system

Abstract

The system according to this embodiment aims to automatically resolve vacant reservations for conference rooms and phone booths. [Solution] The system according to the embodiment comprises a detection unit, a determination unit, and a release unit. The detection unit detects people in the conference room or phone booth. The determination unit determines that the reservation is empty if no people are detected by the detection unit for 5 minutes or more. The release unit automatically releases the reservation when the determination unit determines that the reservation is empty.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that unoccupied reservations for meeting rooms and phone booths occurred, preventing efficient use.

[0005] The system according to the embodiment aims to automatically cancel unoccupied reservations for meeting rooms and phone booths.

Means for Solving the Problems

[0006] The system according to the embodiment includes a detection unit, a determination unit, and a release unit. The detection unit detects people in a meeting room or a phone booth. The determination unit determines that it is an unoccupied reservation when no people are detected by the detection unit for 5 minutes or more. The release unit automatically releases the reservation when it is determined by the determination unit that it is an unoccupied reservation.

Effects of the Invention

[0007] The system according to this embodiment can automatically cancel vacant reservations for conference rooms and phone booths. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that uses AI to detect the usage status of meeting rooms and phone booths and aims to eliminate empty reservations. This system installs sensors to detect people in meeting rooms and phone booths, and if the sensors do not detect a person for more than 5 minutes, the AI ​​determines that the reservation is empty and automatically releases the reservation. This makes it possible for other users to use the meeting room or phone booth. Furthermore, in the case of meeting rooms, the AI ​​automatically checks whether multiple people are using it, and if it detects that only one person is using it, the AI ​​guides the user to use a phone booth instead. This system reduces empty reservations of meeting rooms and phone booths and improves the efficiency of office use. It also reduces employee stress about coming to work and reduces the workload of general affairs departments in managing reservations. Furthermore, it reduces wasted office rent. In this way, the system can automatically release empty reservations of meeting rooms and phone booths and improve utilization efficiency.

[0029] The system according to the embodiment comprises a detection unit, a determination unit, and a release unit. The detection unit detects people in conference rooms and phone booths. The detection unit can detect the presence of people using, for example, an infrared sensor or a camera. The detection unit can detect the movement of people in conference rooms and phone booths using, for example, an infrared sensor. The detection unit can also detect the presence of people in conference rooms and phone booths using a camera. The detection unit can detect the movement of people within a certain range using, for example, an infrared sensor, and collect the data. The detection unit can monitor the presence of people in conference rooms and phone booths in real time using, for example, a camera, and collect the data. The determination unit determines that a reservation is empty if the detection unit has not detected a person for 5 minutes or more. The determination unit can, for example, collect data from sensors in real time and determine that a reservation is empty if no person is detected for a certain period of time (e.g., 5 minutes) or more. The determination unit can, for example, collect data from sensors in real time and determine that a reservation is empty if no person is detected for a certain period of time or more. The determination unit can, for example, collect data from sensors in real time and determine that a reservation is empty if no person is detected for a certain period of time or longer. The release unit automatically releases the reservation when the determination unit determines that it is empty. The release unit can, for example, automatically release a reservation when it is determined to be empty, allowing other users to use the meeting room or phone booth. The release unit can, for example, automatically release a reservation when it is determined to be empty, allowing other users to use the meeting room or phone booth. The release unit can, for example, automatically release a reservation when it is determined to be empty, allowing other users to use the meeting room or phone booth. As a result, the system according to the embodiment can automatically release empty reservations of meeting rooms and phone booths, improving utilization efficiency.

[0030] The detection unit detects people in conference rooms and phone booths. The detection unit can detect the presence of people using, for example, infrared sensors and cameras. Specifically, infrared sensors detect infrared radiation emitted from the human body and detect its presence. Infrared sensors are installed on the ceilings and walls of conference rooms and phone booths, covering a wide area. This allows for accurate determination of the location of people within the room. Cameras, on the other hand, detect people using video analysis technology. Cameras are installed at the entrances and interiors of conference rooms and phone booths, capturing video in real time and analyzing it to confirm the presence of people. AI technology can be used for video analysis; for example, applying an image recognition algorithm using deep learning enables highly accurate person detection. Furthermore, the detection unit can centrally manage and update the data obtained from these sensors and cameras in real time. This allows for always having up-to-date information on the usage status of conference rooms and phone booths. The detection unit can flexibly respond to specific conditions and situations by adjusting the data collection frequency and sensitivity. For example, increasing the sensor sensitivity according to the start and end times of meetings enables more accurate detection. This allows the detection unit to efficiently and effectively detect the presence of people, thereby improving the overall performance of the system.

[0031] The decision unit determines a reservation is vacant if the detection unit has not detected a person for more than 5 minutes. Specifically, it collects data from sensors and cameras in real time and determines a reservation is vacant if no person is detected for a certain period of time (e.g., 5 minutes). The decision unit analyzes the collected data and uses an algorithm that considers a reservation vacant if the presence of a person is not confirmed over time. For example, by utilizing AI-based data analysis technology, it can determine the possibility of a vacant reservation with high accuracy based on past usage patterns and statistical information. Furthermore, the decision unit can use an anomaly detection algorithm to detect patterns that are different from the norm or abnormal data and issue warnings early. As a result, the decision unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. The decision unit centrally manages data from the detection unit and can cooperate with other systems and departments as needed. For example, if a reservation is determined to be vacant, it notifies the management system of that information and updates the reservation status. In addition, the decision unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data analysis. This allows the decision-making unit to efficiently and effectively identify empty reservations, thereby improving the overall performance of the system.

[0032] The release unit automatically releases reservations when the judgment unit determines they are vacant. Specifically, it automatically releases reservations when they are determined to be vacant, allowing other users to use the meeting room or phone booth. The release unit works in conjunction with the management system to update reservation status in real time. For example, when a reservation is determined to be vacant, it notifies the management system of this information and immediately updates the reservation status, allowing other users to quickly reserve the meeting room or phone booth. Furthermore, the release unit can notify users of the reservation release status through the user interface. For example, it can notify users that a vacant reservation has been released via a smartphone app or web portal, enabling them to quickly make a new reservation. The release unit can also send notifications related to reservation releases using multiple communication methods. For example, it can use a combination of email, SMS, and push notifications to ensure that important information reaches users reliably. This allows the release unit to provide users with reservation release information quickly and reliably, improving the utilization efficiency of meeting rooms and phone booths. In addition, the release unit can record the history of reservation releases and use this information for future analysis and improvement. This allows the release unit to improve the overall system performance and enhance user convenience.

