system

Abstract

The system according to this embodiment aims to efficiently handle the reservation and scheduling of meeting rooms. [Solution] The system according to the embodiment comprises a collection unit, a reservation unit, an optimization unit, and a priority setting unit. The collection unit collects meeting information from each department. The reservation unit reserves the most suitable meeting room based on the information collected by the collection unit. The optimization unit optimizes the schedule based on the information reserved by the reservation unit. The priority setting unit sets reservation priorities based on the importance and urgency of the meetings.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

[0007] The system according to this embodiment can efficiently reserve and schedule meeting rooms. [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 applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The meeting room reservation system according to an embodiment of the present invention is a system that uses a generation AI to collect meeting information from each department, reserves the optimal meeting room considering the size of the meeting, the number of participants, the date and time of the meeting, and optimizes the schedule. The meeting room reservation system collects meeting information from each department, and the generation AI reserves the optimal meeting room considering the size of the meeting, the number of participants, and the date and time of the meeting. The generation AI also determines the reservation priority according to importance. As a result, all employees can reserve meeting rooms smoothly, reduce the stress of scheduling adjustments associated with holding meetings, and improve work efficiency. For example, the meeting room reservation system collects meeting information from each department. At this time, detailed information such as the size of the meeting, its purpose, the number of participants, and the date and time of the meeting is collected. For example, if the size of the meeting is large or there are many participants, a large meeting room is required. This information is input into the generation AI. Next, based on the collected information, the generation AI reserves the optimal meeting room considering the size of the meeting, the number of participants, and the date and time of the meeting. The generation AI selects the optimal meeting room considering the usage status, facilities, and capacity of each meeting room. For example, if the size of the meeting is large, a large meeting room is reserved as a priority. Furthermore, the system sets reservation priorities based on the importance and urgency of meetings, ensuring that important meetings are booked first. The generating AI also optimizes schedules, for example, by adjusting them to prevent multiple meetings from overlapping at the same time. If a meeting date or time changes, the generating AI automatically readjusts the schedule and rebooks the most suitable meeting room. This streamlines meeting scheduling and improves work efficiency. This system allows all employees to book meeting rooms smoothly and reduces the stress associated with scheduling meetings. For example, if meeting room reservations overlap or there is a shortage of meeting rooms, the generating AI automatically selects the most suitable room and adjusts the reservation. By setting reservation priorities based on the importance and urgency of meetings, important meetings are booked first. This improves work efficiency and allows all employees to hold meetings smoothly. In summary, the meeting room reservation system allows all employees to book meeting rooms smoothly, reduces the stress associated with scheduling meetings, and improves work efficiency.

[0029] The meeting room reservation system according to this embodiment comprises a collection unit, a reservation unit, an optimization unit, and a priority setting unit. The collection unit collects meeting information from each department. The collection unit collects detailed information such as the size, purpose, participants, and date and time of the meeting. For example, the collection unit determines that a large meeting room is required if the meeting is large or has many participants. This information is input into the generation AI. The reservation unit reserves the most suitable meeting room based on the information collected by the collection unit. For example, the reservation unit selects the most suitable meeting room by considering the usage status, facilities, and capacity of each meeting room. For example, if the meeting is large, the reservation unit prioritizes reserving a large meeting room. The reservation unit also sets reservation priorities based on the importance and urgency of the meeting, ensuring that important meetings are reserved preferentially. The optimization unit optimizes the schedule based on the information reserved by the reservation unit. For example, the optimization unit adjusts the schedule so that multiple meetings do not overlap at the same time. For example, if the date and time of a meeting are changed, the optimization unit readjusts the schedule and re-reserves the most suitable meeting room. The priority setting unit sets reservation priorities based on the importance and urgency of the meetings. For example, the priority setting unit sets reservation priorities based on the importance and urgency of the meetings, ensuring that important meetings are reserved preferentially. As a result, the meeting room reservation system according to this embodiment can improve operational efficiency by collecting meeting information from each department, reserving the most suitable meeting room, and optimizing the schedule.

[0030] The data collection unit collects meeting information from each department. For example, it collects detailed information such as the size, purpose, participants, and date and time of the meeting. Specifically, it provides an interface for departmental staff to input meeting details, allowing the data collection unit to efficiently obtain the necessary information. Larger meetings or those with many participants require larger conference rooms. This information is input into the generative AI. The generative AI analyzes the collected information and generates optimal conference room candidates based on the characteristics of the meeting. For example, the generative AI considers the number of participants and necessary equipment (projector, whiteboard, video conferencing system, etc.) to list appropriate conference rooms. Furthermore, the generative AI can learn from past meeting data and derive optimal conference room selection patterns under specific conditions. This allows the data collection unit to efficiently collect meeting information from each department and quickly identify optimal conference room candidates using the generative AI.

[0031] The reservation department reserves the most suitable meeting room based on information collected by the data collection department. For example, the reservation department selects the optimal meeting room by considering factors such as the usage status, facilities, and capacity of each room. Specifically, the reservation department monitors the reservation status of meeting rooms in real time and uses a database to check availability. For large meetings, larger meeting rooms are prioritized. The reservation department also sets reservation priorities based on the importance and urgency of the meeting, ensuring that important meetings are booked preferentially. For example, urgent meetings and important meetings attended by senior management are booked with higher priority than other meetings. The reservation department selects the most suitable meeting room based on a list of optimal meeting room candidates provided by the AI ​​generation system and confirms the reservation. Furthermore, the reservation department responds quickly to changes or cancellations, re-evaluating the usage status of other meeting rooms and making optimal adjustments. This allows the reservation department to manage meeting room reservations efficiently and flexibly, supporting the smooth running of meetings.

[0032] The optimization unit optimizes the schedule based on the information reserved by the reservation unit. For example, the optimization unit adjusts schedules to prevent multiple meetings from overlapping at the same time. Specifically, the optimization unit monitors meeting schedules in real time and makes adjustments to prevent overlaps and conflicts. For example, if the date and time of a meeting are changed, the schedule is readjusted and the most suitable meeting room is rebooked. The optimization unit uses generative AI to execute an algorithm that optimizes the schedules of multiple meetings simultaneously. This maximizes the efficiency of meeting room utilization and reduces wasted idle time. Furthermore, the optimization unit sets schedule priorities considering the importance and urgency of meetings, adjusting schedules so that important meetings are held first. For example, progress meetings for important projects and urgent problem-solving meetings are scheduled with higher priority than other meetings. This allows the optimization unit to efficiently manage meeting schedules and improve the efficiency of meeting room utilization.

[0033] The priority setting unit sets reservation priorities based on the importance and urgency of meetings. For example, the priority setting unit sets reservation priorities based on the importance and urgency of meetings, ensuring that important meetings are booked preferentially. Specifically, the priority setting unit sets criteria for evaluating the importance and urgency of each meeting and determines the priority of each meeting based on these criteria. For example, meetings attended by senior management and project progress meetings are given high priority. On the other hand, regular meetings and information-sharing meetings are given relatively low priority. The priority setting unit utilizes generative AI to analyze past meeting data and participant feedback, continuously improving the criteria for priority setting. This allows the priority setting unit to always make appropriate priority settings based on the latest information. Furthermore, the priority setting unit can respond quickly to urgent meetings and adjust the schedules of other meetings to prioritize the booking of urgent meetings. This enables the priority setting unit to provide flexible responses according to the importance and urgency of meetings, improving the efficiency and reliability of the meeting room reservation system.

