system

Abstract

The system according to this embodiment aims to automate and efficiently manage internal company events, including scheduling, venue reservations, and task management. [Solution] The system according to the embodiment comprises a schedule adjustment unit, a venue reservation unit, an information generation unit, and a task management unit. The schedule adjustment unit collects and analyzes the schedules of participants. The venue reservation unit checks the availability of venues and makes reservations based on the information collected by the schedule adjustment unit. The information generation unit generates and sends information emails based on the information reserved by the venue reservation unit. The task management unit manages the progress of tasks based on the information sent by the information generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, processes such as schedule adjustment of in-house events, venue reservation, and task management are manually performed, so the efficiency is low and there is a risk of mistakes.

[0005] The system according to the embodiment aims to automate and efficiently manage schedule adjustment of in-house events, venue reservation, and task management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a schedule adjustment unit, a venue reservation unit, an information generation unit, and a task management unit. The schedule adjustment unit collects and analyzes participants' schedules. The venue reservation unit checks venue availability and makes reservations based on the information collected by the schedule adjustment unit. The information generation unit generates and sends information emails based on the reservation information made by the venue reservation unit. The task management unit manages the progress of tasks based on the information sent by the information generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate and efficiently manage internal company events, including scheduling, venue reservations, and task management. [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 manages communication between multiple 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 event management automation system according to an embodiment of the present invention is a system that automates and optimizes the operation of in-house events using an AI agent. This system collects and analyzes participants' schedules and automatically adjusts the optimal date. Next, it checks venue availability in real time and automates reservations. Furthermore, it automatically generates and sends event announcements and reminder emails, and incorporates a task management function to automatically track the progress of event preparations. As a result, event organizers can centrally manage all data on a dashboard and visualize the operation. In addition, the AI ​​also handles tasks such as hosting, recording, and photography on the day of the event, and automatically reflects the schedule derived from the task list in the organizer's calendar, thereby managing and optimizing all aspects of event operation in one place. As a result, the event management automation system can streamline event operation and reduce the burden on organizers.

[0029] The event management automation system according to this embodiment comprises a schedule adjustment unit, a venue reservation unit, an invitation generation unit, and a task management unit. The schedule adjustment unit collects and analyzes the schedules of participants. For example, the schedule adjustment unit obtains the calendar information of participants and automatically adjusts the optimal date. The schedule adjustment unit can compare the schedules of multiple participants and identify a date on which everyone can participate. Some or all of the above processing in the schedule adjustment unit may be performed using AI or not. The venue reservation unit checks the availability of venues based on the information collected by the schedule adjustment unit and makes reservations. For example, the venue reservation unit cooperates with a conference room reservation system to check availability and automatically make reservations. The venue reservation unit can check the availability of venues in real time and select the optimal venue. Some or all of the above processing in the venue reservation unit may be performed using AI or not. The invitation generation unit generates and sends invitation emails based on the information reserved by the venue reservation unit. For example, the invitation generation unit creates an invitation email based on the details of the event and sends it to participants. The information generation unit can also automatically send reminder emails the day before the event. Some or all of the above-mentioned processes in the information generation unit may be performed using AI or not. The task management unit manages the progress of tasks based on the information transmitted by the information generation unit. For example, the task management unit updates the progress of each task in real time and centrally manages it on a dashboard. The task management unit can automatically track the progress of event preparations and provide feedback to the organizers. Some or all of the above-mentioned processes in the task management unit may be performed using AI or not. As a result, the event management automation system according to the embodiment can streamline event management by automating participant scheduling, venue reservations, information email generation and sending, and task management.

[0030] The scheduling unit collects and analyzes participants' schedules. For example, when the scheduling unit obtains participants' calendar information to automatically adjust the optimal schedule, it can obtain permission from each participant and link with calendar applications or schedule management systems. This allows the scheduling unit to acquire each participant's schedule in real time and check for overlaps and free time. The scheduling unit can match the schedules of multiple participants and identify a date when everyone can participate, thus enabling schedule optimization using AI. The AI ​​can use machine learning algorithms to analyze each participant's schedule data and suggest the optimal date. For example, the AI ​​can identify the most suitable date by considering past schedule data and participant priorities. Some or all of the above processing in the scheduling unit may be performed using AI or not. If AI is not used, the scheduling unit can perform schedule adjustments using simple algorithms or rule-based systems. This enables the scheduling unit to efficiently adjust participants' schedules and identify the optimal date when everyone can participate. Furthermore, the scheduling unit can notify participants of the adjustment results and request their confirmation. For example, the adjustment results can be sent via email or notification, and the final date can be confirmed and approved by the participants. This enables the scheduling unit to streamline participant schedule adjustments and contribute to the smooth operation of the event.

[0031] The venue reservation department checks venue availability and makes reservations based on information collected by the scheduling department. The venue reservation department can, for example, link with a meeting room reservation system via an API to check availability and make reservations automatically, thereby obtaining real-time availability. This allows the venue reservation department to select and reserve the optimal venue based on the dates identified by the scheduling department. Because the venue reservation department can check venue availability in real time and select the optimal venue, it can optimize venue selection using AI. The AI ​​can use machine learning algorithms to identify the optimal venue by considering conditions such as venue capacity, facilities, and accessibility. For example, the AI ​​can use past data. Based on reservation data and participant feedback, the venue reservation department can suggest the most suitable venue. Some or all of the above processing in the venue reservation department may be performed using AI or not. If AI is not used, the venue reservation department can use a simple algorithm or rule-based system to select and reserve venues. This allows the venue reservation department to efficiently reserve the optimal venue based on the dates identified by the scheduling department. Furthermore, the venue reservation department can notify participants of the reservation results and request their confirmation. For example, the reservation results can be sent via email or notification, and the final venue can be confirmed and approved by the participants. This allows the venue reservation department to streamline venue reservations and contribute to the smooth operation of events.

[0032] The notification generation unit generates and sends notification emails based on the information reserved by the venue reservation unit. For example, the notification generation unit can automatically generate notification emails that include information such as the date and time of the event, the location, the event content, and the roles of participants, based on the details of the event, and send them to participants. This allows the notification generation unit to provide participants with the necessary information quickly and accurately. The notification generation unit can also automatically send reminder emails the day before the event, and can optimize the content of the notification emails using AI. The AI ​​can use machine learning algorithms to identify the optimal content and timing of sending notification emails based on participants' past participation history and feedback. For example, the AI ​​can use machine learning algorithms based on participants' preferences and interests. The above-mentioned process in the information generation unit, which can generate personalized information emails, may be performed using AI or not. If AI is not used, the information generation unit can generate and send information emails using simple templates or rule-based systems. This allows the information generation unit to efficiently generate and send information emails based on information reserved by the venue reservation unit. Furthermore, the information generation unit can track the sending results and analyze participant responses. For example, it can monitor the open rate and click-through rate of information emails and improve the content and timing of the next information email. This allows the information generation unit to streamline the generation and sending of information emails and contribute to the smooth operation of events.

[0033] The Task Management Department manages the progress of tasks based on information transmitted by the Guidance Generation Department. For example, the Task Management Department can centrally manage information such as the person in charge, deadline, and progress of each task in order to update the progress of each task in real time and manage it centrally on a dashboard. This allows the Task Management Department to grasp the progress of event preparations in real time and provide feedback to the organizers. The Task Management Department can automatically track the progress of event preparations and provide feedback to the organizers, and can optimize the progress of tasks using AI. The AI ​​can analyze the progress of each task and the performance of the person in charge, and can use machine learning algorithms to make optimal task assignments and progress management. For example, the AI ​​can analyze past task data and the performance of the person in charge. Based on skills, the task management unit can suggest the most suitable task assignment. Some or all of the above processes in the task management unit may be performed using AI or not. If AI is not used, the task management unit can manage task progress using simple algorithms or rule-based systems. This allows the task management unit to efficiently manage task progress based on information transmitted by the guidance generation unit. Furthermore, the task management unit can visualize the progress and enable operators to respond quickly. For example, it can highlight tasks that are behind schedule and send alerts to operators to encourage quick responses. This allows the task management unit to streamline task progress management and contribute to the smooth operation of events.

[0034] The task management unit can automatically reflect the schedule derived from the task list onto the operator's calendar. For example, the task management unit can analyze the information in the task list and automatically add the schedule to the operator's calendar. The task management unit can also adjust the schedule considering the priority and deadline of tasks. The task management unit can also reflect any changes to the schedule on the calendar in real time. This reduces the operator's workload by automatically reflecting the schedule derived from the task list onto the calendar. Some or all of the above processes in the task management unit may be performed using AI, or they may not.