[0033] The detection unit can detect the presence of people using infrared sensors or cameras. For example, the detection unit can use infrared sensors to detect the movement of people in conference rooms or phone booths. For example, the detection unit can use infrared sensors to detect the movement of people within a certain range and collect the data. For example, the detection unit can use cameras to monitor the presence of people in conference rooms or phone booths in real time and collect the data. For example, the detection unit can use cameras to monitor the presence of people in conference rooms or phone booths in real time and collect the data. This improves detection accuracy by using infrared sensors or cameras. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data acquired from infrared sensors or cameras into a generating AI and have the generating AI perform the detection of the presence of people.

[0034] The decision unit can collect data from sensors in real time and determine that a reservation is empty if no person is detected for a certain period of time or longer. For example, the decision unit can collect data from sensors in real time and determine that a reservation is empty if no person is detected for a certain period of time (e.g., 5 minutes) or longer. For example, the decision unit can collect data from sensors in real time and determine that a reservation is empty if no person is detected for a certain period of time or longer. For example, the decision unit can collect data from sensors in real time and determine that a reservation is empty if no person is detected for a certain period of time or longer. This improves the accuracy of empty reservation determination by collecting data in real time. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input data acquired from sensors into a generating AI and have the generating AI perform the empty reservation determination.

[0035] The release unit can automatically release a reservation if it is determined to be empty, allowing other users to use that meeting room or phone booth. The release unit can, for example, automatically release a reservation if it is determined to be empty, allowing other users to use that meeting room or phone booth. The release unit can, for example, automatically release a reservation if it is determined to be empty, allowing other users to use that meeting room or phone booth. The release unit can, for example, automatically release a reservation if it is determined to be empty, allowing other users to use that meeting room or phone booth. This improves utilization efficiency by automatically releasing reservations. Some or all of the above processing in the release unit may be performed using AI, for example, or without AI. For example, the release unit can input reservation data determined to be empty into a generating AI and have the generating AI execute the reservation release.

[0036] The decision unit can automatically check if a meeting room is being used by multiple people, and if it detects that it is being used by a single person, it can guide the user to use the phone booth. The decision unit can automatically check if a meeting room is being used by multiple people, and if it detects that it is being used by a single person, it can guide the user to use the phone booth. The decision unit can automatically check if a meeting room is being used by multiple people, and if it detects that it is being used by a single person, it can guide the user to use the phone booth. The decision unit can automatically check if a meeting room is being used by multiple people, and if it detects that it is being used by a single person, it can guide the user to use the phone booth. This improves the efficiency of meeting room use by detecting single users and guiding them to the appropriate location. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input meeting room usage data into a generating AI and have the generating AI perform the detection and guidance of single users.

[0037] The release unit can notify the user that a reservation has been released via email or app notification. The release unit can notify the user that a reservation has been released via email or app notification. The release unit can notify the user that a reservation has been released via email or app notification. The release unit can notify the user that a reservation has been released via email or app notification. This allows users to quickly use meeting rooms or phone booths by notifying them of the release of a reservation. Some or all of the above processing in the release unit may be performed using AI, for example, or without AI. For example, the release unit can input reservation release notification data into a generating AI and have the generating AI send the notification.

[0038] The detection unit can simultaneously detect the temperature and humidity of a conference room or phone booth upon detection and evaluate comfort levels. For example, if the temperature is too high, the detection unit can suggest adjusting the air conditioner. For example, if the humidity is too low, the detection unit can suggest using a humidifier. For example, based on the temperature and humidity data, the detection unit can issue alerts to maintain a comfortable environment. In this way, a comfortable environment can be maintained by detecting temperature and humidity. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input temperature and humidity data into a generating AI and have the generating AI perform a comfort evaluation.

[0039] The detection unit can detect the lighting conditions in conference rooms and phone booths upon detection and make appropriate lighting adjustments. For example, the detection unit can automatically adjust the brightness if the lighting is too dim. For example, the detection unit can automatically adjust the brightness if the lighting is too bright. For example, the detection unit can adjust the color temperature of the lighting to provide a comfortable working environment. In this way, a comfortable working environment can be provided by detecting the lighting conditions and making appropriate adjustments. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input lighting condition data into a generating AI and have the generating AI perform the lighting adjustments.

[0040] The detection unit can detect the audio level in a conference room or phone booth upon detection and take noise reduction measures. For example, if the audio level is too high, the detection unit can suggest noise reduction measures. For example, if the audio level is too low, the detection unit can issue an alert to maintain a quiet environment. For example, based on the audio level data, the detection unit can issue an alert to maintain a comfortable environment. In this way, a comfortable environment can be maintained by detecting the audio level and taking noise reduction measures. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input audio level data into a generating AI and have the generating AI execute noise reduction suggestions.

[0041] The detection unit can detect the air quality in a conference room or phone booth upon detection and determine the need for ventilation. For example, if the air quality is poor, the detection unit can suggest ventilation. For example, if the air quality is good, the detection unit can determine the need for ventilation. For example, based on the air quality data, the detection unit can issue an alert to maintain a comfortable environment. In this way, a comfortable environment can be maintained by detecting air quality and determining the need for ventilation. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input air quality data into a generating AI and have the generating AI perform the determination of the need for ventilation.

[0042] The decision unit can improve its decision accuracy by learning patterns of empty reservations by referring to past usage history when making a decision. For example, the decision unit can learn patterns of empty reservations based on past usage history. For example, the decision unit can improve its accuracy in identifying empty reservations based on past usage history. For example, the decision unit can predict patterns of empty reservations based on past usage history. As a result, the accuracy of identifying empty reservations is improved by referring to past usage history. Some or all of the above processing in the decision unit may be performed using AI, for example, or without using AI. For example, the decision unit can input past usage history data into a generating AI and have the generating AI perform the learning of empty reservation patterns.

