system

Abstract

The system according to this embodiment aims to clarify the agenda and objectives of a meeting and to support the progress of the meeting. [Solution] The system according to the embodiment comprises a reception unit, a progress unit, and a knowledge provision unit. The reception unit clarifies the agenda and objectives of the meeting. The progress unit supports the progress of the meeting based on the agenda and objectives clarified by the reception unit. The knowledge provision unit provides knowledge from non-participants by utilizing AI knowledge in the meeting supported by the progress unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the topics and purposes of meetings may be unclear, or the meetings may be in turmoil and progress may be stalled, leaving room for improvement.

[0005] The system according to the embodiment aims to clarify the topics and purposes of meetings and support the progress of meetings.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a progress management unit, and a knowledge provision unit. The reception unit clarifies the agenda and objectives of the meeting. The progress management unit supports the progress of the meeting based on the agenda and objectives clarified by the reception unit. The knowledge provision unit provides knowledge from non-participants by utilizing AI knowledge in the meeting supported by the progress management unit. [Effects of the Invention]

[0007] The system according to this embodiment can clarify the agenda and objectives of a meeting and support the progress of the meeting. [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 numbered communication I / F (Interface) is an interface including a communication processor, 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 reception 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 reception 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 AI ​​Facilitator system according to the embodiment of the present invention is a system for clarifying the agenda and objectives of "meetings that do not reach a conclusion" or "meetings that do not find answers." The AI ​​Facilitator system supports the progress of meetings, reduces wasted time, and enables constructive meetings. In order to clarify the agenda and objectives of a meeting, the AI ​​supports the progress of the meeting. For example, at the start of the meeting, the AI ​​confirms the agenda and objectives and shares them with the participants. This clarifies the direction of the meeting and helps to avoid unnecessary discussions. Next, if the meeting becomes contentious, the AI ​​joins the meeting as a facilitator. The AI ​​monitors the progress of the meeting in real time and intervenes if the discussion stagnates or no conclusion is reached. For example, the AI ​​can organize the points of discussion and ask specific questions to the participants to move the discussion forward. The AI ​​also provides information necessary for the progress of the meeting and supports participants in making appropriate decisions. Furthermore, the AI's knowledge can be used to add knowledge from people other than the meeting participants. For example, the AI ​​can refer to records of past meetings and related data to provide useful information to the participants. This can lead to the identification of issues and proposed solutions that participants might not have noticed. This mechanism allows meetings to proceed more smoothly and reduces wasted time. Furthermore, leveraging AI knowledge enables more constructive meetings and improves participant satisfaction. For example, by having AI support the meeting process, participants can focus on the discussion and reach conclusions efficiently. Also, new perspectives and ideas may emerge based on the information provided by the AI. In this way, by utilizing AI Facilitator, meetings proceed more smoothly, wasted time is reduced, and meetings become more constructive. This improves corporate productivity and streamlines operations. For example, by having AI support the meeting process, meeting times are shortened, and more time can be allocated to other tasks. Also, more effective decision-making is made based on the information provided by the AI, improving the quality of work. In summary, the AI ​​Facilitator system can realize constructive meetings by clarifying the agenda and objectives, supporting the process, and providing knowledge.

[0029] The AI ​​Facilitator system according to this embodiment comprises a reception unit, a meeting management unit, and a knowledge provision unit. The reception unit clarifies the agenda and objectives of the meeting. For example, the reception unit confirms the agenda and objectives at the start of the meeting and shares them with the participants. The reception unit can clarify the agenda and objectives based on pre-set templates or input from participants. The meeting management unit supports the progress of the meeting based on the agenda and objectives clarified by the reception unit. For example, the meeting management unit monitors the progress of the meeting in real time and intervenes if the discussion stalls. The meeting management unit can organize the points of discussion and ask specific questions to the participants. The meeting management unit can perform tasks such as timekeeping, organizing the discussion, and giving instructions for the progress of the meeting. The knowledge provision unit provides knowledge other than that of the participants by utilizing AI knowledge in meetings supported by the meeting management unit. For example, the knowledge provision unit refers to records of past meetings and related data to provide useful information to the participants. The knowledge provision unit can provide information based on the databases that the AI ​​refers to and how the knowledge is updated. As a result, the AI ​​Facilitator system according to this embodiment can facilitate constructive meetings by clarifying the agenda and objectives of the meeting, supporting its progress, and providing knowledge.

[0030] The reception staff clarifies the meeting agenda and objectives. For example, they confirm and share the agenda and objectives with participants at the start of the meeting. Specifically, the reception staff organizes the meeting agenda and objectives using a pre-set template before the meeting begins. This template includes the meeting title, date and time, location, participant list, agenda summary, objectives, and expected outcomes. The reception staff receives input from participants and incorporates this information into the template. For example, they collect agenda items and questions submitted in advance by participants and organize the agenda based on this information. Based on this information, the reception staff creates the meeting agenda and shares it with participants. The agenda clearly specifies the start time, person in charge, discussion points, and expected outcomes for each agenda item. This allows participants to have a clear understanding of the meeting's objectives and progress. Furthermore, the reception staff reconfirms and shares the agenda and objectives with participants at the start of the meeting. This allows participants to focus on the meeting's progress and engage in constructive discussions. The reception staff can also review and revise the agenda and objectives as needed during the meeting. For example, if the discussion goes off track or new topics are added, the reception staff reconfirms the agenda and objectives and shares them with the participants. This ensures that the meeting proceeds smoothly and that constructive discussions take place. After the meeting, the reception staff also checks the achievement of the agenda and objectives and provides feedback to the participants. This allows participants to confirm the results of the meeting and identify areas for improvement for the next meeting.

[0031] The facilitator supports the progress of the meeting based on the agenda and objectives clarified by the reception team. For example, the facilitator monitors the progress of the meeting in real time and intervenes if the discussion stalls. Specifically, the facilitator uses tools to monitor the progress of the meeting in real time. This includes dashboards that visualize the progress of the discussion and timekeeping tools to support the progress of the discussion. The facilitator uses these tools to understand the progress of the discussion in real time and intervene as needed. For example, if the discussion stalls or if the discussion on a particular agenda item is dragging on, the facilitator will clarify the points of the discussion and ask specific questions to the participants. This will ensure that the discussion progresses smoothly and constructively. The facilitator also manages the progress of each agenda item by keeping time. For example, they set start and end times for each agenda item and manage the discussion to ensure it proceeds on schedule. The facilitator understands the progress of the discussion in real time and adjusts the progress of the agenda items as needed. For example, if a discussion finishes earlier or later than planned, the facilitator adjusts the agenda to ensure the meeting runs smoothly. The facilitator summarizes the discussion and provides specific instructions to the participants. For instance, they organize the points of discussion and ask specific questions to the participants. This ensures that the discussion progresses smoothly and constructively. After the meeting, the facilitator reviews the progress of the discussion and provides feedback to the participants. This allows participants to review the progress of the meeting and identify areas for improvement for the next meeting.