[0035] The task management department can update the progress of event preparations in real time and manage it centrally on a dashboard. For example, the task management department can update the progress of each task in real time and manage it centrally on a dashboard. The task management department can automatically track the progress of event preparations and provide feedback to the organizers. The task management department can also visually display the progress using graphs and charts. This makes it easier for organizers to understand the progress by updating and centrally managing the progress of event preparations in real time. Some or all of the above processes in the task management department may be performed using AI or not.

[0036] The event management automation system according to this embodiment includes a moderation unit that acts as the moderator on the day of the event. The moderation unit acts as the moderator on the day of the event. For example, the moderation unit uses AI to moderate and smoothly support the progress of the event. The moderation unit can make announcements at appropriate times based on the event's progress script. The moderation unit can also analyze participants' reactions in real time and adjust the moderation method. By automating the moderation on the day of the event, the burden on the organizers can be reduced. Some or all of the above-described processes in the moderation unit may be performed using AI or not using AI.

[0037] The event management automation system according to this embodiment includes a recording unit that records the event. The recording unit records the event. For example, the recording unit uses AI to manage the event recording and automatically control the start and end timing of the recording. The recording unit can adjust the viewpoint and camera angle of the recording according to the progress of the event. The recording unit can also edit the recorded data in real time and provide it to participants. By automating the recording of events, the burden on the organizers can be reduced. Some or all of the above-described processes in the recording unit may be performed using AI or not using AI.

[0038] The event management automation system according to this embodiment includes a photography unit that takes photographs of the event. The photography unit takes photographs of the event. For example, the photography unit automatically takes photographs of the event scene using an AI-equipped camera and edits and provides them in real time. The photography unit can adjust the timing and angle of shooting according to the progress of the event. The photography unit can also upload the photographs taken to the cloud and share them with participants. By automating event photography, the burden on the organizers can be reduced. Some or all of the above processing in the photography unit may be performed using AI or not.

[0039] The scheduling unit can analyze participants' past scheduling history and select the optimal scheduling method. For example, the scheduling unit can prioritize scheduling time slots in which participants have frequently participated in the past. The scheduling unit can also predict the time slots that are most likely to be available based on participants' past scheduling history. The scheduling unit can also coordinate with other participants based on participants' past scheduling history. In this way, by analyzing past scheduling history, the optimal scheduling method can be provided for each participant. Some or all of the above processing in the scheduling unit may be performed using generative AI, or it may be performed without using generative AI.

[0040] The scheduling unit can filter participants based on their current projects and areas of interest when scheduling. For example, the scheduling unit can prioritize events related to projects that participants are currently working on. The scheduling unit can also prioritize events that are highly relevant based on participants' areas of interest. The scheduling unit can also propose an optimal schedule considering the progress of participants' current projects. This allows for the provision of a highly relevant schedule by adjusting the schedule based on participants' current projects and areas of interest. Some or all of the above processing in the scheduling unit may be performed using generative AI, or it may be performed without using generative AI.

[0041] The scheduling unit can prioritize scheduling events based on the geographical location of participants, taking their geographical location into consideration. For example, if a participant is nearby, the scheduling unit prioritizes scheduling events that minimize travel time. If a participant is far away, the scheduling unit can also propose a schedule with ample buffer time to account for travel time. The scheduling unit can also select the most suitable venue based on the geographical location of participants and adjust the schedule accordingly. This optimizes travel time by adjusting the schedule while considering the geographical location of participants. Some or all of the above-described processes in the scheduling unit may be performed using a generative AI, or they may be performed without using a generative AI.

[0042] The scheduling unit can analyze participants' social media activity and adjust relevant schedules when scheduling. For example, the scheduling unit can prioritize events that participants have shown interest in on social media. The scheduling unit can also predict the optimal time slots based on participants' social media activity and adjust schedules accordingly. The scheduling unit can also coordinate with other participants based on participants' social media activity. This allows the system to provide highly relevant schedules by analyzing participants' social media activity. Some or all of the above processing in the scheduling unit may be performed using generative AI, or it may be performed without using generative AI.

[0043] The venue reservation department can select the most suitable venue by referring to past reservation history when a venue is reserved. For example, the venue reservation department can select the most suitable venue based on evaluations of venues used in the past. The venue reservation department can also select a venue that suits the preferences of participants from past reservation history. The venue reservation department can also analyze past reservation history and select the venue that is used most frequently. In this way, by referring to past reservation history, the most suitable venue for participants can be provided. Some or all of the above processing in the venue reservation department may be performed using generative AI, or it may be performed without using generative AI.

[0044] The venue reservation department can select the most suitable venue based on its facilities and services when making a reservation. For example, the venue reservation department can select the most suitable venue by considering its facilities (projector, sound system, etc.). The venue reservation department can also select a venue that meets the needs of participants based on the venue's services (catering, Wi-Fi, etc.). The venue reservation department can also compare the facilities and services of venues and select the most suitable venue. In this way, by selecting a venue based on its facilities and services, the department can provide the most suitable venue for participants. Some or all of the above processing in the venue reservation department may be performed using generative AI, or it may be performed without using generative AI.

[0045] The venue reservation department can select the most suitable venue by considering its geographical location when making a reservation. For example, if participants are nearby, the venue reservation department will prioritize selecting a venue that minimizes travel time. If participants are far away, the venue reservation department can also select a venue with ample time to allow for travel. The venue reservation department can also select the most suitable venue based on its geographical location. This allows for the optimization of travel time by selecting a venue while considering its geographical location. Some or all of the above processing in the venue reservation department may be performed using generative AI, or it may be performed without using generative AI.

[0046] The venue reservation department can select the most suitable venue by referring to venue reviews and ratings when making a reservation. For example, the venue reservation department can select a venue with high participant ratings based on venue reviews. The venue reservation department can also select the most suitable venue by referring to venue ratings. The venue reservation department can also analyze venue reviews and ratings to select the most suitable venue. In this way, by referring to venue reviews and ratings, it is possible to provide the most suitable venue for participants. Some or all of the above processing in the venue reservation department may be performed using generative AI, or it may be performed without using generative AI.

[0047] The notification generation unit can adjust the level of detail in notification emails based on the importance of the event when generating them. For example, for important events, the notification generation unit generates notification emails containing detailed information. For general events, the notification generation unit can also generate notification emails containing concise information. For urgent events, the notification generation unit can also generate notification emails that are concise and to the point, allowing for quick response. By adjusting the level of detail in notification emails based on the importance of the event, the unit can provide participants with the most appropriate notification email. Some or all of the above processing in the notification generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0048] The invitation generation unit can apply different invitation email algorithms depending on the event category when generating invitation emails. For example, in the case of a business event, the invitation generation unit can generate an invitation email using formal language. In the case of a casual event, the invitation generation unit can also generate an invitation email using friendly language. In the case of an academic event, the invitation generation unit can also generate an invitation email using specialized language. By applying different invitation email algorithms depending on the event category, it is possible to provide the most suitable invitation email for participants. Some or all of the above processing in the invitation generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0049] The notification generation unit can determine the priority of notification emails based on the timing of the event when generating notification emails. For example, the notification generation unit will prioritize generating notification emails for upcoming events. For long-term events, the notification generation unit can also generate notification emails at an appropriate time. For urgent events, the notification generation unit can also generate notification emails quickly. By prioritizing notification emails based on the timing of the event, the unit can provide participants with the most suitable notification emails. Some or all of the above processing in the notification generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0050] The notification generation unit can adjust the order of notification emails based on the relevance of the events when generating notification emails. For example, the notification generation unit can prioritize notification emails for events that are important to the participant. The notification generation unit can also prioritize notification emails for events that are highly relevant based on the participant's interests. The notification generation unit can also prioritize notification emails for events that are highly relevant based on the participant's past participation history. In this way, by adjusting the order of notification emails based on the relevance of the events, the most suitable notification emails can be provided to the participant. Some or all of the above processing in the notification generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0051] The task management unit can select the optimal task management method by referring to past task history when managing tasks. For example, the task management unit can select the optimal task management method based on past successful task management methods. The task management unit can also select a task management method that suits the participant's preferences from past task history. The task management unit can also analyze past task history and select the most efficient task management method. In this way, by referring to past task history, it is possible to provide the optimal task management method for the participant. Some or all of the above processing in the task management unit may be performed using generative AI, or it may be performed without using generative AI.

[0052] The task management unit can determine task priorities based on the importance of the task during task management. For example, the task management unit can prioritize important tasks. The task management unit can also prioritize urgent tasks. The task management unit can also postpone tasks of lower importance. By determining task priorities based on the importance of the task, it is possible to provide optimal task management for participants. Some or all of the above processes in the task management unit may be performed using generative AI, or they may not be performed using generative AI.