[0043] The decision unit can determine whether a reservation is vacant by considering the purpose of use of the meeting room or phone booth. For example, the decision unit can determine whether a reservation is vacant by considering the purpose of use of the meeting room. For example, the decision unit can determine whether a reservation is vacant by considering the purpose of use of the phone booth. For example, the decision unit can determine whether a reservation is vacant based on data on the purpose of use. This improves the accuracy of vacant reservation determination by considering the purpose of use. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input data on the purpose of use into a generating AI and have the generating AI perform the vacant reservation determination.

[0044] The decision unit can determine whether a reservation is empty by considering the user attribute information of the meeting room or phone booth at the time of decision-making. The decision unit can determine whether a reservation is empty based on, for example, the user attribute information. The decision unit can improve the accuracy of empty reservation determination based on, for example, the user attribute information. The decision unit can predict empty reservation patterns based on, for example, the user attribute information. As a result, the accuracy of empty reservation determination is improved by considering the user attribute information. Some or all of the above processing in the decision unit may be performed using, for example, AI, or without using AI. For example, the decision unit can input user attribute information data into a generating AI and have the generating AI perform the empty reservation determination.

[0045] The decision unit can determine whether a reservation is vacant by considering the usage time of the meeting rooms and phone booths. The decision unit can determine whether a reservation is vacant based on the usage time, for example. The decision unit can improve the accuracy of its vacant reservation determination based on the usage time, for example. The decision unit can predict vacant reservation patterns based on usage time data, for example. This improves the accuracy of vacant reservation determination by considering the usage time. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input usage time data into a generating AI and have the generating AI perform the vacant reservation determination.

[0046] The release unit can select the optimal release timing by considering the reservation status of other users when releasing a reservation. For example, the release unit can select the optimal release timing based on the reservation status of other users. For example, the release unit can adjust the release timing based on the reservation status of other users. For example, the release unit can predict the release timing based on the reservation status of other users. As a result, by considering the reservation status of other users, the reservation is released at the optimal time. Some or all of the above processing in the release unit may be performed using AI, for example, or without using AI. For example, the release unit can input reservation status data of other users into a generating AI and have the generating AI perform the selection of the optimal release timing.

[0047] The release unit can determine the priority of release by referring to the usage history of conference rooms and phone booths when releasing them. The release unit can determine the priority of release based on usage history, for example. The release unit can adjust the priority of release based on usage history, for example. The release unit can predict the priority of release based on usage history data, for example. This ensures that the priority of release is appropriately determined by referring to the usage history. Some or all of the above processing in the release unit may be performed using AI, for example, or without AI. For example, the release unit can input usage history data into a generating AI and have the generating AI perform the determination of the priority of release.

[0048] The release unit can determine the priority of releases by considering the user attribute information of the conference room or phone booth when releasing it. For example, the release unit can determine the priority of releases based on user attribute information. For example, the release unit can adjust the priority of releases based on user attribute information. For example, the release unit can predict the priority of releases based on user attribute information data. In this way, the priority of releases is appropriately determined by considering user attribute information. Some or all of the above processing in the release unit may be performed using AI, for example, or without using AI. For example, the release unit can input user attribute information data into a generating AI and have the generating AI perform the determination of the priority of releases.

[0049] The release unit can determine the priority of releases when releasing conference rooms or phone booths, taking into account the purpose of use. The release unit can determine the priority of releases based on the purpose of use, for example. The release unit can adjust the priority of releases based on the purpose of use, for example. The release unit can predict the priority of releases based on data on the purpose of use, for example. This ensures that the priority of releases is appropriately determined by considering the purpose of use. Some or all of the above-described processes in the release unit may be performed using AI, for example, or without AI. For example, the release unit can input data on the purpose of use into a generating AI and have the generating AI perform the determination of the priority of releases.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The system can not only detect the usage status of meeting rooms and phone booths, but also monitor users' health. For example, the detection unit can measure users' heart rate and body temperature, and issue an alert if an abnormality is detected. It can also send notifications prompting users to stretch or take a break if they remain in the same position for a long time. Furthermore, by accumulating user health data and conducting regular health checks, it can be used as part of health management. This strengthens health management in the office environment and contributes to maintaining the health of employees.

[0052] The system not only detects the usage status of meeting rooms and phone booths, but can also suggest optimal usage times considering the user's schedule. For example, the detection unit can acquire the user's calendar information, analyze available time slots, and suggest the most suitable meeting time. Furthermore, if the user is attending multiple meetings, the system can adjust the schedule to account for travel time. It can also suggest the most suitable meeting room or phone booth based on the user's past usage history. This streamlines user schedule management and reduces wasted time.

[0053] The system not only detects the usage status of meeting rooms and phone booths, but also learns user behavior patterns and can suggest optimal usage methods. For example, the detection unit can analyze the times of day and purposes of use that users frequently use and suggest the optimal usage time. Furthermore, if a user prefers to use a particular meeting room, the system can prioritize suggesting reservations for that meeting room. In addition, it can suggest optimal usage methods based on the user's past usage history. As a result, optimal usage methods are provided based on user behavior patterns, improving utilization efficiency.

[0054] The system not only detects the usage of meeting rooms and phone booths, but also learns user behavior patterns and optimizes energy efficiency. For example, the detection unit can analyze the times when users frequently use the facilities and automatically adjust lighting and air conditioning accordingly. Furthermore, it can turn off lighting and air conditioning during times when users are not using the facilities to minimize energy consumption. It can also suggest ways to optimize energy efficiency based on user behavior patterns. This reduces energy consumption and minimizes environmental impact.

