system

Abstract

The system according to this embodiment aims to improve work efficiency by automating the scheduling of meetings. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a reservation unit, a provision unit, a confirmation unit, and an adjustment unit. The collection unit checks the availability of participants. The generation unit sets up a meeting based on the availability collected by the collection unit. The reservation unit automatically reserves an available meeting room for the meeting set up by the generation unit. The provision unit issues a meeting URL for the meeting set up by the generation unit and embeds it in the meeting notification. The confirmation unit confirms the rescheduling with the participants of the meeting set up by the generation unit. The adjustment unit adjusts the rescheduling based on the confirmation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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 a lot of man-hours are required for meeting adjustment, resulting in a decrease in work efficiency.

[0005] The system according to the embodiment aims to automate meeting adjustment and improve work efficiency.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a reservation unit, a provision unit, a confirmation unit, and an adjustment unit. The collection unit checks the availability of participants. The generation unit sets up a meeting based on the availability collected by the collection unit. The reservation unit automatically reserves an available meeting room for the meeting set up by the generation unit. The provision unit issues a meeting URL for the meeting set up by the generation unit and embeds it in the meeting notification. The confirmation unit confirms the rescheduling with the participants of the meeting set up by the generation unit. The adjustment unit adjusts the rescheduling based on the confirmation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate meeting scheduling and improve work efficiency. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system designed to address the current situation where the number of meetings and the workload involved in scheduling meetings have increased due to the rise in teleworking. When setting up a meeting with relevant parties using a calendar, the AI ​​agent checks the availability of participants and automatically sets up the meeting. Next, it automatically reserves an available meeting room on the calendar and connects to an online meeting system to embed the meeting URL into the meeting notification. Furthermore, by connecting to a communication tool, it can automatically send a message to potential participants whose schedules appear full, asking if they can reschedule and allowing for adjustments. Based on the "yes" or "no" response to whether they can reschedule, further adjustments are made. This mechanism aims to shift the time saved to higher value-added tasks. For example, when setting up a meeting with relevant parties using a calendar, participants are specified. At this time, the AI ​​agent checks the availability of participants and automatically sets up the meeting. For example, if participants A, B, and C are specified, the AI ​​agent checks the availability of each participant and sets up the meeting at a time when everyone can attend. Next, it automatically reserves an available meeting room on the calendar. The AI ​​agent checks the availability of meeting rooms on the calendar and automatically reserves a suitable room for the meeting. For example, it selects and reserves the most suitable room from rooms A, B, and C. Furthermore, by connecting to the online meeting system, it embeds the meeting URL into the meeting notification. The AI ​​agent generates the URL for the online meeting system and embeds it in the calendar's meeting notification. This allows participants to see the online meeting system URL at the same time they receive the meeting notification. In addition, by connecting to the communication tool, it automatically sends a message to potential participants who appear to have full schedules, asking if they can reschedule. For example, it uses the communication tool to send a message to participant A asking, "Can you reschedule?" and accepts a "yes" or "no" response. Based on the response, further adjustments are made.This system reduces the time spent on meeting scheduling, allowing the saved time to be shifted to higher-value tasks. For example, reducing the time spent on meeting scheduling allows for greater focus on other important tasks. Furthermore, since the AI ​​agent automatically sets up meetings, the effort required for scheduling is eliminated. As a result, the AI ​​agent system reduces the time spent on meeting scheduling and improves operational efficiency.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, a generation unit, a reservation unit, a provision unit, a confirmation unit, and an adjustment unit. The collection unit checks the availability of participants. The collection unit checks, for example, the availability of a calendar. The collection unit can also collect availability for a specific time period. The collection unit obtains calendar information to check the availability of participants. The collection unit obtains availability using, for example, a calendar API. The collection unit analyzes the calendar information and identifies availability. The generation unit sets up a meeting based on the availability collected by the collection unit. The generation unit sets up a meeting during a time period when all participants can attend. The generation unit can also automatically adjust the meeting time. The generation unit sets the optimal meeting time considering the availability of participants. The generation unit determines the meeting time based on, for example, the participants' calendar information. The generation unit compares the availability of participants to adjust the meeting time. The reservation unit automatically reserves an available meeting room for the meeting set up by the generation unit. The reservation unit checks, for example, the availability of meeting rooms on a calendar. The reservation department selects and reserves the most suitable meeting room. The reservation department automatically reserves a meeting room suitable for the meeting based on meeting room availability. The reservation department makes reservations considering, for example, the size and facilities of the meeting room. The reservation department checks meeting room availability in real time and selects the most suitable meeting room. The provision department issues a meeting URL for the meeting set up by the generation department and embeds it in the meeting notification. The provision department issues, for example, the URL of the online meeting system. The provision department embeds the meeting URL in the meeting notification. The provision department issues the URL of the online meeting system and embeds it in the meeting notification on the calendar. The provision department makes it possible for participants to check the URL of the online meeting system at the same time they receive the meeting notification. The provision department makes it easy for participants to access the meeting by embedding the meeting URL in the meeting notification. The confirmation department confirms the rescheduling with the participants of the meeting set up by the generation department. The confirmation department sends, for example, a message to participants to ask if rescheduling is possible. The confirmation department sends a message to participants to confirm rescheduling.The confirmation unit sends a message to confirm whether the participant can reschedule. The confirmation unit confirms the rescheduling, for example, using a communication tool. The confirmation unit sends a message to the participant asking, "Can you reschedule?" to confirm the rescheduling. The adjustment unit adjusts the rescheduling based on the confirmation unit. The adjustment unit makes further adjustments, for example, based on the rescheduling confirmation results. The adjustment unit reconfirms the participants' availability in order to adjust the rescheduling. The adjustment unit readjusts the meeting time based on the rescheduling confirmation results. The adjustment unit reconfirms the participants' availability in order to adjust the rescheduling, for example, and sets the optimal meeting time. The adjustment unit collects the participants' availability again in order to adjust the rescheduling. As a result, the AI ​​agent system according to this embodiment can improve work efficiency by confirming the participants' availability, automatically setting up meetings, reserving meeting rooms, issuing meeting URLs, and confirming and adjusting rescheduling.

[0030] The data collection unit checks participants' availability. For example, the unit checks their calendar availability. Specifically, the data collection unit uses a calendar API to obtain participants' calendar information. By using the calendar API, it is possible to obtain participants' availability in real time. The data collection unit analyzes the calendar information to identify available appointments in specific time slots. For example, the data collection unit extracts time slots from participants' calendars that do not have meetings scheduled and identifies those time slots as available. The data collection unit can simultaneously obtain calendar information from multiple participants and compare everyone's availability to identify time slots when everyone can participate. The data collection unit can also use AI to analyze calendar information. AI can not only analyze calendar information and identify participants' availability, but also learn past meeting patterns and participants' behavior patterns to identify availability more accurately. This allows the data collection unit to efficiently check participants' availability and quickly collect the information necessary to set up meetings. Furthermore, the data collection unit can centrally manage calendar information and collaborate with other systems and departments as needed. For example, the collected calendar information is stored on a cloud server, making it accessible to the generation and booking units. The collection unit also regularly updates the calendar information, ensuring it always has access to the latest available appointments. This allows the collection unit to efficiently and effectively collect data, improving the overall system performance.

[0031] The generation unit sets up meetings based on the availability collected by the collection unit. For example, the generation unit sets up meetings at times when all participants can attend. Specifically, the generation unit compares the availability of participants provided by the collection unit and identifies the optimal time slot when everyone can attend. The generation unit can also automatically adjust the meeting time. For example, the generation unit sets the optimal meeting time based on the participants' calendar information. The generation unit uses AI to consider the participants' availability and set the optimal meeting time. The AI ​​analyzes the participants' calendar information, learns past meeting patterns and participants' behavior patterns, and identifies the optimal meeting time. The generation unit compares the participants' availability to adjust the meeting time. For example, the generation unit identifies a time slot when everyone can attend based on the participants' calendar information and sets the meeting at that time. The generation unit checks the participants' availability in real time to adjust the meeting time and identify the optimal time slot. This allows the generation unit to set up meetings at times when all participants can attend and efficiently adjust meetings. Furthermore, the generation unit can not only set the meeting time but also identify the optimal time slot depending on the content and purpose of the meeting. For example, in the case of important or long meetings, selecting a time slot when participants are highly focused can maximize the effectiveness of the meeting. This allows the generation unit to efficiently and effectively schedule meetings and improve the overall performance of the system.

