system

Abstract

The system according to this embodiment aims to efficiently manage the reservation status of meeting rooms and automate the coordination with those who have made reservations. [Solution] The system according to the embodiment comprises a monitoring unit, a contact unit, a schedule review unit, and a cancellation waiting unit. The monitoring unit monitors the reservation status of meeting rooms. The contact unit automatically contacts the reservation holder based on the reservation status monitored by the monitoring unit. The schedule review unit reviews the meeting room reservation details taking into account the user's schedule. The cancellation waiting unit provides a cancellation waiting reservation function in case a reservation is released.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the reservation status of conference rooms has not been efficiently managed and the coordination with the reservers has not been sufficiently automated, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently manage the reservation status of conference rooms and automate the coordination with the reservers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a contact unit, a schedule review unit, and a waiting list unit. The monitoring unit monitors the reservation status of meeting rooms. The contact unit automatically contacts the reservation holder based on the reservation status monitored by the monitoring unit. The schedule review unit reviews the meeting room reservation details taking into account the user's schedule. The waiting list unit provides a waiting list reservation function in case a reservation is released. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage the reservation status of meeting rooms and automate the coordination with those who have made reservations. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The meeting room reservation efficiency system according to an embodiment of the present invention is a system that uses AI to streamline meeting room reservations. This system monitors the reservation status of meeting rooms and, if a reservation is full, automatically contacts the person who made the reservation. Next, it takes into account the user's schedule and scrutinizes the meeting room reservation based on whether the user is working from home or in the office. Furthermore, by adding a waiting list reservation function for when a meeting room is released, it is possible to efficiently secure a meeting room. For example, when monitoring the reservation status of meeting rooms, a schedule management tool is used to grasp the availability of meeting rooms in real time. If a meeting room is full, the AI ​​automatically contacts the person who made the reservation and requests adjustments to the meeting room. Taking into account the user's schedule, it scrutinizes the meeting room reservation based on whether the user is working from home or in the office. For example, if the user is working from home, it can be encouraged to cancel the meeting room reservation. Also, even if the user is in the office, if the meeting room utilization rate is low, it can be asked to give the room to another user. A waiting list reservation function for when a meeting room is released is added. This allows users on the waiting list to be automatically notified when a meeting room becomes available, enabling efficient securing of a meeting room. For example, when a meeting room becomes available, the AI ​​sends a notification to users on the waiting list and confirms the meeting room reservation. This system reduces the effort involved in booking and coordinating meeting rooms, allowing for more efficient room reservations. Furthermore, by taking users' schedules into account, it reduces unnecessary bookings and improves meeting room utilization rates. For example, if a user working from home has booked a meeting room, the AI ​​can automatically prompt them to cancel, freeing up the room for another user. In this way, the meeting room booking efficiency system efficiently monitors meeting room booking status and automatically contacts the user, making meeting room adjustments easier.

[0029] The conference room reservation efficiency system according to this embodiment comprises a monitoring unit, a contact unit, a schedule review unit, and a cancellation waiting unit. The monitoring unit monitors the reservation status of conference rooms. The monitoring unit, for example, uses a schedule management tool to grasp the availability of conference rooms in real time. The monitoring unit, for example, uses a schedule management tool to periodically update the reservation status of conference rooms and obtain the latest information. The monitoring unit, for example, automatically contacts the person who made the reservation if the conference room is fully booked. The contact unit, for example, contacts the person who made the reservation if the conference room is fully booked and requests that they adjust the conference room. The contact unit contacts the person who made the reservation using email or a messaging app. The contact unit, for example, checks the availability of the conference room with the person who made the reservation and requests that they adjust it. The contact unit, for example, requests the person who made the reservation to change the time they are using the conference room. The schedule review unit reviews the conference room reservation details taking into account the user's schedule. The schedule review unit, for example, encourages the user to cancel the conference room reservation if they are working from home. The Schedule Review Department, for example, requests that users relinquish their use of a meeting room to other users if the room's utilization rate is low, even if the user is present at the office. The Schedule Review Department, for example, checks users' schedules and optimizes meeting room usage. The Waiting List Department provides a waiting list reservation function when a meeting room is released. The Waiting List Department, for example, automatically notifies users on the waiting list when a meeting room becomes available and confirms the reservation. The Waiting List Department, for example, notifies users on the waiting list of the meeting room's availability and encourages them to confirm their reservation. The Waiting List Department, for example, confirms the meeting room usage time with users on the waiting list and confirms the reservation. As a result, the meeting room reservation efficiency system according to this embodiment efficiently monitors the meeting room reservation status and automatically contacts users, making it easier to adjust meeting room usage.

[0030] The monitoring department monitors the reservation status of meeting rooms. For example, the monitoring department uses scheduling tools to understand meeting room availability in real time. Specifically, the monitoring department integrates with these tools and periodically retrieves meeting room reservation data via APIs. This ensures that changes in meeting room reservation status are immediately updated, always reflecting the latest information. For example, the monitoring department periodically updates meeting room reservation status using scheduling tools to obtain the latest information. This allows for real-time monitoring of meeting room availability, preventing overlapping reservations and wasted time. For example, if a meeting room is fully booked, the monitoring department automatically contacts the person who made the reservation. This allows the monitoring department to confirm the meeting room's usage status and request adjustments. For example, the monitoring department requests the person who made the reservation to change their reservation time. This maximizes the efficiency of meeting room utilization and resolves the problem of not being able to secure the necessary meeting room. Furthermore, the monitoring department can analyze meeting room usage and identify times and days with low utilization rates. This optimizes meeting room utilization and enables efficient operation. For example, campaigns can be implemented to promote meeting room reservations during times with low utilization rates. Furthermore, the monitoring department can periodically compile reports on the usage of meeting rooms and provide them to administrators. This allows administrators to understand the usage of meeting rooms and take appropriate measures.

[0031] The contact department automatically contacts the user who made the reservation if the meeting room is fully booked, requesting adjustments. Specifically, the contact department contacts the user via email or messaging apps. For example, it checks the meeting room's availability with the user and requests adjustments. The contact department may, for example, ask the user to change the time they are using the meeting room. This maximizes the efficiency of meeting room utilization and resolves the problem of not being able to secure the necessary meeting room. The contact department is required to present specific adjustment options to the user and respond quickly. For example, it can show the user the availability of other meeting rooms and suggest changing the time or moving to another meeting room. The contact department can also consider the user's schedule and propose the most suitable adjustment. This minimizes the burden on the user while improving the efficiency of meeting room utilization. Furthermore, the contact department can collect feedback from the user and use it to improve the system. For example, it can record how the user reacted to the adjustments and what problems occurred, and use this information for future adjustments. This allows the contact department to make more effective adjustments and improve the efficiency of meeting room utilization.

[0032] The Schedule Review Department reviews meeting room reservations, taking into account the user's schedule. Specifically, if a user is working from home, the Schedule Review Department encourages them to cancel their meeting room reservation. For example, if a user is planning to work from home, the Schedule Review Department can automatically cancel the meeting room reservation based on that information and offer the room to another user. Even if a user is in the office, if the meeting room utilization rate is low, the Schedule Review Department will request that they yield the room to another user. The Schedule Review Department checks users' schedules and optimizes meeting room usage. For example, if a user has reserved a meeting room but is not actually using it, the Schedule Review Department can cancel the reservation based on that information and offer it to another user. This maximizes the efficiency of meeting room utilization and prevents unnecessary reservations. Furthermore, the Schedule Review Department can analyze users' schedules and understand meeting room usage patterns. This allows it to predict meeting room usage and propose optimal reservation schedules. For example, if meeting room usage is concentrated during specific time slots or days of the week, it can propose ways to distribute reservations across those time slots. This improves the efficiency of meeting room utilization and reduces reservation competition.