[0032] The Knowledge Provider Department provides knowledge beyond that of the participants by utilizing AI knowledge in meetings supported by the Facilitation Department. Specifically, the Knowledge Provider Department provides useful information to participants by referring to records of past meetings and related data. The Knowledge Provider Department can provide information based on the databases referenced by the AI ​​and the methods of knowledge updates. For example, the Knowledge Provider Department can refer to records of past meetings and provide similar topics and discussion results. This allows participants to refer to the outcomes of past meetings and engage in constructive discussions. The Knowledge Provider Department provides useful information to participants by referring to related data. For example, related data includes industry trends, market developments, and technological advancements. Based on this data, the Knowledge Provider Department provides useful information to participants. This allows participants to engage in discussions based on the latest information and engage in constructive discussions. The Knowledge Provider Department provides information based on the databases referenced by the AI ​​and the methods of knowledge updates. For example, the AI ​​regularly updates its database to obtain the latest information. This allows the knowledge provision department to always provide participants with useful information based on the latest data. The knowledge provision department also provides feedback to participants after the meeting has ended. For example, they summarize the meeting's outcomes and discussion results and provide feedback to participants. This allows participants to review the meeting's results and identify areas for improvement for the next meeting. Based on feedback from participants, the knowledge provision department can improve how knowledge is updated. For example, they can review the frequency of database updates and the method of providing information based on feedback from participants. This allows the knowledge provision department to always provide participants with useful information based on the latest data.

[0033] The facilitator can monitor the progress of the meeting in real time and intervene if the discussion stalls. For example, the facilitator may use sensors or data to monitor the progress of the meeting in real time. The facilitator will determine whether the discussion is stalled based on the frequency of contributions and the progress of the discussion. If the discussion is stalled, the facilitator can move it forward by asking specific questions. For example, the facilitator may summarize the main points of the discussion and ask participants specific questions such as, "What do you think about this point?" This can help to keep the meeting moving smoothly. Some or all of the above processes performed by the facilitator may be performed using AI or not. For example, the facilitator may use AI to analyze the frequency and content of contributions in order to monitor the progress of the meeting in real time and determine whether the discussion is stalled.

[0034] The knowledge provision department can refer to past meeting records and related data to provide participants with useful information. For example, the knowledge provision department can refer to past meeting minutes, audio recordings, and video recordings. The knowledge provision department can also provide information based on relevant literature, databases, and statistical information. Furthermore, the knowledge provision department can use AI to analyze past meeting records and related data to provide participants with useful information. For example, the knowledge provision department can extract information relevant to the current agenda based on past meeting records and provide it to participants. This can improve the quality of meetings. Some or all of the above processes in the knowledge provision department may be performed using AI or not. For example, the knowledge provision department can input past meeting records into AI, which can then extract and provide relevant information.

[0035] The reception desk can confirm and share the agenda and objectives with participants at the start of the meeting. The reception desk can confirm the agenda and objectives based, for example, on a pre-set checklist or feedback from participants. After confirming the agenda and objectives, the reception desk shares them with participants. For example, the reception desk can display the agenda and objectives on a screen at the start of the meeting. The reception desk can also share the agenda and objectives with participants via email or chat tools. This helps to clarify the direction of the meeting. Some or all of the above processes by the reception desk may be performed using AI or not. For example, the reception desk can input the agenda and objectives into an AI, which can then automatically confirm and share them with participants.

[0036] The facilitator can organize the points of discussion and ask specific questions to the participants. For example, the facilitator summarizes the main points of the discussion and organizes the points of discussion. After organizing the points of discussion, the facilitator asks specific questions to the participants. For example, the facilitator asks specific questions such as, "What do you think about this point?" or "Do you have any other opinions?" This allows the discussion to progress. Some or all of the above processes by the facilitator may be performed using AI or not. For example, the facilitator can input the main points of the discussion into an AI, which can then automatically organize the points of discussion and generate specific questions.

[0037] The knowledge provision department can leverage AI knowledge to add knowledge from sources other than participants. For example, the knowledge provision department can provide information based on databases referenced by the AI ​​and methods for updating knowledge. The knowledge provision department can use AI to collect relevant information and provide it to participants. For example, the knowledge provision department can use AI to analyze past meeting records and related data to provide participants with useful information. This can improve the quality of meetings. Some or all of the above processes in the knowledge provision department may be performed using AI or not. For example, the knowledge provision department can input relevant information into the AI, and the AI ​​can automatically collect the information and provide it to participants.

[0038] The reception desk can analyze participants' past speaking history before the meeting begins and optimize how agendas and objectives are shared. For example, the reception desk can prioritize sharing topics of high interest based on participants' past speaking history. Based on participants' past speaking history, the reception desk can concisely summarize and share agendas and objectives. The reception desk can also analyze participants' past speaking history and share agendas and objectives in a visually easy-to-understand manner. This can improve the efficiency of the meeting. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input participants' past speaking history into an AI, which can then automatically analyze and optimize the sharing method.

[0039] The reception desk can customize and share the agenda and objectives at the start of the meeting based on the participants' areas of interest. For example, the reception desk can prioritize sharing relevant agenda items based on the participants' areas of interest. The reception desk can customize and share the agenda and objectives taking into account the participants' areas of interest. The reception desk can also share the agenda and objectives in a visually easy-to-understand manner based on the participants' areas of interest. This helps to clarify the direction of the meeting. Some or all of the above processes at the reception desk may be performed using AI or not. For example, the reception desk can input the participants' areas of interest into an AI, which can then automatically analyze and customize the agenda and objectives for sharing.