[0032] The reservation department automatically reserves available meeting rooms for meetings set by the generation department. For example, the reservation department checks the availability of meeting rooms on a calendar. Specifically, the reservation department uses the meeting room management system's API to obtain meeting room availability information. Using the meeting room management system's API allows for real-time acquisition of meeting room availability information. The reservation department selects and reserves the most suitable meeting room. For example, the reservation department selects a meeting room suitable for the meeting, considering its size and facilities. Based on meeting room availability information, the reservation department automatically reserves a meeting room suitable for the meeting. The reservation department checks meeting room availability information in real time and selects the most suitable meeting room. For example, the reservation department acquires meeting room availability information and selects the most suitable meeting room based on the number of participants and the content of the meeting. The reservation department uses the meeting room management system's API to obtain meeting room availability information. Using the meeting room management system's API allows for real-time acquisition of meeting room availability information. Based on meeting room availability information, the reservation department selects and reserves the most suitable meeting room. This allows the reservation department to reserve meeting rooms efficiently and effectively, improving the overall system performance. Furthermore, the reservation department can centrally manage the reservation status of meeting rooms and collaborate with other systems and departments as needed. For example, information on reserved meeting rooms is stored on a cloud server, making it accessible to the generation and provision departments. The reservation department also regularly updates the reservation status of meeting rooms, ensuring that it is always aware of the latest availability. This allows the reservation department to reserve meeting rooms efficiently and effectively, improving the overall performance of the system.

[0033] The service provider issues a meeting URL for the meeting set up by the generation service provider and embeds it in the meeting notification. For example, the service provider issues a URL for an online meeting system. Specifically, the service provider issues a meeting URL using the API of the online meeting system. By using the API of the online meeting system, the meeting URL can be issued automatically. The service provider embeds the meeting URL in the meeting notification. For example, the service provider generates a meeting notification and embeds the meeting URL within it. The service provider issues a URL for the online meeting system and embeds it in the calendar meeting notification. For example, the service provider makes it possible for participants to see the URL for the online meeting system at the same time they receive the meeting notification. By embedding the meeting URL in the meeting notification, the service provider makes it easy for participants to access it. The service provider can also use the calendar API to generate meeting notifications. By using the calendar API, meeting notifications can be automatically generated and the meeting URL can be embedded. This allows the service provider to issue meeting URLs efficiently and effectively and embed them in meeting notifications. Furthermore, the service provider can centrally manage the sending status of meeting notifications and cooperate with other systems and departments as needed. For example, information from sent meeting notifications is stored on a cloud server, making it accessible to the confirmation and coordination departments. Furthermore, the service provider regularly updates the status of meeting notifications, allowing them to always be aware of the latest transmission status. This enables the service provider to efficiently and effectively issue meeting URLs and embed them in meeting notifications.

[0034] The confirmation unit confirms the rescheduling of the meeting participants set by the generation unit. For example, the confirmation unit sends a message to participants to confirm whether they can reschedule. Specifically, the confirmation unit uses the communication tool's API to send the rescheduling confirmation message to participants. By using the communication tool's API, the rescheduling confirmation message can be sent automatically. The confirmation unit sends a message to participants asking, "Can you reschedule?" in order to confirm the rescheduling. For example, the confirmation unit uses the communication tool to send the rescheduling confirmation message to participants. The confirmation unit sends a message to participants in order to confirm the rescheduling. The confirmation unit sends a message to confirm whether the participant can reschedule. The confirmation unit collects the rescheduling confirmation results and provides them to the coordination unit. This allows the confirmation unit to efficiently and effectively confirm rescheduling and provide the coordination unit with the necessary information. Furthermore, the confirmation unit can centrally manage the rescheduling confirmation status and collaborate with other systems and departments as needed. For example, the collected rescheduling confirmation results can be stored on a cloud server and made accessible to the coordination unit. Furthermore, the verification unit periodically updates the rescheduling status, allowing it to always be aware of the latest status. This enables the verification unit to perform rescheduling checks efficiently and effectively, improving the overall system performance.

[0035] The coordination unit adjusts the rescheduling based on the verification unit's information. The coordination unit then makes further adjustments based on the rescheduling verification results, for example. Specifically, the coordination unit readjusts the meeting time based on the rescheduling verification results provided by the verification unit. The coordination unit reconfirms participants' availability in order to adjust the rescheduling. For example, the coordination unit reconfirms participants' availability provided by the collection unit and sets the optimal meeting time. The coordination unit readjusts the meeting time based on the rescheduling verification results. The coordination unit uses AI to reconfirm participants' availability and set the optimal meeting time. The AI ​​analyzes participants' calendar information, learns past meeting patterns and participants' behavior patterns, and identifies the optimal meeting time. The coordination unit collects participants' availability again in order to adjust the rescheduling. This allows the coordination unit to adjust the rescheduling efficiently and effectively and readjust the meeting time. Furthermore, the coordination unit can centrally manage the rescheduling status and collaborate with other systems and departments as needed. For example, information on coordinated meetings is stored on a cloud server, making it accessible to the generation and delivery departments. Furthermore, the coordination department regularly updates the rescheduling status, ensuring it always has access to the latest information. This allows the coordination department to efficiently and effectively manage rescheduling, improving the overall system performance.

[0036] The data collection unit can analyze participants' past meeting attendance history and select the optimal collection method. For example, the data collection unit can select the optimal collection method based on the time slots that participants have frequently attended in the past. The data collection unit can also suggest the most efficient collection method based on participants' past meeting attendance history. The data collection unit can also analyze participants' past meeting attendance history and customize the collection method. This allows for the selection of the optimal collection method by analyzing participants' past meeting attendance history, enabling efficient collection of available appointments. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input participants' past meeting attendance history data into AI and have the AI ​​select the optimal collection method.

[0037] The collection unit can filter available appointments based on participants' current projects and areas of interest. For example, the collection unit can prioritize collecting available appointments related to projects that participants are currently working on. The collection unit can also filter relevant available appointments based on participants' areas of interest. The collection unit can also collect the most suitable available appointments by considering the participants' current project status. This allows for the collection of highly relevant appointments by filtering available appointments based on participants' current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input participants' project information and area of ​​interest data into an AI and have the AI ​​perform the filtering.

[0038] The collection unit can prioritize collecting highly relevant appointments by considering the geographical location information of participants when collecting available appointments. For example, the collection unit can prioritize collecting appointments that are close to the participant's current location. The collection unit can also filter highly relevant appointments based on the participant's geographical location information. The collection unit can also collect the most suitable available appointments by considering the participant's travel patterns. This allows for efficient collection of available appointments by considering the participant's geographical location information and prioritizing highly relevant appointments. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the participant's geographical location data into AI and have the AI ​​perform the collection of highly relevant appointments.

[0039] The data collection unit can analyze participants' social media activity and collect relevant appointments when collecting available slots. For example, the data collection unit can collect relevant events and appointments from participants' social media activity. The data collection unit can also analyze the content of participants' social media posts and filter relevant appointments. The data collection unit can also collect the most suitable available slots by considering the activity of participants' social media followers and friends. This allows for efficient collection of available slots by analyzing participants' social media activity and collecting relevant appointments. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input participants' social media data into an AI and have the AI ​​perform the collection of relevant appointments.

[0040] The generation unit can adjust the level of detail of a meeting based on the importance of the participants when setting up a meeting. For example, if there are important participants, the generation unit will set up a detailed meeting. If there are less important participants, the generation unit can also set up a simpler meeting. The generation unit can also customize the level of detail of the meeting according to the importance of the participants. This allows for efficient meeting setup by adjusting the level of detail according to the importance of the participants. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input participant importance data into AI and have the AI ​​perform the adjustment of the level of detail of the meeting.

[0041] The generation unit can apply different setting algorithms depending on the participant's category when setting up a meeting. For example, the generation unit applies a specific setting algorithm if the participant is a manager. The generation unit can also apply a different setting algorithm if the participant is a technical professional. The generation unit can also select the optimal setting algorithm depending on the participant's category. This allows for efficient meeting setup by applying different setting algorithms depending on the participant's category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input participant category data into AI and have the AI ​​perform the application of the setting algorithm.

[0042] The generation unit can determine meeting priorities based on participants' submission timing when setting up a meeting. For example, if a participant submits early, the generation unit will prioritize scheduling the meeting. If a participant submits late, the generation unit may also postpone scheduling the meeting. The generation unit can also adjust the meeting priority according to the participants' submission timing. This allows for efficient meeting scheduling by determining meeting priorities based on participants' submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input participant submission timing data into an AI and have the AI ​​determine the meeting priority.

[0043] The generation unit can adjust the order of meetings based on the relevance of the participants when setting up meetings. For example, the generation unit can prioritize setting up meetings for participants who are highly relevant. The generation unit can also postpone setting up meetings for participants who are less relevant. The generation unit can also adjust the order of meetings according to the relevance of the participants. This allows for efficient meeting scheduling by adjusting the order of meetings according to the relevance of the participants. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input participant relevance data into AI and have the AI ​​perform the adjustment of the meeting order.

[0044] The reservation department can analyze the past usage history of a meeting room to select the optimal reservation method when a meeting room is booked. For example, the reservation department can suggest the optimal reservation method based on the meeting room's past usage history. The reservation department can also analyze the meeting room's past usage history and customize the reservation method. The reservation department can also select the most efficient reservation method based on the meeting room's past usage history. This allows for efficient meeting room booking by analyzing the meeting room's past usage history and selecting the optimal reservation method. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input past meeting room usage history data into AI and have the AI ​​select the optimal reservation method.