[0034] The data collection unit can collect detailed information about a meeting, such as its size, purpose, participants, and date and time. For example, if the meeting is large or has many participants, the data collection unit will need a large conference room. This information is input into the generating AI. The data collection unit can collect the purpose of the meeting and select a conference room that suits that purpose, such as information sharing, decision-making, or problem-solving. The data collection unit can collect information such as the titles and number of participants and select an appropriate conference room. In this way, by collecting detailed information about the meeting, it becomes possible to select the optimal conference room. Some or all of the above processing in the data collection unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the data collection unit can input detailed information such as the size, purpose, participants, and date and time of the meeting into the generating AI, and the generating AI can select the optimal conference room.

[0035] The reservation department can select the most suitable meeting room by considering factors such as the usage status, facilities, and capacity of each meeting room. For example, the reservation department can select the most suitable meeting room based on the usage status of each meeting room, such as whether it is booked and how often it is used. For example, the reservation department can select the most suitable meeting room based on the facilities of each meeting room, such as projectors, whiteboards, and sound systems. For example, the reservation department can select the most suitable meeting room based on the capacity of each meeting room, such as the maximum or recommended capacity. In this way, by considering the usage status, facilities, and capacity of each meeting room, it becomes possible to select the most suitable meeting room. Some or all of the above processing in the reservation department may be performed using a generation AI, or it may be performed without a generation AI. For example, the reservation department can input information such as the usage status, facilities, and capacity of each meeting room into a generation AI, which can then select the most suitable meeting room.

[0036] The optimization unit can adjust schedules so that multiple meetings do not overlap at the same time. For example, the optimization unit adjusts the time slots of meetings to set schedules that do not overlap. For example, the optimization unit sets meeting priorities to adjust schedules so that important meetings are prioritized. For example, the optimization unit adjusts the dates and times of meetings to set schedules that do not overlap. By adjusting schedules so that multiple meetings do not overlap at the same time, efficient meeting management becomes possible. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit can input information such as the time slot, priority, and date and time of meetings into a generation AI, and the generation AI can adjust the schedule.

[0037] The optimization unit can readjust the schedule and rebook the most suitable meeting room if the date and time of a meeting are changed. For example, if the date and time of a meeting are changed, the optimization unit readjusts the schedule and resets it to avoid overlaps. For example, if the date and time of a meeting are changed, the optimization unit rebooks the most suitable meeting room to ensure an appropriate meeting room is secured. For example, if the date and time of a meeting are changed, the optimization unit readjusts the schedule and sets it so that important meetings are given priority for rebooking. This allows for flexible scheduling by readjusting the schedule and rebooking the most suitable meeting room when the date and time of a meeting are changed. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit can input information about the change in the date and time of a meeting into the generation AI, and the generation AI can readjust the schedule and rebook the most suitable meeting room.

[0038] The priority setting unit can set reservation priorities based on the importance and urgency of meetings. For example, the priority setting unit can evaluate the importance of meetings and set important meetings to be reserved preferentially. For example, the priority setting unit can evaluate the urgency of meetings and set meetings with high urgency to be reserved preferentially. For example, the priority setting unit can evaluate importance based on the content of the meeting and the positions of the participants and set reservation priorities. In this way, by setting reservation priorities based on the importance and urgency of meetings, important meetings are reserved preferentially. Some or all of the above processing in the priority setting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the priority setting unit can input information on the importance and urgency of meetings into a generation AI, and the generation AI can set reservation priorities.

[0039] The data collection unit can analyze past meeting information from each department and select the optimal collection method. For example, the data collection unit can analyze past meeting information from each department to identify frequently used meeting rooms and time slots, and then select the optimal collection method. For example, the data collection unit can select a collection method based on the size of the meeting and the number of participants, based on past meeting information from each department. For example, the data collection unit can analyze past meeting information from each department and select a collection method that suits the purpose of a specific meeting. In this way, the optimal collection method can be selected by analyzing past meeting information. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input past meeting information from each department into a generation AI, and the generation AI can select the optimal collection method.

[0040] The data collection unit can filter meeting information based on each department's current projects and areas of interest. For example, the data collection unit can collect only relevant meeting information based on each department's current project information. For example, the data collection unit can prioritize collecting highly relevant meeting information based on each department's areas of interest. For example, the data collection unit can collect meeting information at an appropriate time, taking into account the current project progress of each department. This allows for the collection of highly relevant meeting information by filtering based on current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input information on each department's current projects and areas of interest into a generative AI, which can then perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of each department when collecting meeting information. For example, the data collection unit can prioritize the collection of nearby meeting information based on the geographical location of each department. For example, the data collection unit can prioritize the collection of meeting information in distant locations by considering the geographical location of each department. For example, the data collection unit can prioritize the collection of highly relevant meeting information based on the geographical location of each department. In this way, by considering geographical location information, highly relevant meeting information can be prioritized. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the geographical location of each department into a generation AI, and the generation AI can prioritize the collection of highly relevant information.

[0042] The data collection unit can analyze the social media activities of each department and collect relevant information when collecting meeting information. For example, the data collection unit can analyze the social media activities of each department and collect relevant meeting information. For example, the data collection unit can prioritize the collection of important meeting information based on the social media activities of each department. For example, the data collection unit can analyze the social media activities of each department and collect information related to the purpose and content of the meeting. In this way, relevant meeting information can be collected by analyzing social media activities. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input data on the social media activities of each department into a generative AI, and the generative AI can collect relevant information.

[0043] The reservation system can adjust the level of detail required for a reservation based on the importance of the meeting. For example, the reservation system might prompt the user to enter detailed reservation information for a high-priority meeting. For example, it might offer a simplified reservation method for a low-priority meeting. The reservation system adjusts the required reservation information input fields according to the importance of the meeting. This ensures that important meetings are booked by adjusting the level of detail based on the importance of the meeting. Some or all of the above processing in the reservation system may be performed using a generative AI, or not. For example, the reservation system can input meeting importance information into a generative AI, which can then adjust the level of detail of the reservation.

[0044] The reservation department can apply different reservation algorithms depending on the meeting category when a reservation is made. For example, if the meeting category is business, the reservation department applies a business-oriented reservation algorithm. For example, if the meeting category is education, the reservation department applies an education-oriented reservation algorithm. For example, if the meeting category is social, the reservation department applies a social-oriented reservation algorithm. This ensures that the optimal reservation method is provided by applying different reservation algorithms depending on the meeting category. Some or all of the above processing in the reservation department may be performed using a generative AI, or it may be performed without a generative AI. For example, the reservation department can input meeting category information into a generative AI, and the generative AI can apply different reservation algorithms.

