals of a meeting by referring to the user's past input history. For example, the reception desk can automatically display purposes and goals that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest purposes and goals to be used at a specific time period based on the user's past input history. This allows for efficient input work by referring to past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past input history data into a generating AI and have the generating AI perform input assistance.

[0098] The reception desk can determine the priority of input when entering the purpose and goals of meetings, taking into account the user's work schedule. For example, the reception desk can refer to the user's calendar information and prioritize the input of the purpose and goals of important meetings. It can also analyze the user's work progress and prioritize the input of the purpose and goals of high-priority meetings. Furthermore, the reception desk can consider the user's workload and input the purpose and goals of important meetings during less busy times. This allows for efficient input work by determining the priority of input according to the user's work schedule. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's work schedule data into a generating AI and have the generating AI determine the input priority.

[0099] The agenda creation unit can estimate the user's emotions and adjust the agenda content based on those emotions. For example, if the user is relaxed, the agenda creation unit can provide a detailed agenda. If the user is in a hurry, it can provide a concise agenda that gets straight to the point. Furthermore, if the user is excited, the agenda creation unit can provide an agenda with visually stimulating effects. This allows the meeting to proceed smoothly by adjusting the agenda content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the agenda creation unit may be performed using AI or not. For example, the agenda creation unit can input user emotion data into a generative AI and have the generative AI perform the agenda content adjustment.

[0100] The agenda creation unit can automatically select the optimal agenda template by referring to past meeting agendas. For example, the agenda creation unit can select the optimal template based on the agendas of past successful meetings. It can also automatically select an effective template by referring to agendas of similar meetings. Furthermore, the agenda creation unit can analyze past meeting data and prioritize the selection of frequently used templates. This allows for efficient agenda creation by referring to past meeting agendas. Some or all of the above processes in the agenda creation unit may be performed using AI, for example, or without AI. For example, the agenda creation unit can input past meeting agenda data into a generation AI and have the generation AI perform the template selection.

[0101] The agenda creation unit can adjust the level of detail of the agenda based on the purpose and goals of the meeting. For example, the agenda creation unit can provide a detailed agenda for important meetings. It can also provide a concise agenda that gets straight to the point for simpler meetings. Furthermore, the agenda creation unit can provide an agenda with an appropriate level of detail depending on the purpose and goals of the meeting. This allows for efficient agenda creation by adjusting the level of detail according to the purpose and goals of the meeting. Some or all of the above processes in the agenda creation unit may be performed using AI, for example, or not. For example, the agenda creation unit can input meeting purpose and goal data into a generating AI and have the generating AI perform the adjustment of the level of detail of the agenda.

[0102] The agenda creation unit can estimate the user's emotions and adjust the agenda display method based on the estimated emotions. For example, if the user is nervous, the agenda creation unit can provide a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise display method. This improves readability by adjusting the agenda display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the agenda creation unit may be performed using AI or not. For example, the agenda creation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0103] The agenda creation unit can create an optimal agenda by considering the schedules of meeting participants. For example, the agenda creation unit can refer to the calendar information of meeting participants to create an optimal agenda. The agenda creation unit can also consider the workload of meeting participants and set the agenda for times when they have free time. Furthermore, the agenda creation unit can analyze the schedules of meeting participants and create an agenda that everyone can attend. In this way, by considering the schedules of meeting participants, an agenda that everyone can attend can be created. Some or all of the above processes in the agenda creation unit may be performed using AI, for example, or not using AI. For example, the agenda creation unit can input the schedule data of meeting participants into a generation AI and have the generation AI create the agenda.

[0104] The agenda creation unit can automatically add relevant topics by referring to past meeting minutes when creating an agenda. For example, the agenda creation unit can automatically add relevant topics based on past meeting minutes. It can also refer to the minutes of similar meetings and automatically add effective topics. Furthermore, the agenda creation unit can analyze past meeting data and prioritize adding frequently discussed topics. This allows for the efficient addition of relevant topics by referring to past meeting minutes. Some or all of the above processes in the agenda creation unit may be performed using AI, for example, or without AI. For example, the agenda creation unit can input past meeting minutes data into a generating AI and have the generating AI add topics.