[0033] The waiting list function provides a reservation feature for when a meeting room becomes available. Specifically, when a meeting room becomes available, it automatically notifies users on the waiting list and confirms their reservation. For example, it notifies users on the waiting list of the meeting room's availability and encourages them to confirm their reservation. The waiting list function also confirms the meeting room's usage time with users on the waiting list and confirms their reservation. This allows users on the waiting list to quickly confirm their reservation when a meeting room becomes available. The waiting list function is required to consider the priority of users and notify them in the optimal order. For example, if there are multiple users on the waiting list, it notifies and confirms reservations based on the users' priority. This ensures that users on the waiting list can use the meeting room fairly. Furthermore, the waiting list function can collect user feedback and use it to improve the system. For example, it can record how users on the waiting list reacted and what problems occurred, and use this information for future notifications. This allows the waiting list function to provide more effective notifications and improve the efficiency of meeting room utilization.

[0034] The monitoring unit can use a schedule management tool to understand the availability of meeting rooms in real time. The monitoring unit, for example, uses a schedule management tool to periodically update the reservation status of meeting rooms and obtain the latest information. The monitoring unit, for example, uses a schedule management tool to understand the availability of meeting rooms in real time. The monitoring unit, for example, uses a schedule management tool to monitor the reservation status of meeting rooms and understand their availability. This enables efficient use of meeting rooms by understanding their availability in real time. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data obtained from the schedule management tool into a generating AI and have the generating AI perform the task of understanding the availability of meeting rooms.

[0035] The contact unit can automatically contact the person who made the reservation if the meeting room is fully booked and request a meeting room adjustment. For example, if the meeting room is fully booked, the contact unit will contact the person who made the reservation via email or messaging app. For example, the contact unit will check the meeting room's availability with the person who made the reservation and request an adjustment. For example, the contact unit will ask the person who made the reservation to change the time they are using the meeting room. This makes it easier to adjust meeting rooms even when they are fully booked by automatically contacting the person who made the reservation. Some or all of the above processes in the contact unit may be performed using AI, for example, or not using AI. For example, the contact unit can input the person's contact information into a generating AI and have the generating AI execute a meeting room adjustment request.

[0036] The schedule review unit can prompt users to cancel meeting room reservations if they are working from home. For example, the schedule review unit notifies users to cancel meeting room reservations if they are working from home. For example, the schedule review unit checks the user's work-from-home status and prompts them to cancel meeting room reservations. For example, the schedule review unit checks the user's work-from-home status and requests them to cancel meeting room reservations. This improves the efficiency of meeting room utilization by allowing users to cancel reservations when working from home. Some or all of the above processing in the schedule review unit may be performed using AI, for example, or without AI. For example, the schedule review unit can input the user's work-from-home status into a generating AI and have the generating AI execute the meeting room reservation cancellation.

[0037] The Schedule Review Department can request that users relinquish their meeting room to other users even when they are present at the office, if the meeting room utilization rate is below a certain threshold. For example, the Schedule Review Department will notify users to relinquish their meeting room to other users if the meeting room utilization rate is low, even when they are present at the office. For example, the Schedule Review Department will check the meeting room utilization status with users and request that they relinquish their meeting room to other users. For example, the Schedule Review Department will check the meeting room utilization status with users and encourage them to relinquish their meeting room to other users. This improves the efficiency of meeting room utilization by allowing users to relinquish their meeting room to other users when the utilization rate is low. Some or all of the above processes in the Schedule Review Department may be performed using AI, for example, or without AI. For example, the Schedule Review Department can input the user's meeting room utilization status into a generating AI and have the generating AI execute a request to relinquish the meeting room to other users.

[0038] The waiting list system can automatically notify users on the waiting list when a meeting room becomes available and confirm their reservation. For example, the waiting list system can notify users on the waiting list via email or messaging app when a meeting room becomes available. For example, the waiting list system can notify users on the waiting list of the meeting room's availability and encourage them to confirm their reservation. For example, the waiting list system can ask users on the waiting list to check the meeting room's usage time and confirm their reservation. This allows for efficient securing of meeting rooms by automatically notifying users on the waiting list when a meeting room becomes available. Some or all of the above processes in the waiting list system may be performed using AI, for example, or not using AI. For example, the waiting list system can input meeting room release information into a generating AI and have the generating AI execute notifications to users on the waiting list.

[0039] The monitoring unit can analyze past booking patterns to build a predictive model and predict booking congestion in advance when monitoring the booking status of meeting rooms. For example, the monitoring unit can predict congestion on specific days of the week or time slots based on past booking data. For example, the monitoring unit can analyze past booking patterns to predict the frequency of specific events or meetings. For example, the monitoring unit can predict the booking trends of specific users based on past booking history. In this way, by analyzing past booking patterns and building a predictive model, it is possible to predict booking congestion in advance. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past booking data into a generating AI and have the generating AI execute the construction of a predictive model.

[0040] The monitoring unit can set monitoring priorities based on the purpose of meeting room use during monitoring. For example, the monitoring unit will set a high monitoring priority for important meetings and presentations. For example, it will set a medium monitoring priority for workshops and training sessions. For example, it will set a low monitoring priority for casual meetings. This allows monitoring of important meetings and presentations to be prioritized by setting monitoring priorities based on the purpose of meeting room use. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input meeting room usage purpose data into a generating AI and have the generating AI perform the setting of monitoring priorities.

[0041] The monitoring unit can simultaneously monitor the usage status of conference room equipment during monitoring, and determine the availability of the equipment. For example, the monitoring unit can monitor the usage status of the projector and determine its availability. For example, the monitoring unit can monitor the usage status of the whiteboard and determine its availability. For example, the monitoring unit can monitor the usage status of all conference room equipment and determine its availability. In this way, by simultaneously monitoring the usage status of the conference room equipment, the availability of the equipment can be determined. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input conference room equipment usage data into a generating AI and have the generating AI perform the task of determining the availability of the equipment.

[0042] The monitoring unit can also monitor the availability of nearby meeting rooms, taking into account the geographical location of the meeting room during monitoring. For example, the monitoring unit can monitor the availability of nearby meeting rooms and suggest available meeting rooms. For example, the monitoring unit can suggest the optimal meeting room based on the geographical location of the meeting room. For example, the monitoring unit can monitor the usage status of nearby meeting rooms and suggest efficient meeting room use. This enables efficient meeting room use by monitoring the availability of nearby meeting rooms, taking into account the geographical location of the meeting room. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the geographical location of the meeting room into a generating AI and have the generating AI perform monitoring of the availability of nearby meeting rooms.

[0043] The contact unit can select the optimal contact method by referring to past contact history when making contact. For example, the contact unit selects the optimal contact method based on past contact history. For example, the contact unit selects the optimal contact method according to the user's preferences. For example, the contact unit selects the optimal contact method according to the context. By selecting the optimal contact method by referring to past contact history, more effective contact becomes possible. Some or all of the above processing in the contact unit may be performed using AI, for example, or without AI. For example, the contact unit can input past contact history data into a generating AI and have the generating AI perform the selection of the optimal contact method.

[0044] The contact unit can generate different contact messages depending on the reservation holder's job title and department when contacting them. For example, the contact unit can generate a message using appropriate polite language depending on the job title. For example, the contact unit can generate a message containing relevant information depending on the department. For example, the contact unit can generate the optimal message by considering both job title and department. This allows for the sending of more appropriate messages by generating different contact messages depending on the reservation holder's job title and department. Some or all of the above processing in the contact unit may be performed using AI, for example, or without AI. For example, the contact unit can input the reservation holder's job title and department data into a generation AI and have the generation AI perform the generation of contact messages.