[0053] The task management unit can manage the progress of tasks while considering their geographical distribution. For example, the task management unit can prioritize tasks where participants are nearby. It can also postpone tasks where participants are far away. Based on the geographical distribution of tasks, the task management unit can select the optimal task management method. This allows for optimal task management for participants by managing tasks while considering their geographical distribution. Some or all of the above processing in the task management unit may be performed using generative AI, or it may be performed without using generative AI.

[0054] The task management unit can manage the progress of tasks by referring to relevant literature during task management. For example, the task management unit can select the optimal task management method based on the relevant literature. The task management unit can also manage the progress of tasks by referring to relevant literature. The task management unit can also analyze relevant literature and select the most efficient task management method. In this way, by referring to relevant literature, it is possible to provide optimal task management for participants. Some or all of the above processes in the task management unit may be performed using generative AI, or they may be performed without using generative AI.

[0055] The moderator can select the most suitable moderation method by referring to past moderation history during moderation. For example, the moderator can select the most suitable moderation method based on past successful methods. The moderator can also select a moderation method that suits the participants' preferences from past moderation history. The moderator can also analyze past moderation history and select the most efficient moderation method. In this way, by referring to past moderation history, the moderator can provide the most suitable moderation method for the participants. Some or all of the above processing in the moderator may be performed using generative AI, or it may be performed without using generative AI.

[0056] The moderator can customize their moderation methods based on the progress of the event. For example, the moderator can flexibly change the moderation method according to the progress of the event. The moderator can also monitor the progress of the event in real time and select the optimal moderation method. The moderator can also adjust the moderation method while observing the reactions of the participants based on the progress of the event. In this way, by customizing the moderation methods based on the progress of the event, the moderator can provide the best possible moderation experience for the participants. Some or all of the above processes performed by the moderator may be carried out using generative AI, or they may not be carried out using generative AI.

[0057] The moderator can select the most appropriate moderation method during the moderation process, taking into account the geographical location information of the participants. For example, if the participants are nearby, the moderator may select a moderation method that emphasizes direct communication. If the participants are far away, the moderator may also select an online moderation method. The moderator may also select the most appropriate moderation method based on the geographical location information of the participants. By selecting a moderation method that takes into account the geographical location information of the participants, the moderator can provide the most optimal moderation experience for the participants. Some or all of the above processing in the moderator may be performed using generative AI, or it may be performed without using generative AI.

[0058] The moderator can analyze participants' social media activity during moderation and propose methods for moderation. For example, the moderator can propose a moderation method that incorporates topics that participants have shown interest in on social media. The moderator can also propose the optimal moderation method based on participants' social media activity. The moderator can also coordinate with other participants based on participants' social media activity. In this way, by analyzing participants' social media activity, the moderator can provide the optimal moderation for each participant. Some or all of the above processing by the moderator may be performed using generative AI, or it may not be performed using generative AI.

[0059] The recording unit can select the optimal recording method by referring to past recording history during recording. For example, the recording unit can select the optimal recording method based on past successful recording methods. The recording unit can also select a recording method that suits the participant's preferences from past recording history. The recording unit can also analyze past recording history and select the most efficient recording method. In this way, by referring to past recording history, the optimal recording method can be provided for the participant. Some or all of the above processing in the recording unit may be performed using generative AI, or it may be performed without using generative AI.

[0060] The recording unit can customize the recording method based on the progress of the event during recording. For example, the recording unit can flexibly change the recording method according to the progress of the event. The recording unit can also grasp the progress of the event in real time and select the optimal recording method. The recording unit can also adjust the recording method while observing the reactions of participants based on the progress of the event. In this way, by customizing the recording method based on the progress of the event, it is possible to provide the best possible recording for participants. Some or all of the above processing in the recording unit may be performed using generative AI, or it may be performed without using generative AI.

[0061] The recording unit can select the optimal recording method during recording, taking into account the geographical location information of the participants. For example, if the participants are nearby, the recording unit will select a recording method that emphasizes direct communication. If the participants are far away, the recording unit can also select an online recording method. The recording unit can also select the optimal recording method based on the geographical location information of the participants. This allows the recording unit to provide the best possible recording for each participant by selecting a recording method that takes their geographical location into consideration. Some or all of the above processing in the recording unit may be performed using generative AI, or it may be performed without using generative AI.

[0062] The recording unit can analyze participants' social media activity during recording and suggest recording methods. For example, the recording unit can suggest a recording method that incorporates topics that participants have shown interest in on social media. The recording unit can also suggest the optimal recording method based on participants' social media activity. The recording unit can also coordinate with other participants based on participants' social media activity. In this way, by analyzing participants' social media activity, it is possible to provide the optimal recording for each participant. Some or all of the above processing in the recording unit may be performed using generative AI, or it may be performed without using generative AI.

[0063] The photography department can select the optimal photography method by referring to past photography history when taking photographs. For example, the photography department can select the optimal photography method based on past successful photography methods. The photography department can also select a photography method that suits the participant's preferences from past photography history. The photography department can also analyze past photography history and select the most efficient photography method. In this way, by referring to past photography history, the photography department can provide the optimal photography method for the participant. Some or all of the above processes in the photography department may be performed using generative AI, or they may be performed without using generative AI.

[0064] The photography team can customize the photography methods based on the progress of the event. For example, the photography team can flexibly change the photography method according to the progress of the event. The photography team can also monitor the progress of the event in real time and select the optimal photography method. The photography team can also adjust the photography method while observing the reactions of the participants based on the progress of the event. In this way, by customizing the photography methods based on the progress of the event, the photography team can provide the best possible photographs for the participants. Some or all of the above processes performed by the photography team may be carried out using generative AI, or they may not be carried out using generative AI.

[0065] The photography team can select the optimal photography method when taking photographs, taking into account the geographical location information of the participants. For example, if the participants are nearby, the photography team will select a photography method that emphasizes direct communication. If the participants are far away, the photography team can also select an online photography method. The photography team can also select the optimal photography method based on the geographical location information of the participants. By selecting a photography method that takes into account the geographical location information of the participants, the photography team can provide the best possible photographs for each participant. Some or all of the above processing in the photography team may be performed using generative AI, or it may be performed without using generative AI.

[0066] The photography team can analyze participants' social media activity during photo shoots and suggest photography methods. For example, the photography team can suggest a shooting method that incorporates topics that participants have shown interest in on social media. The photography team can also suggest the optimal shooting method based on participants' social media activity. The photography team can also coordinate with other participants based on participants' social media activity. In this way, by analyzing participants' social media activity, the photography team can provide the best possible photo shoot for each participant. Some or all of the above processes in the photography team may be performed using generative AI, or they may not be performed using generative AI.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The automated event management system can also include an interest analysis unit that analyzes participants' interests and preferences. This unit, for example, analyzes participants' past event attendance history and social media activity to identify their interests. Based on these interests, the unit can also suggest relevant events. By providing content tailored to participants' interests, the unit can enhance the appeal of events. This enables event management that considers participants' interests and preferences, thereby improving participant satisfaction.

[0069] The automated event management system can also include a feedback collection unit that collects and analyzes participant feedback. For example, the feedback collection unit can automatically send questionnaires to participants after the event to collect feedback. The feedback collection unit can also analyze the collected feedback to identify areas for improvement in the event. Based on participant feedback, the feedback collection unit can also optimize the planning of future events. This enables event management that reflects participant feedback, thereby improving participant satisfaction.

[0070] The automated event management system can also include a networking support unit to assist with participant networking. This unit can, for example, match participants with shared interests based on their profile information. It can also suggest activities to promote interaction among participants during the event. Furthermore, it can provide a platform to support communication among participants even after the event has ended. This enhances the value of the event by supporting participant networking.

[0071] The automated event management system can also include a mobility support unit to assist participants with their travel. This unit can, for example, suggest the optimal travel route based on participants' geographical location information. It can also monitor traffic conditions in real time and suggest ways to optimize travel time. Furthermore, it can support participants in booking transportation to ensure a smooth arrival at the event venue. By supporting participants' travel, the system can improve the convenience of attending events.

[0072] The automated event management system can also include a learning support unit to further assist participants in their learning. This unit can, for example, track participants' learning progress based on the content provided during the event. It can also suggest learning methods tailored to each participant's learning style. Furthermore, it can provide resources to support participants' learning even after the event has ended. This enhances the value of the event by supporting participants' learning.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The scheduling unit collects and analyzes participants' schedules. For example, it retrieves participants' calendar information and automatically adjusts the optimal date. The scheduling unit can compare the schedules of multiple participants and identify a date when everyone can attend. Step 2: The venue reservation department checks venue availability and makes reservations based on the information collected by the scheduling department. For example, it can integrate with a conference room reservation system to check availability and make reservations automatically. The venue reservation department can check venue availability in real time and select the most suitable venue. Step 3: The invitation generation unit generates and sends invitation emails based on the information reserved by the venue reservation unit. For example, it creates an invitation email based on the event details and sends it to participants. The invitation generation unit can also automatically send a reminder email the day before the event. Step 4: The task management unit manages the progress of tasks based on the information sent by the guidance generation unit. For example, it updates the progress of each task in real time and manages it centrally on a dashboard. The task management unit can automatically track the progress of event preparations and provide feedback to the organizers.