[0045] The reservation department can determine the priority of reservations based on the timing of the meetings. For example, the reservation department might prioritize reservations for meetings to be held in the near future. For example, it might postpone reservations for meetings to be held in the distant future. The reservation department can dynamically adjust the priority of reservations based on the timing of the meetings. This allows for reservations to be made at the appropriate time by determining the priority of reservations based on the timing of the meetings. Some or all of the above processing in the reservation department may be performed using a generative AI, or it may be performed without a generative AI. For example, the reservation department can input information about the timing of meetings into a generative AI, and the generative AI can determine the priority of reservations.

[0046] The reservation department can adjust the order of reservations based on the relevance of the meetings at the time of reservation. For example, the reservation department will prioritize reservations for highly relevant meetings. For example, the reservation department will postpone reservations for less relevant meetings. For example, the reservation department can dynamically adjust the order of reservations based on the relevance of the meetings. This ensures that highly relevant meetings are prioritized by adjusting the order of reservations based on the relevance of the meetings. Some or all of the above processing in the reservation department may be performed using a generative AI, or it may be performed without a generative AI. For example, the reservation department can input information about the relevance of meetings into a generative AI, and the generative AI can adjust the order of reservations.

[0047] The optimization unit can optimize the optimization algorithm by referring to past schedule data during schedule optimization. For example, the optimization unit selects the optimal schedule optimization algorithm based on past schedule data. For example, the optimization unit analyzes past schedule data and applies an optimization algorithm that avoids schedule overlaps. For example, the optimization unit refers to past schedule data and applies an efficient schedule optimization algorithm. In this way, the optimal schedule optimization algorithm can be applied by referring to past schedule data. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the optimization unit can input past schedule data into a generative AI, and the generative AI can optimize the optimization algorithm.

[0048] The optimization unit can apply different optimization methods to each meeting category when optimizing the schedule. For example, in the case of a business meeting, the optimization unit applies a business-oriented optimization method. For example, in the case of an educational meeting, the optimization unit applies an educational-oriented optimization method. For example, in the case of a social meeting, the optimization unit applies a social-oriented optimization method. By applying different optimization methods according to the meeting category, the optimal schedule is provided. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the optimization unit can input meeting category information into a generative AI, and the generative AI can apply different optimization methods.

[0049] The optimization unit can weight schedules based on the timing of meetings during schedule optimization. For example, the optimization unit prioritizes scheduling meetings to be held in the near future. For example, the optimization unit postpones meetings to be held in the distant future. For example, the optimization unit dynamically adjusts the schedule weighting based on the timing of meetings. This makes it possible to schedule meetings at the appropriate time by weighting them based on the timing of meetings. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the optimization unit can input meeting timing information into a generative AI, and the generative AI can perform schedule weighting.

[0050] The optimization unit can perform schedule optimization by referring to relevant market data for meetings. For example, the optimization unit selects the optimal schedule optimization method based on the relevant market data. For example, the optimization unit analyzes the relevant market data and applies an optimization method that avoids schedule overlaps. For example, the optimization unit refers to the relevant market data and applies an efficient schedule optimization method. In this way, the optimal schedule optimization method can be applied by referring to the relevant market data. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit can input relevant market data into a generation AI, and the generation AI can perform the optimization.

[0051] The priority setting unit can optimize its priority setting algorithm by referring to past importance data of meetings when setting priorities. For example, the priority setting unit selects the optimal priority setting algorithm based on past importance data of meetings. For example, the priority setting unit analyzes past importance data of meetings and applies an algorithm that prioritizes meetings with high importance. For example, the priority setting unit refers to past importance data of meetings and applies an efficient priority setting algorithm. In this way, the optimal priority setting algorithm can be applied by referring to past importance data. Some or all of the above processing in the priority setting unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the priority setting unit can input past importance data into a generative AI, and the generative AI can optimize the priority setting algorithm.

[0052] The priority setting unit can apply different priority setting methods to each meeting category when setting priorities. For example, in the case of a business meeting, the priority setting unit applies a business-oriented priority setting method. For example, in the case of an educational meeting, the priority setting unit applies an educational-oriented priority setting method. For example, in the case of a social meeting, the priority setting unit applies a social-oriented priority setting method. This makes it possible to set optimal priorities by applying different priority setting methods according to the meeting category. Some or all of the above processing in the priority setting unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the priority setting unit can input meeting category information into a generation AI, and the generation AI can apply different priority setting methods.

[0053] The priority setting unit can assign priority weights based on the timing of meetings when setting priorities. For example, the priority setting unit may prioritize meetings to be held in the near future. For example, the priority setting unit may postpone meetings to be held in the distant future. For example, the priority setting unit may dynamically adjust the priority weights based on the timing of meetings. This makes it possible to set appropriate priorities by assigning priority weights based on the timing of meetings. Some or all of the above processing in the priority setting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the priority setting unit can input meeting timing information into a generation AI, and the generation AI can perform priority weighting.

[0054] The priority setting unit can improve the accuracy of priority setting by referring to relevant literature for meetings during the priority setting process. For example, the priority setting unit selects the optimal priority setting method based on the relevant literature. For example, the priority setting unit analyzes the relevant literature and applies a method to improve the accuracy of priority setting. For example, the priority setting unit refers to the relevant literature and applies an efficient priority setting method. As a result, the accuracy of priority setting is improved by referring to the relevant literature. Some or all of the above processing in the priority setting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the priority setting unit can input information from the relevant literature into a generation AI, which can then improve the accuracy of priority setting.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The meeting room reservation system can also include a notification function. This function notifies relevant parties regarding the meeting room reservation status and any changes. For example, when a meeting room reservation is confirmed, it can send an email or message to all participants. It can also quickly notify relevant parties if a meeting room reservation changes, prompting schedule adjustments. Furthermore, the notification function can send reminders as the meeting start time approaches, ensuring participants arrive on time. This allows for the rapid sharing of information regarding meeting room reservations and changes, supporting smooth meeting management.

[0057] The meeting room reservation system can also include an analytics department. This department analyzes past meeting data to evaluate meeting room usage trends and efficiency. For example, if a particular meeting room is frequently used, it can analyze its usage in detail and propose ways to improve its efficiency. It can also analyze fluctuations in meeting frequency and participant numbers to forecast future meeting room demand. Furthermore, the analytics department can evaluate the purpose and outcomes of meetings and propose improvements to maximize their effectiveness. This improves meeting room utilization efficiency and supports effective meeting management.

[0058] The meeting room reservation system can also include a feedback department. This department collects feedback from participants after meetings and evaluates the meeting room's usage and facilities. For example, it can send questionnaires to participants after meetings to gather opinions on the comfort and quality of the facilities. Based on the collected feedback, the feedback department can identify areas for improvement in the meeting rooms and propose equipment upgrades or layout changes. Furthermore, the feedback department can implement improvements that reflect participant feedback, thereby increasing satisfaction with the meeting rooms. This allows for continuous improvement of the meeting room environment and enhances participant satisfaction.