[0055] The system not only detects the usage status of meeting rooms and phone booths, but also learns user behavior patterns and can suggest optimal usage methods. For example, the detection unit can analyze the times of day and purposes of use that users frequently use and suggest the optimal usage time. Furthermore, if a user prefers to use a particular meeting room, the system can prioritize suggesting reservations for that meeting room. In addition, it can suggest optimal usage methods based on the user's past usage history. As a result, optimal usage methods are provided based on user behavior patterns, improving utilization efficiency.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The detection unit detects people in the conference room or phone booth. The detection unit can detect the presence of people using infrared sensors or cameras. For example, it can use infrared sensors to detect people's movements in the conference room or phone booth and collect the data. It can also use cameras to monitor the presence of people in the conference room or phone booth in real time and collect the data. Step 2: The decision unit determines that the reservation is empty if the detection unit does not detect a person for more than 5 minutes. The decision unit collects data from the sensor in real time and determines that the reservation is empty if no person is detected for a certain period of time (e.g., 5 minutes) or longer. Step 3: The release unit automatically releases the reservation if the judgment unit determines that it is an empty reservation. The release unit automatically releases the reservation if it is determined to be an empty reservation, allowing other users to use the meeting room or phone booth.

[0058] (Example of form 2) The system according to an embodiment of the present invention is a system that uses AI to detect the usage status of meeting rooms and phone booths and aims to eliminate empty reservations. This system installs sensors to detect people in meeting rooms and phone booths, and if the sensors do not detect a person for more than 5 minutes, the AI ​​determines that the reservation is empty and automatically releases the reservation. This makes it possible for other users to use the meeting room or phone booth. Furthermore, in the case of meeting rooms, the AI ​​automatically checks whether multiple people are using it, and if it detects that only one person is using it, the AI ​​guides the user to use a phone booth instead. This system reduces empty reservations of meeting rooms and phone booths and improves the efficiency of office use. It also reduces employee stress about coming to work and reduces the workload of general affairs departments in managing reservations. Furthermore, it reduces wasted office rent. In this way, the system can automatically release empty reservations of meeting rooms and phone booths and improve utilization efficiency.

[0059] The system according to the embodiment comprises a detection unit, a determination unit, and a release unit. The detection unit detects people in conference rooms and phone booths. The detection unit can detect the presence of people using, for example, an infrared sensor or a camera. The detection unit can detect the movement of people in conference rooms and phone booths using, for example, an infrared sensor. The detection unit can also detect the presence of people in conference rooms and phone booths using a camera. The detection unit can detect the movement of people within a certain range using, for example, an infrared sensor, and collect the data. The detection unit can monitor the presence of people in conference rooms and phone booths in real time using, for example, a camera, and collect the data. The determination unit determines that a reservation is empty if the detection unit has not detected a person for 5 minutes or more. The determination unit can, for example, collect data from sensors in real time and determine that a reservation is empty if no person is detected for a certain period of time (e.g., 5 minutes) or more. The determination unit can, for example, collect data from sensors in real time and determine that a reservation is empty if no person is detected for a certain period of time or more. The determination unit can, for example, collect data from sensors in real time and determine that a reservation is empty if no person is detected for a certain period of time or longer. The release unit automatically releases the reservation when the determination unit determines that it is empty. The release unit can, for example, automatically release a reservation when it is determined to be empty, allowing other users to use the meeting room or phone booth. The release unit can, for example, automatically release a reservation when it is determined to be empty, allowing other users to use the meeting room or phone booth. The release unit can, for example, automatically release a reservation when it is determined to be empty, allowing other users to use the meeting room or phone booth. As a result, the system according to the embodiment can automatically release empty reservations of meeting rooms and phone booths, improving utilization efficiency.

[0060] The detection unit detects people in conference rooms and phone booths. The detection unit can detect the presence of people using, for example, infrared sensors and cameras. Specifically, infrared sensors detect infrared radiation emitted from the human body and detect its presence. Infrared sensors are installed on the ceilings and walls of conference rooms and phone booths, covering a wide area. This allows for accurate determination of the location of people within the room. Cameras, on the other hand, detect people using video analysis technology. Cameras are installed at the entrances and interiors of conference rooms and phone booths, capturing video in real time and analyzing it to confirm the presence of people. AI technology can be used for video analysis; for example, applying an image recognition algorithm using deep learning enables highly accurate person detection. Furthermore, the detection unit can centrally manage and update the data obtained from these sensors and cameras in real time. This allows for always having up-to-date information on the usage status of conference rooms and phone booths. The detection unit can flexibly respond to specific conditions and situations by adjusting the data collection frequency and sensitivity. For example, increasing the sensor sensitivity according to the start and end times of meetings enables more accurate detection. This allows the detection unit to efficiently and effectively detect the presence of people, thereby improving the overall performance of the system.

[0061] The decision unit determines a reservation is vacant if the detection unit has not detected a person for more than 5 minutes. Specifically, it collects data from sensors and cameras in real time and determines a reservation is vacant if no person is detected for a certain period of time (e.g., 5 minutes). The decision unit analyzes the collected data and uses an algorithm that considers a reservation vacant if the presence of a person is not confirmed over time. For example, by utilizing AI-based data analysis technology, it can determine the possibility of a vacant reservation with high accuracy based on past usage patterns and statistical information. Furthermore, the decision unit can use an anomaly detection algorithm to detect patterns that are different from the norm or abnormal data and issue warnings early. As a result, the decision unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. The decision unit centrally manages data from the detection unit and can cooperate with other systems and departments as needed. For example, if a reservation is determined to be vacant, it notifies the management system of that information and updates the reservation status. In addition, the decision unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data analysis. This allows the decision-making unit to efficiently and effectively identify empty reservations, thereby improving the overall performance of the system.

[0062] The release unit automatically releases reservations when the judgment unit determines they are vacant. Specifically, it automatically releases reservations when they are determined to be vacant, allowing other users to use the meeting room or phone booth. The release unit works in conjunction with the management system to update reservation status in real time. For example, when a reservation is determined to be vacant, it notifies the management system of this information and immediately updates the reservation status, allowing other users to quickly reserve the meeting room or phone booth. Furthermore, the release unit can notify users of the reservation release status through the user interface. For example, it can notify users that a vacant reservation has been released via a smartphone app or web portal, enabling them to quickly make a new reservation. The release unit can also send notifications related to reservation releases using multiple communication methods. For example, it can use a combination of email, SMS, and push notifications to ensure that important information reaches users reliably. This allows the release unit to provide users with reservation release information quickly and reliably, improving the utilization efficiency of meeting rooms and phone booths. In addition, the release unit can record the history of reservation releases and use this information for future analysis and improvement. This allows the release unit to improve the overall system performance and enhance user convenience.