[0045] The reservation department can make reservations based on the facility information of the meeting rooms when booking. For example, the reservation department can select the most suitable meeting room based on the facility information. The reservation department can also customize the reservation method considering the facility information of the meeting rooms. The reservation department can also suggest the most efficient reservation method based on the facility information of the meeting rooms. As a result, by making reservations based on the facility information of the meeting rooms, the most suitable meeting room can be selected and the meeting rooms can be booked efficiently. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input meeting room facility information data into AI and have the AI ​​select the reservation method.

[0046] The reservation department can select the most suitable meeting room when booking a meeting room, taking into account the meeting room's geographical location. For example, the reservation department can select the most suitable meeting room based on the meeting room's geographical location. The reservation department can also customize the reservation method, taking into account the meeting room's geographical location. The reservation department can also select the most efficient meeting room based on the meeting room's geographical location. This allows for efficient meeting room reservations by selecting the most suitable meeting room, taking into account the meeting room's geographical location. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input meeting room geographical location data into AI and have the AI ​​select the most suitable meeting room.

[0047] The reservation department can improve the accuracy of reservations by referring to relevant literature on meeting rooms when making a reservation. For example, the reservation department can select the most suitable meeting room based on the relevant literature. The reservation department can also customize the reservation method by referring to the relevant literature on meeting rooms. The reservation department can also suggest the most efficient reservation method based on the relevant literature on meeting rooms. This allows for the selection of the most suitable meeting room and efficient reservation by referring to the relevant literature on meeting rooms. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input the relevant literature data on meeting rooms into an AI and have the AI ​​perform the task of improving the accuracy of reservations.

[0048] The service provider can adjust the level of detail in a meeting URL based on the importance of the meeting when issuing the URL. For example, the service provider can provide a detailed URL issuance method for important meetings. For less important meetings, the service provider can also provide a simpler URL issuance method. The service provider can also customize the level of detail in the URL according to the importance of the meeting. This allows for efficient issuance of meeting URLs by adjusting the level of detail in the URL according to the importance of the meeting. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input meeting importance data into AI and have the AI ​​perform the adjustment of URL detail.

[0049] The service provider can apply different issuance algorithms depending on the conference category when issuing conference URLs. For example, the service provider might apply a specific issuance algorithm for business conferences. It might also apply a different issuance algorithm for educational conferences. The service provider can also select the optimal issuance algorithm depending on the conference category. This allows for efficient issuance of conference URLs by applying different issuance algorithms depending on the conference category. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input conference category data into AI and have the AI ​​apply the issuance algorithm.

[0050] The service provider can determine the priority of meeting URLs based on the submission date of the meeting when issuing the URLs. For example, the service provider will issue URLs preferentially for meetings submitted early. The service provider may also issue URLs later for meetings submitted late. The service provider can also adjust the priority of URLs according to the submission date of the meeting. This allows for efficient issuance of meeting URLs by determining the priority of URLs according to the submission date of the meeting. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input meeting submission date data into AI and have the AI ​​perform the determination of URL priority.

[0051] The service provider can adjust the order of meeting URLs based on their relevance when issuing them. For example, the service provider can prioritize issuing URLs for highly relevant meetings. It can also delay issuing URLs for less relevant meetings. The service provider can adjust the order of URLs according to the relevance of the meetings. This allows for efficient issuance of meeting URLs by adjusting the order of URLs according to their relevance. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input meeting relevance data into AI and have the AI ​​perform the adjustment of the URL order.

[0052] The verification unit can analyze the participant's past rescheduling history and select the optimal verification method when rescheduling. For example, the verification unit can propose the optimal verification method based on the participant's past rescheduling history. The verification unit can also analyze the participant's past rescheduling history and customize the verification method. The verification unit can also select the most efficient verification method based on the participant's past rescheduling history. This allows for efficient rescheduling verification by selecting the optimal verification method through analysis of the participant's past rescheduling history. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the participant's past rescheduling history data into AI and have the AI ​​select the optimal verification method.

[0053] The verification unit can perform verification based on the participant's current project status when rescheduling. For example, the verification unit can propose the optimal verification method considering the participant's current project status. The verification unit can also customize the verification method based on the participant's current project status. The verification unit can also select the most efficient verification method considering the participant's current project status. This allows for efficient rescheduling verification by performing verification based on the participant's current project status. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the participant's project status data into AI and have the AI ​​select the verification method.

[0054] The verification unit can select the optimal verification method when rescheduling, taking into account the geographical location information of the participants. For example, the verification unit can propose the optimal verification method based on the geographical location information of the participants. The verification unit can also customize the verification method, taking into account the geographical location information of the participants. The verification unit can also select the most efficient verification method based on the geographical location information of the participants. This allows for efficient rescheduling verification by selecting the optimal verification method, taking into account the geographical location information of the participants. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the geographical location information data of the participants into AI and have the AI ​​perform the selection of the verification method.

[0055] The verification unit can analyze the participant's social media activity and propose verification methods when rescheduling. For example, the verification unit can propose the most suitable verification method based on the participant's social media activity. The verification unit can also analyze the content of the participant's social media posts and customize the verification method. The verification unit can also select the most suitable verification method by considering the activities of the participant's social media followers and friends. This allows for efficient rescheduling verification by analyzing the participant's social media activity and proposing verification methods. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the participant's social media data into AI and have the AI ​​propose verification methods.

[0056] The adjustment unit can analyze the participant's past rescheduling history and select the optimal adjustment method during rescheduling. For example, the adjustment unit can propose the optimal adjustment method based on the participant's past rescheduling history. The adjustment unit can also analyze the participant's past rescheduling history and customize the adjustment method. The adjustment unit can also select the most efficient adjustment method based on the participant's past rescheduling history. This allows for efficient rescheduling by selecting the optimal adjustment method through analysis of the participant's past rescheduling history. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the participant's past rescheduling history data into AI and have the AI ​​select the optimal adjustment method.

[0057] The adjustment unit can perform rescheduling adjustments based on the participants' current project status. For example, the adjustment unit can propose the optimal adjustment method considering the participants' current project status. The adjustment unit can also customize the adjustment method based on the participants' current project status. The adjustment unit can also select the most efficient adjustment method considering the participants' current project status. This allows for efficient rescheduling adjustments by performing adjustments based on the participants' current project status. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or not. For example, the adjustment unit can input the participants' project status data into AI and have the AI ​​select the adjustment method.

[0058] The adjustment unit can select the optimal adjustment method when rescheduling, taking into account the geographical location information of the participants. For example, the adjustment unit can propose the optimal adjustment method based on the geographical location information of the participants. The adjustment unit can also customize the adjustment method, taking into account the geographical location information of the participants. The adjustment unit can also select the most efficient adjustment method based on the geographical location information of the participants. This allows for efficient rescheduling by selecting the optimal adjustment method, taking into account the geographical location information of the participants. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the geographical location data of the participants into AI and have AI perform the selection of the adjustment method.

[0059] The adjustment unit can analyze participants' social media activity and propose adjustment methods during rescheduling. For example, the adjustment unit can propose the optimal adjustment method based on participants' social media activity. The adjustment unit can also analyze the content of participants' social media posts and customize the adjustment method. The adjustment unit can also select the optimal adjustment method by considering the activities of participants' social media followers and friends. This allows for efficient rescheduling by analyzing participants' social media activity and proposing adjustment methods. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input participants' social media data into AI and have the AI ​​execute the proposal of adjustment methods.

[0060] The adjustment unit can select the optimal adjustment method by referring to the participants' calendar information when rescheduling. For example, the adjustment unit can propose the optimal adjustment method based on the participants' calendar information. The adjustment unit can also customize the adjustment method by referring to the participants' calendar information. The adjustment unit can also select the most efficient adjustment method based on the participants' calendar information. This allows for efficient rescheduling by selecting the optimal adjustment method by referring to the participants' calendar information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the participants' calendar information data into AI and have the AI ​​select the adjustment method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can acquire participants' health data and adjust the timing of collecting available appointments based on their health status. For example, if a participant is fatigued, the collection timing can be delayed until they have rested before collecting their available appointments. If a participant is healthy, their available appointments can be collected immediately, allowing for quick meeting scheduling. This reduces the burden on participants by adjusting the timing of available appointment collection according to their health status.

[0063] The generation unit can analyze participants' past meeting attendance history and select the optimal meeting setting method. For example, it can select the optimal meeting setting method based on the time slots participants have frequently attended in the past. It can also suggest the most efficient setting method based on participants' past meeting attendance history. In this way, by analyzing participants' past meeting attendance history, the optimal meeting setting method can be selected and meetings can be set up efficiently.

[0064] The generation unit can adjust how meetings are scheduled based on the participants' current project status. For example, if participants are working on important projects, the meeting scheduling process can be simplified and expedited. If participants are working on less demanding projects, the system can offer detailed scheduling options and suggest a customizable scheduling method. This allows for efficient meeting scheduling by adjusting the meeting scheduling process according to the participants' current project status.

[0065] The reservation department can analyze the past usage history of meeting rooms to select the optimal reservation method. For example, it can suggest the most suitable reservation method based on the meeting room's past usage history. It can also analyze the past usage history of meeting rooms and customize the reservation method. This allows for efficient meeting room reservations by selecting the optimal reservation method through analysis of past usage history.