[0045] The contact unit can select the optimal contact time when making contact, taking into account the client's schedule. For example, the contact unit selects the optimal contact time based on the client's schedule. For example, the contact unit avoids contacting the client during their busy hours. For example, the contact unit finds the client's free time and contacts them. By selecting the optimal contact time considering the client's schedule, more effective contact becomes possible. Some or all of the above processes in the contact unit may be performed using AI, for example, or without AI. For example, the contact unit can input the client's schedule data into a generating AI and have the generating AI select the optimal contact time.

[0046] The contact unit can determine the priority of scheduling requests by referring to the user's past meeting room usage history when contacting them. For example, the contact unit may determine the priority of scheduling requests based on past meeting room usage history. For example, the contact unit may determine the priority based on the user's frequency of use. For example, the contact unit may determine the priority based on the user's importance. This allows for more effective scheduling by determining the priority of scheduling requests by referring to the user's past meeting room usage history. Some or all of the above processing in the contact unit may be performed using AI, for example, or without AI. For example, the contact unit may input the user's past meeting room usage history data into a generating AI and have the generating AI perform the task of determining the priority of scheduling requests.

[0047] The schedule review unit can analyze the user's past schedule history and select the optimal review method during schedule review. For example, the schedule review unit selects the optimal review method based on the user's past schedule history. For example, the schedule review unit analyzes the user's schedule patterns and selects the optimal review method. For example, the schedule review unit selects an efficient review method based on the user's schedule history. This makes it possible to perform more effective schedule review by analyzing the user's past schedule history and selecting the optimal review method. Some or all of the above processes in the schedule review unit may be performed using AI, for example, or without AI. For example, the schedule review unit can input the user's past schedule history data into a generating AI and have the generating AI select the optimal review method.

[0048] The schedule review unit can apply different review algorithms depending on the user's position and job duties during schedule review. For example, the schedule review unit can apply an appropriate review algorithm depending on the position. For example, the schedule review unit can apply an appropriate review algorithm depending on the job duties. For example, the schedule review unit can apply the optimal review algorithm considering both the position and job duties. This makes it possible to perform more appropriate schedule review by applying different review algorithms depending on the user's position and job duties. Some or all of the above processing in the schedule review unit may be performed using AI, for example, or without AI. For example, the schedule review unit can input user position and job duty data into a generating AI and have the generating AI execute the application of the review algorithm.

[0049] The schedule review unit can determine whether an employee is working from home or in the office by considering the user's geographical location information during schedule review. For example, the schedule review unit determines whether an employee is working from home or in the office based on the user's geographical location information. For example, the schedule review unit updates the user's location information in real time and determines the work status. For example, the schedule review unit determines the optimal work status based on the user's location information. This makes it possible to perform more appropriate schedule review by considering the user's geographical location information when determining whether an employee is working from home or in the office. Some or all of the above processing in the schedule review unit may be performed using AI, for example, or without AI. For example, the schedule review unit can input the user's geographical location information into a generating AI and have the generating AI perform the determination of whether an employee is working from home or in the office.

[0050] The schedule review unit can analyze the user's social media activity during schedule review to evaluate the relevance of schedules. For example, the schedule review unit evaluates the relevance of schedules based on the user's social media activity. For example, the schedule review unit analyzes the content of social media posts to evaluate the relevance of schedules. For example, the schedule review unit evaluates the relevance of schedules based on the user's social media activity history. This allows for more appropriate schedule review by analyzing the user's social media activity to evaluate the relevance of schedules. Some or all of the above processing in the schedule review unit may be performed using AI, for example, or without AI. For example, the schedule review unit can input the user's social media activity data into a generating AI and have the generating AI perform the schedule relevance evaluation.

[0051] The waiting list unit can predict the probability of cancellations by referring to past cancellation history while a user is on the waiting list. For example, the waiting list unit can predict the probability of cancellations on specific days of the week or time slots based on past cancellation history. For example, the waiting list unit can predict the probability of cancellations for specific events or meetings by analyzing past cancellation patterns. For example, the waiting list unit can predict the cancellation tendencies of specific users based on past cancellation history. This makes it possible to have a more effective waiting list by predicting the probability of cancellations by referring to past cancellation history. Some or all of the above processing in the waiting list unit may be performed using AI, for example, or without AI. For example, the waiting list unit can input past cancellation history data into a generating AI and have the generating AI perform the prediction of cancellation probabilities.

[0052] The waiting list unit can generate different notification messages depending on the user's position and job duties when they are on the waiting list. For example, the waiting list unit can generate a notification message using appropriate polite language depending on the position. For example, the waiting list unit can generate a notification message that includes relevant information depending on the job duties. For example, the waiting list unit can generate the optimal notification message by considering both the position and job duties. This allows for more appropriate notifications by generating different notification messages depending on the user's position and job duties. Some or all of the above processing in the waiting list unit may be performed using AI, for example, or without AI. For example, the waiting list unit can input the user's position and job duties data into a generation AI and have the generation AI execute the generation of notification messages.

[0053] The waiting list unit can select the optimal notification time while a user is on the waiting list, taking into account their schedule. For example, the waiting list unit selects the optimal notification time based on the user's schedule. For example, the waiting list unit sends notifications while avoiding the user's busy times. For example, the waiting list unit finds the user's free time and sends notifications. By selecting the optimal notification time while considering the user's schedule, a more effective waiting list becomes possible. Some or all of the above processes in the waiting list unit may be performed using AI, for example, or without AI. For example, the waiting list unit can input the user's schedule data into a generating AI and have the generating AI select the optimal notification time.

[0054] The waiting list unit can determine the priority of a user on the waiting list by referring to the user's past meeting room usage history. For example, the waiting list unit can determine the priority based on the user's past meeting room usage history. For example, the waiting list unit can determine the priority based on the user's frequency of use. For example, the waiting list unit can determine the priority based on the user's importance. This makes it possible to determine the priority of a user on the waiting list by referring to the user's past meeting room usage history, enabling a more effective waiting list. Some or all of the above processing in the waiting list unit may be performed using AI, for example, or without AI. For example, the waiting list unit can input the user's past meeting room usage history data into a generating AI and have the generating AI perform the determination of the waiting list priority.

[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 efficiency system can further prioritize reservations based on the purpose of the meeting room's use. For example, important meetings and presentations can be given a high priority. Workshops and training sessions can be given a medium priority. Furthermore, casual meetings can be given a low priority. This allows for prioritizing important meetings and presentations by setting reservation priorities based on the purpose of the meeting room's use. The reservation priority setting can be done using AI or not. For example, meeting room usage purpose data can be input into a generating AI, and the generating AI can then perform the reservation priority setting.

[0057] The meeting room reservation efficiency system can also provide a dashboard that displays meeting room usage in real time. For example, it can provide a dashboard that allows users to check the availability and reservation status of meeting rooms at a glance. It can also display statistical information such as meeting room utilization rates and reservation cancellation status. Furthermore, it can display detailed information such as the purpose of meeting room use and reservation priority. This enables efficient use of meeting rooms by understanding their usage status in real time. The dashboard display may be generated using AI or not. For example, meeting room usage data can be input into a generating AI, and the generating AI can then generate the dashboard display.