[0059] The meeting room reservation system can also include a forecasting function. This function predicts future meeting room demand based on past meeting data and current reservation status. For example, if there is a tendency for meeting frequency to increase during a particular period, the system can adjust meeting room reservations in advance for that period. Furthermore, if a particular meeting room is experiencing high demand, the forecasting function can suggest ways to distribute its usage. In addition, the forecasting function can predict fluctuations in meeting size and participant numbers, supporting the securing of appropriate meeting rooms. This allows the system to respond to future meeting room demand and achieve efficient meeting management.

[0060] The meeting room reservation system can also include a customization section. This customization section allows for the customization of meeting room reservation methods and settings according to the needs of each department and user. For example, it can be configured to prioritize the reservation of meeting rooms frequently used by specific departments. Furthermore, the customization section can suggest adjustments to meeting room layouts and equipment according to user preferences. In addition, the customization section can suggest the most suitable meeting room based on the user's past reservation history. This enables flexible meeting room reservations tailored to the needs of each department and user, thereby improving user satisfaction.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection department gathers meeting information from each department. The collection department collects detailed information such as the size, purpose, participants, and date and time of the meeting. The collection department determines that a large conference room is required if the meeting is large or has many participants. This information is then input into the generating AI. Step 2: The reservation department reserves the most suitable meeting room based on the information collected by the data collection department. The reservation department selects the most suitable meeting room by considering factors such as the availability, facilities, and capacity of each meeting room. For example, for large meetings, a larger meeting room will be reserved first. In addition, reservation priorities are set based on the importance and urgency of the meeting, ensuring that important meetings are booked first. Step 3: The optimization unit optimizes the schedule based on the information reserved by the reservation unit. The optimization unit adjusts the schedule to avoid overlapping meetings during the same time slot. For example, if the date and time of a meeting are changed, the schedule is readjusted and the most suitable meeting room is rebooked. Step 4: The priority setting section sets booking priorities based on the importance and urgency of the meeting. This ensures that important meetings are booked preferentially.

[0063] (Example of form 2) The meeting room reservation system according to an embodiment of the present invention is a system that uses a generation AI to collect meeting information from each department, reserves the optimal meeting room considering the size of the meeting, the number of participants, the date and time of the meeting, and optimizes the schedule. The meeting room reservation system collects meeting information from each department, and the generation AI reserves the optimal meeting room considering the size of the meeting, the number of participants, and the date and time of the meeting. The generation AI also determines the reservation priority according to importance. As a result, all employees can reserve meeting rooms smoothly, reduce the stress of scheduling adjustments associated with holding meetings, and improve work efficiency. For example, the meeting room reservation system collects meeting information from each department. At this time, detailed information such as the size of the meeting, its purpose, the number of participants, and the date and time of the meeting is collected. For example, if the size of the meeting is large or there are many participants, a large meeting room is required. This information is input into the generation AI. Next, based on the collected information, the generation AI reserves the optimal meeting room considering the size of the meeting, the number of participants, and the date and time of the meeting. The generation AI selects the optimal meeting room considering the usage status, facilities, and capacity of each meeting room. For example, if the size of the meeting is large, a large meeting room is reserved as a priority. Furthermore, the system sets reservation priorities based on the importance and urgency of meetings, ensuring that important meetings are booked first. The generating AI also optimizes schedules, for example, by adjusting them to prevent multiple meetings from overlapping at the same time. If a meeting date or time changes, the generating AI automatically readjusts the schedule and rebooks the most suitable meeting room. This streamlines meeting scheduling and improves work efficiency. This system allows all employees to book meeting rooms smoothly and reduces the stress associated with scheduling meetings. For example, if meeting room reservations overlap or there is a shortage of meeting rooms, the generating AI automatically selects the most suitable room and adjusts the reservation. By setting reservation priorities based on the importance and urgency of meetings, important meetings are booked first. This improves work efficiency and allows all employees to hold meetings smoothly. In summary, the meeting room reservation system allows all employees to book meeting rooms smoothly, reduces the stress associated with scheduling meetings, and improves work efficiency.

[0064] The meeting room reservation system according to this embodiment comprises a collection unit, a reservation unit, an optimization unit, and a priority setting unit. The collection unit collects meeting information from each department. The collection unit collects detailed information such as the size, purpose, participants, and date and time of the meeting. For example, the collection unit determines that a large meeting room is required if the meeting is large or has many participants. This information is input into the generation AI. The reservation unit reserves the most suitable meeting room based on the information collected by the collection unit. For example, the reservation unit selects the most suitable meeting room by considering the usage status, facilities, and capacity of each meeting room. For example, if the meeting is large, the reservation unit prioritizes reserving a large meeting room. The reservation unit also sets reservation priorities based on the importance and urgency of the meeting, ensuring that important meetings are reserved preferentially. The optimization unit optimizes the schedule based on the information reserved by the reservation unit. For example, the optimization unit adjusts the schedule so that multiple meetings do not overlap at the same time. For example, if the date and time of a meeting are changed, the optimization unit readjusts the schedule and re-reserves the most suitable meeting room. The priority setting unit sets reservation priorities based on the importance and urgency of the meetings. For example, the priority setting unit sets reservation priorities based on the importance and urgency of the meetings, ensuring that important meetings are reserved preferentially. As a result, the meeting room reservation system according to this embodiment can improve operational efficiency by collecting meeting information from each department, reserving the most suitable meeting room, and optimizing the schedule.

[0065] The data collection unit collects meeting information from each department. For example, it collects detailed information such as the size, purpose, participants, and date and time of the meeting. Specifically, it provides an interface for departmental staff to input meeting details, allowing the data collection unit to efficiently obtain the necessary information. Larger meetings or those with many participants require larger conference rooms. This information is input into the generative AI. The generative AI analyzes the collected information and generates optimal conference room candidates based on the characteristics of the meeting. For example, the generative AI considers the number of participants and necessary equipment (projector, whiteboard, video conferencing system, etc.) to list appropriate conference rooms. Furthermore, the generative AI can learn from past meeting data and derive optimal conference room selection patterns under specific conditions. This allows the data collection unit to efficiently collect meeting information from each department and quickly identify optimal conference room candidates using the generative AI.

[0066] The reservation department reserves the most suitable meeting room based on information collected by the data collection department. For example, the reservation department selects the optimal meeting room by considering factors such as the usage status, facilities, and capacity of each room. Specifically, the reservation department monitors the reservation status of meeting rooms in real time and uses a database to check availability. For large meetings, larger meeting rooms are prioritized. The reservation department also sets reservation priorities based on the importance and urgency of the meeting, ensuring that important meetings are booked preferentially. For example, urgent meetings and important meetings attended by senior management are booked with higher priority than other meetings. The reservation department selects the most suitable meeting room based on a list of optimal meeting room candidates provided by the AI ​​generation system and confirms the reservation. Furthermore, the reservation department responds quickly to changes or cancellations, re-evaluating the usage status of other meeting rooms and making optimal adjustments. This allows the reservation department to manage meeting room reservations efficiently and flexibly, supporting the smooth running of meetings.