[0040] The reception desk can share the agenda and objectives at the start of the meeting, taking into account the geographical location of the participants. For example, the reception desk can prioritize sharing relevant agenda items based on the participants' geographical location. The reception desk can customize and share agenda items and objectives, taking into account the participants' geographical location. The reception desk can also share agenda items and objectives in a visually easy-to-understand manner based on the participants' geographical location. This helps to clarify the direction of the meeting. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the participants' geographical location information into an AI, which can then automatically analyze and share the agenda and objectives.

[0041] The reception desk can analyze participants' social media activity at the start of the meeting and share relevant topics and objectives. For example, the reception desk can prioritize sharing topics of high interest based on participants' social media activity. Based on participants' social media activity, the reception desk can concisely summarize and share the topics and objectives. The reception desk can also analyze participants' social media activity and share the topics and objectives in a visually easy-to-understand manner. This helps to clarify the direction of the meeting. Some or all of the above processes by the reception desk may be performed using AI or not. For example, the reception desk can input participants' social media activity into an AI, which can then automatically analyze and share the topics and objectives.

[0042] The facilitator can monitor the meeting's progress in real time and optimize its flow based on participants' speaking frequency and content. For example, based on participants' speaking frequency, the facilitator can provide opportunities for less active participants to speak. The facilitator can analyze participants' comments and adjust the direction of the discussion. The facilitator can also optimize the meeting's progress based on participants' speaking frequency and content. This can improve the efficiency of the meeting. Some or all of the above processes in the facilitator may be performed using AI or not. For example, the facilitator can input participants' speaking frequency and content into an AI, which can then automatically analyze and optimize the flow.

[0043] The facilitator can adjust the order and timing of participants' presentations during the meeting, based on their expertise and roles. For example, the facilitator can adjust the order of presentations considering the participants' expertise. The facilitator can adjust the timing of presentations based on the participants' roles. The facilitator can also optimize the order and timing of presentations based on the participants' expertise and roles. This can improve the efficiency of the meeting. Some or all of the above processes performed by the facilitator may be done using AI or not. For example, the facilitator can input the participants' expertise and roles into an AI, which can then automatically analyze and adjust the order and timing of presentations.

[0044] The facilitator can adjust the order and timing of participants' speeches during the meeting, taking into account their geographical location. For example, the facilitator can adjust the order of speeches based on participants' geographical location. The facilitator can adjust the timing of participants' speeches, taking into account their geographical location. The facilitator can also optimize the order and timing of speeches based on participants' geographical location. This can improve the efficiency of the meeting. Some or all of the above processes performed by the facilitator may be performed using AI or not. For example, the facilitator can input participants' geographical location information into an AI, which can then automatically analyze and adjust the order and timing of speeches.

[0045] The facilitator can analyze participants' social media activity during the meeting and provide relevant information. For example, the facilitator can provide relevant information from participants' social media activity. The facilitator can adjust the direction of the discussion based on participants' social media activity. The facilitator can also analyze participants' social media activity and optimize the meeting's progress. This can improve the efficiency of the meeting. Some or all of the above processes performed by the facilitator may be done using AI or not. For example, the facilitator can input participants' social media activity into an AI, which can then automatically analyze and provide relevant information.

[0046] The facilitator can analyze participants' social media activity during the meeting and provide relevant information. For example, the facilitator can provide relevant information from participants' social media activity. The facilitator can adjust the direction of the discussion based on participants' social media activity. The facilitator can also analyze participants' social media activity and optimize the meeting's progress. This can improve the efficiency of the meeting. Some or all of the above processes performed by the facilitator may be done using AI or not. For example, the facilitator can input participants' social media activity into an AI, which can then automatically analyze and provide relevant information.

[0047] The knowledge delivery department can refer to past meeting records and related data and customize information according to the participants' expertise and roles. For example, the knowledge delivery department can provide relevant information considering the participants' expertise. The knowledge delivery department can customize information based on the participants' roles. The knowledge delivery department can also optimize information based on the participants' expertise and roles. This allows for the provision of more appropriate information. Some or all of the above processes in the knowledge delivery department may be performed using AI or not. For example, the knowledge delivery department can input past meeting records and related data into AI, which can then automatically analyze and customize the information.

[0048] The knowledge provision department can provide relevant information in real time based on participants' statements during the meeting. For example, the knowledge provision department can provide relevant information in real time based on participants' statements. The knowledge provision department can analyze participants' statements and adjust the direction of the discussion. The knowledge provision department can also optimize the progress of the meeting based on participants' statements. This can improve the efficiency of the meeting. Some or all of the above processes in the knowledge provision department may be performed using AI or not. For example, the knowledge provision department can input participants' statements into an AI, which can then automatically analyze and provide relevant information in real time.

[0049] The knowledge provision department can provide information by referring to past meeting records and related data, and taking into account the geographical location of participants. For example, the knowledge provision department can provide relevant information based on the geographical location of participants. The knowledge provision department can customize the information by taking into account the geographical location of participants. The knowledge provision department can also optimize the information based on the geographical location of participants. This allows for the provision of more appropriate information. Some or all of the above processing in the knowledge provision department may be performed using AI or not. For example, the knowledge provision department can input past meeting records and related data into AI, which can then automatically analyze and provide information.

[0050] The knowledge provision department can analyze participants' social media activity during the meeting and provide relevant information. For example, the knowledge provision department can provide relevant information from participants' social media activity. The knowledge provision department can adjust the direction of the discussion based on participants' social media activity. The knowledge provision department can also analyze participants' social media activity and optimize the meeting's progress. This can improve the efficiency of the meeting. Some or all of the above processes in the knowledge provision department may be performed using AI or not. For example, the knowledge provision department can input participants' social media activity into an AI, which can then automatically analyze and provide relevant information.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The facilitator can monitor the meeting's progress in real time and adjust the direction of the discussion based on participants' comments. For example, they can analyze participants' comments and ask questions to balance the discussion if it becomes unbalanced. They can also assess the progress of the discussion based on the comments and propose changes or additions to the agenda as needed. Furthermore, they can provide materials and data in real time based on the comments to facilitate the discussion. This ensures a smooth meeting and enables efficient discussion.

[0053] The knowledge provision department can provide relevant information in real time during a meeting based on participants' comments. For example, it can analyze participants' comments and extract and provide necessary information from relevant literature and databases. It can also refer to past meeting records and related data based on comments to provide participants with useful information. Furthermore, it can provide materials and data in real time to support the progress of the discussion based on the comments. This improves the quality of the meeting and enables more efficient discussion.