[0058] The meeting room reservation efficiency system can further collect user feedback on meeting rooms and evaluate user satisfaction. For example, it can send a survey to users after they use a meeting room to request feedback. It can also analyze the content of the feedback to identify areas for improvement in meeting room usage and the reservation system. Furthermore, it can evaluate user satisfaction and use that information to improve meeting room usage and the reservation system. In this way, by collecting user feedback on meeting rooms and evaluating user satisfaction, a better meeting room reservation system can be provided. The collection and evaluation of feedback may be done using AI, or not. For example, user feedback data can be input into a generating AI, and the generating AI can then perform the satisfaction evaluation.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The monitoring department monitors the reservation status of meeting rooms. For example, they use a schedule management tool to grasp the availability of meeting rooms in real time and update it regularly to obtain the latest information. Step 2: If a meeting room is fully booked, the contact department automatically contacts the person who made the reservation and requests that they adjust the meeting room. For example, it contacts the person who made the reservation via email or messaging app to check the meeting room's availability and request a change in the reservation time. Step 3: The Schedule Review Department reviews meeting room reservations, taking into account the user's schedule. For example, if a user is working from home, they are encouraged to cancel the meeting room reservation, and even if they are in the office, they are asked to give up the meeting room to another user if its utilization rate is low. Step 4: The waiting list section provides a waiting list reservation function in case a reservation is released. For example, when a meeting room becomes available, it automatically notifies users on the waiting list and prompts them to confirm their reservation.

[0061] (Example of form 2) The meeting room reservation efficiency system according to an embodiment of the present invention is a system that uses AI to streamline meeting room reservations. This system monitors the reservation status of meeting rooms and, if a reservation is full, automatically contacts the person who made the reservation. Next, it takes into account the user's schedule and scrutinizes the meeting room reservation based on whether the user is working from home or in the office. Furthermore, by adding a waiting list reservation function for when a meeting room is released, it is possible to efficiently secure a meeting room. For example, when monitoring the reservation status of meeting rooms, a schedule management tool is used to grasp the availability of meeting rooms in real time. If a meeting room is full, the AI ​​automatically contacts the person who made the reservation and requests adjustments to the meeting room. Taking into account the user's schedule, it scrutinizes the meeting room reservation based on whether the user is working from home or in the office. For example, if the user is working from home, it can be encouraged to cancel the meeting room reservation. Also, even if the user is in the office, if the meeting room utilization rate is low, it can be asked to give the room to another user. A waiting list reservation function for when a meeting room is released is added. This allows users on the waiting list to be automatically notified when a meeting room becomes available, enabling efficient securing of a meeting room. For example, when a meeting room becomes available, the AI ​​sends a notification to users on the waiting list and confirms the meeting room reservation. This system reduces the effort involved in booking and coordinating meeting rooms, allowing for more efficient room reservations. Furthermore, by taking users' schedules into account, it reduces unnecessary bookings and improves meeting room utilization rates. For example, if a user working from home has booked a meeting room, the AI ​​can automatically prompt them to cancel, freeing up the room for another user. In this way, the meeting room booking efficiency system efficiently monitors meeting room booking status and automatically contacts the user, making meeting room adjustments easier.

[0062] The conference room reservation efficiency system according to this embodiment comprises a monitoring unit, a contact unit, a schedule review unit, and a cancellation waiting unit. The monitoring unit monitors the reservation status of conference rooms. The monitoring unit, for example, uses a schedule management tool to grasp the availability of conference rooms in real time. The monitoring unit, for example, uses a schedule management tool to periodically update the reservation status of conference rooms and obtain the latest information. The monitoring unit, for example, automatically contacts the person who made the reservation if the conference room is fully booked. The contact unit, for example, contacts the person who made the reservation if the conference room is fully booked and requests that they adjust the conference room. The contact unit contacts the person who made the reservation using email or a messaging app. The contact unit, for example, checks the availability of the conference room with the person who made the reservation and requests that they adjust it. The contact unit, for example, requests the person who made the reservation to change the time they are using the conference room. The schedule review unit reviews the conference room reservation details taking into account the user's schedule. The schedule review unit, for example, encourages the user to cancel the conference room reservation if they are working from home. The Schedule Review Department, for example, requests that users relinquish their use of a meeting room to other users if the room's utilization rate is low, even if the user is present at the office. The Schedule Review Department, for example, checks users' schedules and optimizes meeting room usage. The Waiting List Department provides a waiting list reservation function when a meeting room is released. The Waiting List Department, for example, automatically notifies users on the waiting list when a meeting room becomes available and confirms the reservation. The Waiting List Department, for example, notifies users on the waiting list of the meeting room's availability and encourages them to confirm their reservation. The Waiting List Department, for example, confirms the meeting room usage time with users on the waiting list and confirms the reservation. As a result, the meeting room reservation efficiency system according to this embodiment efficiently monitors the meeting room reservation status and automatically contacts users, making it easier to adjust meeting room usage.

[0063] The monitoring department monitors the reservation status of meeting rooms. For example, the monitoring department uses scheduling tools to understand meeting room availability in real time. Specifically, the monitoring department integrates with these tools and periodically retrieves meeting room reservation data via APIs. This ensures that changes in meeting room reservation status are immediately updated, always reflecting the latest information. For example, the monitoring department periodically updates meeting room reservation status using scheduling tools to obtain the latest information. This allows for real-time monitoring of meeting room availability, preventing overlapping reservations and wasted time. For example, if a meeting room is fully booked, the monitoring department automatically contacts the person who made the reservation. This allows the monitoring department to confirm the meeting room's usage status and request adjustments. For example, the monitoring department requests the person who made the reservation to change their reservation time. This maximizes the efficiency of meeting room utilization and resolves the problem of not being able to secure the necessary meeting room. Furthermore, the monitoring department can analyze meeting room usage and identify times and days with low utilization rates. This optimizes meeting room utilization and enables efficient operation. For example, campaigns can be implemented to promote meeting room reservations during times with low utilization rates. Furthermore, the monitoring department can periodically compile reports on the usage of meeting rooms and provide them to administrators. This allows administrators to understand the usage of meeting rooms and take appropriate measures.

[0064] The contact department automatically contacts the user who made the reservation if the meeting room is fully booked, requesting adjustments. Specifically, the contact department contacts the user via email or messaging apps. For example, it checks the meeting room's availability with the user and requests adjustments. The contact department may, for example, ask the user to change the time they are using the meeting room. This maximizes the efficiency of meeting room utilization and resolves the problem of not being able to secure the necessary meeting room. The contact department is required to present specific adjustment options to the user and respond quickly. For example, it can show the user the availability of other meeting rooms and suggest changing the time or moving to another meeting room. The contact department can also consider the user's schedule and propose the most suitable adjustment. This minimizes the burden on the user while improving the efficiency of meeting room utilization. Furthermore, the contact department can collect feedback from the user and use it to improve the system. For example, it can record how the user reacted to the adjustments and what problems occurred, and use this information for future adjustments. This allows the contact department to make more effective adjustments and improve the efficiency of meeting room utilization.

[0065] The Schedule Review Department reviews meeting room reservations, taking into account the user's schedule. Specifically, if a user is working from home, the Schedule Review Department encourages them to cancel their meeting room reservation. For example, if a user is planning to work from home, the Schedule Review Department can automatically cancel the meeting room reservation based on that information and offer the room to another user. Even if a user is in the office, if the meeting room utilization rate is low, the Schedule Review Department will request that they yield the room to another user. The Schedule Review Department checks users' schedules and optimizes meeting room usage. For example, if a user has reserved a meeting room but is not actually using it, the Schedule Review Department can cancel the reservation based on that information and offer it to another user. This maximizes the efficiency of meeting room utilization and prevents unnecessary reservations. Furthermore, the Schedule Review Department can analyze users' schedules and understand meeting room usage patterns. This allows it to predict meeting room usage and propose optimal reservation schedules. For example, if meeting room usage is concentrated during specific time slots or days of the week, it can propose ways to distribute reservations across those time slots. This improves the efficiency of meeting room utilization and reduces reservation competition.