[0105] The document creation unit can estimate the user's emotions and adjust the content of the document based on those emotions. For example, if the user is relaxed, the document creation unit can provide detailed materials. If the user is in a hurry, it can provide concise materials that get straight to the point. Furthermore, if the user is excited, the document creation unit can provide materials with visually stimulating effects. This allows for smoother meeting progress by adjusting the content of the document according 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 document creation unit may be performed using AI or not. For example, the document creation unit can input user emotion data into a generative AI and have the generative AI adjust the content of the document.

[0106] The document creation unit can automatically generate optimal document content by referring to past statements from meeting participants. For example, the document creation unit can automatically generate relevant documents based on past statements from meeting participants. It can also automatically generate effective documents by referring to statements from similar meetings. Furthermore, the document creation unit can analyze past statements and prioritize reflecting frequently discussed topics in the documents. This allows for efficient document creation by referring to past statements. Some or all of the above processes in the document creation unit may be performed using AI, for example, or without AI. For example, the document creation unit can input past statements into a generation AI and have the generation AI generate the document content.

[0107] The document creation department can adjust the level of detail in documents based on the purpose and goals of the meeting. For example, the document creation department can provide detailed documents for important meetings. It can also provide concise documents that highlight the key points for simpler meetings. Furthermore, the document creation department can provide documents with an appropriate level of detail depending on the purpose and goals of the meeting. This allows for efficient document creation by adjusting the level of detail according to the purpose and goals of the meeting. Some or all of the above processes in the document creation department may be performed using AI, for example, or not. For example, the document creation department can input meeting purpose and goal data into a generating AI and have the generating AI adjust the level of detail of the documents.

[0108] The document creation unit can estimate the user's emotions and adjust the way the document is displayed based on the estimated emotions. For example, if the user is nervous, the document creation unit can provide a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. This improves readability by adjusting the document display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the document creation unit may be performed using AI, for example, or not using AI. For example, the document creation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0109] The document creation department can create optimal documents by considering the roles and attributes of meeting participants. For example, the document creation department can provide documents with an appropriate level of detail according to the roles of the meeting participants. The document creation department can also consider the attributes of the meeting participants (such as their areas of expertise) and reflect relevant information in the documents. Furthermore, the document creation department can create optimal documents based on the past statements of the meeting participants. In this way, appropriate documents can be created by considering the roles and attributes of the meeting participants. Some or all of the above processes in the document creation department may be performed using AI, for example, or not using AI. For example, the document creation department can input the roles and attribute data of the meeting participants into a generating AI and have the generating AI create the documents.

[0110] The document creation unit can automatically add relevant information by referring to past meeting materials when creating documents. For example, the document creation unit can automatically add relevant information based on past meeting materials. It can also refer to materials from similar meetings and automatically add effective information. Furthermore, the document creation unit can analyze past meeting data and prioritize adding frequently used information. This allows for the efficient addition of relevant information by referring to past materials. Some or all of the above processes in the document creation unit may be performed using AI, for example, or without AI. For example, the document creation unit can input past document data into a generating AI and have the generating AI perform the information addition.

[0111] The scheduling unit can estimate the user's emotions and adjust the scheduling method based on the estimated emotions. For example, if the user is stressed, the scheduling unit can provide a simple interface and minimize the scheduling process. If the user is relaxed, the scheduling unit can also provide detailed scheduling options and suggest customizable methods. Furthermore, if the user is in a hurry, the scheduling unit can prioritize voice input to enable quick scheduling. This allows for efficient scheduling by adjusting the scheduling method according 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 scheduling unit may be performed using AI or not. For example, the scheduling unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the scheduling method.