[0066] The service provider can adjust the level of detail in the URL based on the importance of the meeting. For example, for important meetings, a detailed URL issuance method can be provided. For less important meetings, a simpler URL issuance method can be provided. This allows for efficient issuance of meeting URLs by adjusting the level of detail according to the importance of the meeting.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit checks the participants' availability. The collection unit checks the calendar's availability, for example, and collects available appointments for specific time slots. The collection unit uses the calendar API to retrieve available appointments and analyzes the calendar information to identify available times. Step 2: The generation unit sets up meetings based on the availability collected by the collection unit. The generation unit sets up meetings during times when all participants can attend and automatically adjusts the meeting times. The generation unit determines the meeting time based on the participants' calendar information and sets the optimal meeting time by comparing the participants' availability. Step 3: The reservation department automatically reserves an available meeting room for the meeting set by the generation department. The reservation department checks the availability of meeting rooms on the calendar, selects the most suitable meeting room, and makes the reservation. The reservation department makes the reservation considering the size and facilities of the meeting room, and selects the most suitable meeting room by checking the availability of meeting rooms in real time. Step 4: The provisioning unit issues a meeting URL for the meeting set up by the generation unit and embeds it in the meeting notification. The provisioning unit issues a URL for the online meeting system and embeds the meeting URL in the meeting notification, making it easy for participants to access the meeting. Step 5: The confirmation unit confirms the rescheduling with the meeting participants set by the generation unit. The confirmation unit sends a message to the participants asking if rescheduling is possible, and sends the message "Is rescheduling possible?" to the participants in order to confirm the rescheduling. Step 6: The coordination unit adjusts the rescheduling based on the confirmation unit's instructions. The coordination unit makes further adjustments based on the rescheduling confirmation results, reconfirms participants' availability, and sets the optimal meeting time. The coordination unit collects participants' availability again in order to adjust the rescheduling.

[0069] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system designed to address the current situation where the number of meetings and the workload involved in scheduling meetings have increased due to the rise in teleworking. When setting up a meeting with relevant parties using a calendar, the AI ​​agent checks the availability of participants and automatically sets up the meeting. Next, it automatically reserves an available meeting room on the calendar and connects to an online meeting system to embed the meeting URL into the meeting notification. Furthermore, by connecting to a communication tool, it can automatically send a message to potential participants whose schedules appear full, asking if they can reschedule and allowing for adjustments. Based on the "yes" or "no" response to whether they can reschedule, further adjustments are made. This mechanism aims to shift the time saved to higher value-added tasks. For example, when setting up a meeting with relevant parties using a calendar, participants are specified. At this time, the AI ​​agent checks the availability of participants and automatically sets up the meeting. For example, if participants A, B, and C are specified, the AI ​​agent checks the availability of each participant and sets up the meeting at a time when everyone can attend. Next, it automatically reserves an available meeting room on the calendar. The AI ​​agent checks the availability of meeting rooms on the calendar and automatically reserves a suitable room for the meeting. For example, it selects and reserves the most suitable room from rooms A, B, and C. Furthermore, by connecting to the online meeting system, it embeds the meeting URL into the meeting notification. The AI ​​agent generates the URL for the online meeting system and embeds it in the calendar's meeting notification. This allows participants to see the online meeting system URL at the same time they receive the meeting notification. In addition, by connecting to the communication tool, it automatically sends a message to potential participants who appear to have full schedules, asking if they can reschedule. For example, it uses the communication tool to send a message to participant A asking, "Can you reschedule?" and accepts a "yes" or "no" response. Based on the response, further adjustments are made.This system reduces the time spent on meeting scheduling, allowing the saved time to be shifted to higher-value tasks. For example, reducing the time spent on meeting scheduling allows for greater focus on other important tasks. Furthermore, since the AI ​​agent automatically sets up meetings, the effort required for scheduling is eliminated. As a result, the AI ​​agent system reduces the time spent on meeting scheduling and improves operational efficiency.

[0070] The AI ​​agent system according to this embodiment comprises a collection unit, a generation unit, a reservation unit, a provision unit, a confirmation unit, and an adjustment unit. The collection unit checks the availability of participants. The collection unit checks, for example, the availability of a calendar. The collection unit can also collect availability for a specific time period. The collection unit obtains calendar information to check the availability of participants. The collection unit obtains availability using, for example, a calendar API. The collection unit analyzes the calendar information and identifies availability. The generation unit sets up a meeting based on the availability collected by the collection unit. The generation unit sets up a meeting during a time period when all participants can attend. The generation unit can also automatically adjust the meeting time. The generation unit sets the optimal meeting time considering the availability of participants. The generation unit determines the meeting time based on, for example, the participants' calendar information. The generation unit compares the availability of participants to adjust the meeting time. The reservation unit automatically reserves an available meeting room for the meeting set up by the generation unit. The reservation unit checks, for example, the availability of meeting rooms on a calendar. The reservation department selects and reserves the most suitable meeting room. The reservation department automatically reserves a meeting room suitable for the meeting based on meeting room availability. The reservation department makes reservations considering, for example, the size and facilities of the meeting room. The reservation department checks meeting room availability in real time and selects the most suitable meeting room. The provision department issues a meeting URL for the meeting set up by the generation department and embeds it in the meeting notification. The provision department issues, for example, the URL of the online meeting system. The provision department embeds the meeting URL in the meeting notification. The provision department issues the URL of the online meeting system and embeds it in the meeting notification on the calendar. The provision department makes it possible for participants to check the URL of the online meeting system at the same time they receive the meeting notification. The provision department makes it easy for participants to access the meeting by embedding the meeting URL in the meeting notification. The confirmation department confirms the rescheduling with the participants of the meeting set up by the generation department. The confirmation department sends, for example, a message to participants to ask if rescheduling is possible. The confirmation department sends a message to participants to confirm rescheduling.The confirmation unit sends a message to confirm whether the participant can reschedule. The confirmation unit confirms the rescheduling, for example, using a communication tool. The confirmation unit sends a message to the participant asking, "Can you reschedule?" to confirm the rescheduling. The adjustment unit adjusts the rescheduling based on the confirmation unit. The adjustment unit makes further adjustments, for example, based on the rescheduling confirmation results. The adjustment unit reconfirms the participants' availability in order to adjust the rescheduling. The adjustment unit readjusts the meeting time based on the rescheduling confirmation results. The adjustment unit reconfirms the participants' availability in order to adjust the rescheduling, for example, and sets the optimal meeting time. The adjustment unit collects the participants' availability again in order to adjust the rescheduling. As a result, the AI ​​agent system according to this embodiment can improve work efficiency by confirming the participants' availability, automatically setting up meetings, reserving meeting rooms, issuing meeting URLs, and confirming and adjusting rescheduling.

[0071] The data collection unit checks participants' availability. For example, the unit checks their calendar availability. Specifically, the data collection unit uses a calendar API to obtain participants' calendar information. By using the calendar API, it is possible to obtain participants' availability in real time. The data collection unit analyzes the calendar information to identify available appointments in specific time slots. For example, the data collection unit extracts time slots from participants' calendars that do not have meetings scheduled and identifies those time slots as available. The data collection unit can simultaneously obtain calendar information from multiple participants and compare everyone's availability to identify time slots when everyone can participate. The data collection unit can also use AI to analyze calendar information. AI can not only analyze calendar information and identify participants' availability, but also learn past meeting patterns and participants' behavior patterns to identify availability more accurately. This allows the data collection unit to efficiently check participants' availability and quickly collect the information necessary to set up meetings. Furthermore, the data collection unit can centrally manage calendar information and collaborate with other systems and departments as needed. For example, the collected calendar information is stored on a cloud server, making it accessible to the generation and booking units. The collection unit also regularly updates the calendar information, ensuring it always has access to the latest available appointments. This allows the collection unit to efficiently and effectively collect data, improving the overall system performance.

[0072] The generation unit sets up meetings based on the availability collected by the collection unit. For example, the generation unit sets up meetings at times when all participants can attend. Specifically, the generation unit compares the availability of participants provided by the collection unit and identifies the optimal time slot when everyone can attend. The generation unit can also automatically adjust the meeting time. For example, the generation unit sets the optimal meeting time based on the participants' calendar information. The generation unit uses AI to consider the participants' availability and set the optimal meeting time. The AI ​​analyzes the participants' calendar information, learns past meeting patterns and participants' behavior patterns, and identifies the optimal meeting time. The generation unit compares the participants' availability to adjust the meeting time. For example, the generation unit identifies a time slot when everyone can attend based on the participants' calendar information and sets the meeting at that time. The generation unit checks the participants' availability in real time to adjust the meeting time and identify the optimal time slot. This allows the generation unit to set up meetings at times when all participants can attend and efficiently adjust meetings. Furthermore, the generation unit can not only set the meeting time but also identify the optimal time slot depending on the content and purpose of the meeting. For example, in the case of important or long meetings, selecting a time slot when participants are highly focused can maximize the effectiveness of the meeting. This allows the generation unit to efficiently and effectively schedule meetings and improve the overall performance of the system.