[0054] The reception desk can analyze participants' past contributions before the meeting begins and optimize how agendas and objectives are shared. For example, the reception desk can prioritize sharing topics of high interest based on participants' past contributions. It can also concisely summarize and share agendas and objectives based on participants' past contributions. Furthermore, the reception desk can analyze participants' past contributions and share agendas and objectives in a visually easy-to-understand format. This can improve the efficiency of the meeting.

[0055] The facilitator can adjust the order and timing of participants' presentations during the meeting, based on their expertise and roles. For example, the facilitator can adjust the order of presentations considering the participants' expertise. They can also adjust the time allotted for each participant's presentation based on their role. Furthermore, they can optimize the order and timing of presentations based on the participants' expertise and roles. This can improve the efficiency of the meeting.

[0056] The knowledge sharing department can refer to past meeting records and related data, and customize information according to the participants' expertise and roles. For example, the knowledge sharing department can provide relevant information considering the participants' expertise. It can also customize information based on the participants' roles. Furthermore, the knowledge sharing department can optimize information based on the participants' expertise and roles. This allows for the provision of more appropriate information.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception staff clarifies the meeting agenda and objectives. For example, the reception staff confirms the agenda and objectives at the start of the meeting and shares them with the participants. The reception staff can clarify the agenda and objectives based on pre-set templates or input from participants. Step 2: The facilitator supports the progress of the meeting based on the agenda and objectives clarified by the reception team. For example, the facilitator can monitor the progress of the meeting in real time and intervene if the discussion stalls. The facilitator can organize the points of discussion and ask specific questions to the participants. The facilitator can also handle timekeeping, summarizing the discussion, and giving instructions on how to proceed. Step 3: The Knowledge Provider team leverages AI knowledge to provide information beyond the participants in meetings supported by the Facilitation Team. For example, the Knowledge Provider team can refer to past meeting records and related data to provide useful information to participants. The Knowledge Provider team can provide information based on the databases referenced by the AI ​​and how the knowledge is updated.

[0059] (Example of form 2) The AI ​​Facilitator system according to the embodiment of the present invention is a system for clarifying the agenda and objectives of "meetings that do not reach a conclusion" or "meetings that do not find answers." The AI ​​Facilitator system supports the progress of meetings, reduces wasted time, and enables constructive meetings. In order to clarify the agenda and objectives of a meeting, the AI ​​supports the progress of the meeting. For example, at the start of the meeting, the AI ​​confirms the agenda and objectives and shares them with the participants. This clarifies the direction of the meeting and helps to avoid unnecessary discussions. Next, if the meeting becomes contentious, the AI ​​joins the meeting as a facilitator. The AI ​​monitors the progress of the meeting in real time and intervenes if the discussion stagnates or no conclusion is reached. For example, the AI ​​can organize the points of discussion and ask specific questions to the participants to move the discussion forward. The AI ​​also provides information necessary for the progress of the meeting and supports participants in making appropriate decisions. Furthermore, the AI's knowledge can be used to add knowledge from people other than the meeting participants. For example, the AI ​​can refer to records of past meetings and related data to provide useful information to the participants. This can lead to the identification of issues and proposed solutions that participants might not have noticed. This mechanism allows meetings to proceed more smoothly and reduces wasted time. Furthermore, leveraging AI knowledge enables more constructive meetings and improves participant satisfaction. For example, by having AI support the meeting process, participants can focus on the discussion and reach conclusions efficiently. Also, new perspectives and ideas may emerge based on the information provided by the AI. In this way, by utilizing AI Facilitator, meetings proceed more smoothly, wasted time is reduced, and meetings become more constructive. This improves corporate productivity and streamlines operations. For example, by having AI support the meeting process, meeting times are shortened, and more time can be allocated to other tasks. Also, more effective decision-making is made based on the information provided by the AI, improving the quality of work. In summary, the AI ​​Facilitator system can realize constructive meetings by clarifying the agenda and objectives, supporting the process, and providing knowledge.

[0060] The AI ​​Facilitator system according to this embodiment comprises a reception unit, a meeting management unit, and a knowledge provision unit. The reception unit clarifies the agenda and objectives of the meeting. For example, the reception unit confirms the agenda and objectives at the start of the meeting and shares them with the participants. The reception unit can clarify the agenda and objectives based on pre-set templates or input from participants. The meeting management unit supports the progress of the meeting based on the agenda and objectives clarified by the reception unit. For example, the meeting management unit monitors the progress of the meeting in real time and intervenes if the discussion stalls. The meeting management unit can organize the points of discussion and ask specific questions to the participants. The meeting management unit can perform tasks such as timekeeping, organizing the discussion, and giving instructions for the progress of the meeting. The knowledge provision unit provides knowledge other than that of the participants by utilizing AI knowledge in meetings supported by the meeting management unit. For example, the knowledge provision unit refers to records of past meetings and related data to provide useful information to the participants. The knowledge provision unit can provide information based on the databases that the AI ​​refers to and how the knowledge is updated. As a result, the AI ​​Facilitator system according to this embodiment can facilitate constructive meetings by clarifying the agenda and objectives of the meeting, supporting its progress, and providing knowledge.

[0061] The reception staff clarifies the meeting agenda and objectives. For example, they confirm and share the agenda and objectives with participants at the start of the meeting. Specifically, the reception staff organizes the meeting agenda and objectives using a pre-set template before the meeting begins. This template includes the meeting title, date and time, location, participant list, agenda summary, objectives, and expected outcomes. The reception staff receives input from participants and incorporates this information into the template. For example, they collect agenda items and questions submitted in advance by participants and organize the agenda based on this information. Based on this information, the reception staff creates the meeting agenda and shares it with participants. The agenda clearly specifies the start time, person in charge, discussion points, and expected outcomes for each agenda item. This allows participants to have a clear understanding of the meeting's objectives and progress. Furthermore, the reception staff reconfirms and shares the agenda and objectives with participants at the start of the meeting. This allows participants to focus on the meeting's progress and engage in constructive discussions. The reception staff can also review and revise the agenda and objectives as needed during the meeting. For example, if the discussion goes off track or new topics are added, the reception staff reconfirms the agenda and objectives and shares them with the participants. This ensures that the meeting proceeds smoothly and that constructive discussions take place. After the meeting, the reception staff also checks the achievement of the agenda and objectives and provides feedback to the participants. This allows participants to confirm the results of the meeting and identify areas for improvement for the next meeting.