[0112] The scheduling unit can automatically suggest the optimal date by referring to the past attendance history of meeting participants. For example, the scheduling unit can suggest the optimal date based on the attendance history of similar meetings held in the past. It can also automatically suggest effective dates by referring to the attendance history of similar meetings. Furthermore, the scheduling unit can analyze past attendance history and prioritize suggesting dates that are frequently attended. This allows for efficient scheduling by referring to past attendance history. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input past attendance history data into a generating AI and have the generating AI execute date suggestions.

[0113] The scheduling unit can determine the priority of dates based on the purpose and goals of the meeting during the scheduling process. For example, the scheduling unit will prioritize important meetings. It can also propose a concise schedule for simpler meetings. Furthermore, the scheduling unit can adjust dates with appropriate priorities according to the purpose and goals of the meeting. This allows for efficient scheduling by determining the priority of dates according to the purpose and goals of the meeting. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input meeting purpose and goal data into a generating AI and have the generating AI determine the priority of dates.

[0114] The scheduling unit can estimate the user's emotions and adjust the display method of scheduling based on the estimated emotions. For example, if the user is nervous, the scheduling unit can provide a simple and highly visible display method. If the user is relaxed, the scheduling unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the scheduling unit can provide a concise display method. This improves visibility by adjusting the display method of scheduling according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI, or not using AI. For example, the scheduling unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0115] The scheduling unit can propose the optimal date when scheduling a meeting, taking into account the geographical location information of the meeting participants. For example, the scheduling unit can propose the optimal date based on the current location of the meeting participants. It can also propose an efficient date by considering the travel time of the meeting participants. Furthermore, the scheduling unit can analyze the geographical location information of the meeting participants and propose a date that is easy for everyone to attend. In this way, scheduling can be done efficiently by taking into account the geographical location information of the meeting participants. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit can input the geographical location data of the meeting participants into a generating AI and have the generating AI execute the date proposal.

[0116] The scheduling unit can automatically select the optimal date by referring to the work schedules of meeting participants during scheduling. For example, the scheduling unit can refer to the calendar information of meeting participants and automatically select the optimal date. The scheduling unit can also consider the workload of meeting participants and set the date during a time when they have more free time. Furthermore, the scheduling unit can analyze the schedules of meeting participants and automatically select a date that everyone can attend. This allows for efficient scheduling by referring to the work schedules of meeting participants. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input the work schedule data of meeting participants into a generating AI and have the generating AI perform the date selection.

[0117] The moderator can estimate the user's emotions and adjust the moderation method based on the estimated emotions. For example, if the user is relaxed, the moderator will proceed at a relaxed pace. If the user is in a hurry, the moderator can also provide a quick and efficient moderation method. Furthermore, if the user is excited, the moderator can provide a moderation method with visually stimulating effects. This allows the meeting to proceed smoothly by adjusting the moderation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the moderator may be performed using AI or not. For example, the moderator can input user emotion data into a generative AI and have the generative AI perform the adjustment of the moderation method.

[0118] The moderator can automatically select the most suitable moderation method by referring to past statements from meeting participants. For example, the moderator can automatically select a relevant moderation method based on past statements from meeting participants. The moderator can also automatically select an effective moderation method by referring to statements from similar meetings. Furthermore, the moderator can analyze past statements and prioritize frequently used moderation methods. This allows for efficient moderation by referring to past statements. Some or all of the above processing in the moderator may be performed using AI, for example, or without AI. For example, the moderator can input past statements into a generating AI and have the generating AI select the moderation method.

[0119] The moderator can adjust the level of detail in the meeting's proceedings based on its purpose and goals. For example, in the case of an important meeting, the moderator can provide a detailed procedure. In the case of a simple meeting, the moderator can also provide a concise procedure that gets straight to the point. Furthermore, the moderator can provide a procedure with an appropriate level of detail depending on the purpose and goals of the meeting. This allows for efficient moderation by adjusting the level of detail according to the purpose and goals of the meeting. Some or all of the above processes in the moderator may be performed using AI, for example, or not. For example, the moderator can input meeting purpose and goal data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proceedings.

