system

Abstract

The system according to this embodiment aims to efficiently set up meetings. [Solution] The system according to the embodiment comprises an adjustment unit, a designation unit, and a setting unit. The adjustment unit adjusts the schedules of the participants. The designation unit specifies the meeting format based on the schedules adjusted by the adjustment unit. The setting unit reserves a meeting room or sets up an online meeting based on the meeting format specified by the designation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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, there is a problem that it takes a lot of man-hours to set up a meeting and it is difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently set up a meeting.

Means for Solving the Problems

[0006] The system according to the embodiment includes an adjustment unit, a designation unit, and a setting unit. The adjustment unit adjusts the schedules of the participants. The designation unit designates a meeting format based on the schedules adjusted by the adjustment unit. The setting unit makes a reservation for a meeting room or sets up an online meeting based on the meeting format designated by the designation unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently set up meetings. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The meeting scheduling system according to an embodiment of the present invention is a system that automates meeting scheduling using an AI agent. When a meeting needs to be scheduled, the user simply specifies the participants, and the AI ​​agent automatically adjusts the participants' schedules and proposes the optimal meeting date and time. Furthermore, by specifying the meeting format (in-person or online), the system automatically handles meeting room reservations and online meeting setup. This mechanism significantly reduces the time and effort required for meeting scheduling. For example, the user specifies the meeting participants. This only requires entering the participants' email addresses and names. For example, the user inputs participant information into the meeting scheduling system interface. This information is sent to the AI ​​agent. Next, the AI ​​agent automatically adjusts the participants' schedules. The AI ​​agent obtains each participant's calendar information and proposes the optimal meeting date and time. For example, the AI ​​agent analyzes each participant's availability and presents candidate dates and times. These candidate dates and times are notified to the user, who can select the most suitable one. Furthermore, the user specifies the meeting format. For in-person meetings, the AI ​​agent automatically reserves the meeting room. For online meetings, the AI ​​agent automatically sets up the online meeting. For example, it generates a meeting link using an online meeting tool. This link will be sent to participants. This mechanism significantly reduces the time and effort required to set up meetings. Users simply specify the participants, and the AI ​​agent automatically completes the meeting setup. This eliminates the hassle of meeting setup and allows for efficient meeting hosting. As a result, the meeting setup system eliminates the hassle of meeting setup and allows for efficient meeting hosting.

[0029] The meeting scheduling system according to this embodiment comprises a coordination unit, a designation unit, and a scheduling unit. The coordination unit coordinates the schedules of the participants. For example, the coordination unit obtains the calendar information of each participant and proposes the optimal meeting date and time. For example, the coordination unit can analyze the calendar information to find everyone's free time and present candidate dates and times. The coordination unit can also propose the optimal meeting date and time considering the participants' high-priority time slots. The designation unit specifies the meeting format based on the schedules coordinated by the coordination unit. For example, the designation unit can specify the format of an in-person meeting or an online meeting. The designation unit can select the optimal meeting format according to the importance of the meeting and the circumstances of the participants. The scheduling unit reserves a meeting room or sets up an online meeting based on the meeting format specified by the designation unit. For example, the scheduling unit can automatically reserve a meeting room using a meeting room reservation system. The scheduling unit can also generate a meeting link using an online meeting tool and notify participants. As a result, the meeting scheduling system can reduce the effort required for meeting scheduling and hold meetings efficiently. Some or all of the above-described processes in the adjustment, designation, and setting units may be performed using AI, or not using AI. For example, the adjustment unit can input participants' calendar information into the AI ​​and have the AI ​​suggest the optimal meeting date and time. The designation unit can have the AI ​​select the meeting format. The setting unit can have the AI ​​reserve a meeting room or set up an online meeting.

[0030] The scheduling department coordinates participants' schedules. For example, it obtains each participant's calendar information and proposes the optimal meeting date and time. Specifically, the scheduling department obtains participants' calendar information in real time and analyzes each participant's availability. The calendar information includes details of appointments, priorities, locations, etc., and uses this information to find the optimal meeting date and time. The scheduling department can use AI to analyze calendar information, find everyone's availability, and present candidate dates and times. The AI ​​learns each participant's past appointments and behavioral patterns to predict the optimal meeting date and time. The scheduling department can also propose the optimal meeting date and time by considering the time slots that are the highest priority for each participant. For example, in the case of an important meeting, it will prioritize the availability of the person with the highest priority among the participants and coordinate with the schedules of other participants. Furthermore, the scheduling department can also consider the geographical location of participants and propose meeting dates and times that minimize travel time. In this way, the scheduling department can efficiently and flexibly adjust meeting dates and times, providing an environment where all participants can easily attend.

[0031] The Designation Department specifies the meeting format based on the schedule coordinated by the Coordination Department. The Designation Department can, for example, specify whether it's an in-person or online meeting. Specifically, the Designation Department selects the most suitable meeting format based on the importance of the meeting and the circumstances of the participants. For example, in-person meetings are often specified for meetings requiring important decision-making or those handling confidential information. On the other hand, online meetings are more appropriate when participants are in remote locations or have difficulty traveling. The Designation Department can also use AI to select the meeting format. The AI ​​proposes the most suitable format based on past meeting data and participant feedback. For example, it can analyze the attendance rate and satisfaction level of past online meetings and propose a meeting format under similar conditions. Furthermore, the Designation Department can specify a hybrid meeting format depending on the purpose and content of the meeting. In a hybrid meeting, some participants can attend in person while others participate online. This allows the Designation Department to provide a flexible meeting format tailored to the needs and circumstances of the participants, maximizing the effectiveness of the meeting.

[0032] The configuration unit reserves meeting rooms and sets up online meetings based on the meeting format specified by the designation unit. Specifically, the configuration unit can automatically reserve meeting rooms using a meeting room reservation system. For example, it can check the availability of meeting rooms in real time, select the most suitable meeting room, and make a reservation. The configuration unit can also generate meeting links and notify participants using online meeting tools. Online meeting tools include functions such as video conferencing, chat, and file sharing, enabling participants to communicate smoothly. The configuration unit can also use AI to reserve meeting rooms and set up online meetings. The AI ​​suggests the most suitable meeting rooms and online meeting tools based on past meeting data and participant feedback. For example, it analyzes evaluations of meeting rooms used in past meetings and the usage status of online meeting tools to make optimal settings under similar conditions. The configuration unit can also send reminders before the start of the meeting to ensure that participants join on time. This reduces the effort required for meeting setup for the configuration unit, allowing for efficient meeting hosting.

[0033] The scheduling unit can obtain each participant's calendar information and propose the optimal meeting date and time. The scheduling unit obtains calendar information from, for example, a calendar management tool. The scheduling unit can analyze the calendar information to find everyone's available time slots and present candidate dates and times. For example, based on the calendar information, the scheduling unit can find time slots when everyone can participate and propose the optimal meeting date and time. The scheduling unit can also propose the optimal meeting date and time by considering the participants' high-priority time slots. For example, based on the participants' calendar information, the scheduling unit can select time slots that are convenient for everyone to participate in and propose the meeting date and time. This allows the scheduling unit to propose the optimal meeting date and time based on each participant's calendar information. Some or all of the above processing in the scheduling unit may be performed using, for example, AI, or not using AI. For example, the scheduling unit can input calendar information into AI and have the AI ​​propose the optimal meeting date and time.

[0034] The configuration unit can generate a meeting link using an online meeting tool. For example, the configuration unit can generate a meeting link using an online meeting tool and notify participants. This automates the setup of the online meeting. Some or all of the above processes in the configuration unit may be performed using AI, for example, or not using AI. For example, the configuration unit can have AI perform the setup of the online meeting tool.