[0062] The facilitator supports the progress of the meeting based on the agenda and objectives clarified by the reception team. For example, the facilitator monitors the progress of the meeting in real time and intervenes if the discussion stalls. Specifically, the facilitator uses tools to monitor the progress of the meeting in real time. This includes dashboards that visualize the progress of the discussion and timekeeping tools to support the progress of the discussion. The facilitator uses these tools to understand the progress of the discussion in real time and intervene as needed. For example, if the discussion stalls or if the discussion on a particular agenda item is dragging on, the facilitator will clarify the points of the discussion and ask specific questions to the participants. This will ensure that the discussion progresses smoothly and constructively. The facilitator also manages the progress of each agenda item by keeping time. For example, they set start and end times for each agenda item and manage the discussion to ensure it proceeds on schedule. The facilitator understands the progress of the discussion in real time and adjusts the progress of the agenda items as needed. For example, if a discussion finishes earlier or later than planned, the facilitator adjusts the agenda to ensure the meeting runs smoothly. The facilitator summarizes the discussion and provides specific instructions to the participants. For instance, they organize the points of discussion and ask specific questions to the participants. This ensures that the discussion progresses smoothly and constructively. After the meeting, the facilitator reviews the progress of the discussion and provides feedback to the participants. This allows participants to review the progress of the meeting and identify areas for improvement for the next meeting.

[0063] The Knowledge Provider Department provides knowledge beyond that of the participants by utilizing AI knowledge in meetings supported by the Facilitation Department. Specifically, the Knowledge Provider Department provides useful information to participants by referring to records of past meetings and related data. The Knowledge Provider Department can provide information based on the databases referenced by the AI ​​and the methods of knowledge updates. For example, the Knowledge Provider Department can refer to records of past meetings and provide similar topics and discussion results. This allows participants to refer to the outcomes of past meetings and engage in constructive discussions. The Knowledge Provider Department provides useful information to participants by referring to related data. For example, related data includes industry trends, market developments, and technological advancements. Based on this data, the Knowledge Provider Department provides useful information to participants. This allows participants to engage in discussions based on the latest information and engage in constructive discussions. The Knowledge Provider Department provides information based on the databases referenced by the AI ​​and the methods of knowledge updates. For example, the AI ​​regularly updates its database to obtain the latest information. This allows the knowledge provision department to always provide participants with useful information based on the latest data. The knowledge provision department also provides feedback to participants after the meeting has ended. For example, they summarize the meeting's outcomes and discussion results and provide feedback to participants. This allows participants to review the meeting's results and identify areas for improvement for the next meeting. Based on feedback from participants, the knowledge provision department can improve how knowledge is updated. For example, they can review the frequency of database updates and the method of providing information based on feedback from participants. This allows the knowledge provision department to always provide participants with useful information based on the latest data.

[0064] The facilitator can monitor the progress of the meeting in real time and intervene if the discussion stalls. For example, the facilitator may use sensors or data to monitor the progress of the meeting in real time. The facilitator will determine whether the discussion is stalled based on the frequency of contributions and the progress of the discussion. If the discussion is stalled, the facilitator can move it forward by asking specific questions. For example, the facilitator may summarize the main points of the discussion and ask participants specific questions such as, "What do you think about this point?" This can help to keep the meeting moving smoothly. Some or all of the above processes performed by the facilitator may be performed using AI or not. For example, the facilitator may use AI to analyze the frequency and content of contributions in order to monitor the progress of the meeting in real time and determine whether the discussion is stalled.

[0065] The knowledge provision department can refer to past meeting records and related data to provide participants with useful information. For example, the knowledge provision department can refer to past meeting minutes, audio recordings, and video recordings. The knowledge provision department can also provide information based on relevant literature, databases, and statistical information. Furthermore, the knowledge provision department can use AI to analyze past meeting records and related data to provide participants with useful information. For example, the knowledge provision department can extract information relevant to the current agenda based on past meeting records and provide it to participants. This can improve the quality of meetings. Some or all of the above processes in the knowledge provision department may be performed using AI or not. For example, the knowledge provision department can input past meeting records into AI, which can then extract and provide relevant information.

[0066] The reception desk can confirm and share the agenda and objectives with participants at the start of the meeting. The reception desk can confirm the agenda and objectives based, for example, on a pre-set checklist or feedback from participants. After confirming the agenda and objectives, the reception desk shares them with participants. For example, the reception desk can display the agenda and objectives on a screen at the start of the meeting. The reception desk can also share the agenda and objectives with participants via email or chat tools. This helps to clarify the direction of the meeting. Some or all of the above processes by the reception desk may be performed using AI or not. For example, the reception desk can input the agenda and objectives into an AI, which can then automatically confirm and share them with participants.

[0067] The facilitator can organize the points of discussion and ask specific questions to the participants. For example, the facilitator summarizes the main points of the discussion and organizes the points of discussion. After organizing the points of discussion, the facilitator asks specific questions to the participants. For example, the facilitator asks specific questions such as, "What do you think about this point?" or "Do you have any other opinions?" This allows the discussion to progress. Some or all of the above processes by the facilitator may be performed using AI or not. For example, the facilitator can input the main points of the discussion into an AI, which can then automatically organize the points of discussion and generate specific questions.

[0068] The knowledge provision department can leverage AI knowledge to add knowledge from sources other than participants. For example, the knowledge provision department can provide information based on databases referenced by the AI ​​and methods for updating knowledge. The knowledge provision department can use AI to collect relevant information and provide it to participants. For example, the knowledge provision department can use AI to analyze past meeting records and related data to provide participants with useful information. This can improve the quality of meetings. Some or all of the above processes in the knowledge provision department may be performed using AI or not. For example, the knowledge provision department can input relevant information into the AI, and the AI ​​can automatically collect the information and provide it to participants.

[0069] The reception desk can estimate the user's emotions and adjust the method of confirming the agenda and objectives based on the estimated emotions. For example, if the user is nervous, the reception desk can provide a concise and clear method of confirming the agenda and objectives. If the user is relaxed, the reception desk can provide a detailed method of confirming the agenda and objectives. If the user is anxious, the reception desk can provide a quick method of confirming the agenda and objectives. This allows for the provision of a more appropriate confirmation method. Emotion estimation is achieved using an emotion estimation function, such as 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 the user's emotions into the AI, which can automatically estimate the emotions and adjust the confirmation method.