[0120] The moderator can estimate the user's emotions and adjust the display method of the moderator's presentation based on the estimated emotions. For example, if the user is nervous, the moderator can provide a simple and highly visible display method. If the user is relaxed, the moderator can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the moderator can provide a concise display method. This improves visibility by adjusting the display method of the moderator's presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the moderator may be performed using AI or not using AI. For example, the moderator can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0121] The moderator can select the most appropriate moderation method during the moderation process, taking into account the roles and attributes of the meeting participants. For example, the moderator can provide a moderation method with an appropriate level of detail depending on the role of the meeting participants. The moderator can also consider the attributes of the meeting participants (such as their areas of expertise) and reflect relevant information in the moderation. Furthermore, the moderator can select the most appropriate moderation method based on the past statements of the meeting participants. In this way, an appropriate moderation method can be selected by considering the roles and attributes of the meeting participants. Some or all of the above processing in the moderator may be performed using AI, for example, or not using AI. For example, the moderator can input the roles and attribute data of the meeting participants into a generating AI and have the generating AI perform the selection of the moderation method.

[0122] The moderator can automatically add relevant agenda items by referring to past meeting minutes during the moderation process. For example, the moderator can automatically add relevant agenda items based on past minutes. The moderator can also refer to minutes of similar meetings and automatically add effective agenda items. Furthermore, the moderator can analyze past meeting data and prioritize adding frequently discussed agenda items. This allows for the efficient addition of relevant agenda items by referring to past minutes. Some or all of the above processes in the moderator may be performed using AI, for example, or not. For example, the moderator can input past meeting minutes data into a generating AI and have the generating AI perform the addition of agenda items.

[0123] The closing unit can estimate the user's emotions and adjust the closing method based on the estimated emotions. For example, if the user is relaxed, the closing unit will close the meeting at a relaxed pace. If the user is in a hurry, the closing unit can also provide a quick and efficient closing method. Furthermore, if the user is excited, the closing unit can provide a closing method with visually stimulating effects. This allows for a smoother conclusion to the meeting by adjusting the closing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 closing unit may be performed using AI or not using AI. For example, the closing unit can input user emotion data into a generative AI and have the generative AI adjust the closing method.

[0124] The closing unit can automatically select the optimal closing method by referring to past statements from meeting participants. For example, the closing unit can automatically select a relevant closing method based on past statements from meeting participants. The closing unit can also automatically select an effective closing method by referring to statements from similar meetings. Furthermore, the closing unit can analyze past statements and prioritize the selection of frequently used closing methods. This allows for efficient closing by referring to past statements. Some or all of the above processing in the closing unit may be performed using AI, for example, or without AI. For example, the closing unit can input past statements into a generating AI and have the generating AI select a closing method.

[0125] The closing unit can adjust the level of detail in the closing based on the purpose and goals of the meeting. For example, in the case of an important meeting, the closing unit can provide a detailed closing method. In the case of a simple meeting, the closing unit can also provide a concise closing method that gets straight to the point. Furthermore, the closing unit can provide a closing method with an appropriate level of detail depending on the purpose and goals of the meeting. This allows for efficient closing by adjusting the level of detail in the closing according to the purpose and goals of the meeting. Some or all of the above processing in the closing unit may be performed using AI, for example, or not using AI. For example, the closing unit can input meeting purpose and goal data into a generating AI and have the generating AI perform the adjustment of the level of detail in the closing.