[0035] The configuration unit can automatically reserve meeting rooms. The configuration unit can, for example, automatically reserve meeting rooms using a meeting room reservation system. The configuration unit can use a meeting room reservation system to check the availability of meeting rooms and reserve the most suitable meeting room. The configuration unit can, for example, use a meeting room reservation system to check the availability of meeting rooms and reserve the most suitable meeting room. The configuration unit can also use a meeting room reservation system to automatically reserve meeting rooms. The configuration unit can also use a meeting room reservation system to automatically reserve meeting rooms. This allows for automatic meeting room reservations. Some or all of the above processes in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit can have AI perform the meeting room reservation.

[0036] A reminder unit sends reminders. The reminder unit can send reminders. The reminder unit can send reminders by methods such as email, SMS, and app notifications. The reminder unit can send reminders according to the start time of a meeting. For example, the reminder unit can send an email reminder one hour before the start time of a meeting. It can also send an SMS reminder 30 minutes before the start time of a meeting. The reminder unit can also send an app notification reminder 15 minutes before the start time of a meeting. This allows for the automatic sending of meeting reminders. Some or all of the above processes in the reminder unit may be performed using AI, for example, or not using AI. For example, the reminder unit can have AI execute the timing of sending reminders.

[0037] A sharing section for sharing agendas and materials. The sharing section can share agendas and materials. The sharing section can share agendas and materials in ways such as PDF files, presentation materials, and link sharing. The sharing section can share agendas and materials before the start of a meeting. For example, the sharing section can share PDF files via email before the start of a meeting. The sharing section can also upload presentation materials to cloud storage and share the link before the start of a meeting. The sharing section can also share links before the start of a meeting so that participants can access the materials. This allows for the automatic sharing of meeting agendas and materials. Some or all of the above processes in the sharing section may be performed using AI, for example, or not. For example, the sharing section can have AI perform the methods for sharing agendas and materials.

[0038] The Minutes Creation Department is responsible for creating meeting minutes. The Minutes Creation Department can create meeting minutes. For example, the Minutes Creation Department records the key points of the meeting, the content of the discussions, and the decisions made. The Minutes Creation Department can create meeting minutes after the meeting has ended and share them with the participants. For example, the Minutes Creation Department can create meeting minutes after the meeting has ended and share them with the participants via email. The Minutes Creation Department can also upload the meeting minutes to cloud storage after the meeting has ended and share the link. The Minutes Creation Department can also create meeting minutes after the meeting has ended and make them accessible to the participants. This allows for the automatic creation of meeting minutes. Some or all of the above processes in the Minutes Creation Department may be performed using AI, for example, or not. For example, the Minutes Creation Department can have AI perform the creation of meeting minutes.

[0039] The scheduling unit can analyze participants' past meeting attendance history and select the optimal meeting date and time. For example, the scheduling unit can suggest the optimal meeting date and time based on the time slots of meetings the participant has attended in the past. The scheduling unit can also analyze the frequency of meetings the participant has attended in the past and set meetings at appropriate intervals. The scheduling unit can also select the time slot most likely to be attended based on the attendance rate of meetings the participant has attended in the past. This allows for the selection of the optimal meeting date and time based on past meeting attendance history. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input past meeting attendance history into AI and have the AI ​​select the optimal meeting date and time.

[0040] The scheduling unit can adjust meeting dates and times based on participants' current projects and work status. For example, the scheduling unit can propose the optimal meeting date and time considering the progress of projects currently underway for each participant. The scheduling unit can also analyze participants' workloads and propose meeting dates and times that are less burdensome. The scheduling unit can also consider participants' work priorities and select times that will not affect important tasks. This allows for the scheduling of meeting dates and times based on current projects and work status. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input current project and work status into AI and have the AI ​​perform the scheduling of the optimal meeting date and time.

[0041] The coordination unit can propose the optimal meeting date and time considering the geographical location information of the participants. For example, if the participants are in different time zones, the coordination unit will select a time that is convenient for everyone to attend. If the participants are traveling, the coordination unit can also propose a meeting date and time that minimizes travel time. If the participants are in a specific location, the coordination unit can also propose the optimal meeting date and time tailored to that location. In this way, the coordination unit can propose the optimal meeting date and time considering geographical location information. Some or all of the above processing in the coordination unit may be performed using AI, for example, or not. For example, the coordination unit can input geographical location information into AI and have the AI ​​propose the optimal meeting date and time.

[0042] The coordination unit can analyze participants' social media activity and suggest relevant meeting dates and times. For example, the coordination unit can suggest the optimal meeting date and time based on the times when participants are most active on social media. The coordination unit can also analyze the content of participants' social media posts and suggest relevant meeting dates and times. The coordination unit can also analyze participants' social media activity patterns and select the times when they are most likely to participate. This allows the coordination unit to suggest the optimal meeting date and time based on social media activity. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not. For example, the coordination unit can input social media activity into AI and have the AI ​​suggest the optimal meeting date and time.

[0043] The designator can specify the meeting format based on the importance of the meeting. For example, for a highly important meeting, the designator can specify an in-person meeting. For a less important meeting, the designator can also specify an online meeting. Depending on the importance, the designator can also specify a hybrid meeting format. This allows for the selection of the optimal meeting format based on the importance of the meeting. Some or all of the above processing in the designator may be performed using AI, for example, or not using AI. For example, the designator can input the importance of the meeting into the AI ​​and have the AI ​​specify the meeting format.

[0044] The designator can specify different meeting formats depending on the meeting category. For example, for a project meeting, the designator can specify an in-person meeting. For a regular meeting, the designator can also specify an online meeting. For a brainstorming meeting, the designator can also specify a hybrid meeting format. This allows the designator to specify the most suitable meeting format depending on the meeting category. Some or all of the above processing in the designator may be performed using AI, for example, or not using AI. For example, the designator can input the meeting category into the AI ​​and have the AI ​​specify the meeting format.

[0045] The designator can specify the meeting format based on the submission deadline. For example, if the deadline is approaching, the designator can specify an online meeting. If there is ample time before the deadline, the designator can also specify an in-person meeting. Depending on the deadline, the designator can also specify a hybrid meeting format. This allows for the selection of the most suitable meeting format based on the submission deadline. Some or all of the above processing in the designator may be performed using AI, for example, or not. For example, the designator can input the meeting submission deadline into the AI ​​and have the AI ​​specify the meeting format.

[0046] The designator can specify the meeting format based on the relevance of the meetings. For example, the designator can specify an in-person meeting for highly relevant meetings. For less relevant meetings, the designator can also specify an online meeting. Depending on the relevance, the designator can also specify a hybrid meeting format. This allows for the selection of the most suitable meeting format based on the relevance of the meetings. Some or all of the above processing in the designator may be performed using AI, for example, or not using AI. For example, the designator can input the relevance of the meetings into the AI ​​and have the AI ​​perform the designation of the meeting format.

[0047] The configuration unit can reserve meeting rooms or set up online meetings based on the importance of the meeting. For example, for a high-priority meeting, the configuration unit will reserve a large meeting room. For a low-priority meeting, the configuration unit can reserve a small meeting room. The configuration unit can also set up online meetings according to their importance. This allows for the optimal reservation of meeting rooms or online meeting settings based on the importance of the meeting. Some or all of the above processing in the configuration unit may be performed using AI, for example, or not using AI. For example, the configuration unit can input the importance of the meeting into the AI ​​and have the AI ​​execute the reservation of meeting rooms or the setting up of online meetings.

[0048] The settings unit can reserve different meeting rooms or configure online meeting settings depending on the meeting category. For example, for a project meeting, the settings unit can reserve a large meeting room. For a regular meeting, it can reserve a smaller meeting room. For a brainstorming meeting, the settings unit can configure online meeting settings. This allows for the optimal meeting room reservation or online meeting configuration depending on the meeting category. Some or all of the above processing in the settings unit may be performed using AI, for example, or not. For example, the settings unit can input the meeting category into the AI ​​and have the AI ​​execute the meeting room reservation or online meeting configuration.