[0075] (Example of form 2) The event management automation system according to an embodiment of the present invention is a system that automates and optimizes the operation of in-house events using an AI agent. This system collects and analyzes participants' schedules and automatically adjusts the optimal date. Next, it checks venue availability in real time and automates reservations. Furthermore, it automatically generates and sends event announcements and reminder emails, and incorporates a task management function to automatically track the progress of event preparations. As a result, event organizers can centrally manage all data on a dashboard and visualize the operation. In addition, the AI ​​also handles tasks such as hosting, recording, and photography on the day of the event, and automatically reflects the schedule derived from the task list in the organizer's calendar, thereby managing and optimizing all aspects of event operation in one place. As a result, the event management automation system can streamline event operation and reduce the burden on organizers.

[0076] The event management automation system according to this embodiment comprises a schedule adjustment unit, a venue reservation unit, an invitation generation unit, and a task management unit. The schedule adjustment unit collects and analyzes the schedules of participants. For example, the schedule adjustment unit obtains the calendar information of participants and automatically adjusts the optimal date. The schedule adjustment unit can compare the schedules of multiple participants and identify a date on which everyone can participate. Some or all of the above processing in the schedule adjustment unit may be performed using AI or not. The venue reservation unit checks the availability of venues based on the information collected by the schedule adjustment unit and makes reservations. For example, the venue reservation unit cooperates with a conference room reservation system to check availability and automatically make reservations. The venue reservation unit can check the availability of venues in real time and select the optimal venue. Some or all of the above processing in the venue reservation unit may be performed using AI or not. The invitation generation unit generates and sends invitation emails based on the information reserved by the venue reservation unit. For example, the invitation generation unit creates an invitation email based on the details of the event and sends it to participants. The information generation unit can also automatically send reminder emails the day before the event. Some or all of the above-mentioned processes in the information generation unit may be performed using AI or not. The task management unit manages the progress of tasks based on the information transmitted by the information generation unit. For example, the task management unit updates the progress of each task in real time and centrally manages it on a dashboard. The task management unit can automatically track the progress of event preparations and provide feedback to the organizers. Some or all of the above-mentioned processes in the task management unit may be performed using AI or not. As a result, the event management automation system according to the embodiment can streamline event management by automating participant scheduling, venue reservations, information email generation and sending, and task management.

[0077] The scheduling unit collects and analyzes participants' schedules. For example, when the scheduling unit obtains participants' calendar information to automatically adjust the optimal schedule, it can obtain permission from each participant and link with calendar applications or schedule management systems. This allows the scheduling unit to acquire each participant's schedule in real time and check for overlaps and free time. The scheduling unit can match the schedules of multiple participants and identify a date when everyone can participate, thus enabling schedule optimization using AI. The AI ​​can use machine learning algorithms to analyze each participant's schedule data and suggest the optimal date. For example, the AI ​​can identify the most suitable date by considering past schedule data and participant priorities. Some or all of the above processing in the scheduling unit may be performed using AI or not. If AI is not used, the scheduling unit can perform schedule adjustments using simple algorithms or rule-based systems. This enables the scheduling unit to efficiently adjust participants' schedules and identify the optimal date when everyone can participate. Furthermore, the scheduling unit can notify participants of the adjustment results and request their confirmation. For example, the adjustment results can be sent via email or notification, and the final date can be confirmed and approved by the participants. This enables the scheduling unit to streamline participant schedule adjustments and contribute to the smooth operation of the event.

[0078] The venue reservation department checks venue availability and makes reservations based on information collected by the scheduling department. The venue reservation department can, for example, link with a meeting room reservation system via an API to check availability and make reservations automatically, thereby obtaining real-time availability. This allows the venue reservation department to select and reserve the optimal venue based on the dates identified by the scheduling department. Because the venue reservation department can check venue availability in real time and select the optimal venue, it can optimize venue selection using AI. The AI ​​can use machine learning algorithms to identify the optimal venue by considering conditions such as venue capacity, facilities, and accessibility. For example, the AI ​​can use past data. Based on reservation data and participant feedback, the venue reservation department can suggest the most suitable venue. Some or all of the above processing in the venue reservation department may be performed using AI or not. If AI is not used, the venue reservation department can use a simple algorithm or rule-based system to select and reserve venues. This allows the venue reservation department to efficiently reserve the optimal venue based on the dates identified by the scheduling department. Furthermore, the venue reservation department can notify participants of the reservation results and request their confirmation. For example, the reservation results can be sent via email or notification, and the final venue can be confirmed and approved by the participants. This allows the venue reservation department to streamline venue reservations and contribute to the smooth operation of events.

[0079] The notification generation unit generates and sends notification emails based on the information reserved by the venue reservation unit. For example, the notification generation unit can automatically generate notification emails that include information such as the date and time of the event, the location, the event content, and the roles of participants, based on the details of the event, and send them to participants. This allows the notification generation unit to provide participants with the necessary information quickly and accurately. The notification generation unit can also automatically send reminder emails the day before the event, and can optimize the content of the notification emails using AI. The AI ​​can use machine learning algorithms to identify the optimal content and timing of sending notification emails based on participants' past participation history and feedback. For example, the AI ​​can use machine learning algorithms based on participants' preferences and interests. The above-mentioned process in the information generation unit, which can generate personalized information emails, may be performed using AI or not. If AI is not used, the information generation unit can generate and send information emails using simple templates or rule-based systems. This allows the information generation unit to efficiently generate and send information emails based on information reserved by the venue reservation unit. Furthermore, the information generation unit can track the sending results and analyze participant responses. For example, it can monitor the open rate and click-through rate of information emails and improve the content and timing of the next information email. This allows the information generation unit to streamline the generation and sending of information emails and contribute to the smooth operation of events.

[0080] The Task Management Department manages the progress of tasks based on information transmitted by the Guidance Generation Department. For example, the Task Management Department can centrally manage information such as the person in charge, deadline, and progress of each task in order to update the progress of each task in real time and manage it centrally on a dashboard. This allows the Task Management Department to grasp the progress of event preparations in real time and provide feedback to the organizers. The Task Management Department can automatically track the progress of event preparations and provide feedback to the organizers, and can optimize the progress of tasks using AI. The AI ​​can analyze the progress of each task and the performance of the person in charge, and can use machine learning algorithms to make optimal task assignments and progress management. For example, the AI ​​can analyze past task data and the performance of the person in charge. Based on skills, the task management unit can suggest the most suitable task assignment. Some or all of the above processes in the task management unit may be performed using AI or not. If AI is not used, the task management unit can manage task progress using simple algorithms or rule-based systems. This allows the task management unit to efficiently manage task progress based on information transmitted by the guidance generation unit. Furthermore, the task management unit can visualize the progress and enable operators to respond quickly. For example, it can highlight tasks that are behind schedule and send alerts to operators to encourage quick responses. This allows the task management unit to streamline task progress management and contribute to the smooth operation of events.

[0081] The task management unit can automatically reflect the schedule derived from the task list onto the operator's calendar. For example, the task management unit can analyze the information in the task list and automatically add the schedule to the operator's calendar. The task management unit can also adjust the schedule considering the priority and deadline of tasks. The task management unit can also reflect any changes to the schedule on the calendar in real time. This reduces the operator's workload by automatically reflecting the schedule derived from the task list onto the calendar. Some or all of the above processes in the task management unit may be performed using AI, or they may not.

[0082] The task management department can update the progress of event preparations in real time and manage it centrally on a dashboard. For example, the task management department can update the progress of each task in real time and manage it centrally on a dashboard. The task management department can automatically track the progress of event preparations and provide feedback to the organizers. The task management department can also visually display the progress using graphs and charts. This makes it easier for organizers to understand the progress by updating and centrally managing the progress of event preparations in real time. Some or all of the above processes in the task management department may be performed using AI or not.

[0083] The event management automation system according to this embodiment includes a moderation unit that acts as the moderator on the day of the event. The moderation unit acts as the moderator on the day of the event. For example, the moderation unit uses AI to moderate and smoothly support the progress of the event. The moderation unit can make announcements at appropriate times based on the event's progress script. The moderation unit can also analyze participants' reactions in real time and adjust the moderation method. By automating the moderation on the day of the event, the burden on the organizers can be reduced. Some or all of the above-described processes in the moderation unit may be performed using AI or not using AI.