[0063] The detection unit can detect the presence of people using infrared sensors or cameras. For example, the detection unit can use infrared sensors to detect the movement of people in conference rooms or phone booths. For example, the detection unit can use infrared sensors to detect the movement of people within a certain range and collect the data. For example, the detection unit can use cameras to monitor the presence of people in conference rooms or phone booths in real time and collect the data. For example, the detection unit can use cameras to monitor the presence of people in conference rooms or phone booths in real time and collect the data. This improves detection accuracy by using infrared sensors or cameras. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data acquired from infrared sensors or cameras into a generating AI and have the generating AI perform the detection of the presence of people.

[0064] The decision unit can collect data from sensors in real time and determine that a reservation is empty if no person is detected for a certain period of time or longer. For example, the decision unit can collect data from sensors in real time and determine that a reservation is empty if no person is detected for a certain period of time (e.g., 5 minutes) or longer. For example, the decision unit can collect data from sensors in real time and determine that a reservation is empty if no person is detected for a certain period of time or longer. For example, the decision unit can collect data from sensors in real time and determine that a reservation is empty if no person is detected for a certain period of time or longer. This improves the accuracy of empty reservation determination by collecting data in real time. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input data acquired from sensors into a generating AI and have the generating AI perform the empty reservation determination.

[0065] The release unit can automatically release a reservation if it is determined to be empty, allowing other users to use that meeting room or phone booth. The release unit can, for example, automatically release a reservation if it is determined to be empty, allowing other users to use that meeting room or phone booth. The release unit can, for example, automatically release a reservation if it is determined to be empty, allowing other users to use that meeting room or phone booth. The release unit can, for example, automatically release a reservation if it is determined to be empty, allowing other users to use that meeting room or phone booth. This improves utilization efficiency by automatically releasing reservations. Some or all of the above processing in the release unit may be performed using AI, for example, or without AI. For example, the release unit can input reservation data determined to be empty into a generating AI and have the generating AI execute the reservation release.

[0066] The decision unit can automatically check if a meeting room is being used by multiple people, and if it detects that it is being used by a single person, it can guide the user to use the phone booth. The decision unit can automatically check if a meeting room is being used by multiple people, and if it detects that it is being used by a single person, it can guide the user to use the phone booth. The decision unit can automatically check if a meeting room is being used by multiple people, and if it detects that it is being used by a single person, it can guide the user to use the phone booth. The decision unit can automatically check if a meeting room is being used by multiple people, and if it detects that it is being used by a single person, it can guide the user to use the phone booth. This improves the efficiency of meeting room use by detecting single users and guiding them to the appropriate location. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input meeting room usage data into a generating AI and have the generating AI perform the detection and guidance of single users.

[0067] The release unit can notify the user that a reservation has been released via email or app notification. The release unit can notify the user that a reservation has been released via email or app notification. The release unit can notify the user that a reservation has been released via email or app notification. The release unit can notify the user that a reservation has been released via email or app notification. This allows users to quickly use meeting rooms or phone booths by notifying them of the release of a reservation. Some or all of the above processing in the release unit may be performed using AI, for example, or without AI. For example, the release unit can input reservation release notification data into a generating AI and have the generating AI send the notification.

[0068] The detection unit can estimate the user's emotions and adjust the detection accuracy based on the estimated emotions. For example, if the user is stressed, the detection unit can increase the detection accuracy to reduce false positives. For example, if the user is relaxed, the detection unit can return the detection accuracy to normal to reduce the system load. For example, if the user is in a hurry, the detection unit can temporarily increase the detection accuracy to enable a quick response. This reduces false positives by adjusting the detection accuracy 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 detection unit may be performed using AI or not using AI. For example, the detection unit can input user emotion data into the generative AI and have the generative AI adjust the detection accuracy.

[0069] The detection unit can simultaneously detect the temperature and humidity of a conference room or phone booth upon detection and evaluate comfort levels. For example, if the temperature is too high, the detection unit can suggest adjusting the air conditioner. For example, if the humidity is too low, the detection unit can suggest using a humidifier. For example, based on the temperature and humidity data, the detection unit can issue alerts to maintain a comfortable environment. In this way, a comfortable environment can be maintained by detecting temperature and humidity. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input temperature and humidity data into a generating AI and have the generating AI perform a comfort evaluation.

[0070] The detection unit can detect the lighting conditions in conference rooms and phone booths upon detection and make appropriate lighting adjustments. For example, the detection unit can automatically adjust the brightness if the lighting is too dim. For example, the detection unit can automatically adjust the brightness if the lighting is too bright. For example, the detection unit can adjust the color temperature of the lighting to provide a comfortable working environment. In this way, a comfortable working environment can be provided by detecting the lighting conditions and making appropriate adjustments. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input lighting condition data into a generating AI and have the generating AI perform the lighting adjustments.

[0071] The detection unit can estimate the user's emotions and adjust the detection frequency based on the estimated emotions. For example, if the user is stressed, the detection unit can increase the detection frequency to provide a sense of security. For example, if the user is relaxed, the detection unit can return the detection frequency to normal to reduce the system load. For example, if the user is in a hurry, the detection unit can temporarily increase the detection frequency to enable a quick response. In this way, a sense of security can be provided by adjusting the detection 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 detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into the generative AI and have the generative AI adjust the detection frequency.

[0072] The detection unit can detect the audio level in a conference room or phone booth upon detection and take noise reduction measures. For example, if the audio level is too high, the detection unit can suggest noise reduction measures. For example, if the audio level is too low, the detection unit can issue an alert to maintain a quiet environment. For example, based on the audio level data, the detection unit can issue an alert to maintain a comfortable environment. In this way, a comfortable environment can be maintained by detecting the audio level and taking noise reduction measures. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input audio level data into a generating AI and have the generating AI execute noise reduction suggestions.

[0073] The detection unit can detect the air quality in a conference room or phone booth upon detection and determine the need for ventilation. For example, if the air quality is poor, the detection unit can suggest ventilation. For example, if the air quality is good, the detection unit can determine the need for ventilation. For example, based on the air quality data, the detection unit can issue an alert to maintain a comfortable environment. In this way, a comfortable environment can be maintained by detecting air quality and determining the need for ventilation. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input air quality data into a generating AI and have the generating AI perform the determination of the need for ventilation.