[0126] The closing section can estimate the user's emotions and adjust the closing display method based on the estimated emotions. For example, if the user is nervous, the closing section can provide a simple and highly visible display method. If the user is relaxed, the closing section can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the closing section can provide a concise display method. This improves visibility by adjusting the closing display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the closing section may be performed using AI, for example, or without AI. For example, the closing section can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0127] The closing unit can select the optimal closing method at the time of closing, taking into account the roles and attributes of the meeting participants. For example, the closing unit can provide a closing method with an appropriate level of detail depending on the role of the meeting participants. The closing unit can also consider the attributes of the meeting participants (such as their areas of expertise) and reflect relevant information in the closing. Furthermore, the closing unit can select the optimal closing method based on the past statements of the meeting participants. This allows for the selection of an appropriate closing method by considering the roles and attributes of the meeting participants. Some or all of the above processing in the closing unit may be performed using AI, for example, or without AI. For example, the closing unit can input the roles and attribute data of the meeting participants into a generating AI and have the generating AI select the closing method.

[0128] The closing unit can automatically add relevant information by referring to past meeting minutes during the closing process. For example, the closing unit can automatically add relevant information based on past meeting minutes. It can also refer to minutes from similar meetings and automatically add effective information. Furthermore, the closing unit can analyze past meeting data and prioritize adding frequently used information. This allows for the efficient addition of relevant information by referring to past meeting minutes. Some or all of the above processing in the closing unit may be performed using AI, for example, or without AI. For example, the closing unit can input past meeting minutes data into a generating AI and have the generating AI perform the information addition.

[0129] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0130] The meeting efficiency system can also be equipped with a real-time translation unit. This unit can translate what is said during a meeting in real time and provide it immediately to participants who speak different languages. For example, it can translate what is said in English into Japanese and display it to Japanese-speaking participants. Furthermore, the real-time translation unit can also speak the translated content using speech synthesis technology. This facilitates smoother communication between participants who speak different languages ​​and improves the efficiency of international meetings.

[0131] The meeting efficiency system can also be equipped with a concentration monitoring unit that monitors the level of concentration of participants. The concentration monitoring unit uses cameras and sensors to analyze participants' gaze and posture and evaluate their concentration level in real time. For example, if a participant's gaze is diverted from the display or their posture is poor, it will be determined that their concentration level is low. The concentration monitoring unit can also issue an alert and notify the facilitator if the concentration level drops. This helps maintain participants' concentration throughout the meeting and allows for efficient discussion.

[0132] The meeting efficiency system can also include an emotion estimation unit that estimates participants' emotions and adjusts the meeting's progress based on those estimates. The emotion estimation unit analyzes participants' facial expressions and tone of voice to estimate their emotions in real time. For example, if a participant is tired, the system can temporarily suspend the meeting and suggest a break. If a participant is excited, it can ask additional questions to deepen the discussion. This allows for flexible meeting management that responds to participants' emotions, improving the effectiveness of the meeting.

[0133] The meeting efficiency system can also include a summarization unit that automatically summarizes participants' remarks. The summarization unit analyzes what is said during the meeting in real time, extracting and summarizing the key points. For example, it can shorten long statements and clarify the main points. Furthermore, the summarization unit can display the summarized content on a screen and share it with all participants. This ensures smoother meeting progress and that important information is shared without being overlooked.

[0134] The meeting efficiency system can also estimate participants' emotions and adjust the meeting closing based on those estimates. For example, if participants are satisfied, the closing can be done quickly, and the next action can be taken. If participants are dissatisfied, additional discussion or explanation can be provided to resolve the issues. This allows for flexible closing that responds to participants' emotions, resulting in a more satisfactory conclusion to the meeting.

[0135] The meeting efficiency system can also include a recording unit that automatically records participants' remarks and saves them in a searchable format for later use. The recording unit records what is said during the meeting in text format and organizes it by speaker. For example, it can make it possible to search for remarks by keyword, allowing for quick retrieval of comments on specific topics. The recording unit can also save audio recordings, allowing for review in both text and audio formats. This is helpful when reviewing meeting content later, preventing information omissions and misunderstandings.