[0084] The event management automation system according to this embodiment includes a recording unit that records the event. The recording unit records the event. For example, the recording unit uses AI to manage the event recording and automatically control the start and end timing of the recording. The recording unit can adjust the viewpoint and camera angle of the recording according to the progress of the event. The recording unit can also edit the recorded data in real time and provide it to participants. By automating the recording of events, the burden on the organizers can be reduced. Some or all of the above-described processes in the recording unit may be performed using AI or not using AI.

[0085] The event management automation system according to this embodiment includes a photography unit that takes photographs of the event. The photography unit takes photographs of the event. For example, the photography unit automatically takes photographs of the event scene using an AI-equipped camera and edits and provides them in real time. The photography unit can adjust the timing and angle of shooting according to the progress of the event. The photography unit can also upload the photographs taken to the cloud and share them with participants. By automating event photography, the burden on the organizers can be reduced. Some or all of the above processing in the photography unit may be performed using AI or not.

[0086] The scheduling unit can estimate participants' emotions and make optimal schedule adjustments based on the estimated emotions. For example, if a participant is feeling stressed, the scheduling unit will prioritize scheduling times that allow for relaxation. If a participant is busy, the scheduling unit can also select time slots that allow for efficient participation in a short amount of time. If a participant is relaxed, the scheduling unit can also suggest a schedule with ample time. In this way, by adjusting schedules based on participants' emotions, the optimal schedule for each participant can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using AI or not.

[0087] The scheduling unit can analyze participants' past scheduling history and select the optimal scheduling method. For example, the scheduling unit can prioritize scheduling time slots in which participants have frequently participated in the past. The scheduling unit can also predict the time slots that are most likely to be available based on participants' past scheduling history. The scheduling unit can also coordinate with other participants based on participants' past scheduling history. In this way, by analyzing past scheduling history, the optimal scheduling method can be provided for each participant. Some or all of the above processing in the scheduling unit may be performed using generative AI, or it may be performed without using generative AI.

[0088] The scheduling unit can filter participants based on their current projects and areas of interest when scheduling. For example, the scheduling unit can prioritize events related to projects that participants are currently working on. The scheduling unit can also prioritize events that are highly relevant based on participants' areas of interest. The scheduling unit can also propose an optimal schedule considering the progress of participants' current projects. This allows for the provision of a highly relevant schedule by adjusting the schedule based on participants' current projects and areas of interest. Some or all of the above processing in the scheduling unit may be performed using generative AI, or it may be performed without using generative AI.

[0089] The scheduling unit can estimate participants' emotions and determine scheduling priorities based on the estimated emotions. For example, if a participant is feeling stressed, the scheduling unit will prioritize scheduling times that allow for relaxation. If a participant is busy, the scheduling unit can also select times that allow for efficient participation in a short amount of time. If a participant is relaxed, the scheduling unit can also suggest a more relaxed schedule. By prioritizing scheduling based on participants' emotions, the system can provide participants with the most optimal schedule. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using or without generative AI.

[0090] The scheduling unit can prioritize scheduling events based on the geographical location of participants, taking their geographical location into consideration. For example, if a participant is nearby, the scheduling unit prioritizes scheduling events that minimize travel time. If a participant is far away, the scheduling unit can also propose a schedule with ample buffer time to account for travel time. The scheduling unit can also select the most suitable venue based on the geographical location of participants and adjust the schedule accordingly. This optimizes travel time by adjusting the schedule while considering the geographical location of participants. Some or all of the above-described processes in the scheduling unit may be performed using a generative AI, or they may be performed without using a generative AI.

[0091] The scheduling unit can analyze participants' social media activity and adjust relevant schedules when scheduling. For example, the scheduling unit can prioritize events that participants have shown interest in on social media. The scheduling unit can also predict the optimal time slots based on participants' social media activity and adjust schedules accordingly. The scheduling unit can also coordinate with other participants based on participants' social media activity. This allows the system to provide highly relevant schedules by analyzing participants' social media activity. Some or all of the above processing in the scheduling unit may be performed using generative AI, or it may be performed without using generative AI.

[0092] The venue reservation department can estimate the emotions of participants and select a venue based on those estimated emotions. For example, the venue reservation department may prioritize venues with a relaxing atmosphere for participants. It may also select quiet venues where participants can concentrate. It may also select venues with activities that participants can enjoy. In this way, by selecting venues based on participants' emotions, the optimal venue for each participant can be provided. 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 venue reservation department may be performed using generative AI or not.

[0093] The venue reservation department can select the most suitable venue by referring to past reservation history when a venue is reserved. For example, the venue reservation department can select the most suitable venue based on evaluations of venues used in the past. The venue reservation department can also select a venue that suits the preferences of participants from past reservation history. The venue reservation department can also analyze past reservation history and select the venue that is used most frequently. In this way, by referring to past reservation history, the most suitable venue for participants can be provided. Some or all of the above processing in the venue reservation department may be performed using generative AI, or it may be performed without using generative AI.

[0094] The venue reservation department can select the most suitable venue based on its facilities and services when making a reservation. For example, the venue reservation department can select the most suitable venue by considering its facilities (projector, sound system, etc.). The venue reservation department can also select a venue that meets the needs of participants based on the venue's services (catering, Wi-Fi, etc.). The venue reservation department can also compare the facilities and services of venues and select the most suitable venue. In this way, by selecting a venue based on its facilities and services, the department can provide the most suitable venue for participants. Some or all of the above processing in the venue reservation department may be performed using generative AI, or it may be performed without using generative AI.

[0095] The venue booking department can estimate participants' emotions and determine venue booking priorities based on the estimated emotions. For example, the venue booking department might prioritize venues with a relaxing atmosphere for participants. It could also select quiet venues where participants can concentrate. It could also select venues with activities that participants can enjoy. By prioritizing venue bookings based on participants' emotions, the department can provide the most suitable venue for each participant. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the venue booking department may be performed using generative AI or not.

[0096] The venue reservation department can select the most suitable venue by considering its geographical location when making a reservation. For example, if participants are nearby, the venue reservation department will prioritize selecting a venue that minimizes travel time. If participants are far away, the venue reservation department can also select a venue with ample time to allow for travel. The venue reservation department can also select the most suitable venue based on its geographical location. This allows for the optimization of travel time by selecting a venue while considering its geographical location. Some or all of the above processing in the venue reservation department may be performed using generative AI, or it may be performed without using generative AI.

[0097] The venue reservation department can select the most suitable venue by referring to venue reviews and ratings when making a reservation. For example, the venue reservation department can select a venue with high participant ratings based on venue reviews. The venue reservation department can also select the most suitable venue by referring to venue ratings. The venue reservation department can also analyze venue reviews and ratings to select the most suitable venue. In this way, by referring to venue reviews and ratings, it is possible to provide the most suitable venue for participants. Some or all of the above processing in the venue reservation department may be performed using generative AI, or it may be performed without using generative AI.

[0098] The guidance generation unit can estimate the emotions of participants and adjust the wording of the guidance email based on the estimated emotions. For example, if a participant is relaxed, the guidance generation unit can generate a guidance email using casual language. If a participant is nervous, the guidance generation unit can also generate a guidance email using polite and calm language. If a participant is excited, the guidance generation unit can also generate a guidance email using bright and cheerful language. In this way, by adjusting the wording of the guidance email based on the emotions of participants, the system can provide the most suitable guidance email for each participant. 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-described processing in the guidance generation unit may be performed using a generative AI or not.

[0099] The notification generation unit can adjust the level of detail in notification emails based on the importance of the event when generating them. For example, for important events, the notification generation unit generates notification emails containing detailed information. For general events, the notification generation unit can also generate notification emails containing concise information. For urgent events, the notification generation unit can also generate notification emails that are concise and to the point, allowing for quick response. By adjusting the level of detail in notification emails based on the importance of the event, the unit can provide participants with the most appropriate notification email. Some or all of the above processing in the notification generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0100] The invitation generation unit can apply different invitation email algorithms depending on the event category when generating invitation emails. For example, in the case of a business event, the invitation generation unit can generate an invitation email using formal language. In the case of a casual event, the invitation generation unit can also generate an invitation email using friendly language. In the case of an academic event, the invitation generation unit can also generate an invitation email using specialized language. By applying different invitation email algorithms depending on the event category, it is possible to provide the most suitable invitation email for participants. Some or all of the above processing in the invitation generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0101] The guidance generation unit can estimate the participant's emotions and adjust the length of the guidance email based on the estimated emotions. For example, if the participant is relaxed, the guidance generation unit can generate a longer guidance email containing detailed information. If the participant is busy, the guidance generation unit can also generate a shorter guidance email containing concise information. If the participant is excited, the guidance generation unit can also generate a concise guidance email. In this way, by adjusting the length of the guidance email based on the participant's emotions, the guidance generation unit can provide the most suitable guidance email for the participant. 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 guidance generation unit may be performed using a generative AI or not.