[0074] The decision unit can estimate the user's emotions and adjust the criteria for ignoring bookings based on the estimated emotions. For example, if the user is stressed, the decision unit can tighten the criteria for ignoring bookings. For example, if the user is relaxed, the decision unit can return the criteria for ignoring bookings to normal. For example, if the user is in a hurry, the decision unit can temporarily tighten the criteria for ignoring bookings. This allows for appropriate decisions by adjusting the criteria for ignoring bookings 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 decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input user emotion data into the generative AI and have the generative AI adjust the criteria for ignoring bookings.

[0075] The decision unit can improve its decision accuracy by learning patterns of empty reservations by referring to past usage history when making a decision. For example, the decision unit can learn patterns of empty reservations based on past usage history. For example, the decision unit can improve its accuracy in identifying empty reservations based on past usage history. For example, the decision unit can predict patterns of empty reservations based on past usage history. As a result, the accuracy of identifying empty reservations is improved by referring to past usage history. Some or all of the above processing in the decision unit may be performed using AI, for example, or without using AI. For example, the decision unit can input past usage history data into a generating AI and have the generating AI perform the learning of empty reservation patterns.

[0076] The decision unit can determine whether a reservation is vacant by considering the purpose of use of the meeting room or phone booth. For example, the decision unit can determine whether a reservation is vacant by considering the purpose of use of the meeting room. For example, the decision unit can determine whether a reservation is vacant by considering the purpose of use of the phone booth. For example, the decision unit can determine whether a reservation is vacant based on data on the purpose of use. This improves the accuracy of vacant reservation determination by considering the purpose of use. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input data on the purpose of use into a generating AI and have the generating AI perform the vacant reservation determination.

[0077] The decision unit can estimate the user's emotions and adjust the notification method for empty reservations based on the estimated user emotions. For example, if the user is stressed, the decision unit can provide a simple notification method. For example, if the user is relaxed, the decision unit can provide a detailed notification method. For example, if the user is in a hurry, the decision unit can provide a rapid notification method. This allows for appropriate notifications by adjusting the notification method 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 decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the notification method.

[0078] The decision unit can determine whether a reservation is empty by considering the user attribute information of the meeting room or phone booth at the time of decision-making. The decision unit can determine whether a reservation is empty based on, for example, the user attribute information. The decision unit can improve the accuracy of empty reservation determination based on, for example, the user attribute information. The decision unit can predict empty reservation patterns based on, for example, the user attribute information. As a result, the accuracy of empty reservation determination is improved by considering the user attribute information. Some or all of the above processing in the decision unit may be performed using, for example, AI, or without using AI. For example, the decision unit can input user attribute information data into a generating AI and have the generating AI perform the empty reservation determination.

[0079] The decision unit can determine whether a reservation is vacant by considering the usage time of the meeting rooms and phone booths. The decision unit can determine whether a reservation is vacant based on the usage time, for example. The decision unit can improve the accuracy of its vacant reservation determination based on the usage time, for example. The decision unit can predict vacant reservation patterns based on usage time data, for example. This improves the accuracy of vacant reservation determination by considering the usage time. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input usage time data into a generating AI and have the generating AI perform the vacant reservation determination.

[0080] The release unit can estimate the user's emotions and adjust the timing of the reservation release based on the estimated user emotions. For example, if the user is feeling stressed, the release unit can advance the timing of the reservation release. For example, if the user is relaxed, the release unit can return the timing of the reservation release to normal. For example, if the user is in a hurry, the release unit can temporarily advance the timing of the reservation release. In this way, by adjusting the timing of the reservation release according to the user's emotions, the release is performed at an appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the release unit may be performed using AI, for example, or without using AI. For example, the release unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the reservation release timing.

[0081] The release unit can select the optimal release timing by considering the reservation status of other users when releasing a reservation. For example, the release unit can select the optimal release timing based on the reservation status of other users. For example, the release unit can adjust the release timing based on the reservation status of other users. For example, the release unit can predict the release timing based on the reservation status of other users. As a result, by considering the reservation status of other users, the reservation is released at the optimal time. Some or all of the above processing in the release unit may be performed using AI, for example, or without using AI. For example, the release unit can input reservation status data of other users into a generating AI and have the generating AI perform the selection of the optimal release timing.

[0082] The release unit can determine the priority of release by referring to the usage history of conference rooms and phone booths when releasing them. The release unit can determine the priority of release based on usage history, for example. The release unit can adjust the priority of release based on usage history, for example. The release unit can predict the priority of release based on usage history data, for example. This ensures that the priority of release is appropriately determined by referring to the usage history. Some or all of the above processing in the release unit may be performed using AI, for example, or without AI. For example, the release unit can input usage history data into a generating AI and have the generating AI perform the determination of the priority of release.

[0083] The release unit can estimate the user's emotions and adjust the notification method for reservation release based on the estimated user emotions. For example, if the user is stressed, the release unit can provide a simple notification method. For example, if the user is relaxed, the release unit can provide a detailed notification method. For example, if the user is in a hurry, the release unit can provide a rapid notification method. This ensures that appropriate notifications are made by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the release unit may be performed using AI or not using AI. For example, the release unit can input user emotion data into a generative AI and have the generative AI adjust the notification method.

[0084] The release unit can determine the priority of releases by considering the user attribute information of the conference room or phone booth when releasing it. For example, the release unit can determine the priority of releases based on user attribute information. For example, the release unit can adjust the priority of releases based on user attribute information. For example, the release unit can predict the priority of releases based on user attribute information data. In this way, the priority of releases is appropriately determined by considering user attribute information. Some or all of the above processing in the release unit may be performed using AI, for example, or without using AI. For example, the release unit can input user attribute information data into a generating AI and have the generating AI perform the determination of the priority of releases.