[0049] The configuration unit can reserve meeting rooms or set up online meetings based on the meeting submission deadline. For example, if the submission deadline is approaching, the configuration unit will prioritize setting up online meetings. If there is ample time before the submission deadline, the configuration unit can also prioritize reserving meeting rooms. The configuration unit can reserve meeting rooms or set up online meetings according to the submission deadline. This allows for the optimal reservation of meeting rooms or setting up online meetings based on the meeting submission deadline. Some or all of the above processes in the configuration unit may be performed using AI, for example, or not. For example, the configuration unit can input the meeting submission deadline into AI and have the AI ​​execute the reservation of meeting rooms or setting up online meetings.

[0050] The configuration unit can reserve meeting rooms and set up online meetings based on the relevance of the meetings. For example, the configuration unit can reserve a large meeting room for highly relevant meetings. For less relevant meetings, it can reserve a smaller meeting room. The configuration unit can also set up online meetings according to their relevance. This allows for the optimal reservation of meeting rooms and online meeting settings based on the relevance of the meetings. Some or all of the above processing in the configuration unit may be performed using AI, for example, or not using AI. For example, the configuration unit can input the relevance of the meetings into the AI ​​and have the AI ​​execute the reservation of meeting rooms and online meeting settings.

[0051] The reminder unit can analyze a participant's past reminder reception history and select the optimal sending method. For example, the reminder unit can suggest the optimal sending timing based on the time periods when participants have received reminders in the past. The reminder unit can also analyze the frequency of reminders received in the past and send reminders at appropriate intervals. The reminder unit can also select the time period when participants are most likely to receive reminders based on their past reminder reception rate. This allows the system to select the optimal sending method based on past reminder reception history. Some or all of the above processes in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input past reminder reception history into AI and have the AI ​​select the optimal sending method.

[0052] The reminder unit can send reminders while considering the geographical location of participants. For example, if a participant is on the move, the reminder unit will send a reminder during a time when there is less movement. If a participant is in a specific location, the reminder unit can also send an optimal reminder tailored to that location. If participants are in different time zones, the reminder unit can also send a reminder during a time when everyone is likely to receive it. This allows for the sending of optimal reminders while considering geographical location. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input geographical location information into AI and have the AI ​​execute the reminder sending process.

[0053] The sharing unit can analyze participants' past sharing history and select the optimal sharing method. For example, the sharing unit can suggest the optimal sharing method based on the format of materials previously shared by participants. The sharing unit can also analyze the frequency of materials previously shared by participants and share materials at appropriate intervals. The sharing unit can also share materials in the most easily received format based on the reception rate of materials previously shared by participants. This allows the optimal sharing method to be selected based on past sharing history. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input past sharing history into AI and have the AI ​​select the optimal sharing method.

[0054] The sharing function can share agendas and materials while considering the geographical location of participants. For example, if participants are in different time zones, the sharing function will share agendas and materials at a time when everyone is likely to receive them. If participants are on the move, the sharing function can also share agendas and materials at a time when they are less likely to be traveling. If participants are in a specific location, the sharing function can also share agendas and materials that are best suited to that location. This allows for the sharing of optimal agendas and materials while considering geographical location. Some or all of the above processing in the sharing function may be performed using AI, for example, or not. For example, the sharing function can input geographical location information into AI and have the AI ​​perform the sharing of agendas and materials.

[0055] The minutes creation department can analyze participants' past minutes creation history and select the optimal creation method. For example, the minutes creation department can suggest the optimal creation method based on the format of minutes previously created by participants. The minutes creation department can also analyze the frequency of minutes previously created by participants and create minutes at appropriate intervals. The minutes creation department can also create minutes in the most easily received format based on the reception rate of minutes previously created by participants. This allows for the selection of the optimal creation method based on past minutes creation history. Some or all of the above processes in the minutes creation department may be performed using AI, for example, or not. For example, the minutes creation department can input past minutes creation history into AI and have the AI ​​select the optimal creation method.

[0056] The minutes creation unit can create meeting minutes while taking into account the geographical location information of the participants. For example, if the participants are in different time zones, the minutes creation unit will create the minutes at a time when everyone is likely to receive them. If the participants are traveling, the minutes creation unit can also create the minutes at a time when they are traveling less. If the participants are in a specific location, the minutes creation unit can also create the most suitable minutes for that location. This allows for the creation of optimal minutes while taking geographical location information into account. Some or all of the above processes in the minutes creation unit may be performed using AI, for example, or not. For example, the minutes creation unit can input geographical location information into AI and have the AI ​​create the minutes.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The scheduling unit can propose the optimal meeting date and time, taking into account the participants' health data. For example, it can select the time of day when participants are most focused based on their sleep data. The scheduling unit can also propose a meeting date and time by selecting a time when participants are less fatigued based on their exercise data. Furthermore, it can select a time when participants have high energy levels, taking into account their dietary data. This allows the scheduling unit to propose the optimal meeting date and time based on the participants' health status. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input health data into AI and have the AI ​​propose the optimal meeting date and time.

[0059] The scheduling unit can analyze participants' feedback on past meetings and propose optimal meeting dates and times. For example, it can propose optimal meeting dates and times based on the time slots in meetings that participants have previously given high ratings to. The scheduling unit can also propose meeting dates and times that avoid the time slots in meetings that participants have previously given low ratings to. Furthermore, it can adjust the frequency and time of meetings based on participants' feedback. This allows the scheduling unit to propose optimal meeting dates and times based on past feedback. Some or all of the above processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input feedback data into AI and have the AI ​​propose optimal meeting dates and times.

[0060] The scheduling unit can analyze participants' past meeting attendance rates and propose the optimal meeting frequency. For example, it can propose the optimal meeting frequency based on the frequency of meetings in which participants have shown high attendance rates in the past. The scheduling unit can also avoid proposing meeting frequencies in which participants have shown low attendance rates in the past. Furthermore, it can adjust the meeting frequency based on participants' attendance rates. This allows the scheduling unit to propose the optimal meeting frequency based on past attendance rates. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input attendance rate data into AI and have the AI ​​propose the optimal meeting frequency.

[0061] The coordination unit can adjust the meeting agenda considering the current project progress of the participants. For example, if a participant is approaching a key milestone in an ongoing project, it will prioritize agenda items related to that project. The coordination unit can also propose the most effective agenda based on the participants' project progress. It can also prioritize important agenda items considering the priorities of the participants' projects. This allows for the adjustment of the meeting agenda to be optimal based on the current project progress. Some or all of the above processes in the coordination unit may or may not be performed using AI. For example, the coordination unit can input project progress data into the AI ​​and have the AI ​​perform the agenda adjustments.

[0062] The coordination unit can analyze participants' past meeting contributions and propose the optimal meeting format. For example, it can propose the optimal format based on meeting formats in which participants have actively participated in the past. The coordination unit can also avoid proposing meeting formats in which participants have been passive in the past. Furthermore, it can adjust the meeting format based on the participants' contributions. This allows it to propose the optimal meeting format based on past contributions. Some or all of the above processing in the coordination unit may be performed using AI or not. For example, the coordination unit can input contribution data into AI and have the AI ​​propose the optimal meeting format.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The scheduling department coordinates the participants' schedules. Specifically, it obtains each participant's calendar information and proposes the optimal meeting date and time. The scheduling department analyzes the calendar information to find everyone's availability and presents candidate dates and times. It can also propose the optimal meeting date and time by considering the participants' highest priority time slots. Step 2: The Designating Department specifies the meeting format based on the schedule coordinated by the Coordinating Department. Specifically, they can specify whether it is an in-person meeting or an online meeting. The Designating Department selects the most suitable meeting format based on the importance of the meeting and the circumstances of the participants. Step 3: The configuration unit reserves meeting rooms and sets up online meetings based on the meeting format specified by the designation unit. Specifically, it can automatically reserve meeting rooms using a meeting room reservation system. It can also generate meeting links using online meeting tools and notify participants.