[0067] The optimization unit optimizes the schedule based on the information reserved by the reservation unit. For example, the optimization unit adjusts schedules to prevent multiple meetings from overlapping at the same time. Specifically, the optimization unit monitors meeting schedules in real time and makes adjustments to prevent overlaps and conflicts. For example, if the date and time of a meeting are changed, the schedule is readjusted and the most suitable meeting room is rebooked. The optimization unit uses generative AI to execute an algorithm that optimizes the schedules of multiple meetings simultaneously. This maximizes the efficiency of meeting room utilization and reduces wasted idle time. Furthermore, the optimization unit sets schedule priorities considering the importance and urgency of meetings, adjusting schedules so that important meetings are held first. For example, progress meetings for important projects and urgent problem-solving meetings are scheduled with higher priority than other meetings. This allows the optimization unit to efficiently manage meeting schedules and improve the efficiency of meeting room utilization.

[0068] The priority setting unit sets reservation priorities based on the importance and urgency of meetings. For example, the priority setting unit sets reservation priorities based on the importance and urgency of meetings, ensuring that important meetings are booked preferentially. Specifically, the priority setting unit sets criteria for evaluating the importance and urgency of each meeting and determines the priority of each meeting based on these criteria. For example, meetings attended by senior management and project progress meetings are given high priority. On the other hand, regular meetings and information-sharing meetings are given relatively low priority. The priority setting unit utilizes generative AI to analyze past meeting data and participant feedback, continuously improving the criteria for priority setting. This allows the priority setting unit to always make appropriate priority settings based on the latest information. Furthermore, the priority setting unit can respond quickly to urgent meetings and adjust the schedules of other meetings to prioritize the booking of urgent meetings. This enables the priority setting unit to provide flexible responses according to the importance and urgency of meetings, improving the efficiency and reliability of the meeting room reservation system.

[0069] The data collection unit can collect detailed information about a meeting, such as its size, purpose, participants, and date and time. For example, if the meeting is large or has many participants, the data collection unit will need a large conference room. This information is input into the generating AI. The data collection unit can collect the purpose of the meeting and select a conference room that suits that purpose, such as information sharing, decision-making, or problem-solving. The data collection unit can collect information such as the titles and number of participants and select an appropriate conference room. In this way, by collecting detailed information about the meeting, it becomes possible to select the optimal conference room. Some or all of the above processing in the data collection unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the data collection unit can input detailed information such as the size, purpose, participants, and date and time of the meeting into the generating AI, and the generating AI can select the optimal conference room.

[0070] The reservation department can select the most suitable meeting room by considering factors such as the usage status, facilities, and capacity of each meeting room. For example, the reservation department can select the most suitable meeting room based on the usage status of each meeting room, such as whether it is booked and how often it is used. For example, the reservation department can select the most suitable meeting room based on the facilities of each meeting room, such as projectors, whiteboards, and sound systems. For example, the reservation department can select the most suitable meeting room based on the capacity of each meeting room, such as the maximum or recommended capacity. In this way, by considering the usage status, facilities, and capacity of each meeting room, it becomes possible to select the most suitable meeting room. Some or all of the above processing in the reservation department may be performed using a generation AI, or it may be performed without a generation AI. For example, the reservation department can input information such as the usage status, facilities, and capacity of each meeting room into a generation AI, which can then select the most suitable meeting room.

[0071] The optimization unit can adjust schedules so that multiple meetings do not overlap at the same time. For example, the optimization unit adjusts the time slots of meetings to set schedules that do not overlap. For example, the optimization unit sets meeting priorities to adjust schedules so that important meetings are prioritized. For example, the optimization unit adjusts the dates and times of meetings to set schedules that do not overlap. By adjusting schedules so that multiple meetings do not overlap at the same time, efficient meeting management becomes possible. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit can input information such as the time slot, priority, and date and time of meetings into a generation AI, and the generation AI can adjust the schedule.

[0072] The optimization unit can readjust the schedule and rebook the most suitable meeting room if the date and time of a meeting are changed. For example, if the date and time of a meeting are changed, the optimization unit readjusts the schedule and resets it to avoid overlaps. For example, if the date and time of a meeting are changed, the optimization unit rebooks the most suitable meeting room to ensure an appropriate meeting room is secured. For example, if the date and time of a meeting are changed, the optimization unit readjusts the schedule and sets it so that important meetings are given priority for rebooking. This allows for flexible scheduling by readjusting the schedule and rebooking the most suitable meeting room when the date and time of a meeting are changed. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit can input information about the change in the date and time of a meeting into the generation AI, and the generation AI can readjust the schedule and rebook the most suitable meeting room.

[0073] The priority setting unit can set reservation priorities based on the importance and urgency of meetings. For example, the priority setting unit can evaluate the importance of meetings and set important meetings to be reserved preferentially. For example, the priority setting unit can evaluate the urgency of meetings and set meetings with high urgency to be reserved preferentially. For example, the priority setting unit can evaluate importance based on the content of the meeting and the positions of the participants and set reservation priorities. In this way, by setting reservation priorities based on the importance and urgency of meetings, important meetings are reserved preferentially. Some or all of the above processing in the priority setting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the priority setting unit can input information on the importance and urgency of meetings into a generation AI, and the generation AI can set reservation priorities.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of meeting information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect the information when the user is relaxed. For example, if the user is relaxed, the data collection unit can immediately collect meeting information and process it quickly. For example, if the user is in a hurry, the data collection unit can advance the collection timing and collect meeting information quickly. In this way, the user's stress is reduced by adjusting the timing of meeting information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the collection timing.

[0075] The data collection unit can analyze past meeting information from each department and select the optimal collection method. For example, the data collection unit can analyze past meeting information from each department to identify frequently used meeting rooms and time slots, and then select the optimal collection method. For example, the data collection unit can select a collection method based on the size of the meeting and the number of participants, based on past meeting information from each department. For example, the data collection unit can analyze past meeting information from each department and select a collection method that suits the purpose of a specific meeting. In this way, the optimal collection method can be selected by analyzing past meeting information. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input past meeting information from each department into a generation AI, and the generation AI can select the optimal collection method.

[0076] The data collection unit can filter meeting information based on each department's current projects and areas of interest. For example, the data collection unit can collect only relevant meeting information based on each department's current project information. For example, the data collection unit can prioritize collecting highly relevant meeting information based on each department's areas of interest. For example, the data collection unit can collect meeting information at an appropriate time, taking into account the current project progress of each department. This allows for the collection of highly relevant meeting information by filtering based on current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input information on each department's current projects and areas of interest into a generative AI, which can then perform the filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of meeting information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important meeting information and prioritize collecting more important information. For example, if the user is relaxed, the data collection unit will collect all meeting information equally. For example, if the user is in a hurry, the data collection unit will prioritize collecting more urgent meeting information. In this way, important information can be collected preferentially by determining the priority of meeting information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input user emotion data into a generative AI, and the generative AI can determine the priority of meeting information to collect.

[0078] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of each department when collecting meeting information. For example, the data collection unit can prioritize the collection of nearby meeting information based on the geographical location of each department. For example, the data collection unit can prioritize the collection of meeting information in distant locations by considering the geographical location of each department. For example, the data collection unit can prioritize the collection of highly relevant meeting information based on the geographical location of each department. In this way, by considering geographical location information, highly relevant meeting information can be prioritized. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the geographical location of each department into a generation AI, and the generation AI can prioritize the collection of highly relevant information.