[0066] The waiting list function provides a reservation feature for when a meeting room becomes available. Specifically, when a meeting room becomes available, it automatically notifies users on the waiting list and confirms their reservation. For example, it notifies users on the waiting list of the meeting room's availability and encourages them to confirm their reservation. The waiting list function also confirms the meeting room's usage time with users on the waiting list and confirms their reservation. This allows users on the waiting list to quickly confirm their reservation when a meeting room becomes available. The waiting list function is required to consider the priority of users and notify them in the optimal order. For example, if there are multiple users on the waiting list, it notifies and confirms reservations based on the users' priority. This ensures that users on the waiting list can use the meeting room fairly. Furthermore, the waiting list function can collect user feedback and use it to improve the system. For example, it can record how users on the waiting list reacted and what problems occurred, and use this information for future notifications. This allows the waiting list function to provide more effective notifications and improve the efficiency of meeting room utilization.

[0067] The monitoring unit can use a schedule management tool to understand the availability of meeting rooms in real time. The monitoring unit, for example, uses a schedule management tool to periodically update the reservation status of meeting rooms and obtain the latest information. The monitoring unit, for example, uses a schedule management tool to understand the availability of meeting rooms in real time. The monitoring unit, for example, uses a schedule management tool to monitor the reservation status of meeting rooms and understand their availability. This enables efficient use of meeting rooms by understanding their availability in real time. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data obtained from the schedule management tool into a generating AI and have the generating AI perform the task of understanding the availability of meeting rooms.

[0068] The contact unit can automatically contact the person who made the reservation if the meeting room is fully booked and request a meeting room adjustment. For example, if the meeting room is fully booked, the contact unit will contact the person who made the reservation via email or messaging app. For example, the contact unit will check the meeting room's availability with the person who made the reservation and request an adjustment. For example, the contact unit will ask the person who made the reservation to change the time they are using the meeting room. This makes it easier to adjust meeting rooms even when they are fully booked by automatically contacting the person who made the reservation. Some or all of the above processes in the contact unit may be performed using AI, for example, or not using AI. For example, the contact unit can input the person's contact information into a generating AI and have the generating AI execute a meeting room adjustment request.

[0069] The schedule review unit can prompt users to cancel meeting room reservations if they are working from home. For example, the schedule review unit notifies users to cancel meeting room reservations if they are working from home. For example, the schedule review unit checks the user's work-from-home status and prompts them to cancel meeting room reservations. For example, the schedule review unit checks the user's work-from-home status and requests them to cancel meeting room reservations. This improves the efficiency of meeting room utilization by allowing users to cancel reservations when working from home. Some or all of the above processing in the schedule review unit may be performed using AI, for example, or without AI. For example, the schedule review unit can input the user's work-from-home status into a generating AI and have the generating AI execute the meeting room reservation cancellation.

[0070] The Schedule Review Department can request that users relinquish their meeting room to other users even when they are present at the office, if the meeting room utilization rate is below a certain threshold. For example, the Schedule Review Department will notify users to relinquish their meeting room to other users if the meeting room utilization rate is low, even when they are present at the office. For example, the Schedule Review Department will check the meeting room utilization status with users and request that they relinquish their meeting room to other users. For example, the Schedule Review Department will check the meeting room utilization status with users and encourage them to relinquish their meeting room to other users. This improves the efficiency of meeting room utilization by allowing users to relinquish their meeting room to other users when the utilization rate is low. Some or all of the above processes in the Schedule Review Department may be performed using AI, for example, or without AI. For example, the Schedule Review Department can input the user's meeting room utilization status into a generating AI and have the generating AI execute a request to relinquish the meeting room to other users.

[0071] The waiting list system can automatically notify users on the waiting list when a meeting room becomes available and confirm their reservation. For example, the waiting list system can notify users on the waiting list via email or messaging app when a meeting room becomes available. For example, the waiting list system can notify users on the waiting list of the meeting room's availability and encourage them to confirm their reservation. For example, the waiting list system can ask users on the waiting list to check the meeting room's usage time and confirm their reservation. This allows for efficient securing of meeting rooms by automatically notifying users on the waiting list when a meeting room becomes available. Some or all of the above processes in the waiting list system may be performed using AI, for example, or not using AI. For example, the waiting list system can input meeting room release information into a generating AI and have the generating AI execute notifications to users on the waiting list.

[0072] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit will reduce the monitoring frequency and refrain from sending notifications. For example, if the user is relaxed, the monitoring unit will increase the monitoring frequency and provide more detailed information. For example, if the user is in a hurry, the monitoring unit will increase the monitoring frequency and provide a quick response. This allows for more appropriate monitoring by adjusting the monitoring frequency according to 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 monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the monitoring frequency.

[0073] The monitoring unit can analyze past booking patterns to build a predictive model and predict booking congestion in advance when monitoring the booking status of meeting rooms. For example, the monitoring unit can predict congestion on specific days of the week or time slots based on past booking data. For example, the monitoring unit can analyze past booking patterns to predict the frequency of specific events or meetings. For example, the monitoring unit can predict the booking trends of specific users based on past booking history. In this way, by analyzing past booking patterns and building a predictive model, it is possible to predict booking congestion in advance. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past booking data into a generating AI and have the generating AI execute the construction of a predictive model.

[0074] The monitoring unit can set monitoring priorities based on the purpose of meeting room use during monitoring. For example, the monitoring unit will set a high monitoring priority for important meetings and presentations. For example, it will set a medium monitoring priority for workshops and training sessions. For example, it will set a low monitoring priority for casual meetings. This allows monitoring of important meetings and presentations to be prioritized by setting monitoring priorities based on the purpose of meeting room use. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input meeting room usage purpose data into a generating AI and have the generating AI perform the setting of monitoring priorities.

[0075] The monitoring unit can estimate the user's emotions and adjust the notification method of the monitoring results based on the estimated emotions of the user. For example, if the user is feeling stressed, the monitoring unit sends a brief notification. For example, if the user is relaxed, the monitoring unit sends a detailed notification. For example, if the user is in a hurry, the monitoring unit sends a rapid notification. This allows for more appropriate notifications by adjusting the notification method of the monitoring results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the notification method.

[0076] The monitoring unit can simultaneously monitor the usage status of conference room equipment during monitoring, and determine the availability of the equipment. For example, the monitoring unit can monitor the usage status of the projector and determine its availability. For example, the monitoring unit can monitor the usage status of the whiteboard and determine its availability. For example, the monitoring unit can monitor the usage status of all conference room equipment and determine its availability. In this way, by simultaneously monitoring the usage status of the conference room equipment, the availability of the equipment can be determined. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input conference room equipment usage data into a generating AI and have the generating AI perform the task of determining the availability of the equipment.

[0077] The monitoring unit can also monitor the availability of nearby meeting rooms, taking into account the geographical location of the meeting room during monitoring. For example, the monitoring unit can monitor the availability of nearby meeting rooms and suggest available meeting rooms. For example, the monitoring unit can suggest the optimal meeting room based on the geographical location of the meeting room. For example, the monitoring unit can monitor the usage status of nearby meeting rooms and suggest efficient meeting room use. This enables efficient meeting room use by monitoring the availability of nearby meeting rooms, taking into account the geographical location of the meeting room. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the geographical location of the meeting room into a generating AI and have the generating AI perform monitoring of the availability of nearby meeting rooms.

[0078] The contact unit can estimate the user's emotions and adjust the timing of contact based on the estimated emotions. For example, if the user is stressed, the contact unit will delay the timing of contact. For example, if the user is relaxed, the contact unit will speed up the timing of contact. For example, if the user is in a hurry, the contact unit will make contact quickly. By adjusting the timing of contact according to the user's emotions, contact can be made at a more appropriate time. 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 contact unit may be performed using AI or not using AI. For example, the contact unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the timing of contact.