[0065] (Example of form 2) The meeting scheduling system according to an embodiment of the present invention is a system that automates meeting scheduling using an AI agent. When a meeting needs to be scheduled, the user simply specifies the participants, and the AI ​​agent automatically adjusts the participants' schedules and proposes the optimal meeting date and time. Furthermore, by specifying the meeting format (in-person or online), the system automatically handles meeting room reservations and online meeting setup. This mechanism significantly reduces the time and effort required for meeting scheduling. For example, the user specifies the meeting participants. This only requires entering the participants' email addresses and names. For example, the user inputs participant information into the meeting scheduling system interface. This information is sent to the AI ​​agent. Next, the AI ​​agent automatically adjusts the participants' schedules. The AI ​​agent obtains each participant's calendar information and proposes the optimal meeting date and time. For example, the AI ​​agent analyzes each participant's availability and presents candidate dates and times. These candidate dates and times are notified to the user, who can select the most suitable one. Furthermore, the user specifies the meeting format. For in-person meetings, the AI ​​agent automatically reserves the meeting room. For online meetings, the AI ​​agent automatically sets up the online meeting. For example, it generates a meeting link using an online meeting tool. This link will be sent to participants. This mechanism significantly reduces the time and effort required to set up meetings. Users simply specify the participants, and the AI ​​agent automatically completes the meeting setup. This eliminates the hassle of meeting setup and allows for efficient meeting hosting. As a result, the meeting setup system eliminates the hassle of meeting setup and allows for efficient meeting hosting.

[0066] The meeting scheduling system according to this embodiment comprises a coordination unit, a designation unit, and a scheduling unit. The coordination unit coordinates the schedules of the participants. For example, the coordination unit obtains the calendar information of each participant and proposes the optimal meeting date and time. For example, the coordination unit can analyze the calendar information to find everyone's free time and present candidate dates and times. The coordination unit can also propose the optimal meeting date and time considering the participants' high-priority time slots. The designation unit specifies the meeting format based on the schedules coordinated by the coordination unit. For example, the designation unit can specify the format of an in-person meeting or an online meeting. The designation unit can select the optimal meeting format according to the importance of the meeting and the circumstances of the participants. The scheduling unit reserves a meeting room or sets up an online meeting based on the meeting format specified by the designation unit. For example, the scheduling unit can automatically reserve a meeting room using a meeting room reservation system. The scheduling unit can also generate a meeting link using an online meeting tool and notify participants. As a result, the meeting scheduling system can reduce the effort required for meeting scheduling and hold meetings efficiently. Some or all of the above-described processes in the adjustment, designation, and setting units may be performed using AI, or not using AI. For example, the adjustment unit can input participants' calendar information into the AI ​​and have the AI ​​suggest the optimal meeting date and time. The designation unit can have the AI ​​select the meeting format. The setting unit can have the AI ​​reserve a meeting room or set up an online meeting.

[0067] The scheduling department coordinates participants' schedules. For example, it obtains each participant's calendar information and proposes the optimal meeting date and time. Specifically, the scheduling department obtains participants' calendar information in real time and analyzes each participant's availability. The calendar information includes details of appointments, priorities, locations, etc., and uses this information to find the optimal meeting date and time. The scheduling department can use AI to analyze calendar information, find everyone's availability, and present candidate dates and times. The AI ​​learns each participant's past appointments and behavioral patterns to predict the optimal meeting date and time. The scheduling department can also propose the optimal meeting date and time by considering the time slots that are the highest priority for each participant. For example, in the case of an important meeting, it will prioritize the availability of the person with the highest priority among the participants and coordinate with the schedules of other participants. Furthermore, the scheduling department can also consider the geographical location of participants and propose meeting dates and times that minimize travel time. In this way, the scheduling department can efficiently and flexibly adjust meeting dates and times, providing an environment where all participants can easily attend.

[0068] The Designation Department specifies the meeting format based on the schedule coordinated by the Coordination Department. The Designation Department can, for example, specify whether it's an in-person or online meeting. Specifically, the Designation Department selects the most suitable meeting format based on the importance of the meeting and the circumstances of the participants. For example, in-person meetings are often specified for meetings requiring important decision-making or those handling confidential information. On the other hand, online meetings are more appropriate when participants are in remote locations or have difficulty traveling. The Designation Department can also use AI to select the meeting format. The AI ​​proposes the most suitable format based on past meeting data and participant feedback. For example, it can analyze the attendance rate and satisfaction level of past online meetings and propose a meeting format under similar conditions. Furthermore, the Designation Department can specify a hybrid meeting format depending on the purpose and content of the meeting. In a hybrid meeting, some participants can attend in person while others participate online. This allows the Designation Department to provide a flexible meeting format tailored to the needs and circumstances of the participants, maximizing the effectiveness of the meeting.

[0069] The configuration unit reserves meeting rooms and sets up online meetings based on the meeting format specified by the designation unit. Specifically, the configuration unit can automatically reserve meeting rooms using a meeting room reservation system. For example, it can check the availability of meeting rooms in real time, select the most suitable meeting room, and make a reservation. The configuration unit can also generate meeting links and notify participants using online meeting tools. Online meeting tools include functions such as video conferencing, chat, and file sharing, enabling participants to communicate smoothly. The configuration unit can also use AI to reserve meeting rooms and set up online meetings. The AI ​​suggests the most suitable meeting rooms and online meeting tools based on past meeting data and participant feedback. For example, it analyzes evaluations of meeting rooms used in past meetings and the usage status of online meeting tools to make optimal settings under similar conditions. The configuration unit can also send reminders before the start of the meeting to ensure that participants join on time. This reduces the effort required for meeting setup for the configuration unit, allowing for efficient meeting hosting.

[0070] The scheduling unit can obtain each participant's calendar information and propose the optimal meeting date and time. The scheduling unit obtains calendar information from, for example, a calendar management tool. The scheduling unit can analyze the calendar information to find everyone's available time slots and present candidate dates and times. For example, based on the calendar information, the scheduling unit can find time slots when everyone can participate and propose the optimal meeting date and time. The scheduling unit can also propose the optimal meeting date and time by considering the participants' high-priority time slots. For example, based on the participants' calendar information, the scheduling unit can select time slots that are convenient for everyone to participate in and propose the meeting date and time. This allows the scheduling unit to propose the optimal meeting date and time based on each participant's calendar information. Some or all of the above processing in the scheduling unit may be performed using, for example, AI, or not using AI. For example, the scheduling unit can input calendar information into AI and have the AI ​​propose the optimal meeting date and time.

[0071] The configuration unit can generate a meeting link using an online meeting tool. For example, the configuration unit can generate a meeting link using an online meeting tool and notify participants. This automates the setup of the online meeting. Some or all of the above processes in the configuration unit may be performed using AI, for example, or not using AI. For example, the configuration unit can have AI perform the setup of the online meeting tool.

[0072] The configuration unit can automatically reserve meeting rooms. The configuration unit can, for example, automatically reserve meeting rooms using a meeting room reservation system. The configuration unit can use a meeting room reservation system to check the availability of meeting rooms and reserve the most suitable meeting room. The configuration unit can, for example, use a meeting room reservation system to check the availability of meeting rooms and reserve the most suitable meeting room. The configuration unit can also use a meeting room reservation system to automatically reserve meeting rooms. The configuration unit can also use a meeting room reservation system to automatically reserve meeting rooms. This allows for automatic meeting room reservations. Some or all of the above processes in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit can have AI perform the meeting room reservation.