[0079] The data collection unit can analyze the social media activities of each department and collect relevant information when collecting meeting information. For example, the data collection unit can analyze the social media activities of each department and collect relevant meeting information. For example, the data collection unit can prioritize the collection of important meeting information based on the social media activities of each department. For example, the data collection unit can analyze the social media activities of each department and collect information related to the purpose and content of the meeting. In this way, relevant meeting information can be collected by analyzing social media activities. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input data on the social media activities of each department into a generative AI, and the generative AI can collect relevant information.

[0080] The reservation system can estimate the user's emotions and adjust the meeting room reservation method based on the estimated emotions. For example, if the user is stressed, the reservation system may provide a simple reservation method to minimize hassle. If the user is relaxed, the reservation system may provide detailed reservation options and suggest a customizable reservation method. If the user is in a hurry, the reservation system may prioritize voice input to allow for quick meeting room reservations. This reduces user stress by adjusting the meeting room reservation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation system may be performed using or without generative AI. For example, the reservation system can input user emotion data into a generative AI, which can then adjust the reservation method.

[0081] The reservation system can adjust the level of detail required for a reservation based on the importance of the meeting. For example, the reservation system might prompt the user to enter detailed reservation information for a high-priority meeting. For example, it might offer a simplified reservation method for a low-priority meeting. The reservation system adjusts the required reservation information input fields according to the importance of the meeting. This ensures that important meetings are booked by adjusting the level of detail based on the importance of the meeting. Some or all of the above processing in the reservation system may be performed using a generative AI, or not. For example, the reservation system can input meeting importance information into a generative AI, which can then adjust the level of detail of the reservation.

[0082] The reservation department can apply different reservation algorithms depending on the meeting category when a reservation is made. For example, if the meeting category is business, the reservation department applies a business-oriented reservation algorithm. For example, if the meeting category is education, the reservation department applies an education-oriented reservation algorithm. For example, if the meeting category is social, the reservation department applies a social-oriented reservation algorithm. This ensures that the optimal reservation method is provided by applying different reservation algorithms depending on the meeting category. Some or all of the above processing in the reservation department may be performed using a generative AI, or it may be performed without a generative AI. For example, the reservation department can input meeting category information into a generative AI, and the generative AI can apply different reservation algorithms.

[0083] The booking system can estimate the user's emotions and adjust the length of the booking based on those emotions. For example, if the user is stressed, the booking system will prioritize shorter bookings. If the user is relaxed, the booking system will suggest longer bookings. If the user is in a hurry, the booking system will prioritize the shortest booking time. By adjusting the booking length based on the user's emotions, bookings can be tailored to the user's needs. 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 booking system may be performed using or without a generative AI. For example, the booking system can input user emotion data into a generative AI, which can then adjust the booking length.

[0084] The reservation department can determine the priority of reservations based on the timing of the meetings. For example, the reservation department might prioritize reservations for meetings to be held in the near future. For example, it might postpone reservations for meetings to be held in the distant future. The reservation department can dynamically adjust the priority of reservations based on the timing of the meetings. This allows for reservations to be made at the appropriate time by determining the priority of reservations based on the timing of the meetings. Some or all of the above processing in the reservation department may be performed using a generative AI, or it may be performed without a generative AI. For example, the reservation department can input information about the timing of meetings into a generative AI, and the generative AI can determine the priority of reservations.

[0085] The reservation department can adjust the order of reservations based on the relevance of the meetings at the time of reservation. For example, the reservation department will prioritize reservations for highly relevant meetings. For example, the reservation department will postpone reservations for less relevant meetings. For example, the reservation department can dynamically adjust the order of reservations based on the relevance of the meetings. This ensures that highly relevant meetings are prioritized by adjusting the order of reservations based on the relevance of the meetings. Some or all of the above processing in the reservation department may be performed using a generative AI, or it may be performed without a generative AI. For example, the reservation department can input information about the relevance of meetings into a generative AI, and the generative AI can adjust the order of reservations.

[0086] The optimization unit can estimate the user's emotions and adjust the schedule optimization method based on the estimated user emotions. For example, if the user is stressed, the optimization unit provides a simple schedule optimization method to minimize effort. For example, if the user is relaxed, the optimization unit provides detailed schedule optimization options and proposes a customizable optimization method. For example, if the user is in a hurry, the optimization unit provides a method to quickly optimize the schedule. This reduces user stress by adjusting the schedule optimization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using or without a generative AI. For example, the optimization unit can input user emotion data into a generative AI, which can then adjust the schedule optimization method.

[0087] The optimization unit can optimize the optimization algorithm by referring to past schedule data during schedule optimization. For example, the optimization unit selects the optimal schedule optimization algorithm based on past schedule data. For example, the optimization unit analyzes past schedule data and applies an optimization algorithm that avoids schedule overlaps. For example, the optimization unit refers to past schedule data and applies an efficient schedule optimization algorithm. In this way, the optimal schedule optimization algorithm can be applied by referring to past schedule data. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the optimization unit can input past schedule data into a generative AI, and the generative AI can optimize the optimization algorithm.

[0088] The optimization unit can apply different optimization methods to each meeting category when optimizing the schedule. For example, in the case of a business meeting, the optimization unit applies a business-oriented optimization method. For example, in the case of an educational meeting, the optimization unit applies an educational-oriented optimization method. For example, in the case of a social meeting, the optimization unit applies a social-oriented optimization method. By applying different optimization methods according to the meeting category, the optimal schedule is provided. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the optimization unit can input meeting category information into a generative AI, and the generative AI can apply different optimization methods.

[0089] The optimization unit can estimate the user's emotions and determine schedule priorities based on the estimated emotions. For example, if the user is stressed, the optimization unit will postpone less important schedules and prioritize more important ones. For example, if the user is relaxed, the optimization unit will process all schedules equally. For example, if the user is in a hurry, the optimization unit will prioritize urgent schedules. In this way, important schedules are processed preferentially by determining schedule priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using or without a generative AI. For example, the optimization unit can input user emotion data into a generative AI, which can then determine schedule priorities.

[0090] The optimization unit can weight schedules based on the timing of meetings during schedule optimization. For example, the optimization unit prioritizes scheduling meetings to be held in the near future. For example, the optimization unit postpones meetings to be held in the distant future. For example, the optimization unit dynamically adjusts the schedule weighting based on the timing of meetings. This makes it possible to schedule meetings at the appropriate time by weighting them based on the timing of meetings. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the optimization unit can input meeting timing information into a generative AI, and the generative AI can perform schedule weighting.

[0091] The optimization unit can perform schedule optimization by referring to relevant market data for meetings. For example, the optimization unit selects the optimal schedule optimization method based on the relevant market data. For example, the optimization unit analyzes the relevant market data and applies an optimization method that avoids schedule overlaps. For example, the optimization unit refers to the relevant market data and applies an efficient schedule optimization method. In this way, the optimal schedule optimization method can be applied by referring to the relevant market data. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit can input relevant market data into a generation AI, and the generation AI can perform the optimization.