[0136] The meeting efficiency system can also estimate participants' emotions and dynamically adjust the meeting agenda based on those estimates. For example, if participants are tired, the system can change the order of the agenda and discuss important topics first. If participants are excited, it can allocate additional time to deepen the discussion. This allows for flexible agenda adjustments based on participants' emotions, improving the effectiveness of meetings.

[0137] The meeting efficiency system can also include an information provision unit that analyzes participants' statements in real time and automatically provides relevant external information. The information provision unit analyzes the statements and retrieves relevant data and materials from the internet or internal databases. For example, it can instantly provide specific market data or technical information to support discussions. Furthermore, the information provision unit can display the retrieved information on a screen and share it with all participants. This ensures that necessary information is quickly provided during the meeting, facilitating smoother decision-making.

[0138] The meeting efficiency system can also estimate participants' emotions and adjust the meeting pace based on those estimates. For example, if participants are relaxed, the meeting will proceed at a leisurely pace. Conversely, if participants are in a hurry, it can provide a quick and efficient approach. This allows for flexible adjustment of the meeting pace according to participants' emotions, improving the effectiveness of the meeting.

[0139] The meeting efficiency system can also include an evaluation unit that automatically assesses participants' contributions and provides feedback. The evaluation unit analyzes the content of the contributions and evaluates their logic and persuasiveness. For example, it evaluates whether the contribution is logical and based on specific data, and provides feedback. The evaluation unit can also point out areas for improvement and provide advice to help with future contributions. This improves participants' communication skills and enhances the quality of meetings.

[0140] The following briefly describes the processing flow for example form 2.

[0141] Step 1: The reception desk accepts input of the meeting's purpose and goals. For example, it accepts the user entering the meeting's purpose and goals into an input form. It can also accept the meeting's purpose and goals using voice input. Furthermore, it can refer to past meeting data and automatically suggest similar meeting purposes and goals. Step 2: The agenda creation team creates the agenda based on the information received by the reception team. For example, they create a list of topics based on the meeting's purpose and goals. They can also create the agenda in conjunction with the company's internal drive, portal site, and calendar functions. Step 3: The document creation team creates meeting materials based on the agenda created by the agenda creation team. For example, they input information (attributes and positions) and audio data of meeting participants to learn the characteristics of each participant. They can also refer to past meeting materials and automatically add relevant information. Step 4: The scheduling department schedules the meeting based on the materials prepared by the document creation department. For example, they propose the best date considering the schedules of the meeting participants. They can also propose the best date considering the geographical location of the meeting participants. Step 5: The moderator conducts the meeting based on the schedule set by the scheduling team. For example, they may project their information onto a screen and speak to facilitate the meeting. They can also supplement the content of speakers who are unclear and clarify any misunderstandings among participants. Step 6: The closing team closes the meeting based on the content facilitated by the moderator. For example, they control the scope of the discussion, review all comments, and provide an optimal summary. They can also organize the meeting's tasks (assignments), assign them to appropriate participants, and set deadlines.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the reception unit, agenda creation unit, document creation unit, scheduling unit, moderation unit, and closing unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit uses the reception device 38 of the smart device 14 to receive the purpose and goals of the meeting. The agenda creation unit creates the agenda using the specific processing unit 290 of the data processing unit 12. The document creation unit creates meeting materials using the specific processing unit 290 of the data processing unit 12. The scheduling unit schedules the meeting using the specific processing unit 290 of the data processing unit 12. The moderation unit conducts the meeting using the output device 40 of the smart device 14. The closing unit closes the meeting using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0146] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, agenda creation unit, document creation unit, scheduling unit, moderation unit, and closing unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit uses the microphone 238 of the smart glasses 214 to receive the purpose and goals of the meeting. The agenda creation unit creates the agenda using the specific processing unit 290 of the data processing unit 12. The document creation unit creates meeting materials using the specific processing unit 290 of the data processing unit 12. The scheduling unit schedules the meeting using the specific processing unit 290 of the data processing unit 12. The moderation unit conducts the meeting using the speaker 240 of the smart glasses 214. The closing unit closes the meeting using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0162] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the reception unit, agenda creation unit, document creation unit, scheduling unit, moderation unit, and closing unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit uses the microphone 238 of the headset terminal 314 to receive the purpose and goals of the meeting. The agenda creation unit creates the agenda using the specific processing unit 290 of the data processing unit 12. The document creation unit creates meeting materials using the specific processing unit 290 of the data processing unit 12. The scheduling unit schedules the meeting using the specific processing unit 290 of the data processing unit 12. The moderation unit conducts the meeting using the speaker 240 of the headset terminal 314. The closing unit closes the meeting using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0178] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0183] 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).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] Each of the multiple elements described above, including the reception unit, agenda creation unit, document creation unit, scheduling unit, moderation unit, and closing unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit uses the microphone 238 of the robot 414 to receive the purpose and goals of the meeting. The agenda creation unit creates the agenda using the specific processing unit 290 of the data processing unit 12. The document creation unit creates meeting materials using the specific processing unit 290 of the data processing unit 12. The scheduling unit schedules the meeting using the specific processing unit 290 of the data processing unit 12. The moderation unit conducts the meeting using the speaker 240 of the robot 414. The closing unit closes the meeting using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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."