[0079] The contact unit can select the optimal contact method by referring to past contact history when making contact. For example, the contact unit selects the optimal contact method based on past contact history. For example, the contact unit selects the optimal contact method according to the user's preferences. For example, the contact unit selects the optimal contact method according to the context. By selecting the optimal contact method by referring to past contact history, more effective contact becomes possible. Some or all of the above processing in the contact unit may be performed using AI, for example, or without AI. For example, the contact unit can input past contact history data into a generating AI and have the generating AI perform the selection of the optimal contact method.

[0080] The contact unit can generate different contact messages depending on the reservation holder's job title and department when contacting them. For example, the contact unit can generate a message using appropriate polite language depending on the job title. For example, the contact unit can generate a message containing relevant information depending on the department. For example, the contact unit can generate the optimal message by considering both job title and department. This allows for the sending of more appropriate messages by generating different contact messages depending on the reservation holder's job title and department. Some or all of the above processing in the contact unit may be performed using AI, for example, or without AI. For example, the contact unit can input the reservation holder's job title and department data into a generation AI and have the generation AI perform the generation of contact messages.

[0081] The contact unit can estimate the user's emotions and adjust the content of contact messages based on the estimated emotions. For example, if the user is stressed, the contact unit sends a concise and gentle message. If the user is relaxed, the contact unit sends a message with detailed information. If the user is in a hurry, the contact unit sends a quick and concise message. This allows for the sending of more appropriate messages by adjusting the content of contact messages according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the contact unit may be performed using AI or not. For example, the contact unit can input user emotion data into a generative AI and have the generative AI adjust the content of contact messages.

[0082] The contact unit can select the optimal contact time when making contact, taking into account the client's schedule. For example, the contact unit selects the optimal contact time based on the client's schedule. For example, the contact unit avoids contacting the client during their busy hours. For example, the contact unit finds the client's free time and contacts them. By selecting the optimal contact time considering the client's schedule, more effective contact becomes possible. Some or all of the above processes in the contact unit may be performed using AI, for example, or without AI. For example, the contact unit can input the client's schedule data into a generating AI and have the generating AI select the optimal contact time.

[0083] The contact unit can determine the priority of scheduling requests by referring to the user's past meeting room usage history when contacting them. For example, the contact unit may determine the priority of scheduling requests based on past meeting room usage history. For example, the contact unit may determine the priority based on the user's frequency of use. For example, the contact unit may determine the priority based on the user's importance. This allows for more effective scheduling by determining the priority of scheduling requests by referring to the user's past meeting room usage history. Some or all of the above processing in the contact unit may be performed using AI, for example, or without AI. For example, the contact unit may input the user's past meeting room usage history data into a generating AI and have the generating AI perform the task of determining the priority of scheduling requests.

[0084] The schedule review unit can estimate the user's emotions and adjust the schedule review criteria based on the estimated emotions. For example, if the user is stressed, the schedule review unit will relax the schedule review criteria. For example, if the user is relaxed, the schedule review unit will tighten the schedule review criteria. For example, if the user is in a hurry, the schedule review unit will speed up the schedule review criteria. By adjusting the schedule review criteria according to the user's emotions, more appropriate schedule review 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the schedule review unit may be performed using AI, for example, or without AI. For example, the schedule review unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the schedule review criteria.

[0085] The schedule review unit can analyze the user's past schedule history and select the optimal review method during schedule review. For example, the schedule review unit selects the optimal review method based on the user's past schedule history. For example, the schedule review unit analyzes the user's schedule patterns and selects the optimal review method. For example, the schedule review unit selects an efficient review method based on the user's schedule history. This makes it possible to perform more effective schedule review by analyzing the user's past schedule history and selecting the optimal review method. Some or all of the above processes in the schedule review unit may be performed using AI, for example, or without AI. For example, the schedule review unit can input the user's past schedule history data into a generating AI and have the generating AI select the optimal review method.

[0086] The schedule review unit can apply different review algorithms depending on the user's position and job duties during schedule review. For example, the schedule review unit can apply an appropriate review algorithm depending on the position. For example, the schedule review unit can apply an appropriate review algorithm depending on the job duties. For example, the schedule review unit can apply the optimal review algorithm considering both the position and job duties. This makes it possible to perform more appropriate schedule review by applying different review algorithms depending on the user's position and job duties. Some or all of the above processing in the schedule review unit may be performed using AI, for example, or without AI. For example, the schedule review unit can input user position and job duty data into a generating AI and have the generating AI execute the application of the review algorithm.

[0087] The schedule review unit can estimate the user's emotions and determine the priority of schedule review based on the estimated emotions. For example, if the user is stressed, the schedule review unit will lower the priority of the schedule review. For example, if the user is relaxed, the schedule review unit will raise the priority of the schedule review. For example, if the user is in a hurry, the schedule review unit will raise the priority of the schedule review. This allows for more appropriate schedule review by determining the priority of the schedule review according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the schedule review unit may be performed using AI, for example, or without AI. For example, the schedule review unit can input the user's emotion data into a generative AI and have the generative AI perform the determination of schedule review priorities.

[0088] The schedule review unit can determine whether an employee is working from home or in the office by considering the user's geographical location information during schedule review. For example, the schedule review unit determines whether an employee is working from home or in the office based on the user's geographical location information. For example, the schedule review unit updates the user's location information in real time and determines the work status. For example, the schedule review unit determines the optimal work status based on the user's location information. This makes it possible to perform more appropriate schedule review by considering the user's geographical location information when determining whether an employee is working from home or in the office. Some or all of the above processing in the schedule review unit may be performed using AI, for example, or without AI. For example, the schedule review unit can input the user's geographical location information into a generating AI and have the generating AI perform the determination of whether an employee is working from home or in the office.

[0089] The schedule review unit can analyze the user's social media activity during schedule review to evaluate the relevance of schedules. For example, the schedule review unit evaluates the relevance of schedules based on the user's social media activity. For example, the schedule review unit analyzes the content of social media posts to evaluate the relevance of schedules. For example, the schedule review unit evaluates the relevance of schedules based on the user's social media activity history. This allows for more appropriate schedule review by analyzing the user's social media activity to evaluate the relevance of schedules. Some or all of the above processing in the schedule review unit may be performed using AI, for example, or without AI. For example, the schedule review unit can input the user's social media activity data into a generating AI and have the generating AI perform the schedule relevance evaluation.

[0090] The waiting list unit can estimate the user's emotions and adjust the waiting list notification method based on the estimated emotions. For example, if the user is stressed, the waiting list unit sends a brief notification. For example, if the user is relaxed, the waiting list unit sends a detailed notification. For example, if the user is in a hurry, the waiting list unit sends a quick notification. This allows for more appropriate notifications by adjusting the waiting list notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the waiting list unit may be performed using AI or not using AI. For example, the waiting list unit can input the user's emotion data into the generative AI and have the generative AI adjust the notification method.

[0091] The waiting list unit can predict the probability of cancellations by referring to past cancellation history while a user is on the waiting list. For example, the waiting list unit can predict the probability of cancellations on specific days of the week or time slots based on past cancellation history. For example, the waiting list unit can predict the probability of cancellations for specific events or meetings by analyzing past cancellation patterns. For example, the waiting list unit can predict the cancellation tendencies of specific users based on past cancellation history. This makes it possible to have a more effective waiting list by predicting the probability of cancellations by referring to past cancellation history. Some or all of the above processing in the waiting list unit may be performed using AI, for example, or without AI. For example, the waiting list unit can input past cancellation history data into a generating AI and have the generating AI perform the prediction of cancellation probabilities.