[0070] The reception desk can analyze participants' past speaking history before the meeting begins and optimize how agendas and objectives are shared. For example, the reception desk can prioritize sharing topics of high interest based on participants' past speaking history. Based on participants' past speaking history, the reception desk can concisely summarize and share agendas and objectives. The reception desk can also analyze participants' past speaking history and share agendas and objectives in a visually easy-to-understand manner. This can improve the efficiency of the meeting. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input participants' past speaking history into an AI, which can then automatically analyze and optimize the sharing method.

[0071] The reception desk can customize and share the agenda and objectives at the start of the meeting based on the participants' areas of interest. For example, the reception desk can prioritize sharing relevant agenda items based on the participants' areas of interest. The reception desk can customize and share the agenda and objectives taking into account the participants' areas of interest. The reception desk can also share the agenda and objectives in a visually easy-to-understand manner based on the participants' areas of interest. This helps to clarify the direction of the meeting. Some or all of the above processes at the reception desk may be performed using AI or not. For example, the reception desk can input the participants' areas of interest into an AI, which can then automatically analyze and customize the agenda and objectives for sharing.

[0072] The reception desk can estimate the user's emotions and adjust the timing of sharing the agenda and objectives based on the estimated emotions. For example, if the user is nervous, the reception desk can share the agenda and objectives immediately after the meeting begins. If the user is relaxed, the reception desk can share the agenda and objectives during the meeting. If the user is anxious, the reception desk can share the agenda and objectives before the meeting begins. This allows for sharing at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as 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 the user's emotions into the AI, which can automatically estimate the emotions and adjust the sharing timing.

[0073] The reception desk can share the agenda and objectives at the start of the meeting, taking into account the geographical location of the participants. For example, the reception desk can prioritize sharing relevant agenda items based on the participants' geographical location. The reception desk can customize and share agenda items and objectives, taking into account the participants' geographical location. The reception desk can also share agenda items and objectives in a visually easy-to-understand manner based on the participants' geographical location. This helps to clarify the direction of the meeting. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the participants' geographical location information into an AI, which can then automatically analyze and share the agenda and objectives.

[0074] The reception desk can analyze participants' social media activity at the start of the meeting and share relevant topics and objectives. For example, the reception desk can prioritize sharing topics of high interest based on participants' social media activity. Based on participants' social media activity, the reception desk can concisely summarize and share the topics and objectives. The reception desk can also analyze participants' social media activity and share the topics and objectives in a visually easy-to-understand manner. This helps to clarify the direction of the meeting. Some or all of the above processes by the reception desk may be performed using AI or not. For example, the reception desk can input participants' social media activity into an AI, which can then automatically analyze and share the topics and objectives.

[0075] The facilitator can estimate the user's emotions and adjust the meeting's progress based on those emotions. For example, if the user is nervous, the facilitator can conduct the meeting at a slow pace. If the user is relaxed, the facilitator can conduct the meeting smoothly. If the user is anxious, the facilitator can conduct the meeting quickly. This allows for a more appropriate approach to the meeting. 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-described processes in the facilitator may be performed using AI or not. For example, the facilitator can input the user's emotions into an AI, which can then automatically estimate the emotions and adjust the meeting's progress accordingly.

[0076] The facilitator can monitor the meeting's progress in real time and optimize its flow based on participants' speaking frequency and content. For example, based on participants' speaking frequency, the facilitator can provide opportunities for less active participants to speak. The facilitator can analyze participants' comments and adjust the direction of the discussion. The facilitator can also optimize the meeting's progress based on participants' speaking frequency and content. This can improve the efficiency of the meeting. Some or all of the above processes in the facilitator may be performed using AI or not. For example, the facilitator can input participants' speaking frequency and content into an AI, which can then automatically analyze and optimize the flow.

[0077] The facilitator can adjust the order and timing of participants' presentations during the meeting, based on their expertise and roles. For example, the facilitator can adjust the order of presentations considering the participants' expertise. The facilitator can adjust the timing of presentations based on the participants' roles. The facilitator can also optimize the order and timing of presentations based on the participants' expertise and roles. This can improve the efficiency of the meeting. Some or all of the above processes performed by the facilitator may be done using AI or not. For example, the facilitator can input the participants' expertise and roles into an AI, which can then automatically analyze and adjust the order and timing of presentations.

[0078] The facilitator can estimate the user's emotions and adjust the meeting's pace based on the estimated emotions. For example, if the user is nervous, the facilitator can conduct the meeting at a slow pace. If the user is relaxed, the facilitator can conduct the meeting smoothly. If the user is anxious, the facilitator can conduct the meeting quickly. This allows for a more appropriate pace. 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 facilitator may be performed using AI or not. For example, the facilitator can input the user's emotions into the AI, which can then automatically estimate the emotions and adjust the pace.

[0079] The facilitator can adjust the order and timing of participants' speeches during the meeting, taking into account their geographical location. For example, the facilitator can adjust the order of speeches based on participants' geographical location. The facilitator can adjust the timing of participants' speeches, taking into account their geographical location. The facilitator can also optimize the order and timing of speeches based on participants' geographical location. This can improve the efficiency of the meeting. Some or all of the above processes performed by the facilitator may be performed using AI or not. For example, the facilitator can input participants' geographical location information into an AI, which can then automatically analyze and adjust the order and timing of speeches.

[0080] The facilitator can analyze participants' social media activity during the meeting and provide relevant information. For example, the facilitator can provide relevant information from participants' social media activity. The facilitator can adjust the direction of the discussion based on participants' social media activity. The facilitator can also analyze participants' social media activity and optimize the meeting's progress. This can improve the efficiency of the meeting. Some or all of the above processes performed by the facilitator may be done using AI or not. For example, the facilitator can input participants' social media activity into an AI, which can then automatically analyze and provide relevant information.

[0081] The facilitator can analyze participants' social media activity during the meeting and provide relevant information. For example, the facilitator can provide relevant information from participants' social media activity. The facilitator can adjust the direction of the discussion based on participants' social media activity. The facilitator can also analyze participants' social media activity and optimize the meeting's progress. This can improve the efficiency of the meeting. Some or all of the above processes performed by the facilitator may be done using AI or not. For example, the facilitator can input participants' social media activity into an AI, which can then automatically analyze and provide relevant information.