[0073] The reservation department automatically reserves available meeting rooms for meetings set by the generation department. For example, the reservation department checks the availability of meeting rooms on a calendar. Specifically, the reservation department uses the meeting room management system's API to obtain meeting room availability information. Using the meeting room management system's API allows for real-time acquisition of meeting room availability information. The reservation department selects and reserves the most suitable meeting room. For example, the reservation department selects a meeting room suitable for the meeting, considering its size and facilities. Based on meeting room availability information, the reservation department automatically reserves a meeting room suitable for the meeting. The reservation department checks meeting room availability information in real time and selects the most suitable meeting room. For example, the reservation department acquires meeting room availability information and selects the most suitable meeting room based on the number of participants and the content of the meeting. The reservation department uses the meeting room management system's API to obtain meeting room availability information. Using the meeting room management system's API allows for real-time acquisition of meeting room availability information. Based on meeting room availability information, the reservation department selects and reserves the most suitable meeting room. This allows the reservation department to reserve meeting rooms efficiently and effectively, improving the overall system performance. Furthermore, the reservation department can centrally manage the reservation status of meeting rooms and collaborate with other systems and departments as needed. For example, information on reserved meeting rooms is stored on a cloud server, making it accessible to the generation and provision departments. The reservation department also regularly updates the reservation status of meeting rooms, ensuring that it is always aware of the latest availability. This allows the reservation department to reserve meeting rooms efficiently and effectively, improving the overall performance of the system.

[0074] The service provider issues a meeting URL for the meeting set up by the generation service provider and embeds it in the meeting notification. For example, the service provider issues a URL for an online meeting system. Specifically, the service provider issues a meeting URL using the API of the online meeting system. By using the API of the online meeting system, the meeting URL can be issued automatically. The service provider embeds the meeting URL in the meeting notification. For example, the service provider generates a meeting notification and embeds the meeting URL within it. The service provider issues a URL for the online meeting system and embeds it in the calendar meeting notification. For example, the service provider makes it possible for participants to see the URL for the online meeting system at the same time they receive the meeting notification. By embedding the meeting URL in the meeting notification, the service provider makes it easy for participants to access it. The service provider can also use the calendar API to generate meeting notifications. By using the calendar API, meeting notifications can be automatically generated and the meeting URL can be embedded. This allows the service provider to issue meeting URLs efficiently and effectively and embed them in meeting notifications. Furthermore, the service provider can centrally manage the sending status of meeting notifications and cooperate with other systems and departments as needed. For example, information from sent meeting notifications is stored on a cloud server, making it accessible to the confirmation and coordination departments. Furthermore, the service provider regularly updates the status of meeting notifications, allowing them to always be aware of the latest transmission status. This enables the service provider to efficiently and effectively issue meeting URLs and embed them in meeting notifications.

[0075] The confirmation unit confirms the rescheduling of the meeting participants set by the generation unit. For example, the confirmation unit sends a message to participants to confirm whether they can reschedule. Specifically, the confirmation unit uses the communication tool's API to send the rescheduling confirmation message to participants. By using the communication tool's API, the rescheduling confirmation message can be sent automatically. The confirmation unit sends a message to participants asking, "Can you reschedule?" in order to confirm the rescheduling. For example, the confirmation unit uses the communication tool to send the rescheduling confirmation message to participants. The confirmation unit sends a message to participants in order to confirm the rescheduling. The confirmation unit sends a message to confirm whether the participant can reschedule. The confirmation unit collects the rescheduling confirmation results and provides them to the coordination unit. This allows the confirmation unit to efficiently and effectively confirm rescheduling and provide the coordination unit with the necessary information. Furthermore, the confirmation unit can centrally manage the rescheduling confirmation status and collaborate with other systems and departments as needed. For example, the collected rescheduling confirmation results can be stored on a cloud server and made accessible to the coordination unit. Furthermore, the verification unit periodically updates the rescheduling status, allowing it to always be aware of the latest status. This enables the verification unit to perform rescheduling checks efficiently and effectively, improving the overall system performance.

[0076] The coordination unit adjusts the rescheduling based on the verification unit's information. The coordination unit then makes further adjustments based on the rescheduling verification results, for example. Specifically, the coordination unit readjusts the meeting time based on the rescheduling verification results provided by the verification unit. The coordination unit reconfirms participants' availability in order to adjust the rescheduling. For example, the coordination unit reconfirms participants' availability provided by the collection unit and sets the optimal meeting time. The coordination unit readjusts the meeting time based on the rescheduling verification results. The coordination unit uses AI to reconfirm participants' availability and set the optimal meeting time. The AI ​​analyzes participants' calendar information, learns past meeting patterns and participants' behavior patterns, and identifies the optimal meeting time. The coordination unit collects participants' availability again in order to adjust the rescheduling. This allows the coordination unit to adjust the rescheduling efficiently and effectively and readjust the meeting time. Furthermore, the coordination unit can centrally manage the rescheduling status and collaborate with other systems and departments as needed. For example, information on coordinated meetings is stored on a cloud server, making it accessible to the generation and delivery departments. Furthermore, the coordination department regularly updates the rescheduling status, ensuring it always has access to the latest information. This allows the coordination department to efficiently and effectively manage rescheduling, improving the overall system performance.

[0077] The collection unit can estimate the emotions of participants and adjust the timing of collecting available appointments based on the estimated emotions. For example, if a participant is feeling stressed, the collection unit can delay the collection timing to collect available appointments when the participant is relaxed. If the participant is relaxed, the collection unit can also collect available appointments immediately and quickly schedule a meeting. If a participant is busy, the collection unit can adjust the collection timing to reduce the participant's burden. In this way, the burden on participants can be reduced by adjusting the timing of collecting available appointments according to their emotions. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input participant emotion data into AI and have the AI ​​perform the adjustment of the collection timing.

[0078] The data collection unit can analyze participants' past meeting attendance history and select the optimal collection method. For example, the data collection unit can select the optimal collection method based on the time slots that participants have frequently attended in the past. The data collection unit can also suggest the most efficient collection method based on participants' past meeting attendance history. The data collection unit can also analyze participants' past meeting attendance history and customize the collection method. This allows for the selection of the optimal collection method by analyzing participants' past meeting attendance history, enabling efficient collection of available appointments. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input participants' past meeting attendance history data into AI and have the AI ​​select the optimal collection method.

[0079] The collection unit can filter available appointments based on participants' current projects and areas of interest. For example, the collection unit can prioritize collecting available appointments related to projects that participants are currently working on. The collection unit can also filter relevant available appointments based on participants' areas of interest. The collection unit can also collect the most suitable available appointments by considering the participants' current project status. This allows for the collection of highly relevant appointments by filtering available appointments based on participants' current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input participants' project information and area of ​​interest data into an AI and have the AI ​​perform the filtering.

[0080] The data collection unit can estimate the emotions of participants and determine the priority of available appointments to collect based on the estimated emotions. For example, if a participant is feeling stressed, the data collection unit will prioritize collecting less important available appointments. If a participant is relaxed, the data collection unit may also prioritize collecting more important available appointments. If a participant is busy, the data collection unit may also prioritize collecting more important available appointments. This reduces the burden on participants by prioritizing available appointments according to their 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input participant emotion data into a generative AI and have the generative AI determine the priority of available appointments.

[0081] The collection unit can prioritize collecting highly relevant appointments by considering the geographical location information of participants when collecting available appointments. For example, the collection unit can prioritize collecting appointments that are close to the participant's current location. The collection unit can also filter highly relevant appointments based on the participant's geographical location information. The collection unit can also collect the most suitable available appointments by considering the participant's travel patterns. This allows for efficient collection of available appointments by considering the participant's geographical location information and prioritizing highly relevant appointments. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the participant's geographical location data into AI and have the AI ​​perform the collection of highly relevant appointments.

[0082] The data collection unit can analyze participants' social media activity and collect relevant appointments when collecting available slots. For example, the data collection unit can collect relevant events and appointments from participants' social media activity. The data collection unit can also analyze the content of participants' social media posts and filter relevant appointments. The data collection unit can also collect the most suitable available slots by considering the activity of participants' social media followers and friends. This allows for efficient collection of available slots by analyzing participants' social media activity and collecting relevant appointments. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input participants' social media data into an AI and have the AI ​​perform the collection of relevant appointments.

[0083] The generation unit can estimate the emotions of participants and adjust the meeting setup method based on the estimated emotions. For example, if a participant is stressed, the generation unit can provide a simple setup method and minimize the meeting setup procedure. If a participant is relaxed, the generation unit can also provide detailed setup options and suggest a customizable setup method. If a participant is in a hurry, the generation unit can also enable quick meeting setup. This reduces the burden on participants by adjusting the meeting setup method according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input participant emotion data into a generative AI and have the generative AI adjust the meeting setup method.

[0084] The generation unit can adjust the level of detail of a meeting based on the importance of the participants when setting up a meeting. For example, if there are important participants, the generation unit will set up a detailed meeting. If there are less important participants, the generation unit can also set up a simpler meeting. The generation unit can also customize the level of detail of the meeting according to the importance of the participants. This allows for efficient meeting setup by adjusting the level of detail according to the importance of the participants. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input participant importance data into AI and have the AI ​​perform the adjustment of the level of detail of the meeting.