[0092] The waiting list unit can generate different notification messages depending on the user's position and job duties when they are on the waiting list. For example, the waiting list unit can generate a notification message using appropriate polite language depending on the position. For example, the waiting list unit can generate a notification message that includes relevant information depending on the job duties. For example, the waiting list unit can generate the optimal notification message by considering both the position and job duties. This allows for more appropriate notifications by generating different notification messages depending on the user's position and job duties. Some or all of the above processing in the waiting list unit may be performed using AI, for example, or without AI. For example, the waiting list unit can input the user's position and job duties data into a generation AI and have the generation AI execute the generation of notification messages.

[0093] The waiting list unit can estimate the user's emotions and determine the priority of the waiting list based on the estimated emotions. For example, if the user is stressed, the waiting list unit will lower the priority of the waiting list. For example, if the user is relaxed, the waiting list unit will raise the priority of the waiting list. For example, if the user is in a hurry, the waiting list unit will raise the priority of the waiting list. This allows for more appropriate waiting list placement by determining the priority of the waiting list according to the user's emotions. 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 these examples. Some or all of the above processing in the waiting list unit may be performed using AI, for example, or without AI. For example, the waiting list unit can input the user's emotion data into a generative AI and have the generative AI perform the determination of the waiting list priority.

[0094] The waiting list unit can select the optimal notification time while a user is on the waiting list, taking into account their schedule. For example, the waiting list unit selects the optimal notification time based on the user's schedule. For example, the waiting list unit sends notifications while avoiding the user's busy times. For example, the waiting list unit finds the user's free time and sends notifications. By selecting the optimal notification time while considering the user's schedule, a more effective waiting list becomes possible. Some or all of the above processes in the waiting list unit may be performed using AI, for example, or without AI. For example, the waiting list unit can input the user's schedule data into a generating AI and have the generating AI select the optimal notification time.

[0095] The waiting list unit can determine the priority of a user on the waiting list by referring to the user's past meeting room usage history. For example, the waiting list unit can determine the priority based on the user's past meeting room usage history. For example, the waiting list unit can determine the priority based on the user's frequency of use. For example, the waiting list unit can determine the priority based on the user's importance. This makes it possible to determine the priority of a user on the waiting list by referring to the user's past meeting room usage history, enabling a more effective waiting list. Some or all of the above processing in the waiting list unit may be performed using AI, for example, or without AI. For example, the waiting list unit can input the user's past meeting room usage history data into a generating AI and have the generating AI perform the determination of the waiting list priority.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The meeting room reservation efficiency system can further prioritize reservations based on the purpose of the meeting room's use. For example, important meetings and presentations can be given a high priority. Workshops and training sessions can be given a medium priority. Furthermore, casual meetings can be given a low priority. This allows for prioritizing important meetings and presentations by setting reservation priorities based on the purpose of the meeting room's use. The reservation priority setting can be done using AI or not. For example, meeting room usage purpose data can be input into a generating AI, and the generating AI can then perform the reservation priority setting.

[0098] The meeting room reservation efficiency system can also provide a dashboard that displays meeting room usage in real time. For example, it can provide a dashboard that allows users to check the availability and reservation status of meeting rooms at a glance. It can also display statistical information such as meeting room utilization rates and reservation cancellation status. Furthermore, it can display detailed information such as the purpose of meeting room use and reservation priority. This enables efficient use of meeting rooms by understanding their usage status in real time. The dashboard display may be generated using AI or not. For example, meeting room usage data can be input into a generating AI, and the generating AI can then generate the dashboard display.

[0099] The meeting room reservation efficiency system can further collect user feedback on meeting rooms and evaluate user satisfaction. For example, it can send a survey to users after they use a meeting room to request feedback. It can also analyze the content of the feedback to identify areas for improvement in meeting room usage and the reservation system. Furthermore, it can evaluate user satisfaction and use that information to improve meeting room usage and the reservation system. In this way, by collecting user feedback on meeting rooms and evaluating user satisfaction, a better meeting room reservation system can be provided. The collection and evaluation of feedback may be done using AI, or not. For example, user feedback data can be input into a generating AI, and the generating AI can then perform the satisfaction evaluation.

[0100] The meeting room reservation efficiency system can further estimate the emotions of meeting room users and optimize meeting room usage based on those emotions. For example, if a user is feeling stressed, it can encourage them to shorten their meeting room usage time. Conversely, if a user is relaxed, it can encourage them to extend their meeting room usage time. Furthermore, if a user is in a hurry, their meeting room usage can be prioritized. This optimizes meeting room usage according to user emotions, enabling a more comfortable meeting room experience. 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. Optimization of meeting room usage may be performed using AI or without AI. For example, user emotion data can be input into a generative AI, and the generative AI can be made to perform the optimization of usage.

[0101] The meeting room reservation efficiency system can further estimate the emotions of meeting room users and adjust the meeting room reservation based on those emotions. For example, if a user is feeling stressed, the system can encourage them to cancel the meeting room reservation. Conversely, if a user is relaxed, the system can encourage them to keep the reservation. Furthermore, if a user is in a hurry, the system can prioritize the meeting room reservation. This allows for more appropriate meeting room reservations by adjusting the reservation 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. The adjustment of meeting room reservations may be performed using AI or not. For example, user emotion data can be input into a generative AI, and the generative AI can be made to perform the adjustment of the reservation.

[0102] The meeting room reservation efficiency system can further estimate the emotions of meeting room users and adjust the meeting room usage rules based on the estimated emotions. For example, if a user is stressed, the meeting room usage rules can be relaxed. Conversely, if a user is relaxed, the meeting room usage rules can be made stricter. Furthermore, if a user is in a hurry, the meeting room usage rules can be applied quickly. In this way, by adjusting the meeting room usage rules according to the user's emotions, more appropriate meeting room use becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The adjustment of meeting room usage rules may be done using AI or not using AI. For example, user emotion data can be input into a generative AI, and the generative AI can be made to perform the adjustment of usage rules.

[0103] The meeting room reservation efficiency system can further estimate the emotions of meeting room users and provide support to them based on those emotions. For example, if a user is feeling stressed, it can prompt the system to provide a relaxing environment. If a user is relaxed, it can prompt the system to provide an environment conducive to concentration. Furthermore, if a user is in a hurry, it can prompt the system to provide prompt support. This allows for a more comfortable meeting room experience by providing support to meeting room users according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The provision of support to meeting room users may be done using AI or not. For example, user emotion data can be input into a generative AI, and the generative AI can be made to provide support.

[0104] The meeting room reservation efficiency system can further estimate the emotions of meeting room users and adjust the content of notifications sent to them based on the estimated emotions. For example, if a user is feeling stressed, a concise and gentle notification can be sent. If a user is relaxed, a notification containing detailed information can be sent. Furthermore, if a user is in a hurry, a quick and concise notification can be sent. This allows for more appropriate notifications by adjusting the content of notifications sent to meeting room users according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The adjustment of notification content to meeting room users may be done using AI or not. For example, user emotion data can be input into a generative AI, and the generative AI can be made to adjust the notification content.

[0105] The meeting room reservation efficiency system can further estimate the emotions of meeting room users and provide feedback to them based on those emotions. For example, if a user is feeling stressed, it can provide advice to help them relax. If a user is relaxed, it can provide advice to help them concentrate. Furthermore, if a user is in a hurry, it can provide quick advice. This allows for more appropriate advice by providing feedback to meeting room users according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The provision of feedback to meeting room users may be done using AI or not. For example, user emotion data can be input into a generative AI, and the generative AI can be made to provide feedback.