[0082] The knowledge provider can estimate the user's emotions and adjust the content and format of the information it provides based on the estimated emotions. For example, if the user is nervous, the knowledge provider can provide concise and clear information. If the user is relaxed, the knowledge provider can provide detailed information. If the user is anxious, the knowledge provider can provide information quickly. This allows for the provision of more appropriate information. 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 knowledge provider may be performed using AI or not. For example, the knowledge provider can input the user's emotions into the AI, which can then automatically estimate the emotions and adjust the content and format of the information.

[0083] The knowledge delivery department can refer to past meeting records and related data and customize information according to the participants' expertise and roles. For example, the knowledge delivery department can provide relevant information considering the participants' expertise. The knowledge delivery department can customize information based on the participants' roles. The knowledge delivery department can also optimize information based on the participants' expertise and roles. This allows for the provision of more appropriate information. Some or all of the above processes in the knowledge delivery department may be performed using AI or not. For example, the knowledge delivery department can input past meeting records and related data into AI, which can then automatically analyze and customize the information.

[0084] The knowledge provision department can provide relevant information in real time based on participants' statements during the meeting. For example, the knowledge provision department can provide relevant information in real time based on participants' statements. The knowledge provision department can analyze participants' statements and adjust the direction of the discussion. The knowledge provision department can also optimize the progress of the meeting based on participants' statements. This can improve the efficiency of the meeting. Some or all of the above processes in the knowledge provision department may be performed using AI or not. For example, the knowledge provision department can input participants' statements into an AI, which can then automatically analyze and provide relevant information in real time.

[0085] The knowledge delivery unit can estimate the user's emotions and prioritize the information it provides based on those emotions. For example, if the user is nervous, the knowledge delivery unit will prioritize providing important information. If the user is relaxed, the knowledge delivery unit can prioritize providing detailed information. If the user is anxious, the knowledge delivery unit will quickly provide important information. This allows for the provision of more relevant information. 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 knowledge delivery unit may be performed using AI or not. For example, the knowledge delivery unit can input the user's emotions into an AI, which can then automatically estimate the emotions and determine the priority of the information.

[0086] The knowledge provision department can provide information by referring to past meeting records and related data, and taking into account the geographical location of participants. For example, the knowledge provision department can provide relevant information based on the geographical location of participants. The knowledge provision department can customize the information by taking into account the geographical location of participants. The knowledge provision department can also optimize the information based on the geographical location of participants. This allows for the provision of more appropriate information. Some or all of the above processing in the knowledge provision department may be performed using AI or not. For example, the knowledge provision department can input past meeting records and related data into AI, which can then automatically analyze and provide information.

[0087] The knowledge provision department can analyze participants' social media activity during the meeting and provide relevant information. For example, the knowledge provision department can provide relevant information from participants' social media activity. The knowledge provision department can adjust the direction of the discussion based on participants' social media activity. The knowledge provision department can also analyze participants' social media activity and optimize the meeting's progress. This can improve the efficiency of the meeting. Some or all of the above processes in the knowledge provision department may be performed using AI or not. For example, the knowledge provision department can input participants' social media activity into an AI, which can then automatically analyze and provide relevant information.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The facilitator can monitor the meeting's progress in real time and adjust the direction of the discussion based on participants' comments. For example, they can analyze participants' comments and ask questions to balance the discussion if it becomes unbalanced. They can also assess the progress of the discussion based on the comments and propose changes or additions to the agenda as needed. Furthermore, they can provide materials and data in real time based on the comments to facilitate the discussion. This ensures a smooth meeting and enables efficient discussion.

[0090] The knowledge provision department can provide relevant information in real time during a meeting based on participants' comments. For example, it can analyze participants' comments and extract and provide necessary information from relevant literature and databases. It can also refer to past meeting records and related data based on comments to provide participants with useful information. Furthermore, it can provide materials and data in real time to support the progress of the discussion based on the comments. This improves the quality of the meeting and enables more efficient discussion.

[0091] The reception desk can analyze participants' past contributions before the meeting begins and optimize how agendas and objectives are shared. For example, the reception desk can prioritize sharing topics of high interest based on participants' past contributions. It can also concisely summarize and share agendas and objectives based on participants' past contributions. Furthermore, the reception desk can analyze participants' past contributions and share agendas and objectives in a visually easy-to-understand format. This can improve the efficiency of the meeting.

[0092] The facilitator can adjust the order and timing of participants' presentations during the meeting, based on their expertise and roles. For example, the facilitator can adjust the order of presentations considering the participants' expertise. They can also adjust the time allotted for each participant's presentation based on their role. Furthermore, they can optimize the order and timing of presentations based on the participants' expertise and roles. This can improve the efficiency of the meeting.

[0093] The knowledge sharing department can refer to past meeting records and related data, and customize information according to the participants' expertise and roles. For example, the knowledge sharing department can provide relevant information considering the participants' expertise. It can also customize information based on the participants' roles. Furthermore, the knowledge sharing department can optimize information based on the participants' expertise and roles. This allows for the provision of more appropriate information.

[0094] The facilitator can estimate the user's emotions and adjust the meeting's progress based on those estimates. For example, if the user is nervous, the facilitator can conduct the meeting at a slower pace. If the user is relaxed, the facilitator can conduct the meeting smoothly. Furthermore, if the user is anxious, the facilitator can conduct the meeting quickly. This allows for a more appropriate meeting progression. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI.

[0095] The reception desk can estimate the user's emotions and adjust the way agendas and objectives are confirmed based on those estimated emotions. For example, if the user is nervous, the reception desk can provide a concise and clear way to confirm agendas and objectives. If the user is relaxed, the reception desk can provide a more detailed way to confirm agendas and objectives. Furthermore, if the user is anxious, the reception desk can provide a quick way to confirm agendas and objectives. This allows for the provision of more appropriate confirmation methods. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI.

[0096] The knowledge delivery unit can estimate the user's emotions and adjust the content and format of the information it provides based on those emotions. For example, if the user is nervous, the knowledge delivery unit can provide concise and clear information. If the user is relaxed, the knowledge delivery unit can provide detailed information. Furthermore, if the user is anxious, the knowledge delivery unit can provide information quickly. This allows for the provision of more appropriate information. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI.