[0102] The notification generation unit can determine the priority of notification emails based on the timing of the event when generating notification emails. For example, the notification generation unit will prioritize generating notification emails for upcoming events. For long-term events, the notification generation unit can also generate notification emails at an appropriate time. For urgent events, the notification generation unit can also generate notification emails quickly. By prioritizing notification emails based on the timing of the event, the unit can provide participants with the most suitable notification emails. Some or all of the above processing in the notification generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0103] The notification generation unit can adjust the order of notification emails based on the relevance of the events when generating notification emails. For example, the notification generation unit can prioritize notification emails for events that are important to the participant. The notification generation unit can also prioritize notification emails for events that are highly relevant based on the participant's interests. The notification generation unit can also prioritize notification emails for events that are highly relevant based on the participant's past participation history. In this way, by adjusting the order of notification emails based on the relevance of the events, the most suitable notification emails can be provided to the participant. Some or all of the above processing in the notification generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0104] The task management unit can estimate participants' emotions and manage task progress based on those estimated emotions. For example, if a participant is feeling stressed, the task management unit can closely monitor task progress and provide support. If a participant is relaxed, the task management unit can also manage task progress more loosely. If a participant is busy, the task management unit can prioritize important tasks. This allows for optimal task management for participants by managing task progress based on 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-described processes in the task management unit may be performed using generative AI or not.

[0105] The task management unit can select the optimal task management method by referring to past task history when managing tasks. For example, the task management unit can select the optimal task management method based on past successful task management methods. The task management unit can also select a task management method that suits the participant's preferences from past task history. The task management unit can also analyze past task history and select the most efficient task management method. In this way, by referring to past task history, it is possible to provide the optimal task management method for the participant. Some or all of the above processing in the task management unit may be performed using generative AI, or it may be performed without using generative AI.

[0106] The task management unit can determine task priorities based on the importance of the task during task management. For example, the task management unit can prioritize important tasks. The task management unit can also prioritize urgent tasks. The task management unit can also postpone tasks of lower importance. By determining task priorities based on the importance of the task, it is possible to provide optimal task management for participants. Some or all of the above processes in the task management unit may be performed using generative AI, or they may not be performed using generative AI.

[0107] The task management unit can estimate the emotions of participants and adjust the order in which task progress is displayed based on the estimated emotions of the participants. For example, if a participant is feeling stressed, the task management unit can prioritize displaying important tasks. If a participant is relaxed, the task management unit can also display all tasks equally. If a participant is busy, the task management unit can also prioritize displaying urgent tasks. In this way, by adjusting the order in which task progress is displayed based on the emotions of the participants, optimal task management can be provided for the participants. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the task management unit may be performed using generative AI or not using generative AI.

[0108] The task management unit can manage the progress of tasks while considering their geographical distribution. For example, the task management unit can prioritize tasks where participants are nearby. It can also postpone tasks where participants are far away. Based on the geographical distribution of tasks, the task management unit can select the optimal task management method. This allows for optimal task management for participants by managing tasks while considering their geographical distribution. Some or all of the above processing in the task management unit may be performed using generative AI, or it may be performed without using generative AI.

[0109] The task management unit can manage the progress of tasks by referring to relevant literature during task management. For example, the task management unit can select the optimal task management method based on the relevant literature. The task management unit can also manage the progress of tasks by referring to relevant literature. The task management unit can also analyze relevant literature and select the most efficient task management method. In this way, by referring to relevant literature, it is possible to provide optimal task management for participants. Some or all of the above processes in the task management unit may be performed using generative AI, or they may be performed without using generative AI.

[0110] The moderator can estimate the participants' emotions and adjust the moderation method based on the estimated emotions. For example, if the participants are relaxed, the moderator may adopt a casual moderation method. If the participants are nervous, the moderator may adopt a polite and calm moderation method. If the participants are excited, the moderator may adopt a bright and energetic moderation method. In this way, by adjusting the moderation method based on the participants' emotions, the moderator can provide the most suitable moderation experience for the participants. 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 moderator may be performed using generative AI or not.

[0111] The moderator can select the most suitable moderation method by referring to past moderation history during moderation. For example, the moderator can select the most suitable moderation method based on past successful methods. The moderator can also select a moderation method that suits the participants' preferences from past moderation history. The moderator can also analyze past moderation history and select the most efficient moderation method. In this way, by referring to past moderation history, the moderator can provide the most suitable moderation method for the participants. Some or all of the above processing in the moderator may be performed using generative AI, or it may be performed without using generative AI.

[0112] The moderator can customize their moderation methods based on the progress of the event. For example, the moderator can flexibly change the moderation method according to the progress of the event. The moderator can also monitor the progress of the event in real time and select the optimal moderation method. The moderator can also adjust the moderation method while observing the reactions of the participants based on the progress of the event. In this way, by customizing the moderation methods based on the progress of the event, the moderator can provide the best possible moderation experience for the participants. Some or all of the above processes performed by the moderator may be carried out using generative AI, or they may not be carried out using generative AI.

[0113] The moderator can estimate the emotions of the participants and determine the priority of the moderation based on the estimated emotions. For example, if the participants are relaxed, the moderator may prioritize a casual moderation style. If the participants are nervous, the moderator may also prioritize a polite and calm moderation style. If the participants are excited, the moderator may also prioritize a bright and energetic moderation style. By determining the priority of the moderation based on the emotions of the participants, the moderator can provide the most suitable moderation experience for the participants. 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 moderator may be performed using generative AI or not.

[0114] The moderator can select the most appropriate moderation method during the moderation process, taking into account the geographical location information of the participants. For example, if the participants are nearby, the moderator may select a moderation method that emphasizes direct communication. If the participants are far away, the moderator may also select an online moderation method. The moderator may also select the most appropriate moderation method based on the geographical location information of the participants. By selecting a moderation method that takes into account the geographical location information of the participants, the moderator can provide the most optimal moderation experience for the participants. Some or all of the above processing in the moderator may be performed using generative AI, or it may be performed without using generative AI.

[0115] The moderator can analyze participants' social media activity during moderation and propose methods for moderation. For example, the moderator can propose a moderation method that incorporates topics that participants have shown interest in on social media. The moderator can also propose the optimal moderation method based on participants' social media activity. The moderator can also coordinate with other participants based on participants' social media activity. In this way, by analyzing participants' social media activity, the moderator can provide the optimal moderation for each participant. Some or all of the above processing by the moderator may be performed using generative AI, or it may not be performed using generative AI.

[0116] The recording unit can estimate the emotions of the participants and adjust the recording method based on the estimated emotions. For example, if the participant is relaxed, the recording unit may adopt a recording method that proceeds at a relaxed pace. If the participant is in a hurry, the recording unit may also adopt a recording method that emphasizes important points. If the participant is excited, the recording unit may also adopt a recording method that adds visually stimulating effects. In this way, by adjusting the recording method based on the emotions of the participants, the optimal recording can be provided for the participants. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using generative AI or not using generative AI.

[0117] The recording unit can select the optimal recording method by referring to past recording history during recording. For example, the recording unit can select the optimal recording method based on past successful recording methods. The recording unit can also select a recording method that suits the participant's preferences from past recording history. The recording unit can also analyze past recording history and select the most efficient recording method. In this way, by referring to past recording history, the optimal recording method can be provided for the participant. Some or all of the above processing in the recording unit may be performed using generative AI, or it may be performed without using generative AI.

[0118] The recording unit can customize the recording method based on the progress of the event during recording. For example, the recording unit can flexibly change the recording method according to the progress of the event. The recording unit can also grasp the progress of the event in real time and select the optimal recording method. The recording unit can also adjust the recording method while observing the reactions of participants based on the progress of the event. In this way, by customizing the recording method based on the progress of the event, it is possible to provide the best possible recording for participants. Some or all of the above processing in the recording unit may be performed using generative AI, or it may be performed without using generative AI.

[0119] The recording unit can estimate the emotions of participants and prioritize recordings based on those emotions. For example, if a participant is relaxed, the recording unit will prioritize a recording method that proceeds at a relaxed pace. If a participant is in a hurry, the recording unit may also prioritize a recording method that highlights important points. If a participant is excited, the recording unit may also prioritize a recording method that adds visually stimulating effects. This allows the recording unit to provide the most suitable recording for each participant by prioritizing recordings based on 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 recording unit may be performed using or without generative AI.