[0106] The meeting room reservation efficiency system can further estimate the emotions of meeting room users and provide support to them based on those emotions. For example, if a user is feeling stressed, it can prompt the system to provide a relaxing environment. If a user is relaxed, it can prompt the system to provide an environment conducive to concentration. Furthermore, if a user is in a hurry, it can prompt the system to provide prompt support. This allows for a more comfortable meeting room experience by providing support to meeting room users according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The provision of support to meeting room users may be done using AI or not. For example, user emotion data can be input into a generative AI, and the generative AI can be made to provide support.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The monitoring department monitors the reservation status of meeting rooms. For example, they use a schedule management tool to grasp the availability of meeting rooms in real time and update it regularly to obtain the latest information. Step 2: If a meeting room is fully booked, the contact department automatically contacts the person who made the reservation and requests that they adjust the meeting room. For example, it contacts the person who made the reservation via email or messaging app to check the meeting room's availability and request a change in the reservation time. Step 3: The Schedule Review Department reviews meeting room reservations, taking into account the user's schedule. For example, if a user is working from home, they are encouraged to cancel the meeting room reservation, and even if they are in the office, they are asked to give up the meeting room to another user if its utilization rate is low. Step 4: The waiting list section provides a waiting list reservation function in case a reservation is released. For example, when a meeting room becomes available, it automatically notifies users on the waiting list and prompts them to confirm their reservation.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the monitoring unit, contact unit, schedule review unit, and waiting list unit, is implemented by, for example, at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit is implemented by the control unit 46A of the smart device 14 and uses a schedule management tool to grasp the availability of meeting rooms in real time. The contact unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and automatically contacts the person making the reservation and requests adjustments to the meeting room. The schedule review unit is implemented by, for example, the control unit 46A of the smart device 14 and reviews the meeting room reservation details taking into account the user's schedule. The waiting list unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and automatically notifies the user on the waiting list when a meeting room is released and confirms the reservation of the meeting room. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the monitoring unit, contact unit, schedule review unit, and waiting list unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart glasses 214 and uses a schedule management tool to grasp the availability of meeting rooms in real time. The contact unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically contacts the person making the reservation and requests adjustments to the meeting room. The schedule review unit is implemented by the control unit 46A of the smart glasses 214 and reviews the meeting room reservation details taking into account the user's schedule. The waiting list unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically notifies users on the waiting list when a meeting room is released and confirms the reservation of the meeting room. 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.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the monitoring unit, contact unit, schedule review unit, and waiting list unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the headset terminal 314 and uses a schedule management tool to grasp the availability of meeting rooms in real time. The contact unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically contacts the person making the reservation and requests adjustments to the meeting room. The schedule review unit is implemented by the control unit 46A of the headset terminal 314 and reviews the meeting room reservation details taking into account the user's schedule. The waiting list unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically notifies users on the waiting list when a meeting room is released and confirms the reservation of the meeting room. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the monitoring unit, contact unit, schedule review unit, and waiting list unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the robot 414 and uses a schedule management tool to grasp the availability of meeting rooms in real time. The contact unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically contacts the person making the reservation and requests adjustments to the meeting room. The schedule review unit is implemented by, for example, the control unit 46A of the robot 414 and reviews the meeting room reservation details taking into account the user's schedule. The waiting list unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically notifies the user on the waiting list when a meeting room is released and confirms the reservation of the meeting room. 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The monitoring department monitors the reservation status of the meeting rooms, A contact unit that automatically contacts the person who made the reservation based on the reservation status monitored by the aforementioned monitoring unit, The Schedule Review Department scrutinizes meeting room reservations, taking into account the user's schedule, It includes a waiting list section that provides a waiting list reservation function in case of a release. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, Use a scheduling tool to check meeting room availability in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned contact portion is If the meeting room is fully booked, the system will automatically contact the person who made the reservation and request that they arrange for a different meeting room. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned schedule review department, If an employee is working from home, they should be asked to cancel their meeting room reservation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned schedule review department, Even if you are present at the office, if the meeting room utilization rate falls below a certain threshold, you will be asked to give it up to other users. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned cancellation waiting section is, When a meeting room becomes available, users on the waiting list will be automatically notified and their reservation will be confirmed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, The system estimates the user's emotions and adjusts the monitoring frequency based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, When monitoring meeting room reservations, we analyze past reservation patterns to build a predictive model and forecast reservation congestion in advance. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, During monitoring, prioritize monitoring based on the purpose of meeting room use. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, The system estimates the user's emotions and adjusts the notification method of monitoring results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, During monitoring, the usage status of the conference room equipment is also monitored to determine the availability of the equipment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, During monitoring, the system also monitors the availability of nearby meeting rooms based on the geographical location of the meeting room. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned contact portion is It estimates the user's emotions and adjusts the timing of contact based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned contact portion is When making contact, the system will refer to past contact history to select the most suitable contact method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned contact portion is When contacting someone, different contact messages are generated depending on the person's job title and department. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned contact portion is The system estimates the user's emotions and adjusts the content of contact messages based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned contact portion is When making contact, we select the optimal contact time considering the client's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned contact portion is When contacting a user, we refer to their past meeting room usage history to determine the priority of scheduling requests. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned schedule review department, The system estimates the user's emotions and adjusts the criteria for schedule refinement based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned schedule review department, During schedule review, the user's past schedule history is analyzed to select the most suitable review method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned schedule review department, When reviewing schedules, different review algorithms are applied depending on the user's position and job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned schedule review department, The system estimates the user's emotions and determines the priority of schedule review based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned schedule review department, When reviewing schedules, we consider the user's geographical location to determine whether they should work from home or come into the office. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned schedule review department, During schedule review, we analyze users' social media activity to evaluate the relevance of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned cancellation waiting section is, We estimate the user's emotions and adjust the notification method for waiting list cancellations based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned cancellation waiting section is, When waiting for a cancellation, we refer to past cancellation history to predict the probability of a cancellation occurring. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned cancellation waiting section is, When a user is on the waiting list, different notification messages are generated depending on their job title and duties. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned cancellation waiting section is, The system estimates the user's emotions and determines the priority of the waiting list based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned cancellation waiting section is, When a user is on the waiting list, the system will select the optimal notification time, taking their schedule into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned cancellation waiting section is, When a user is on the waiting list, their past meeting room usage history is referenced to determine their priority on the waiting list. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 monitoring department monitors the reservation status of the meeting rooms, A contact unit that automatically contacts the person who made the reservation based on the reservation status monitored by the aforementioned monitoring unit, The Schedule Review Department scrutinizes meeting room reservations, taking into account the user's schedule, It includes a waiting list section that provides a waiting list reservation function in case of a release. A system characterized by the following features.

2. The aforementioned monitoring unit, Use a scheduling tool to check meeting room availability in real time. The system according to feature 1.

3. The aforementioned contact portion is If the meeting room is fully booked, the system will automatically contact the person who made the reservation and request that they arrange for a different meeting room. The system according to feature 1.

4. The aforementioned schedule review department, If an employee is working from home, they should be asked to cancel their meeting room reservation. The system according to feature 1.

5. The aforementioned schedule review department, Even if you are present at the office, if the meeting room utilization rate falls below a certain threshold, you will be asked to give it up to other users. The system according to feature 1.

6. The aforementioned cancellation waiting section is, When a meeting room becomes available, users on the waiting list will be automatically notified and their reservation will be confirmed. The system according to feature 1.

7. The aforementioned monitoring unit, The system estimates the user's emotions and adjusts the monitoring frequency based on those emotions. The system according to feature 1.

8. The aforementioned monitoring unit, When monitoring meeting room reservations, we analyze past reservation patterns to build a predictive model and forecast reservation congestion in advance. The system according to feature 1.

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