[0085] The generation unit can apply different setting algorithms depending on the participant's category when setting up a meeting. For example, the generation unit applies a specific setting algorithm if the participant is a manager. The generation unit can also apply a different setting algorithm if the participant is a technical professional. The generation unit can also select the optimal setting algorithm depending on the participant's category. This allows for efficient meeting setup by applying different setting algorithms depending on the participant's category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input participant category data into AI and have the AI ​​perform the application of the setting algorithm.

[0086] The generation unit can estimate the emotions of participants and adjust the length of the meeting based on the estimated emotions. For example, if a participant is feeling stressed, the generation unit can set a shorter meeting. If a participant is relaxed, the generation unit can also set a longer meeting. If a participant is in a hurry, the generation unit can also set a meeting that ends quickly. This reduces the burden on participants by adjusting the length of the meeting according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input participant emotion data into a generation AI and have the generation AI adjust the length of the meeting.

[0087] The generation unit can determine meeting priorities based on participants' submission timing when setting up a meeting. For example, if a participant submits early, the generation unit will prioritize scheduling the meeting. If a participant submits late, the generation unit may also postpone scheduling the meeting. The generation unit can also adjust the meeting priority according to the participants' submission timing. This allows for efficient meeting scheduling by determining meeting priorities based on participants' submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input participant submission timing data into an AI and have the AI ​​determine the meeting priority.

[0088] The generation unit can adjust the order of meetings based on the relevance of the participants when setting up meetings. For example, the generation unit can prioritize setting up meetings for participants who are highly relevant. The generation unit can also postpone setting up meetings for participants who are less relevant. The generation unit can also adjust the order of meetings according to the relevance of the participants. This allows for efficient meeting scheduling by adjusting the order of meetings according to the relevance of the participants. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input participant relevance data into AI and have the AI ​​perform the adjustment of the meeting order.

[0089] The booking system can estimate participants' emotions and adjust the meeting room booking method based on the estimated emotions. For example, if a participant is stressed, the booking system can offer a simple booking method and minimize the booking procedure. If a participant is relaxed, the booking system can also offer detailed booking options and suggest a customizable booking method. If a participant is in a hurry, the booking system can also enable quick booking of a meeting room. This reduces the burden on participants by adjusting the meeting room booking method according to their 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 booking system may be performed using AI or not. For example, the booking system can input participant emotion data into a generative AI and have the generative AI adjust the meeting room booking method.

[0090] The reservation department can analyze the past usage history of a meeting room to select the optimal reservation method when a meeting room is booked. For example, the reservation department can suggest the optimal reservation method based on the meeting room's past usage history. The reservation department can also analyze the meeting room's past usage history and customize the reservation method. The reservation department can also select the most efficient reservation method based on the meeting room's past usage history. This allows for efficient meeting room booking by analyzing the meeting room's past usage history and selecting the optimal reservation method. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input past meeting room usage history data into AI and have the AI ​​select the optimal reservation method.

[0091] The reservation department can make reservations based on the facility information of the meeting rooms when booking. For example, the reservation department can select the most suitable meeting room based on the facility information. The reservation department can also customize the reservation method considering the facility information of the meeting rooms. The reservation department can also suggest the most efficient reservation method based on the facility information of the meeting rooms. As a result, by making reservations based on the facility information of the meeting rooms, the most suitable meeting room can be selected and the meeting rooms can be booked efficiently. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input meeting room facility information data into AI and have the AI ​​select the reservation method.

[0092] The reservation system can estimate participants' emotions and prioritize meeting rooms based on those estimated emotions. For example, if a participant is feeling stressed, the reservation system will prioritize booking a less important meeting room. If a participant is relaxed, the reservation system may also prioritize booking a more important meeting room. If a participant is busy, the reservation system may also prioritize booking a more important meeting room. This reduces the burden on participants by prioritizing meeting rooms according to their 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 reservation system may be performed using AI or not. For example, the reservation system can input participant emotion data into a generative AI and have the generative AI determine the priority of meeting rooms.

[0093] The reservation department can select the most suitable meeting room when booking a meeting room, taking into account the meeting room's geographical location. For example, the reservation department can select the most suitable meeting room based on the meeting room's geographical location. The reservation department can also customize the reservation method, taking into account the meeting room's geographical location. The reservation department can also select the most efficient meeting room based on the meeting room's geographical location. This allows for efficient meeting room reservations by selecting the most suitable meeting room, taking into account the meeting room's geographical location. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input meeting room geographical location data into AI and have the AI ​​select the most suitable meeting room.

[0094] The reservation department can improve the accuracy of reservations by referring to relevant literature on meeting rooms when making a reservation. For example, the reservation department can select the most suitable meeting room based on the relevant literature. The reservation department can also customize the reservation method by referring to the relevant literature on meeting rooms. The reservation department can also suggest the most efficient reservation method based on the relevant literature on meeting rooms. This allows for the selection of the most suitable meeting room and efficient reservation by referring to the relevant literature on meeting rooms. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input the relevant literature data on meeting rooms into an AI and have the AI ​​perform the task of improving the accuracy of reservations.

[0095] The service provider can estimate participants' emotions and adjust how meeting URLs are issued based on the estimated emotions. For example, if a participant is stressed, the service provider can provide a simple issuance method and minimize the URL issuance procedure. If a participant is relaxed, the service provider can also provide detailed issuance options and suggest a customizable issuance method. If a participant is in a hurry, the service provider can also enable the rapid issuance of meeting URLs. This reduces the burden on participants by adjusting how meeting URLs are issued according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 service provider may be performed using AI or not. For example, the service provider can input participant emotion data into a generative AI and have the generative AI adjust how meeting URLs are issued.

[0096] The service provider can adjust the level of detail in a meeting URL based on the importance of the meeting when issuing the URL. For example, the service provider can provide a detailed URL issuance method for important meetings. For less important meetings, the service provider can also provide a simpler URL issuance method. The service provider can also customize the level of detail in the URL according to the importance of the meeting. This allows for efficient issuance of meeting URLs by adjusting the level of detail in the URL according to the importance of the meeting. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input meeting importance data into AI and have the AI ​​perform the adjustment of URL detail.

[0097] The service provider can apply different issuance algorithms depending on the conference category when issuing conference URLs. For example, the service provider might apply a specific issuance algorithm for business conferences. It might also apply a different issuance algorithm for educational conferences. The service provider can also select the optimal issuance algorithm depending on the conference category. This allows for efficient issuance of conference URLs by applying different issuance algorithms depending on the conference category. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input conference category data into AI and have the AI ​​apply the issuance algorithm.

[0098] The service provider can estimate participants' emotions and adjust the length of the meeting URL based on the estimated emotions. For example, if a participant is stressed, the service provider can issue a shorter URL. If a participant is relaxed, the service provider can also issue a longer URL. If a participant is in a hurry, the service provider can also issue a short URL for quick access. This reduces the burden on participants by adjusting the length of the meeting URL according to their 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 service provider may be performed using AI or not using AI. For example, the service provider can input participant emotion data into a generative AI and have the generative AI adjust the length of the meeting URL.

[0099] The service provider can determine the priority of meeting URLs based on the submission date of the meeting when issuing the URLs. For example, the service provider will issue URLs preferentially for meetings submitted early. The service provider may also issue URLs later for meetings submitted late. The service provider can also adjust the priority of URLs according to the submission date of the meeting. This allows for efficient issuance of meeting URLs by determining the priority of URLs according to the submission date of the meeting. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input meeting submission date data into AI and have the AI ​​perform the determination of URL priority.

[0100] The service provider can adjust the order of meeting URLs based on their relevance when issuing them. For example, the service provider can prioritize issuing URLs for highly relevant meetings. It can also delay issuing URLs for less relevant meetings. The service provider can adjust the order of URLs according to the relevance of the meetings. This allows for efficient issuance of meeting URLs by adjusting the order of URLs according to their relevance. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input meeting relevance data into AI and have the AI ​​perform the adjustment of the URL order.

[0101] The confirmation unit can estimate the participant's emotions and adjust the rescheduling confirmation method based on the estimated emotions. For example, if the participant is stressed, the confirmation unit can provide a simple confirmation method and minimize the confirmation procedure. If the participant is relaxed, the confirmation unit can also provide detailed confirmation options and suggest a customizable confirmation method. If the participant is in a hurry, the confirmation unit can also enable quick rescheduling confirmation. This reduces the burden on participants by adjusting the rescheduling confirmation method according to their 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 confirmation unit may be performed using AI or not using AI. For example, the confirmation unit can input participant emotion data into a generative AI and have the generative AI adjust the rescheduling confirmation method.