[0073] A reminder unit sends reminders. The reminder unit can send reminders. The reminder unit can send reminders by methods such as email, SMS, and app notifications. The reminder unit can send reminders according to the start time of a meeting. For example, the reminder unit can send an email reminder one hour before the start time of a meeting. It can also send an SMS reminder 30 minutes before the start time of a meeting. The reminder unit can also send an app notification reminder 15 minutes before the start time of a meeting. This allows for the automatic sending of meeting reminders. Some or all of the above processes in the reminder unit may be performed using AI, for example, or not using AI. For example, the reminder unit can have AI execute the timing of sending reminders.

[0074] A sharing section for sharing agendas and materials. The sharing section can share agendas and materials. The sharing section can share agendas and materials in ways such as PDF files, presentation materials, and link sharing. The sharing section can share agendas and materials before the start of a meeting. For example, the sharing section can share PDF files via email before the start of a meeting. The sharing section can also upload presentation materials to cloud storage and share the link before the start of a meeting. The sharing section can also share links before the start of a meeting so that participants can access the materials. This allows for the automatic sharing of meeting agendas and materials. Some or all of the above processes in the sharing section may be performed using AI, for example, or not. For example, the sharing section can have AI perform the methods for sharing agendas and materials.

[0075] The Minutes Creation Department is responsible for creating meeting minutes. The Minutes Creation Department can create meeting minutes. For example, the Minutes Creation Department records the key points of the meeting, the content of the discussions, and the decisions made. The Minutes Creation Department can create meeting minutes after the meeting has ended and share them with the participants. For example, the Minutes Creation Department can create meeting minutes after the meeting has ended and share them with the participants via email. The Minutes Creation Department can also upload the meeting minutes to cloud storage after the meeting has ended and share the link. The Minutes Creation Department can also create meeting minutes after the meeting has ended and make them accessible to the participants. This allows for the automatic creation of meeting minutes. Some or all of the above processes in the Minutes Creation Department may be performed using AI, for example, or not. For example, the Minutes Creation Department can have AI perform the creation of meeting minutes.

[0076] The scheduling unit can estimate the emotions of participants and propose the optimal meeting date and time based on the estimated emotions. For example, if a participant is feeling stressed, the scheduling unit will prioritize suggesting a meeting time that allows for relaxation. If a participant is busy, the scheduling unit can also select the least burdensome time to suggest a meeting time. If a participant is relaxed, the scheduling unit can also select a time when their concentration will be highest to suggest a meeting time. This allows the scheduling unit to propose the optimal meeting date and time based on the participants' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit can input participant emotion data into an AI and have the AI ​​propose the optimal meeting date and time.

[0077] The scheduling unit can analyze participants' past meeting attendance history and select the optimal meeting date and time. For example, the scheduling unit can suggest the optimal meeting date and time based on the time slots of meetings the participant has attended in the past. The scheduling unit can also analyze the frequency of meetings the participant has attended in the past and set meetings at appropriate intervals. The scheduling unit can also select the time slot most likely to be attended based on the attendance rate of meetings the participant has attended in the past. This allows for the selection of the optimal meeting date and time based on past meeting attendance history. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input past meeting attendance history into AI and have the AI ​​select the optimal meeting date and time.

[0078] The scheduling unit can adjust meeting dates and times based on participants' current projects and work status. For example, the scheduling unit can propose the optimal meeting date and time considering the progress of projects currently underway for each participant. The scheduling unit can also analyze participants' workloads and propose meeting dates and times that are less burdensome. The scheduling unit can also consider participants' work priorities and select times that will not affect important tasks. This allows for the scheduling of meeting dates and times based on current projects and work status. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input current project and work status into AI and have the AI ​​perform the scheduling of the optimal meeting date and time.

[0079] The scheduling unit can estimate the emotions of participants and determine meeting priorities based on those estimated emotions. For example, if a participant is feeling stressed, the scheduling unit may postpone less important meetings. If a participant is relaxed, the scheduling unit may prioritize more important meetings. If a participant is busy, the scheduling unit may prioritize more urgent meetings. This allows for meeting priorities to be determined based on participants' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input participant emotion data into an AI and have the AI ​​determine meeting priorities.

[0080] The coordination unit can propose the optimal meeting date and time considering the geographical location information of the participants. For example, if the participants are in different time zones, the coordination unit will select a time that is convenient for everyone to attend. If the participants are traveling, the coordination unit can also propose a meeting date and time that minimizes travel time. If the participants are in a specific location, the coordination unit can also propose the optimal meeting date and time tailored to that location. In this way, the coordination unit can propose the optimal meeting date and time considering geographical location information. Some or all of the above processing in the coordination unit may be performed using AI, for example, or not. For example, the coordination unit can input geographical location information into AI and have the AI ​​propose the optimal meeting date and time.

[0081] The coordination unit can analyze participants' social media activity and suggest relevant meeting dates and times. For example, the coordination unit can suggest the optimal meeting date and time based on the times when participants are most active on social media. The coordination unit can also analyze the content of participants' social media posts and suggest relevant meeting dates and times. The coordination unit can also analyze participants' social media activity patterns and select the times when they are most likely to participate. This allows the coordination unit to suggest the optimal meeting date and time based on social media activity. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not. For example, the coordination unit can input social media activity into AI and have the AI ​​suggest the optimal meeting date and time.

[0082] The designator can estimate the emotions of participants and specify the meeting format based on the estimated emotions. For example, if a participant is feeling stressed, the designator may prioritize an online meeting. If a participant is relaxed, the designator may also specify an in-person meeting. If a participant is busy, the designator may specify a short online meeting. This allows the designator to specify the most suitable meeting format based on the participants' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the designator may be performed using AI or not. For example, the designator can input participant emotion data into an AI and have the AI ​​specify the meeting format.

[0083] The designator can specify the meeting format based on the importance of the meeting. For example, for a highly important meeting, the designator can specify an in-person meeting. For a less important meeting, the designator can also specify an online meeting. Depending on the importance, the designator can also specify a hybrid meeting format. This allows for the selection of the optimal meeting format based on the importance of the meeting. Some or all of the above processing in the designator may be performed using AI, for example, or not using AI. For example, the designator can input the importance of the meeting into the AI ​​and have the AI ​​specify the meeting format.

[0084] The designator can specify different meeting formats depending on the meeting category. For example, for a project meeting, the designator can specify an in-person meeting. For a regular meeting, the designator can also specify an online meeting. For a brainstorming meeting, the designator can also specify a hybrid meeting format. This allows the designator to specify the most suitable meeting format depending on the meeting category. Some or all of the above processing in the designator may be performed using AI, for example, or not using AI. For example, the designator can input the meeting category into the AI ​​and have the AI ​​specify the meeting format.

[0085] The scheduling unit can estimate participants' emotions and determine meeting format priorities based on those estimated emotions. For example, if a participant is feeling stressed, the scheduling unit may prioritize an online meeting. If a participant is relaxed, the scheduling unit may also prioritize an in-person meeting. If a participant is busy, the scheduling unit may also prioritize a shorter online meeting. This allows for the prioritization of meeting formats based on participants' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input participant emotion data into an AI and have the AI ​​determine the meeting format priorities.

[0086] The designator can specify the meeting format based on the submission deadline. For example, if the deadline is approaching, the designator can specify an online meeting. If there is ample time before the deadline, the designator can also specify an in-person meeting. Depending on the deadline, the designator can also specify a hybrid meeting format. This allows for the selection of the most suitable meeting format based on the submission deadline. Some or all of the above processing in the designator may be performed using AI, for example, or not. For example, the designator can input the meeting submission deadline into the AI ​​and have the AI ​​specify the meeting format.