[0092] The priority setting unit can estimate the user's emotions and adjust the reservation priority setting method based on the estimated user emotions. For example, if the user is stressed, the priority setting unit will lower the priority of low-priority meetings and prioritize high-priority meetings. For example, if the user is relaxed, the priority setting unit will process all meetings equally. For example, if the user is in a hurry, the priority setting unit will prioritize high-priority meetings. In this way, important meetings are prioritized for booking by adjusting the reservation priority setting method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the priority setting unit may be performed using or without a generative AI. For example, the priority setting unit can input user emotion data into a generative AI, and the generative AI can adjust the reservation priority setting method.

[0093] The priority setting unit can optimize its priority setting algorithm by referring to past importance data of meetings when setting priorities. For example, the priority setting unit selects the optimal priority setting algorithm based on past importance data of meetings. For example, the priority setting unit analyzes past importance data of meetings and applies an algorithm that prioritizes meetings with high importance. For example, the priority setting unit refers to past importance data of meetings and applies an efficient priority setting algorithm. In this way, the optimal priority setting algorithm can be applied by referring to past importance data. Some or all of the above processing in the priority setting unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the priority setting unit can input past importance data into a generative AI, and the generative AI can optimize the priority setting algorithm.

[0094] The priority setting unit can apply different priority setting methods to each meeting category when setting priorities. For example, in the case of a business meeting, the priority setting unit applies a business-oriented priority setting method. For example, in the case of an educational meeting, the priority setting unit applies an educational-oriented priority setting method. For example, in the case of a social meeting, the priority setting unit applies a social-oriented priority setting method. This makes it possible to set optimal priorities by applying different priority setting methods according to the meeting category. Some or all of the above processing in the priority setting unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the priority setting unit can input meeting category information into a generation AI, and the generation AI can apply different priority setting methods.

[0095] The priority setting unit can estimate the user's emotions and adjust the display method of reservation priorities based on the estimated user emotions. For example, if the user is stressed, the priority setting unit provides a simple and highly visible display method. For example, if the user is relaxed, the priority setting unit provides a display method that includes detailed information. For example, if the user is in a hurry, the priority setting unit provides a display method that gets straight to the point. By adjusting the display method of reservation priorities based on the user's emotions, a highly visible display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the priority setting unit may be performed using the generative AI or not using the generative AI. For example, the priority setting unit can input user emotion data into the generative AI, and the generative AI can adjust the display method.

[0096] The priority setting unit can assign priority weights based on the timing of meetings when setting priorities. For example, the priority setting unit may prioritize meetings to be held in the near future. For example, the priority setting unit may postpone meetings to be held in the distant future. For example, the priority setting unit may dynamically adjust the priority weights based on the timing of meetings. This makes it possible to set appropriate priorities by assigning priority weights based on the timing of meetings. Some or all of the above processing in the priority setting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the priority setting unit can input meeting timing information into a generation AI, and the generation AI can perform priority weighting.

[0097] The priority setting unit can improve the accuracy of priority setting by referring to relevant literature for meetings during the priority setting process. For example, the priority setting unit selects the optimal priority setting method based on the relevant literature. For example, the priority setting unit analyzes the relevant literature and applies a method to improve the accuracy of priority setting. For example, the priority setting unit refers to the relevant literature and applies an efficient priority setting method. As a result, the accuracy of priority setting is improved by referring to the relevant literature. Some or all of the above processing in the priority setting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the priority setting unit can input information from the relevant literature into a generation AI, which can then improve the accuracy of priority setting.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The meeting room reservation system can also include a notification function. This function notifies relevant parties regarding the meeting room reservation status and any changes. For example, when a meeting room reservation is confirmed, it can send an email or message to all participants. It can also quickly notify relevant parties if a meeting room reservation changes, prompting schedule adjustments. Furthermore, the notification function can send reminders as the meeting start time approaches, ensuring participants arrive on time. This allows for the rapid sharing of information regarding meeting room reservations and changes, supporting smooth meeting management.

[0100] The meeting room reservation system can also include an analytics department. This department analyzes past meeting data to evaluate meeting room usage trends and efficiency. For example, if a particular meeting room is frequently used, it can analyze its usage in detail and propose ways to improve its efficiency. It can also analyze fluctuations in meeting frequency and participant numbers to forecast future meeting room demand. Furthermore, the analytics department can evaluate the purpose and outcomes of meetings and propose improvements to maximize their effectiveness. This improves meeting room utilization efficiency and supports effective meeting management.

[0101] The meeting room reservation system can also include a feedback department. This department collects feedback from participants after meetings and evaluates the meeting room's usage and facilities. For example, it can send questionnaires to participants after meetings to gather opinions on the comfort and quality of the facilities. Based on the collected feedback, the feedback department can identify areas for improvement in the meeting rooms and propose equipment upgrades or layout changes. Furthermore, the feedback department can implement improvements that reflect participant feedback, thereby increasing satisfaction with the meeting rooms. This allows for continuous improvement of the meeting room environment and enhances participant satisfaction.

[0102] The meeting room reservation system can also include a forecasting function. This function predicts future meeting room demand based on past meeting data and current reservation status. For example, if there is a tendency for meeting frequency to increase during a particular period, the system can adjust meeting room reservations in advance for that period. Furthermore, if a particular meeting room is experiencing high demand, the forecasting function can suggest ways to distribute its usage. In addition, the forecasting function can predict fluctuations in meeting size and participant numbers, supporting the securing of appropriate meeting rooms. This allows the system to respond to future meeting room demand and achieve efficient meeting management.

[0103] The meeting room reservation system can also include a customization section. This customization section allows for the customization of meeting room reservation methods and settings according to the needs of each department and user. For example, it can be configured to prioritize the reservation of meeting rooms frequently used by specific departments. Furthermore, the customization section can suggest adjustments to meeting room layouts and equipment according to user preferences. In addition, the customization section can suggest the most suitable meeting room based on the user's past reservation history. This enables flexible meeting room reservations tailored to the needs of each department and user, thereby improving user satisfaction.

[0104] The meeting room reservation system can further utilize emotion estimation functionality to adjust the meeting room environment based on the user's emotions. For example, if a user is feeling stressed, the lighting and temperature can be adjusted to provide a relaxing environment. Conversely, if a user is relaxed, an environment conducive to concentration can be provided. Furthermore, if a user is in a hurry, necessary equipment can be prepared in advance to ensure the meeting proceeds quickly. This allows for the provision of an optimal meeting environment tailored to the user's emotions, maximizing the effectiveness of the meeting.

[0105] The meeting room reservation system can further utilize emotion estimation to adjust the meeting process based on the user's emotions. For example, if a user is feeling stressed, it can suggest a concise agenda or a shorter meeting to ensure a smoother flow. Conversely, if a user is relaxed, it can suggest a more detailed agenda or a longer meeting. Furthermore, if a user is in a hurry, it can provide support to quickly move the agenda forward. This enables optimal meeting management tailored to the user's emotions, maximizing the effectiveness of the meeting.