[0102] The verification unit can analyze the participant's past rescheduling history and select the optimal verification method when rescheduling. For example, the verification unit can propose the optimal verification method based on the participant's past rescheduling history. The verification unit can also analyze the participant's past rescheduling history and customize the verification method. The verification unit can also select the most efficient verification method based on the participant's past rescheduling history. This allows for efficient rescheduling verification by selecting the optimal verification method through analysis of the participant's past rescheduling history. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the participant's past rescheduling history data into AI and have the AI ​​select the optimal verification method.

[0103] The verification unit can perform verification based on the participant's current project status when rescheduling. For example, the verification unit can propose the optimal verification method considering the participant's current project status. The verification unit can also customize the verification method based on the participant's current project status. The verification unit can also select the most efficient verification method considering the participant's current project status. This allows for efficient rescheduling verification by performing verification based on the participant's current project status. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the participant's project status data into AI and have the AI ​​select the verification method.

[0104] The confirmation unit can estimate the participant's emotions and determine the priority of rescheduling based on the estimated emotions. For example, if a participant is feeling stressed, the confirmation unit will prioritize rescheduling for lower importance. If a participant is relaxed, the confirmation unit may also prioritize rescheduling for higher importance. If a participant is busy, the confirmation unit may also prioritize rescheduling for higher importance. This reduces the burden on participants by determining the priority of rescheduling according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or not using AI. For example, the confirmation unit can input participant emotion data into a generative AI and have the generative AI determine the priority of rescheduling.

[0105] The verification unit can select the optimal verification method when rescheduling, taking into account the geographical location information of the participants. For example, the verification unit can propose the optimal verification method based on the geographical location information of the participants. The verification unit can also customize the verification method, taking into account the geographical location information of the participants. The verification unit can also select the most efficient verification method based on the geographical location information of the participants. This allows for efficient rescheduling verification by selecting the optimal verification method, taking into account the geographical location information of the participants. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the geographical location information data of the participants into AI and have the AI ​​perform the selection of the verification method.

[0106] The verification unit can analyze the participant's social media activity and propose verification methods when rescheduling. For example, the verification unit can propose the most suitable verification method based on the participant's social media activity. The verification unit can also analyze the content of the participant's social media posts and customize the verification method. The verification unit can also select the most suitable verification method by considering the activities of the participant's social media followers and friends. This allows for efficient rescheduling verification by analyzing the participant's social media activity and proposing verification methods. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the participant's social media data into AI and have the AI ​​propose verification methods.

[0107] The adjustment unit can estimate the participants' emotions and adjust the rescheduling method based on the estimated emotions. For example, if a participant is stressed, the adjustment unit can provide a simple adjustment method and minimize the adjustment procedure. If a participant is relaxed, the adjustment unit can also provide detailed adjustment options and suggest a customizable adjustment method. If a participant is in a hurry, the adjustment unit can also enable rapid rescheduling. This reduces the burden on participants by adjusting the rescheduling method according to their 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 adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input participant emotion data into a generative AI and have the generative AI perform the adjustment of the rescheduling method.

[0108] The adjustment unit can analyze the participant's past rescheduling history and select the optimal adjustment method during rescheduling. For example, the adjustment unit can propose the optimal adjustment method based on the participant's past rescheduling history. The adjustment unit can also analyze the participant's past rescheduling history and customize the adjustment method. The adjustment unit can also select the most efficient adjustment method based on the participant's past rescheduling history. This allows for efficient rescheduling by selecting the optimal adjustment method through analysis of the participant's past rescheduling history. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the participant's past rescheduling history data into AI and have the AI ​​select the optimal adjustment method.

[0109] The adjustment unit can perform rescheduling adjustments based on the participants' current project status. For example, the adjustment unit can propose the optimal adjustment method considering the participants' current project status. The adjustment unit can also customize the adjustment method based on the participants' current project status. The adjustment unit can also select the most efficient adjustment method considering the participants' current project status. This allows for efficient rescheduling adjustments by performing adjustments based on the participants' current project status. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or not. For example, the adjustment unit can input the participants' project status data into AI and have the AI ​​select the adjustment method.

[0110] The adjustment unit can estimate the emotions of participants and determine the priority of rescheduling based on the estimated emotions. For example, if a participant is feeling stressed, the adjustment unit will prioritize rescheduling of lower importance. If a participant is relaxed, the adjustment unit may also prioritize rescheduling of higher importance. If a participant is busy, the adjustment unit may also prioritize rescheduling of higher importance. This reduces the burden on participants by determining the priority of rescheduling according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input participant emotion data into a generative AI and have the generative AI determine the priority of rescheduling.

[0111] The adjustment unit can select the optimal adjustment method when rescheduling, taking into account the geographical location information of the participants. For example, the adjustment unit can propose the optimal adjustment method based on the geographical location information of the participants. The adjustment unit can also customize the adjustment method, taking into account the geographical location information of the participants. The adjustment unit can also select the most efficient adjustment method based on the geographical location information of the participants. This allows for efficient rescheduling by selecting the optimal adjustment method, taking into account the geographical location information of the participants. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the geographical location data of the participants into AI and have AI perform the selection of the adjustment method.

[0112] The adjustment unit can analyze participants' social media activity and propose adjustment methods during rescheduling. For example, the adjustment unit can propose the optimal adjustment method based on participants' social media activity. The adjustment unit can also analyze the content of participants' social media posts and customize the adjustment method. The adjustment unit can also select the optimal adjustment method by considering the activities of participants' social media followers and friends. This allows for efficient rescheduling by analyzing participants' social media activity and proposing adjustment methods. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input participants' social media data into AI and have the AI ​​execute the proposal of adjustment methods.

[0113] The adjustment unit can select the optimal adjustment method by referring to the participants' calendar information when rescheduling. For example, the adjustment unit can propose the optimal adjustment method based on the participants' calendar information. The adjustment unit can also customize the adjustment method by referring to the participants' calendar information. The adjustment unit can also select the most efficient adjustment method based on the participants' calendar information. This allows for efficient rescheduling by selecting the optimal adjustment method by referring to the participants' calendar information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the participants' calendar information data into AI and have the AI ​​select the adjustment method.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The data collection unit can acquire participants' health data and adjust the timing of collecting available appointments based on their health status. For example, if a participant is fatigued, the collection timing can be delayed until they have rested before collecting their available appointments. If a participant is healthy, their available appointments can be collected immediately, allowing for quick meeting scheduling. This reduces the burden on participants by adjusting the timing of available appointment collection according to their health status.

[0116] The collection unit can estimate participants' emotions and customize the collection method for available appointments based on those emotions. For example, if a participant is feeling stressed, the collection unit can offer a simple collection method to reduce their burden. If a participant is relaxed, it can offer more detailed collection options and suggest a customizable collection method. This allows for a reduction in participants' burden by customizing the collection method for available appointments according to their emotions.

[0117] The generation unit can analyze participants' past meeting attendance history and select the optimal meeting setting method. For example, it can select the optimal meeting setting method based on the time slots participants have frequently attended in the past. It can also suggest the most efficient setting method based on participants' past meeting attendance history. In this way, by analyzing participants' past meeting attendance history, the optimal meeting setting method can be selected and meetings can be set up efficiently.

[0118] The generation unit can estimate the participants' emotions and adjust the meeting setup method based on those estimates. For example, if a participant is feeling stressed, it can provide a simple setup method and minimize the meeting setup procedure. If a participant is relaxed, it can provide detailed setup options and suggest a customizable setup method. This reduces the burden on participants by adjusting the meeting setup method according to their emotions.

[0119] The generation unit can adjust how meetings are scheduled based on the participants' current project status. For example, if participants are working on important projects, the meeting scheduling process can be simplified and expedited. If participants are working on less demanding projects, the system can offer detailed scheduling options and suggest a customizable scheduling method. This allows for efficient meeting scheduling by adjusting the meeting scheduling process according to the participants' current project status.

[0120] The booking system can estimate participants' emotions and adjust the meeting room booking process based on those estimates. For example, if participants are feeling stressed, it can offer a simple booking method and minimize the booking procedure. If participants are relaxed, it can offer detailed booking options and suggest a customizable booking method. This reduces the burden on participants by adjusting the meeting room booking process according to their emotions.

[0121] The reservation department can analyze the past usage history of meeting rooms to select the optimal reservation method. For example, it can suggest the most suitable reservation method based on the meeting room's past usage history. It can also analyze the past usage history of meeting rooms and customize the reservation method. This allows for efficient meeting room reservations by selecting the optimal reservation method through analysis of past usage history.

[0122] The service provider can estimate participants' emotions and adjust how the meeting URL is issued based on those estimates. For example, if a participant is feeling stressed, a simple issuance method can be provided, minimizing the URL issuance procedure. If a participant is relaxed, more detailed issuance options can be provided, and a customizable issuance method can be suggested. This reduces the burden on participants by adjusting how the meeting URL is issued according to their emotions.

[0123] The service provider can adjust the level of detail in the URL based on the importance of the meeting. For example, for important meetings, a detailed URL issuance method can be provided. For less important meetings, a simpler URL issuance method can be provided. This allows for efficient issuance of meeting URLs by adjusting the level of detail according to the importance of the meeting.