[0087] The designator can specify the meeting format based on the relevance of the meetings. For example, the designator can specify an in-person meeting for highly relevant meetings. For less relevant meetings, the designator can also specify an online meeting. Depending on the relevance, the designator can also specify a hybrid meeting format. This allows for the selection of the most suitable meeting format based on the relevance of the meetings. Some or all of the above processing in the designator may be performed using AI, for example, or not using AI. For example, the designator can input the relevance of the meetings into the AI ​​and have the AI ​​perform the designation of the meeting format.

[0088] The configuration unit can estimate the emotions of participants and reserve meeting rooms or set up online meetings based on the estimated emotions. For example, if a participant is feeling stressed, the configuration unit will prioritize reserving a meeting room where they can relax. If a participant is relaxed, the configuration unit can also reserve a meeting room where they can concentrate. If a participant is busy, the configuration unit can also set up a short online meeting. This allows for the optimal reservation of meeting rooms or online meeting settings based on the emotions of participants. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the configuration unit may be performed using AI, or not using AI. For example, the configuration unit can input participant emotion data into AI and have the AI ​​reserve meeting rooms or set up online meetings.

[0089] The configuration unit can reserve meeting rooms or set up online meetings based on the importance of the meeting. For example, for a high-priority meeting, the configuration unit will reserve a large meeting room. For a low-priority meeting, the configuration unit can reserve a small meeting room. The configuration unit can also set up online meetings according to their importance. This allows for the optimal reservation of meeting rooms or online meeting settings based on the importance of the meeting. Some or all of the above processing in the configuration unit may be performed using AI, for example, or not using AI. For example, the configuration unit can input the importance of the meeting into the AI ​​and have the AI ​​execute the reservation of meeting rooms or the setting up of online meetings.

[0090] The settings unit can reserve different meeting rooms or configure online meeting settings depending on the meeting category. For example, for a project meeting, the settings unit can reserve a large meeting room. For a regular meeting, it can reserve a smaller meeting room. For a brainstorming meeting, the settings unit can configure online meeting settings. This allows for the optimal meeting room reservation or online meeting configuration depending on the meeting category. Some or all of the above processing in the settings unit may be performed using AI, for example, or not. For example, the settings unit can input the meeting category into the AI ​​and have the AI ​​execute the meeting room reservation or online meeting configuration.

[0091] The configuration unit can estimate the emotions of participants and determine the priority of meeting room reservations and online meeting scheduling based on the estimated emotions. For example, if a participant is feeling stressed, the configuration unit will prioritize booking a meeting room where they can relax. If a participant is relaxed, the configuration unit may also prioritize booking a meeting room where they can concentrate. If a participant is busy, the configuration unit may also prioritize scheduling a short online meeting. This allows the configuration unit to determine the priority of meeting room reservations and online meeting scheduling based on the emotions of participants. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the configuration unit may be performed using AI or not using AI. For example, the configuration unit can input participant emotion data into AI and have the AI ​​determine the priority of meeting room reservations and online meeting scheduling.

[0092] The configuration unit can reserve meeting rooms or set up online meetings based on the meeting submission deadline. For example, if the submission deadline is approaching, the configuration unit will prioritize setting up online meetings. If there is ample time before the submission deadline, the configuration unit can also prioritize reserving meeting rooms. The configuration unit can reserve meeting rooms or set up online meetings according to the submission deadline. This allows for the optimal reservation of meeting rooms or setting up online meetings based on the meeting submission deadline. Some or all of the above processes in the configuration unit may be performed using AI, for example, or not. For example, the configuration unit can input the meeting submission deadline into AI and have the AI ​​execute the reservation of meeting rooms or setting up online meetings.

[0093] The configuration unit can reserve meeting rooms and set up online meetings based on the relevance of the meetings. For example, the configuration unit can reserve a large meeting room for highly relevant meetings. For less relevant meetings, it can reserve a smaller meeting room. The configuration unit can also set up online meetings according to their relevance. This allows for the optimal reservation of meeting rooms and online meeting settings based on the relevance of the meetings. Some or all of the above processing in the configuration unit may be performed using AI, for example, or not using AI. For example, the configuration unit can input the relevance of the meetings into the AI ​​and have the AI ​​execute the reservation of meeting rooms and online meeting settings.

[0094] The reminder unit can estimate the participant's emotions and adjust the timing of reminder delivery based on the estimated emotions. For example, if a participant is feeling stressed, the reminder unit can send a reminder during a time when they can relax. If a participant is relaxed, the reminder unit can also send a reminder during a time when they are more focused. If a participant is busy, the reminder unit can also send a reminder during the least stressful time. This allows for optimal reminder delivery timing adjustment based on the participant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 reminder unit may be performed using AI or not. For example, the reminder unit can input participant emotion data into an AI and have the AI ​​adjust the timing of reminder delivery.

[0095] The reminder unit can analyze a participant's past reminder reception history and select the optimal sending method. For example, the reminder unit can suggest the optimal sending timing based on the time periods when participants have received reminders in the past. The reminder unit can also analyze the frequency of reminders received in the past and send reminders at appropriate intervals. The reminder unit can also select the time period when participants are most likely to receive reminders based on their past reminder reception rate. This allows the system to select the optimal sending method based on past reminder reception history. Some or all of the above processes in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input past reminder reception history into AI and have the AI ​​select the optimal sending method.

[0096] The reminder unit can estimate the participant's emotions and adjust the content of the reminder based on the estimated emotions. For example, if a participant is feeling stressed, the reminder unit can send a reminder with relaxing content. If a participant is relaxed, the reminder unit can also send a reminder with content that will increase their concentration. If a participant is busy, the reminder unit can also send a reminder with content that is least burdensome. This allows for the optimal adjustment of reminder content based on the participant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input participant emotion data into an AI and have the AI ​​adjust the content of the reminder.

[0097] The reminder unit can send reminders while considering the geographical location of participants. For example, if a participant is on the move, the reminder unit will send a reminder during a time when there is less movement. If a participant is in a specific location, the reminder unit can also send an optimal reminder tailored to that location. If participants are in different time zones, the reminder unit can also send a reminder during a time when everyone is likely to receive it. This allows for the sending of optimal reminders while considering geographical location. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input geographical location information into AI and have the AI ​​execute the reminder sending process.

[0098] The sharing unit can estimate participants' emotions and adjust how agendas and materials are shared based on those estimated emotions. For example, if a participant is stressed, the sharing unit can share simple, easy-to-read materials. If a participant is relaxed, the sharing unit can also share materials containing detailed information. If a participant is busy, the sharing unit can share concise materials. This allows for the optimal adjustment of agenda and material sharing methods based on participants' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input participant emotion data into an AI and have the AI ​​adjust how agendas and materials are shared.

[0099] The sharing unit can analyze participants' past sharing history and select the optimal sharing method. For example, the sharing unit can suggest the optimal sharing method based on the format of materials previously shared by participants. The sharing unit can also analyze the frequency of materials previously shared by participants and share materials at appropriate intervals. The sharing unit can also share materials in the most easily received format based on the reception rate of materials previously shared by participants. This allows the optimal sharing method to be selected based on past sharing history. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input past sharing history into AI and have the AI ​​select the optimal sharing method.

[0100] The sharing unit can estimate participants' emotions and prioritize the topics and materials to be shared based on those estimated emotions. For example, if a participant is stressed, the sharing unit may postpone less important topics and materials. If a participant is relaxed, the sharing unit may prioritize sharing more important topics and materials. If a participant is busy, the sharing unit may prioritize sharing more urgent topics and materials. This allows for prioritization of topics and materials based on participants' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input participant emotion data into an AI and have the AI ​​determine the prioritization of topics and materials.