[0106] The meeting room reservation system can further utilize emotion estimation to adjust meeting content based on the user's emotions. For example, if a user is stressed, important topics can be prioritized and unnecessary topics omitted. Conversely, if a user is relaxed, detailed topics can be addressed to facilitate deeper discussion. Furthermore, if a user is in a hurry, support can be provided to help reach conclusions quickly. This allows for the provision of optimal meeting content tailored to the user's emotions, maximizing the effectiveness of the meeting.

[0107] The meeting room reservation system can further utilize emotion estimation functionality to adjust meeting follow-up methods based on the user's emotions. For example, if a user is feeling stressed, a concise follow-up can be provided to alleviate their burden. Conversely, if a user is relaxed, a detailed follow-up can be provided to facilitate preparation for the next meeting. Furthermore, if a user is in a hurry, a quick follow-up can be provided to deliver the necessary information. This allows for optimal follow-up tailored to the user's emotions, maximizing the effectiveness of the meeting.

[0108] The meeting room reservation system can further utilize emotion estimation functionality to adjust the way meeting feedback is given based on the user's emotions. For example, if a user is feeling stressed, concise feedback can be provided to alleviate their burden. If a user is relaxed, detailed feedback can be provided, along with suggestions for improvement for the next meeting. Furthermore, if a user is in a hurry, quick feedback can be given, providing the necessary information. This allows for the provision of optimal feedback tailored to the user's emotions, maximizing the effectiveness of the meeting.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection department gathers meeting information from each department. The collection department collects detailed information such as the size, purpose, participants, and date and time of the meeting. The collection department determines that a large conference room is required if the meeting is large or has many participants. This information is then input into the generating AI. Step 2: The reservation department reserves the most suitable meeting room based on the information collected by the data collection department. The reservation department selects the most suitable meeting room by considering factors such as the availability, facilities, and capacity of each meeting room. For example, for large meetings, a larger meeting room will be reserved first. In addition, reservation priorities are set based on the importance and urgency of the meeting, ensuring that important meetings are booked first. Step 3: The optimization unit optimizes the schedule based on the information reserved by the reservation unit. The optimization unit adjusts the schedule to avoid overlapping meetings during the same time slot. For example, if the date and time of a meeting are changed, the schedule is readjusted and the most suitable meeting room is rebooked. Step 4: The priority setting section sets booking priorities based on the importance and urgency of the meeting. This ensures that important meetings are booked preferentially.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the data collection unit, reservation unit, optimization unit, and priority setting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects meeting information from each department. The reservation unit is implemented by the specific processing unit 290 of the data processing unit 12 and reserves the optimal meeting room based on the collected information. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the schedule. The priority setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets reservation priorities based on the importance and urgency of the meetings. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, reservation unit, optimization unit, and priority setting unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects meeting information from each department. The reservation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and reserves the optimal meeting room based on the collected information. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and optimizes the schedule. The priority setting unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and sets reservation priorities based on the importance and urgency of the meetings. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The 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.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 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.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the 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.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 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.

[0146] Each of the multiple elements described above, including the data collection unit, reservation unit, optimization unit, and priority setting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects meeting information from each department. The reservation unit is implemented by the specific processing unit 290 of the data processing unit 12 and reserves the optimal meeting room based on the collected information. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the schedule. The priority setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets reservation priorities based on the importance and urgency of the meetings. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The 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.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the data collection unit, reservation unit, optimization unit, and priority setting unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects meeting information from each department. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reserves the optimal meeting room based on the collected information. The optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and optimizes the schedule. The priority setting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sets reservation priorities based on the importance and urgency of the meetings. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) The collection department collects meeting information from each department, A reservation unit reserves the most suitable meeting room based on the information collected by the aforementioned collection unit, An optimization unit that optimizes the schedule based on the information reserved by the aforementioned reservation unit, It includes a priority setting unit that sets reservation priorities based on the importance and urgency of meetings. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect detailed information such as the size, purpose, participants, and date and time of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reservation section is, We will select the most suitable meeting room by considering factors such as the usage status, facilities, and capacity of each meeting room. The system described in Appendix 1, characterized by the features described herein. (Note 4) The optimization unit, Schedule meetings to avoid overlapping at the same time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The optimization unit, If the meeting date and time are changed, readjust the schedule and rebook the most suitable meeting room. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned priority setting unit, Prioritize bookings based on the importance and urgency of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of meeting information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past meeting information from each department and select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting meeting information, filter it based on each department's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and prioritizes the meeting information to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting meeting information, prioritize the collection of highly relevant information, taking into account the geographical location of each department. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering meeting information, we analyze the social media activities of each department and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reservation section is, The system estimates the user's emotions and adjusts the meeting room reservation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reservation section is, When making a reservation, adjust the level of detail based on the importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reservation section is, When making a reservation, different reservation algorithms are applied depending on the meeting category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reservation section is, It estimates the user's emotions and adjusts the length of the reservation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reservation section is, When making a reservation, priority will be determined based on the timing of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reservation section is, When making a reservation, the order of reservations will be adjusted based on the relevance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, It estimates the user's emotions and adjusts the scheduling optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, During schedule optimization, the optimization algorithm is optimized by referring to past schedule data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, When optimizing the schedule, different optimization methods are applied to each category of meeting. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, It estimates the user's emotions and determines schedule priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, When optimizing the schedule, weight the schedule based on when the meetings are held. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, When optimizing the schedule, we refer to relevant market data for meetings. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned priority setting unit, It estimates the user's emotions and adjusts the reservation priority setting method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned priority setting unit, When setting priorities, the prioritization algorithm is optimized by referencing past importance data of meetings. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned priority setting unit, When setting priorities, apply different prioritization methods for each meeting category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned priority setting unit, The system estimates the user's emotions and adjusts how reservation priority is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned priority setting unit, When setting priorities, weight priorities based on when the meetings are scheduled. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned priority setting unit, When setting priorities, refer to relevant literature for meetings to improve the accuracy of priority setting. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects meeting information from each department, A reservation unit reserves the most suitable meeting room based on the information collected by the aforementioned collection unit, An optimization unit that optimizes the schedule based on the information reserved by the aforementioned reservation unit, It includes a priority setting unit that sets reservation priorities based on the importance and urgency of meetings. A system characterized by the following features.

2. The aforementioned collection unit is Collect detailed information such as the size, purpose, participants, and date and time of the meeting. The system according to feature 1.

3. The aforementioned reservation section is, We will select the most suitable meeting room by considering factors such as the usage status, facilities, and capacity of each meeting room. The system according to feature 1.

4. The optimization unit, Schedule meetings to avoid overlapping at the same time. The system according to feature 1.

5. The optimization unit, If the meeting date and time are changed, readjust the schedule and rebook the most suitable meeting room. The system according to feature 1.

6. The aforementioned priority setting unit, Prioritize bookings based on the importance and urgency of the meeting. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of meeting information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past meeting information from each department and select the most suitable collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting meeting information, filter it based on each department's current projects and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates user sentiment and prioritizes the meeting information to collect based on the estimated user sentiment. The system according to feature 1.

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