[0124] The confirmation unit can estimate the participant's emotions and adjust the rescheduling confirmation method based on the estimated emotions. For example, if a participant is feeling stressed, it can provide a simple confirmation method and minimize the confirmation procedure. If the participant is relaxed, it can provide detailed confirmation options and suggest a customizable confirmation method. This reduces the burden on participants by adjusting the rescheduling confirmation method according to their emotions.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The collection unit checks the participants' availability. The collection unit checks the calendar's availability, for example, and collects available appointments for specific time slots. The collection unit uses the calendar API to retrieve available appointments and analyzes the calendar information to identify available times. Step 2: The generation unit sets up meetings based on the availability collected by the collection unit. The generation unit sets up meetings during times when all participants can attend and automatically adjusts the meeting times. The generation unit determines the meeting time based on the participants' calendar information and sets the optimal meeting time by comparing the participants' availability. Step 3: The reservation department automatically reserves an available meeting room for the meeting set by the generation department. The reservation department checks the availability of meeting rooms on the calendar, selects the most suitable meeting room, and makes the reservation. The reservation department makes the reservation considering the size and facilities of the meeting room, and selects the most suitable meeting room by checking the availability of meeting rooms in real time. Step 4: The provisioning unit issues a meeting URL for the meeting set up by the generation unit and embeds it in the meeting notification. The provisioning unit issues a URL for the online meeting system and embeds the meeting URL in the meeting notification, making it easy for participants to access the meeting. Step 5: The confirmation unit confirms the rescheduling with the meeting participants set by the generation unit. The confirmation unit sends a message to the participants asking if rescheduling is possible, and sends the message "Is rescheduling possible?" to the participants in order to confirm the rescheduling. Step 6: The coordination unit adjusts the rescheduling based on the confirmation unit's instructions. The coordination unit makes further adjustments based on the rescheduling confirmation results, reconfirms participants' availability, and sets the optimal meeting time. The coordination unit collects participants' availability again in order to adjust the rescheduling.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, generation unit, reservation unit, provision unit, confirmation unit, and adjustment unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and checks the availability of time on the calendar. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and sets up a meeting based on the available appointments collected by the collection unit. The reservation unit is implemented, for example, by the control unit 46A of the smart device 14 and checks the availability of meeting rooms and automatically reserves the most suitable meeting room. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and issues a URL for the online meeting system and embeds it in the meeting notification. The confirmation unit is implemented, for example, by the control unit 46A of the smart device 14 and sends a message to participants asking if rescheduling is possible. The adjustment unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and makes further adjustments based on the rescheduling confirmation results. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, generation unit, reservation unit, provision unit, confirmation unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and checks the availability of time on the calendar. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and sets up a meeting based on the availability collected by the collection unit. The reservation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and checks the availability of meeting rooms and automatically reserves the most suitable meeting room. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and issues a URL for the online meeting system and embeds it in the meeting notification. The confirmation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and sends a message to participants asking if rescheduling is possible. The adjustment unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and makes further adjustments based on the rescheduling confirmation result. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, generation unit, reservation unit, provision unit, confirmation unit, and adjustment unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and checks the availability of time on the calendar. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sets up a meeting based on the available appointments collected by the collection unit. The reservation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and checks the availability of meeting rooms and automatically reserves the most suitable meeting room. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and issues a URL for the online meeting system and embeds it in the meeting notification. The confirmation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and sends a message to participants asking if rescheduling is possible. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs further adjustments based on the rescheduling confirmation result. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each of the multiple elements described above, including the collection unit, generation unit, reservation unit, provision unit, confirmation unit, and adjustment unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and checks the availability of time on the calendar. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and sets up a meeting based on the available appointments collected by the collection unit. The reservation unit is implemented by, for example, the control unit 46A of the robot 414 and checks the availability of meeting rooms and automatically reserves the most suitable meeting room. The provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and issues a URL for the online meeting system and embeds it in the meeting notification. The confirmation unit is implemented by, for example, the control unit 46A of the robot 414 and sends a message to participants asking if rescheduling is possible. The adjustment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and makes further adjustments based on the rescheduling confirmation results. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) The collection department checks the availability of participants, A generation unit sets up meetings based on the available schedules collected by the collection unit, A reservation unit that automatically reserves an available meeting room for a meeting set by the generation unit, A providing unit that issues a meeting URL for the meeting set by the generation unit and embeds it in the meeting notification, A confirmation unit that confirms the rescheduling of the meeting for the participants set by the generation unit, The system includes an adjustment unit that adjusts the rescheduling based on the aforementioned confirmation unit, The aforementioned verification unit includes a specific method for verifying the rescheduling, The adjustment unit includes a specific method for adjusting the rescheduling. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system estimates the participants' emotions and adjusts the timing of collecting available appointments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze participants' past meeting attendance history and select the appropriate data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting available time slots, filter them based on participants' current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates the participants' emotions and determines the priority of available appointments to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting available schedules, the system prioritizes collecting schedules that are highly relevant, taking into account the geographical location of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting available appointments, analyze participants' social media activity and collect relevant appointments. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is The system estimates the participants' emotions and adjusts how the meeting is set up based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When setting up a meeting, adjust the level of detail based on the importance of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When setting up a meeting, different setting algorithms are applied depending on the participant's category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is The system estimates the participants' emotions and adjusts the meeting length based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When scheduling a meeting, prioritize the meeting based on when participants submit their submissions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When setting up a meeting, adjust the meeting order based on the relevance of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reservation section is, The system estimates the emotions of the participants and adjusts the meeting room reservation method based on the estimated emotions of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reservation section is, When booking a meeting room, the system analyzes the room's past usage history to select the most appropriate booking method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reservation section is, When booking a meeting room, make the reservation based on the meeting room's facilities and equipment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reservation section is, The system estimates the emotions of the participants and determines the priority of meeting rooms based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reservation section is, When booking a meeting room, the system selects the most suitable room by considering its geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reservation section is, When booking a meeting room, refer to relevant literature to improve the accuracy of the booking. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, We estimate the emotions of the participants and adjust how the meeting URL is issued based on the estimated emotions of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When issuing a meeting URL, adjust the level of detail in the URL based on the importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When issuing a meeting URL, different issuance algorithms are applied depending on the meeting category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates the participants' emotions and adjusts the length of the meeting URL based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When issuing meeting URLs, priority is determined based on when the meeting was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When issuing meeting URLs, adjust the order of URLs based on the relevance of the meetings. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned verification unit is We estimate the participants' emotions and adjust the rescheduling confirmation method based on the estimated emotions of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned verification unit is When confirming rescheduling, the participant's past rescheduling history is analyzed to select the most appropriate confirmation method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned verification unit is When confirming the rescheduling, the confirmation will be based on the current project status of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned verification unit is The system estimates the emotions of the participants and determines the priority of rescheduling based on the estimated emotions of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned verification unit is When confirming the rescheduling, the most suitable confirmation method will be selected, taking into account the geographical location information of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned verification unit is When confirming rescheduling, we will analyze participants' social media activity and propose methods for confirmation. The system described in Appendix 1, characterized by the features described herein. (Note 32) The adjustment unit is, We estimate the participants' emotions and adjust the rescheduling method based on the estimated emotions of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 33) The adjustment unit is, When rescheduling, we analyze the participants' past rescheduling history to select the most suitable adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The adjustment unit is, When rescheduling, adjustments will be made based on the current project status of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 35) The adjustment unit is, The system estimates the emotions of the participants and determines the priority of rescheduling based on the estimated emotions of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 36) The adjustment unit is, When rescheduling, the optimal adjustment method will be selected considering the geographical location information of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 37) The adjustment unit is, During the rescheduling process, we will analyze participants' social media activity and propose methods for adjustment. The system described in Appendix 1, characterized by the features described herein. (Note 38) The adjustment unit is, When rescheduling, refer to the participants' calendar information to select the most suitable adjustment method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0199] 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 collection department checks the availability of participants, A generation unit sets up meetings based on the available schedules collected by the collection unit, A reservation unit that automatically reserves an available meeting room for a meeting set by the generation unit, A providing unit that issues a meeting URL for the meeting set by the generation unit and embeds it in the meeting notification, A confirmation unit that confirms the rescheduling of the meeting for the participants set by the generation unit, The system includes an adjustment unit that adjusts the rescheduling based on the aforementioned confirmation unit, The aforementioned verification unit includes a specific method for verifying the rescheduling, The adjustment unit includes a specific method for adjusting the rescheduling. A system characterized by the following features.

2. The aforementioned collection unit is The system estimates the participants' emotions and adjusts the timing of collecting available appointments based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze participants' past meeting attendance history and select the appropriate data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting available time slots, filter them based on participants' current projects and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is The system estimates the participants' emotions and determines the priority of available appointments to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting available schedules, the system prioritizes collecting schedules that are highly relevant, taking into account the geographical location of the participants. The system according to feature 1.

7. The aforementioned collection unit is When collecting available appointments, analyze participants' social media activity and collect relevant appointments. The system according to feature 1.

8. The generating unit is The system estimates the participants' emotions and adjusts how the meeting is set up based on those estimated emotions. The system according to feature 1.

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