[0101] The sharing function can share agendas and materials while considering the geographical location of participants. For example, if participants are in different time zones, the sharing function will share agendas and materials at a time when everyone is likely to receive them. If participants are on the move, the sharing function can also share agendas and materials at a time when they are less likely to be traveling. If participants are in a specific location, the sharing function can also share agendas and materials that are best suited to that location. This allows for the sharing of optimal agendas and materials while considering geographical location. Some or all of the above processing in the sharing function may be performed using AI, for example, or not. For example, the sharing function can input geographical location information into AI and have the AI ​​perform the sharing of agendas and materials.

[0102] The minutes-taking unit can estimate the emotions of participants and adjust the minutes-taking method based on the estimated emotions. For example, if a participant is stressed, the minutes-taking unit can create simple and easy-to-read minutes. If a participant is relaxed, the minutes-taking unit can also create minutes that include detailed information. If a participant is busy, the minutes-taking unit can also create minutes that are to the point. This allows for the adjustment of the minutes-taking method to the optimal one based on the emotions of the participants. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the minutes-taking unit may be performed using AI or not. For example, the minutes-taking unit can input participant emotion data into AI and have the AI ​​adjust the minutes-taking method.

[0103] The minutes creation department can analyze participants' past minutes creation history and select the optimal creation method. For example, the minutes creation department can suggest the optimal creation method based on the format of minutes previously created by participants. The minutes creation department can also analyze the frequency of minutes previously created by participants and create minutes at appropriate intervals. The minutes creation department can also create minutes in the most easily received format based on the reception rate of minutes previously created by participants. This allows for the selection of the optimal creation method based on past minutes creation history. Some or all of the above processes in the minutes creation department may be performed using AI, for example, or not. For example, the minutes creation department can input past minutes creation history into AI and have the AI ​​select the optimal creation method.

[0104] The minutes-taking unit can estimate the emotions of participants and adjust the content of the minutes based on the estimated emotions. For example, if a participant is stressed, the minutes-taking unit can create simple and easy-to-read minutes. If a participant is relaxed, the minutes-taking unit can also create minutes that include detailed information. If a participant is busy, the minutes-taking unit can also create minutes that are concise and to the point. This allows for the optimal adjustment of the minutes content based on the emotions of the participants. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the minutes-taking unit may be performed using AI or not. For example, the minutes-taking unit can input participant emotion data into AI and have the AI ​​adjust the content of the minutes.

[0105] The minutes creation unit can create meeting minutes while taking into account the geographical location information of the participants. For example, if the participants are in different time zones, the minutes creation unit will create the minutes at a time when everyone is likely to receive them. If the participants are traveling, the minutes creation unit can also create the minutes at a time when they are traveling less. If the participants are in a specific location, the minutes creation unit can also create the most suitable minutes for that location. This allows for the creation of optimal minutes while taking geographical location information into account. Some or all of the above processes in the minutes creation unit may be performed using AI, for example, or not. For example, the minutes creation unit can input geographical location information into AI and have the AI ​​create the minutes.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The scheduling unit can propose the optimal meeting date and time, taking into account the participants' health data. For example, it can select the time of day when participants are most focused based on their sleep data. The scheduling unit can also propose a meeting date and time by selecting a time when participants are less fatigued based on their exercise data. Furthermore, it can select a time when participants have high energy levels, taking into account their dietary data. This allows the scheduling unit to propose the optimal meeting date and time based on the participants' health status. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input health data into AI and have the AI ​​propose the optimal meeting date and time.

[0108] The scheduling unit can analyze participants' feedback on past meetings and propose optimal meeting dates and times. For example, it can propose optimal meeting dates and times based on the time slots in meetings that participants have previously given high ratings to. The scheduling unit can also propose meeting dates and times that avoid the time slots in meetings that participants have previously given low ratings to. Furthermore, it can adjust the frequency and time of meetings based on participants' feedback. This allows the scheduling unit to propose optimal meeting dates and times based on past feedback. Some or all of the above processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input feedback data into AI and have the AI ​​propose optimal meeting dates and times.

[0109] The adjustment unit can estimate the emotions of the participants and adjust the length of the meeting based on the estimated emotions. For example, if a participant is feeling stressed, it may suggest a shorter meeting. If a participant is relaxed, the adjustment unit may also suggest a longer meeting. If a participant is busy, it may suggest a shorter meeting to ensure efficient progress. This allows for the optimal meeting length to be adjusted based on the participants' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input emotion data into an AI and have the AI ​​adjust the length of the meeting.

[0110] The coordination unit can estimate the emotions of the participants and select a meeting location based on those emotions. For example, if a participant is feeling stressed, it can select a meeting room with a relaxing environment. If a participant is relaxed, the coordination unit can also select a meeting room with an environment conducive to concentration. Furthermore, if a participant is busy, it can select a meeting room in an easily accessible location. This allows for the selection of the optimal meeting location based on the participants' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processes in the coordination unit may be performed using AI or not. For example, the coordination unit can input emotion data into AI and have the AI ​​select the meeting location.

[0111] The coordination unit can estimate the emotions of participants and adjust the meeting agenda based on those emotions. For example, if a participant is stressed, it can postpone less important agenda items. If a participant is relaxed, the coordination unit can prioritize more important agenda items. If a participant is busy, it can prioritize more urgent agenda items. This allows for the adjustment of the meeting agenda to be optimal based on the participants' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the coordination unit may be performed using AI or not. For example, the coordination unit can input emotion data into an AI and have the AI ​​adjust the meeting agenda.

[0112] The scheduling unit can analyze participants' past meeting attendance rates and propose the optimal meeting frequency. For example, it can propose the optimal meeting frequency based on the frequency of meetings in which participants have shown high attendance rates in the past. The scheduling unit can also avoid proposing meeting frequencies in which participants have shown low attendance rates in the past. Furthermore, it can adjust the meeting frequency based on participants' attendance rates. This allows the scheduling unit to propose the optimal meeting frequency based on past attendance rates. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input attendance rate data into AI and have the AI ​​propose the optimal meeting frequency.

[0113] The coordination unit can adjust the meeting agenda considering the current project progress of the participants. For example, if a participant is approaching a key milestone in an ongoing project, it will prioritize agenda items related to that project. The coordination unit can also propose the most effective agenda based on the participants' project progress. It can also prioritize important agenda items considering the priorities of the participants' projects. This allows for the adjustment of the meeting agenda to be optimal based on the current project progress. Some or all of the above processes in the coordination unit may or may not be performed using AI. For example, the coordination unit can input project progress data into the AI ​​and have the AI ​​perform the agenda adjustments.

[0114] The adjustment unit can estimate the emotions of participants and adjust the meeting follow-up based on the estimated emotions. For example, if a participant is feeling stressed, the frequency of follow-ups can be reduced. The adjustment unit can also increase the frequency of follow-ups if a participant is relaxed. Furthermore, if a participant is busy, the content of the follow-ups can be made more concise. This allows for the adjustment of the meeting follow-up to be optimal based on the participants' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input emotion data into AI and have the AI ​​perform the adjustment of follow-ups.

[0115] The coordination unit can analyze participants' past meeting contributions and propose the optimal meeting format. For example, it can propose the optimal format based on meeting formats in which participants have actively participated in the past. The coordination unit can also avoid proposing meeting formats in which participants have been passive in the past. Furthermore, it can adjust the meeting format based on the participants' contributions. This allows it to propose the optimal meeting format based on past contributions. Some or all of the above processing in the coordination unit may be performed using AI or not. For example, the coordination unit can input contribution data into AI and have the AI ​​propose the optimal meeting format.