[0120] The recording unit can select the optimal recording method during recording, taking into account the geographical location information of the participants. For example, if the participants are nearby, the recording unit will select a recording method that emphasizes direct communication. If the participants are far away, the recording unit can also select an online recording method. The recording unit can also select the optimal recording method based on the geographical location information of the participants. This allows the recording unit to provide the best possible recording for each participant by selecting a recording method that takes their geographical location into consideration. Some or all of the above processing in the recording unit may be performed using generative AI, or it may be performed without using generative AI.

[0121] The recording unit can analyze participants' social media activity during recording and suggest recording methods. For example, the recording unit can suggest a recording method that incorporates topics that participants have shown interest in on social media. The recording unit can also suggest the optimal recording method based on participants' social media activity. The recording unit can also coordinate with other participants based on participants' social media activity. In this way, by analyzing participants' social media activity, it is possible to provide the optimal recording for each participant. Some or all of the above processing in the recording unit may be performed using generative AI, or it may be performed without using generative AI.

[0122] The photography team can estimate the emotions of the participants and adjust the photography method based on the estimated emotions. For example, if a participant is relaxed, the photography team will employ a method that elicits natural expressions. If a participant is tense, the photography team can create a relaxing environment for the photoshoot. If a participant is excited, the photography team can take dynamic photos. By adjusting the photography method based on the participants' emotions, the team can provide the best possible photography experience for each participant. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the photography team may be performed using generative AI or not.

[0123] The photography department can select the optimal photography method by referring to past photography history when taking photographs. For example, the photography department can select the optimal photography method based on past successful photography methods. The photography department can also select a photography method that suits the participant's preferences from past photography history. The photography department can also analyze past photography history and select the most efficient photography method. In this way, by referring to past photography history, the photography department can provide the optimal photography method for the participant. Some or all of the above processes in the photography department may be performed using generative AI, or they may be performed without using generative AI.

[0124] The photography team can customize the photography methods based on the progress of the event. For example, the photography team can flexibly change the photography method according to the progress of the event. The photography team can also monitor the progress of the event in real time and select the optimal photography method. The photography team can also adjust the photography method while observing the reactions of the participants based on the progress of the event. In this way, by customizing the photography methods based on the progress of the event, the photography team can provide the best possible photographs for the participants. Some or all of the above processes performed by the photography team may be carried out using generative AI, or they may not be carried out using generative AI.

[0125] The photography team can estimate the emotions of participants and determine the priority of photography based on the estimated emotions. For example, if a participant is relaxed, the photography team will prioritize photography methods that bring out natural expressions. If a participant is tense, the photography team can create a relaxing environment for photography. If a participant is excited, the photography team can prioritize taking photos of them in motion. In this way, by determining the priority of photography based on the emotions of participants, the team can provide the best possible photography for each participant. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the photography team may be performed using generative AI or not.

[0126] The photography team can select the optimal photography method when taking photographs, taking into account the geographical location information of the participants. For example, if the participants are nearby, the photography team will select a photography method that emphasizes direct communication. If the participants are far away, the photography team can also select an online photography method. The photography team can also select the optimal photography method based on the geographical location information of the participants. By selecting a photography method that takes into account the geographical location information of the participants, the photography team can provide the best possible photographs for each participant. Some or all of the above processing in the photography team may be performed using generative AI, or it may be performed without using generative AI.

[0127] The photography team can analyze participants' social media activity during photo shoots and suggest photography methods. For example, the photography team can suggest a shooting method that incorporates topics that participants have shown interest in on social media. The photography team can also suggest the optimal shooting method based on participants' social media activity. The photography team can also coordinate with other participants based on participants' social media activity. In this way, by analyzing participants' social media activity, the photography team can provide the best possible photo shoot for each participant. Some or all of the above processes in the photography team may be performed using generative AI, or they may not be performed using generative AI.

[0128] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0129] The automated event management system can also include a health management unit to monitor participants' health. This unit can, for example, monitor participants' heart rates and stress levels in real time and notify organizers with alerts based on their health status. If a participant is experiencing excessive stress, the health management unit can also suggest ways to provide a more relaxing environment. Furthermore, the health management unit can adjust the event's progress based on participants' health status. This allows for event management that takes participants' health into consideration, thereby improving participant satisfaction.

[0130] The automated event management system can also include an interest analysis unit that analyzes participants' interests and preferences. This unit, for example, analyzes participants' past event attendance history and social media activity to identify their interests. Based on these interests, the unit can also suggest relevant events. By providing content tailored to participants' interests, the unit can enhance the appeal of events. This enables event management that considers participants' interests and preferences, thereby improving participant satisfaction.

[0131] The automated event management system can also include a feedback collection unit that collects and analyzes participant feedback. For example, the feedback collection unit can automatically send questionnaires to participants after the event to collect feedback. The feedback collection unit can also analyze the collected feedback to identify areas for improvement in the event. Based on participant feedback, the feedback collection unit can also optimize the planning of future events. This enables event management that reflects participant feedback, thereby improving participant satisfaction.

[0132] The automated event management system can also include a networking support unit to assist with participant networking. This unit can, for example, match participants with shared interests based on their profile information. It can also suggest activities to promote interaction among participants during the event. Furthermore, it can provide a platform to support communication among participants even after the event has ended. This enhances the value of the event by supporting participant networking.

[0133] The automated event management system can also include an emotion adjustment unit that estimates participants' emotions and adjusts the event's progress based on those estimates. For example, if participants are relaxed, the emotion adjustment unit might suggest a relaxed pace. If participants are excited, it might suggest a more energetic pace. If participants are stressed, it might suggest relaxing activities. This allows the system to adjust the event's progress based on participants' emotions, providing them with the optimal event experience.

[0134] The automated event management system can also include a mobility support unit to assist participants with their travel. This unit can, for example, suggest the optimal travel route based on participants' geographical location information. It can also monitor traffic conditions in real time and suggest ways to optimize travel time. Furthermore, it can support participants in booking transportation to ensure a smooth arrival at the event venue. By supporting participants' travel, the system can improve the convenience of attending events.

[0135] The automated event management system can also include a content customization unit that estimates participants' emotions and customizes event content based on those emotions. For example, if participants are relaxed, the content customization unit can provide relaxing content. If participants are excited, it can provide stimulating content. If participants are stressed, it can provide relaxing activities. This allows for the provision of an optimal event experience for participants by customizing event content based on their emotions.

[0136] The automated event management system can also include a learning support unit to further assist participants in their learning. This unit can, for example, track participants' learning progress based on the content provided during the event. It can also suggest learning methods tailored to each participant's learning style. Furthermore, it can provide resources to support participants' learning even after the event has ended. This enhances the value of the event by supporting participants' learning.

[0137] The automated event management system can further include a feedback sentiment analysis unit that estimates participants' emotions and collects event feedback based on those estimated emotions. For example, if a participant is relaxed, the feedback sentiment analysis unit might request detailed feedback. If a participant is busy, it might request concise feedback. If a participant is excited, it might request positive feedback. This allows for more accurate feedback by collecting feedback based on participants' emotions.

[0138] The automated event management system can also include a networking sentiment analysis unit that estimates participants' emotions and supports event networking based on those estimates. For example, if participants are relaxed, the networking sentiment analysis unit might suggest a casual networking event. If participants are tense, it might suggest a small-group networking event. If participants are excited, it might suggest a large-scale networking event. This allows the system to provide the optimal networking experience for participants by supporting networking based on their emotions.

[0139] The following briefly describes the processing flow for example form 2.