[0085] The release unit can determine the priority of releases when releasing conference rooms or phone booths, taking into account the purpose of use. The release unit can determine the priority of releases based on the purpose of use, for example. The release unit can adjust the priority of releases based on the purpose of use, for example. The release unit can predict the priority of releases based on data on the purpose of use, for example. This ensures that the priority of releases is appropriately determined by considering the purpose of use. Some or all of the above-described processes in the release unit may be performed using AI, for example, or without AI. For example, the release unit can input data on the purpose of use into a generating AI and have the generating AI perform the determination of the priority of releases.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The system can not only detect the usage status of meeting rooms and phone booths, but also monitor users' health. For example, the detection unit can measure users' heart rate and body temperature, and issue an alert if an abnormality is detected. It can also send notifications prompting users to stretch or take a break if they remain in the same position for a long time. Furthermore, by accumulating user health data and conducting regular health checks, it can be used as part of health management. This strengthens health management in the office environment and contributes to maintaining the health of employees.

[0088] The system not only detects the usage status of meeting rooms and phone booths, but can also estimate the user's emotions and adjust the environment of the meeting rooms and phone booths based on those emotions. For example, if the detection unit detects that the user is feeling stressed, it can soften the lighting and play relaxing music. If the user is concentrating, it can activate noise cancellation to block out external noise. Furthermore, if the user is tired, it can send a notification prompting them to take a break. This allows for environmental adjustments tailored to the user's emotions, providing a comfortable working environment.

[0089] The system not only detects the usage status of meeting rooms and phone booths, but can also suggest optimal usage times considering the user's schedule. For example, the detection unit can acquire the user's calendar information, analyze available time slots, and suggest the most suitable meeting time. Furthermore, if the user is attending multiple meetings, the system can adjust the schedule to account for travel time. It can also suggest the most suitable meeting room or phone booth based on the user's past usage history. This streamlines user schedule management and reduces wasted time.

[0090] The system not only detects the usage status of meeting rooms and phone booths, but can also estimate users' emotions and support the progress of meetings based on those emotions. For example, if a user is feeling tense, the detection unit can offer advice to help them relax. If a user is concentrating, it can offer suggestions to help the meeting proceed smoothly. Furthermore, if a user is tired, it can suggest when to encourage them to take a break. This ensures smoother meetings and improves participant satisfaction.

[0091] The system not only detects the usage of meeting rooms and phone booths, but can also estimate users' emotions and record meeting content based on those estimated emotions. For example, if the detection unit detects a user showing strong emotion regarding an important point, it can highlight and record that part. Furthermore, if a user expresses doubt, that part can be recorded in detail for later review. Additionally, if a user is satisfied, that part can be recorded concisely. This ensures that meeting content is recorded efficiently and easily reviewed later.

[0092] The system not only detects the usage status of meeting rooms and phone booths, but also learns user behavior patterns and can suggest optimal usage methods. For example, the detection unit can analyze the times of day and purposes of use that users frequently use and suggest the optimal usage time. Furthermore, if a user prefers to use a particular meeting room, the system can prioritize suggesting reservations for that meeting room. In addition, it can suggest optimal usage methods based on the user's past usage history. As a result, optimal usage methods are provided based on user behavior patterns, improving utilization efficiency.

[0093] The system can not only detect the usage of meeting rooms and phone booths, but also estimate users' emotions and provide meeting feedback based on those emotions. For example, the detection unit can analyze the stress and satisfaction levels users felt during the meeting and suggest areas for improvement. It can also highlight successful points based on the positive emotions users felt during the meeting. Furthermore, it can specifically suggest areas that need improvement based on the negative emotions users felt during the meeting. This improves the quality of meetings and increases participant satisfaction.

[0094] The system not only detects the usage of meeting rooms and phone booths, but also learns user behavior patterns and optimizes energy efficiency. For example, the detection unit can analyze the times when users frequently use the facilities and automatically adjust lighting and air conditioning accordingly. Furthermore, it can turn off lighting and air conditioning during times when users are not using the facilities to minimize energy consumption. It can also suggest ways to optimize energy efficiency based on user behavior patterns. This reduces energy consumption and minimizes environmental impact.

[0095] The system not only detects the usage status of meeting rooms and phone booths, but can also estimate users' emotions and support the progress of meetings based on those emotions. For example, if a user is feeling tense, the detection unit can offer advice to help them relax. If a user is concentrating, it can offer suggestions to help the meeting proceed smoothly. Furthermore, if a user is tired, it can suggest when to encourage them to take a break. This ensures smoother meetings and improves participant satisfaction.