[0116] The adjustment unit can estimate the emotions of the participants and adjust the meeting format based on the estimated emotions. For example, if a participant is feeling stressed, it can suggest an informal format. If a participant is relaxed, the adjustment unit can also suggest a formal format. Furthermore, if a participant is busy, it can suggest an efficient format. This allows the system to adjust the meeting format to the optimal level based on the participants' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input emotion data into an AI and have the AI ​​perform the adjustment of the meeting format.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The scheduling department coordinates the participants' schedules. Specifically, it obtains each participant's calendar information and proposes the optimal meeting date and time. The scheduling department analyzes the calendar information to find everyone's availability and presents candidate dates and times. It can also propose the optimal meeting date and time by considering the participants' highest priority time slots. Step 2: The Designating Department specifies the meeting format based on the schedule coordinated by the Coordinating Department. Specifically, they can specify whether it is an in-person meeting or an online meeting. The Designating Department selects the most suitable meeting format based on the importance of the meeting and the circumstances of the participants. Step 3: The configuration unit reserves meeting rooms and sets up online meetings based on the meeting format specified by the designation unit. Specifically, it can automatically reserve meeting rooms using a meeting room reservation system. It can also generate meeting links using online meeting tools and notify participants.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the above-mentioned elements, including the adjustment unit, designation unit, setting unit, reminder unit, sharing unit, and minutes creation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the adjustment unit is implemented by the control unit 46A of the smart device 14 or the designation processing unit 290 of the data processing device 12. The designation unit is implemented, for example, by the control unit 46A of the smart device 14 or the designation processing unit 290 of the data processing device 12. The setting unit is implemented, for example, by the control unit 46A of the smart device 14 or the designation processing unit 290 of the data processing device 12. The reminder unit is implemented, for example, by the control unit 46A of the smart device 14 or the designation processing unit 290 of the data processing device 12. The sharing unit is implemented, for example, by the control unit 46A of the smart device 14 or the designation processing unit 290 of the data processing device 12. The minutes creation unit is implemented, for example, by the control unit 46A of the smart device 14 or the designation processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the above-mentioned elements, including the adjustment unit, designation unit, setting unit, reminder unit, sharing unit, and minutes creation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the adjustment unit is implemented by the control unit 46A of the smart glasses 214 or the designation unit 290 of the data processing device 12. The designation unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the designation unit 290 of the data processing device 12. The setting unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the designation unit 290 of the data processing device 12. The reminder unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the designation unit 290 of the data processing device 12. The sharing unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the designation unit 290 of the data processing device 12. The minutes creation unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the designation unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the adjustment unit, designation unit, setting unit, reminder unit, sharing unit, and meeting minutes creation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the adjustment unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The designation unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The setting unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The reminder unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The sharing unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The minutes creation function is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the above-mentioned elements, including the adjustment unit, designation unit, setting unit, reminder unit, sharing unit, and minutes creation unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the adjustment unit is implemented by the control unit 46A of the robot 414 or the designation unit 290 of the data processing device 12. The designation unit is implemented, for example, by the control unit 46A of the robot 414 or the designation unit 290 of the data processing device 12. The setting unit is implemented, for example, by the control unit 46A of the robot 414 or the designation unit 290 of the data processing device 12. The reminder unit is implemented, for example, by the control unit 46A of the robot 414 or the designation unit 290 of the data processing device 12. The sharing unit is implemented, for example, by the control unit 46A of the robot 414 or the designation unit 290 of the data processing device 12. The minutes creation unit is implemented, for example, by the control unit 46A of the robot 414 or the designation unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) The coordination department adjusts the schedules of the participants, A designation unit that specifies the meeting format based on the schedule adjusted by the aforementioned adjustment unit, A setting unit that reserves meeting rooms and sets up online meetings based on the meeting format specified by the aforementioned designation unit, Equipped with A system characterized by the following features. (Note 2) The adjustment unit is, It retrieves each participant's calendar information and suggests the most suitable meeting date and time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned setting unit is, Generate a meeting link using an online meeting tool. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned setting unit is, Automate meeting room reservations. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a reminder function for sending reminders. The system described in Appendix 1, characterized by the features described herein. (Note 6) It has a shared area for sharing agendas and documents. The system described in Appendix 1, characterized by the features described herein. (Note 7) The facility includes a meeting minutes preparation department. The system described in Appendix 1, characterized by the features described herein. (Note 8) The adjustment unit is, It estimates the participants' emotions and suggests the optimal meeting date and time based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The adjustment unit is, Analyze participants' past meeting attendance history to select the optimal meeting date and time. The system described in Appendix 1, characterized by the features described herein. (Note 10) The adjustment unit is, We will adjust the meeting date and time based on the participants' current projects and work status. The system described in Appendix 1, characterized by the features described herein. (Note 11) The adjustment unit is, The system estimates the emotions of the participants and determines the priority of meetings based on the estimated emotions of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 12) The adjustment unit is, We will suggest the optimal meeting date and time, taking into account the geographical location of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 13) The adjustment unit is, We will analyze participants' social media activity and suggest relevant meeting dates. The system described in Appendix 1, characterized by the features described herein. (Note 14) The designated part is, The system estimates the participants' emotions and specifies the meeting format based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The designated part is, Specify the meeting format based on the importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 16) The designated part is, Specify different meeting formats depending on the meeting category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The designated part is, The system estimates the emotions of the participants and prioritizes meeting formats based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The designated part is, Specify the meeting format based on the submission deadline for the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 19) The designated part is, Specify the meeting format based on the relevance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned setting unit is, The system estimates the emotions of the participants and uses those emotions to book meeting rooms and schedule online meetings. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned setting unit is, Book meeting rooms or schedule online meetings based on the importance of each meeting. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned setting unit is, Depending on the meeting category, you can book different meeting rooms or set up online meetings. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned setting unit is, The system estimates participants' emotions and prioritizes meeting room reservations and online meeting scheduling based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned setting unit is, Based on the submission deadline for the meeting, we will reserve meeting rooms and set up online meetings. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned setting unit is, Book meeting rooms or schedule online meetings based on the relevance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 26) The reminder unit is, The system estimates participants' emotions and adjusts the timing of reminder sending based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The reminder unit is, Analyze participants' past reminder reception history to select the optimal sending method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The reminder unit is, The system estimates the participants' emotions and adjusts the content of reminders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The reminder unit is, Send reminders taking participants' geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned shared portion is, We estimate the participants' emotions and adjust the way agenda items and materials are shared based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned shared portion is, Analyze participants' past sharing history to select the most suitable sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned shared portion is, Estimate the participants' emotions and prioritize the topics and materials to share based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned shared portion is, Share agenda items and materials while taking participants' geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned minutes preparation department, We estimate the emotions of the participants and adjust the way meeting minutes are created based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned minutes preparation department, Analyze the participants' past meeting minute-taking history and select the most suitable method. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned minutes preparation department, Estimate the emotions of the participants and adjust the content of the meeting minutes based on the estimated emotions of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned minutes preparation department, The meeting minutes will be prepared taking into account the geographical location of the participants. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 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 coordination department adjusts the schedules of the participants, A designation unit that specifies the meeting format based on the schedule adjusted by the aforementioned adjustment unit, A setting unit that reserves meeting rooms and sets up online meetings based on the meeting format specified by the aforementioned designation unit, Equipped with A system characterized by the following features.

2. The adjustment unit is, It retrieves each participant's calendar information and suggests the most suitable meeting date and time. The system according to feature 1.

3. The setting unit is, Generate a meeting link using an online meeting tool. The system according to feature 1.

4. The setting unit is, Automate meeting room reservations. The system according to feature 1.

5. It includes a reminder function for sending reminders. The system according to feature 1.

6. It has a shared area for sharing agendas and documents. The system according to feature 1.

7. The facility includes a meeting minutes preparation department. The system according to feature 1.

8. The adjustment unit is, It estimates the emotions of the participants and suggests the optimal meeting date and time based on the estimated emotions. The system according to feature 1.

9. The adjustment unit is, Analyze participants' past meeting attendance history to select the optimal meeting date and time. The system according to feature 1.

10. The adjustment unit is, We will adjust the meeting date and time based on the participants' current projects and work status. The system according to feature 1.

Images