[0140] Step 1: The scheduling unit collects and analyzes participants' schedules. For example, it retrieves participants' calendar information and automatically adjusts the optimal date. The scheduling unit can compare the schedules of multiple participants and identify a date when everyone can attend. Step 2: The venue reservation department checks venue availability and makes reservations based on the information collected by the scheduling department. For example, it can integrate with a conference room reservation system to check availability and make reservations automatically. The venue reservation department can check venue availability in real time and select the most suitable venue. Step 3: The invitation generation unit generates and sends invitation emails based on the information reserved by the venue reservation unit. For example, it creates an invitation email based on the event details and sends it to participants. The invitation generation unit can also automatically send a reminder email the day before the event. Step 4: The task management unit manages the progress of tasks based on the information sent by the guidance generation unit. For example, it updates the progress of each task in real time and manages it centrally on a dashboard. The task management unit can automatically track the progress of event preparations and provide feedback to the organizers.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements mentioned above, including the schedule adjustment unit, venue reservation unit, announcement generation unit, task management unit, moderator unit, recording unit, and photography unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the schedule adjustment unit is implemented by the control unit 46A of the smart device 14, which acquires participants' calendar information and automatically adjusts the optimal schedule. The venue reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which checks availability in cooperation with the conference room reservation system and makes reservations. The announcement generation unit is implemented by, for example, the control unit 46A of the smart device 14, which creates an announcement email based on the event details and sends it to participants. The task management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which updates the progress of each task in real time and centrally manages it on a dashboard. The moderator unit is implemented by, for example, the control unit 46A of the smart device 14, which makes announcements at the appropriate time based on the event progress script. The recording unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically controls the start and end timing of recording. The photo-taking unit is implemented, for example, by the control unit 46A of the smart device 14, and automatically takes photos of the event scene, edits them in real time, and provides them. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0145] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements mentioned above, including the schedule adjustment unit, venue reservation unit, announcement generation unit, task management unit, moderator unit, recording unit, and photography unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the schedule adjustment unit is implemented by the control unit 46A of the smart glasses 214, which acquires participants' calendar information and automatically adjusts the optimal schedule. The venue reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which checks availability in cooperation with the conference room reservation system and makes reservations. The announcement generation unit is implemented by, for example, the control unit 46A of the smart glasses 214, which creates an announcement email based on detailed event information and sends it to participants. The task management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which updates the progress of each task in real time and centrally manages it on a dashboard. The moderator unit is implemented by, for example, the control unit 46A of the smart glasses 214, which makes announcements at the appropriate time based on the event progress script. The recording unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically controls the start and end timing of recording. The photo-taking unit is implemented, for example, by the control unit 46A of the smart glasses 214, and automatically takes photos of the event scene, edits them in real time, and provides them. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0161] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements mentioned above, including the schedule adjustment unit, venue reservation unit, announcement generation unit, task management unit, moderator unit, recording unit, and photography unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the schedule adjustment unit is implemented by the control unit 46A of the headset terminal 314, which acquires participants' calendar information and automatically adjusts the optimal schedule. The venue reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which checks availability in cooperation with the conference room reservation system and makes reservations. The announcement generation unit is implemented by, for example, the control unit 46A of the headset terminal 314, which creates an announcement email based on detailed event information and sends it to participants. The task management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which updates the progress of each task in real time and centrally manages it on a dashboard. The moderator unit is implemented by, for example, the control unit 46A of the headset terminal 314, which makes announcements at appropriate times based on the event progress script. The recording unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically controls the start and end timing of recording. The photo-taking unit is implemented, for example, by the control unit 46A of the headset terminal 314, and automatically takes photos of the event scene, edits them in real time, and provides them. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0177] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] Each of the multiple elements mentioned above, including the schedule adjustment unit, venue reservation unit, announcement generation unit, task management unit, moderator unit, recording unit, and photography unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the schedule adjustment unit is implemented by the control unit 46A of the robot 414, which acquires participants' calendar information and automatically adjusts the optimal schedule. The venue reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which checks availability in cooperation with the conference room reservation system and makes reservations. The announcement generation unit is implemented by, for example, the control unit 46A of the robot 414, which creates an announcement email based on detailed event information and sends it to participants. The task management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which updates the progress of each task in real time and centrally manages it on a dashboard. The moderator unit is implemented by, for example, the control unit 46A of the robot 414, which makes announcements at the appropriate time based on the event progress script. The recording unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically controls the start and end timing of recording. The photography unit is implemented, for example, by the control unit 46A of the robot 414, and automatically takes photographs of the event scene, edits them in real time, and provides them. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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."

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] (Note 1) The scheduling department collects and analyzes the participants' schedules, A venue reservation department checks the availability of venues and makes reservations based on the information collected by the aforementioned scheduling department, An information generation unit generates and sends information emails based on the information reserved by the venue reservation unit, A task management unit manages the progress of tasks based on the information transmitted by the guidance generation unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned task management unit, The schedule derived from the task list is automatically reflected in the operator's calendar. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned task management unit, Update the progress of event preparations in real time and manage it centrally on a dashboard. The system described in Appendix 1, characterized by the features described herein. (Note 4) The event will be hosted by a team of emcees. The system described in Appendix 1, characterized by the features described herein. (Note 5) It is equipped with a recording unit for recording events. The system described in Appendix 1, characterized by the features described herein. (Note 6) The event includes a photography department to take pictures of the event. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned schedule adjustment unit, The system estimates the participants' emotions and adjusts the schedule to the optimal level based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned schedule adjustment unit, Analyze participants' past schedule history and select the optimal scheduling method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned schedule adjustment unit, When scheduling, filter participants based on their current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned schedule adjustment unit, The system estimates the emotions of the participants and prioritizes scheduling adjustments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned schedule adjustment unit, When scheduling, prioritize relevant schedules by considering participants' geographical locations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned schedule adjustment unit, When scheduling, we analyze participants' social media activity and adjust relevant schedules accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned venue reservation department, The emotions of the participants are estimated, and the venue is selected based on the estimated emotions of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned venue reservation department, When booking a venue, the system will refer to past booking history to select the most suitable venue. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned venue reservation department, When booking a venue, select the most suitable venue based on its facilities and services. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned venue reservation department, The system estimates the emotions of the participants and prioritizes venue reservations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned venue reservation department, When booking a venue, the most suitable venue will be selected considering its geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned venue reservation department, When booking a venue, refer to venue reviews and ratings to select the most suitable venue. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned guide generation unit, We estimate the participants' emotions and adjust the wording of the invitation email based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide generation unit, When generating the invitation email, adjust the level of detail in the email based on the importance of the event. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide generation unit, When generating notification emails, different notification email algorithms are applied depending on the event category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned guide generation unit, The system estimates the participants' emotions and adjusts the length of the invitation email based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned guide generation unit, When generating invitation emails, the priority of the invitation emails is determined based on the timing of the event. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide generation unit, When generating notification emails, adjust the order of the emails based on the relevance of the events. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned task management unit, The system estimates participants' emotions and manages task progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned task management unit, When managing tasks, refer to past task history to select the optimal task management method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned task management unit, When managing tasks, prioritize tasks based on their importance. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned task management unit, The system estimates the participants' emotions and adjusts the order in which task progress is displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned task management unit, When managing tasks, consider the geographical distribution of tasks to track their progress. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned task management unit, When managing tasks, refer to relevant literature related to the task to manage the task's progress. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned moderators and presenters The moderator estimates the participants' emotions and adjusts the moderation method based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned moderators and presenters When moderating an event, refer to past moderation records to select the most suitable moderation method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned moderators and presenters During the event, the moderator's approach is customized based on the progress of the event. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned moderators and presenters The system estimates the emotions of the participants and determines the priority of the moderator's role based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned moderators and presenters When moderating an event, the moderator will select the most appropriate moderation method, taking into account the geographical location of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned moderators and presenters During the moderation process, we will analyze the participants' social media activity and propose methods for moderating the session. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned recording unit is The system estimates the participants' emotions and adjusts the recording method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned recording unit is When recording, the system will refer to past recording history to select the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned recording unit is During recording, customize the recording method based on the progress of the event. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned recording unit is The system estimates the emotions of the participants and prioritizes the recordings based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned recording unit is During recording, the optimal recording method is selected considering the geographical location information of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned recording unit is During recording, we analyze participants' social media activity and suggest recording methods. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned photography unit, The system estimates the participants' emotions and adjusts the photography method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned photography unit, When taking a photograph, the system will refer to past photography history to select the optimal photography method. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned photography unit, When taking photos, customize the photography method based on the progress of the event. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned photography unit, The system estimates the emotions of the participants and determines the priority of photo shoots based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned photography unit, When taking photographs, the optimal photography method is selected considering the geographical location information of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned photography unit, During the photo shoot, we analyze participants' social media activity and suggest appropriate photography methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0213] 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 scheduling department collects and analyzes the participants' schedules, A venue reservation department checks the availability of venues and makes reservations based on the information collected by the aforementioned schedule adjustment department. An information generation unit generates and sends information emails based on the information reserved by the venue reservation unit, A task management unit manages the progress of tasks based on the information transmitted by the aforementioned guidance generation unit, Equipped with A system characterized by the following features.

2. The aforementioned task management unit, The schedule derived from the task list is automatically reflected in the operator's calendar. The system according to feature 1.

3. The aforementioned task management unit, Update the progress of event preparations in real time and manage it centrally on a dashboard. The system according to feature 1.

4. The event will be hosted by a dedicated MC team. The system according to feature 1.

5. It is equipped with a recording unit for recording events. The system according to feature 1.

6. The event includes a photography department to take pictures of the event. The system according to feature 1.

7. The aforementioned schedule adjustment unit, The system estimates the participants' emotions and adjusts the schedule to the optimal level based on those estimated emotions. The system according to feature 1.

8. The aforementioned schedule adjustment unit, Analyze participants' past schedule history and select the optimal scheduling method. The system according to feature 1.

9. The aforementioned schedule adjustment unit, When scheduling, filter participants based on their current projects and areas of interest. The system according to feature 1.

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