[0096] The system not only detects the usage status of meeting rooms and phone booths, but also learns user behavior patterns and can suggest optimal usage methods. For example, the detection unit can analyze the times of day and purposes of use that users frequently use and suggest the optimal usage time. Furthermore, if a user prefers to use a particular meeting room, the system can prioritize suggesting reservations for that meeting room. In addition, it can suggest optimal usage methods based on the user's past usage history. As a result, optimal usage methods are provided based on user behavior patterns, improving utilization efficiency.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The detection unit detects people in the conference room or phone booth. The detection unit can detect the presence of people using infrared sensors or cameras. For example, it can use infrared sensors to detect people's movements in the conference room or phone booth and collect the data. It can also use cameras to monitor the presence of people in the conference room or phone booth in real time and collect the data. Step 2: The decision unit determines that the reservation is empty if the detection unit does not detect a person for more than 5 minutes. The decision unit collects data from the sensor in real time and determines that the reservation is empty if no person is detected for a certain period of time (e.g., 5 minutes) or longer. Step 3: The release unit automatically releases the reservation if the judgment unit determines that it is an empty reservation. The release unit automatically releases the reservation if it is determined to be an empty reservation, allowing other users to use the meeting room or phone booth.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the detection unit, decision unit, and release unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the detection unit uses the camera 42 or infrared sensor of the smart device 14 to detect the presence of a person in the conference room or phone booth, and the control unit 46A collects the data. The decision unit is implemented, for example, in the identification processing unit 290 of the data processing device 12, which collects data from the sensor in real time and determines that the reservation is empty if no person is detected for a certain period of time (e.g., 5 minutes) or longer. The release unit is implemented, for example, in the identification processing unit 290 of the data processing device 12, which automatically releases the reservation when it is determined to be empty, allowing other users to use the conference room or phone booth. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements, including the detection unit, decision unit, and release unit described above, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the detection unit uses the camera 42 and infrared sensor of the smart glasses 214 to detect the presence of a person in the conference room or phone booth, and the control unit 46A collects the data. The decision unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which collects data from the sensor in real time and determines that the reservation is empty if no person is detected for a certain period of time (e.g., 5 minutes) or longer. The release unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which automatically releases the reservation when it is determined to be empty, allowing other users to use the conference room or phone booth. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements, including the detection unit, decision unit, and release unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the detection unit uses the camera 42 and infrared sensor of the headset terminal 314 to detect the presence of a person in the conference room or phone booth, and the control unit 46A collects the data. The decision unit is implemented in the specific processing unit 290 of the data processing unit 12, which collects data from the sensor in real time and determines that the reservation is empty if no person is detected for a certain period of time (e.g., 5 minutes) or longer. The release unit is implemented in the specific processing unit 290 of the data processing unit 12, which automatically releases the reservation when it is determined to be empty, allowing other users to use the conference room or phone booth. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the detection unit, decision unit, and release unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the detection unit uses the camera 42 or infrared sensor of the robot 414 to detect the presence of a person in the conference room or phone booth, and the control unit 46A collects the data. The decision unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which collects data from the sensor in real time and determines that the reservation is empty if no person is detected for a certain period of time (e.g., 5 minutes) or longer. The release unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which automatically releases the reservation when it is determined to be empty, allowing other users to use the conference room or phone booth. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A detection unit that detects people in conference rooms and phone booths, The detection unit determines that a reservation is invalid if no person is detected for 5 minutes or more, The system includes a release unit that automatically releases a reservation if the determination unit determines that it is an empty reservation. A system characterized by the following features. (Note 2) The detection unit is Infrared sensors and cameras are used to detect the presence of people. The system described in Appendix 1, characterized by the features described herein. (Note 3) The unit that makes the determination said, Data from sensors is collected in real time, and if no person is detected for a certain period of time, it is determined to be an empty reservation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned opening portion is If a reservation is determined to be vacant, it will be automatically released, allowing other users to use the meeting room or phone booth. The system described in Appendix 1, characterized by the features described herein. (Note 5) The unit that makes the determination said, In the case of a meeting room, the system automatically checks whether multiple people are using it, and if it detects that only one person is using it, it guides the user to use a phone booth instead. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned opening portion is Notify users via email or app notifications when reservations become available. The system described in Appendix 1, characterized by the features described herein. (Note 7) The detection unit is It estimates the user's emotions and adjusts the accuracy of the detection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The detection unit is Upon detection, the system simultaneously monitors the temperature and humidity of the conference room and phone booth to evaluate comfort levels. The system described in Appendix 1, characterized by the features described herein. (Note 9) The detection unit is Upon detection, the system will monitor the lighting conditions in conference rooms and phone booths and adjust the lighting accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 10) The detection unit is It estimates the user's emotions and adjusts the detection frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The detection unit is Upon detection, the system will assess the audio level in conference rooms and phone booths and implement noise reduction measures. The system described in Appendix 1, characterized by the features described herein. (Note 12) The detection unit is Upon detection, the system will assess the air quality in conference rooms and phone booths and determine the need for ventilation. The system described in Appendix 1, characterized by the features described herein. (Note 13) The unit that makes the determination said, The system estimates the user's emotions and adjusts the criteria for determining whether a booking is invalid based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The unit that makes the determination said, When making a decision, the system learns patterns of vacant bookings by referring to past usage history, thereby improving the accuracy of the decision. The system described in Appendix 1, characterized by the features described herein. (Note 15) The unit that makes the determination said, When making a decision, the purpose of using the meeting room or phone booth will be taken into consideration when determining whether to make an empty reservation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The unit that makes the determination said, The system estimates the user's emotions and adjusts the notification method for empty bookings based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The unit that makes the determination said, When making a decision, the system considers the user attribute information of the meeting room or phone booth to determine whether a reservation is invalid. The system described in Appendix 1, characterized by the features described herein. (Note 18) The unit that makes the determination said, When making a decision, the decision to reserve an empty room will be made considering the usage times of meeting rooms and phone booths. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned opening portion is The system estimates user sentiment and adjusts the timing of reservation release based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned opening portion is When releasing a reservation, the system will select the optimal release timing, taking into account the reservation status of other users. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned opening portion is When releasing a room, the priority for release is determined by referring to the usage history of the meeting room or phone booth. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned opening portion is We estimate the user's emotions and adjust the notification method for reservation release based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned opening portion is When releasing a room, the priority for release is determined by considering the user attribute information of the meeting room or phone booth. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned opening portion is When making rooms available, the priority for availability will be determined by considering the intended use of the meeting rooms and phone booths. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 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. A detection unit that detects people in conference rooms and phone booths, The detection unit determines that a reservation is invalid if no person is detected for 5 minutes or more, The system includes a release unit that automatically releases a reservation if the determination unit determines that it is an empty reservation. A system characterized by the following features.

2. The detection unit is Infrared sensors and cameras are used to detect the presence of people. The system according to feature 1.

3. The unit that makes the determination said, Data from sensors is collected in real time, and if no person is detected for a certain period of time, it is determined to be an empty reservation. The system according to feature 1.

4. The aforementioned opening portion is If a reservation is determined to be vacant, it will be automatically released, allowing other users to use the meeting room or phone booth. The system according to feature 1.

5. The unit that makes the determination said, In the case of a meeting room, the system automatically checks whether multiple people are using it, and if it detects that only one person is using it, it guides the user to use a phone booth instead. The system according to feature 1.

6. The aforementioned opening portion is Notify users via email or app notifications when reservations become available. The system according to feature 1.

7. The detection unit is It estimates the user's emotions and adjusts the accuracy of the detection based on the estimated user emotions. The system according to feature 1.

8. The detection unit is Upon detection, the system simultaneously monitors the temperature and humidity of the conference room and phone booth to evaluate comfort levels. The system according to feature 1.

9. The detection unit is Upon detection, the system will monitor the lighting conditions in conference rooms and phone booths and adjust the lighting accordingly. The system according to feature 1.

10. The detection unit is It estimates the user's emotions and adjusts the detection frequency based on the estimated emotions. The system according to feature 1.

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