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] (Note 1) A reception desk that accepts input of the meeting's purpose and goals, An agenda creation unit creates an agenda based on the information received by the aforementioned reception unit, A document creation unit that creates meeting materials based on the agenda created by the aforementioned agenda creation unit, The scheduling department is responsible for scheduling meetings based on the materials prepared by the aforementioned document preparation department. The moderator and facilitation team conducts the meeting based on the schedule adjusted by the aforementioned scheduling team, The system includes a closing unit that closes the meeting based on the content of the meeting conducted by the moderator. A system characterized by the following features. (Note 2) The agenda creation unit, Create agendas by integrating with company drives, portal sites, and calendar functions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned document creation unit, It inputs information and audio data of meeting participants and learns the characteristics of each participant. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned moderators and presenters The presenter is projected onto a screen and speaks to conduct the proceedings. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned moderators and presenters To supplement the statements of speakers who have not given sufficient explanation and to clarify any discrepancies in understanding among participants. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned moderators and presenters If someone is speaking for too long, interrupt them at an appropriate time to stop them from continuing. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned moderators and presenters If the discussion topics in a meeting expand, necessary numerical data and other information can be quickly retrieved from the company's internal drive to support decision-making. The system described in Appendix 1, characterized by the features described herein. (Note 8) The closing section is, Control the scope of the discussion, review all comments, and then provide an optimal summary. The system described in Appendix 1, characterized by the features described herein. (Note 9) The closing section is, Organize the meeting tasks, assign them to the appropriate participants, and set deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts how the meeting's purpose and goals are entered based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is By referencing past meeting data, the system automatically suggests the objectives and goals of similar meetings. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input the purpose and goals of a meeting, the system suggests the optimal timing for input based on their work situation. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When entering the purpose and goals of a meeting, the system provides input assistance by referring to the user's past input history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is When entering the purpose and goals of a meeting, prioritize the input based on the user's work schedule. The system described in Appendix 1, characterized by the features described herein. (Note 16) The agenda creation unit, We estimate the user's emotions and adjust the agenda content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The agenda creation unit, Referencing past meeting agendas, the system automatically selects the most suitable agenda template. The system described in Appendix 1, characterized by the features described herein. (Note 18) The agenda creation unit, When creating the agenda, adjust the level of detail based on the purpose and goals of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 19) The agenda creation unit, It estimates the user's emotions and adjusts how the agenda is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The agenda creation unit, When creating the agenda, take into account the schedules of the meeting participants to create the most suitable agenda. The system described in Appendix 1, characterized by the features described herein. (Note 21) The agenda creation unit, When creating an agenda, automatically add relevant topics by referencing past meeting minutes. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned document creation unit, The system estimates the user's emotions and adjusts the content of the materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned document creation unit, The system automatically generates optimal document content by referencing past statements from meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned document creation unit, When creating materials, adjust the level of detail based on the purpose and goals of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned document creation unit, It estimates the user's emotions and adjusts how the materials are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned document creation unit, When creating materials, consider the roles and attributes of meeting participants to create the most suitable materials. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned document creation unit, When creating documents, automatically add relevant information by referencing past meeting materials. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned scheduling department, It estimates the user's emotions and adjusts the scheduling method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned scheduling department, The system automatically suggests the optimal date by referring to the past attendance history of meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned scheduling department, When scheduling, prioritize dates based on the purpose and goals of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned scheduling department, The system estimates the user's emotions and adjusts how scheduling is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned scheduling department, When scheduling, we will propose the optimal date considering the geographical location of the meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned scheduling department, When scheduling a meeting, the system automatically selects the most suitable date by referring to the work schedules of the meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned moderators and presenters The system estimates the user's emotions and adjusts the moderation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned moderators and presenters The system automatically selects the most suitable moderation method by referencing past statements from meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned moderators and presenters When moderating, adjust the level of detail based on the purpose and goals of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned moderators and presenters It estimates the user's emotions and adjusts the way the moderator's presentation is displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned moderators and presenters When moderating a meeting, select the most appropriate method of proceeding, taking into account the roles and attributes of the meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned moderators and presenters During the meeting, the system automatically adds relevant agenda items by referencing past meeting minutes. The system described in Appendix 1, characterized by the features described herein. (Note 40) The closing section is, It estimates the user's emotions and adjusts the closing method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The closing section is, The system automatically selects the optimal closing method by referencing past statements from meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 42) The closing section is, At the closing, adjust the level of detail in the closing based on the purpose and goals of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 43) The closing section is, It estimates the user's emotions and adjusts how closing messages are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The closing section is, At the closing of the meeting, select the most appropriate closing method considering the roles and attributes of the meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 45) The closing section is, At the closing of the meeting, the system automatically adds relevant information by referencing past meeting minutes. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0214] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts input of the meeting's purpose and goals, An agenda creation unit creates an agenda based on the information received by the aforementioned reception unit, A document creation unit that creates meeting materials based on the agenda created by the aforementioned agenda creation unit, The scheduling department is responsible for scheduling meetings based on the materials prepared by the aforementioned document preparation department. The moderator and facilitation team conducts the meeting based on the schedule adjusted by the aforementioned scheduling team, The system includes a closing unit that closes the meeting based on the content of the meeting conducted by the moderator. A system characterized by the following features.

2. The agenda creation unit, Create agendas by integrating with company drives, portal sites, and calendar functions. The system according to feature 1.

3. The aforementioned document creation unit, It inputs information and audio data of meeting participants and learns the characteristics of each participant. The system according to feature 1.

4. The aforementioned moderators and presenters The presenter is projected onto a screen and speaks to conduct the proceedings. The system according to feature 1.

5. The aforementioned moderators and presenters To supplement the statements of speakers who have not given sufficient explanation and to clarify any discrepancies in understanding among participants. The system according to feature 1.

6. The aforementioned moderators and presenters If someone is speaking for too long, interrupt them at an appropriate time to stop them from continuing. The system according to feature 1.

7. The aforementioned moderators and presenters If the discussion topics in a meeting expand, necessary numerical data and other information can be quickly retrieved from the company's internal drive to support decision-making. The system according to feature 1.

8. The closing section is, Control the scope of the discussion, review all comments, and then provide an optimal summary. The system according to feature 1.

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