[0097] The facilitator can estimate the user's emotions and adjust the meeting's pace based on those emotions. For example, if the user is nervous, the facilitator can conduct the meeting at a slower pace. If the user is relaxed, the facilitator can conduct the meeting smoothly. Furthermore, if the user is anxious, the facilitator can conduct the meeting quickly. This allows for a more appropriate pace. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI.

[0098] The knowledge delivery unit can estimate the user's emotions and prioritize the information it provides based on those emotions. For example, if the user is nervous, the knowledge delivery unit can prioritize providing important information. If the user is relaxed, the knowledge delivery unit can prioritize providing detailed information. Furthermore, if the user is anxious, the knowledge delivery unit can quickly provide important information. This allows for the provision of more appropriate information. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The reception staff clarifies the meeting agenda and objectives. For example, the reception staff confirms the agenda and objectives at the start of the meeting and shares them with the participants. The reception staff can clarify the agenda and objectives based on pre-set templates or input from participants. Step 2: The facilitator supports the progress of the meeting based on the agenda and objectives clarified by the reception team. For example, the facilitator can monitor the progress of the meeting in real time and intervene if the discussion stalls. The facilitator can organize the points of discussion and ask specific questions to the participants. The facilitator can also handle timekeeping, summarizing the discussion, and giving instructions on how to proceed. Step 3: The Knowledge Provider team leverages AI knowledge to provide information beyond the participants in meetings supported by the Facilitation Team. For example, the Knowledge Provider team can refer to past meeting records and related data to provide useful information to participants. The Knowledge Provider team can provide information based on the databases referenced by the AI ​​and how the knowledge is updated.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the reception unit, progress unit, and knowledge provision 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 is implemented by the control unit 46A of the smart device 14, which confirms the agenda and objectives at the start of the meeting and shares them with the participants. The progress unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors the progress of the meeting in real time and intervenes when the discussion stalls. The knowledge provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which refers to records of past meetings and related data and provides useful information to the participants. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the reception unit, progress unit, and knowledge provision 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 is implemented by the control unit 46A of the smart glasses 214, which confirms the agenda and objectives at the start of the meeting and shares them with the participants. The progress unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors the progress of the meeting in real time and intervenes when the discussion stalls. The knowledge provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which refers to records of past meetings and related data and provides useful information to the participants. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception unit, progress unit, and knowledge provision 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 is implemented by the control unit 46A of the headset terminal 314, which confirms the agenda and objectives at the start of the meeting and shares them with the participants. The progress unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors the progress of the meeting in real time and intervenes when the discussion stalls. The knowledge provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which refers to records of past meetings and related data and provides useful information to the participants. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the reception unit, progress unit, and knowledge provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, which confirms the agenda and objectives at the start of the meeting and shares them with the participants. The progress unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors the progress of the meeting in real time and intervenes when the discussion stalls. The knowledge provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which refers to records of past meetings and related data and provides useful information to the participants. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) The reception desk clarifies the agenda and objectives of the meeting, The meeting facilitation department supports the progress of the meeting based on the agenda and objectives clarified by the aforementioned reception department, The system includes a knowledge provision unit that utilizes AI knowledge to provide knowledge from participants other than those present in a meeting whose progress is supported by the aforementioned facilitation unit. A system characterized by the following features. (Note 2) The aforementioned progress section is, The meeting's progress is monitored in real time, and intervention is performed if the discussion stalls. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned knowledge provision department, Refer to past meeting records and related data to provide participants with useful information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is Confirm the agenda and objectives at the start of the meeting and share them with the participants. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned progress section is, Organize the points of discussion and ask specific questions to the participants. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned knowledge provision department, Leveraging AI knowledge to add knowledge from outside the participants. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the user's emotions and adjust the way we confirm agenda items and objectives based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Before the meeting begins, we analyze participants' past contributions to optimize how the agenda and objectives are shared. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is At the start of the meeting, customize and share the agenda and objectives based on the participants' areas of interest. 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 the timing of sharing agenda items and objectives based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is At the start of the meeting, the agenda and objectives will be shared while taking into account the geographical location of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is At the start of the meeting, we will analyze participants' social media activity and share relevant topics and objectives. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned progress section is, It estimates the user's emotions and adjusts the meeting's progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned progress section is, The meeting progress is monitored in real time, and the flow is optimized based on the frequency and content of participants' contributions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned progress section is, During the meeting, adjust the order and timing of participants' presentations according to their expertise and roles. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned progress section is, It estimates the user's emotions and adjusts the pace of the meeting based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned progress section is, During the meeting, the order and timing of participants' presentations will be adjusted to take their geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned progress section is, During the meeting, we will analyze participants' social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned progress section is, During the meeting, we will analyze participants' social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned knowledge provision department, It estimates the user's emotions and adjusts the content and format of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned knowledge provision department, Refer to past meeting records and related data, and customize the information according to the participants' expertise and roles. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned knowledge provision department, During the meeting, relevant information will be provided in real time based on what participants are saying. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned knowledge provision department, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned knowledge provision department, Information will be provided by referring to past meeting records and related data, and taking into account the geographical location of participants. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned knowledge provision department, During the meeting, we will analyze participants' social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk clarifies the agenda and objectives of the meeting, The meeting facilitation department supports the progress of the meeting based on the agenda and objectives clarified by the aforementioned reception department, The system includes a knowledge provision unit that utilizes AI knowledge to provide knowledge from participants other than those present in a meeting whose progress is supported by the aforementioned facilitation unit. A system characterized by the following features.

2. The aforementioned progress section is, The meeting's progress is monitored in real time, and intervention is performed if the discussion stalls. The system according to feature 1.

3. The aforementioned knowledge provision department, Refer to past meeting records and related data to provide participants with useful information. The system according to feature 1.

4. The aforementioned reception unit is Confirm the agenda and objectives at the start of the meeting and share them with the participants. The system according to feature 1.

5. The aforementioned progress section is, Organize the points of discussion and ask specific questions to the participants. The system according to feature 1.

6. The aforementioned knowledge provision department, By utilizing AI knowledge, we can add knowledge from outside the participants. The system according to feature 1.

7. The aforementioned reception unit is We estimate the user's emotions and adjust the way we confirm agenda items and objectives based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Before the meeting begins, we analyze participants' past contributions to optimize how the agenda and objectives are shared. The system according to feature 1.

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