system

Abstract

The system according to this embodiment aims to streamline meeting room reservations and promote their appropriate use. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a reservation unit. The collection unit collects the user's reservation history and meeting patterns. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes the optimal meeting room and time based on the analysis results obtained by the analysis unit. The reservation unit reserves the meeting room and time proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems such as difficulty in reserving a meeting room, overlapping reservations, or a reserved room not being actually used.

[0005] The system according to the embodiment aims to improve the reservation efficiency of meeting rooms and promote appropriate utilization.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a reservation unit. The data collection unit collects the user's reservation history and meeting patterns. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes the optimal meeting room and time based on the analysis results obtained by the analysis unit. The reservation unit reserves the meeting room and time proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline meeting room reservations and promote appropriate use. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The meeting room reservation system according to an embodiment of the present invention is an AI agent for streamlining meeting room reservations and promoting appropriate use. This meeting room reservation system learns the user's reservation history and meeting patterns and optimizes the reservation status in real time. For example, the AI ​​learns the user's reservation history and meeting patterns. In this process, the AI ​​analyzes past reservation data, meeting frequency, participant trends, etc. For example, if there is a tendency for meetings to be concentrated on specific days of the week or time slots, the AI ​​can grasp that pattern. Next, the AI ​​optimizes the reservation status in real time. Based on the current reservation status and the user's reservation history, the AI ​​proposes the most appropriate meeting room and time. For example, if meeting room A is already reserved, the AI ​​proposes meeting room B. Also, if the user wishes to hold a meeting during a specific time slot, the AI ​​proposes an available meeting room during that time slot. Furthermore, the meeting room reservation system automatically reserves the meeting room and time proposed by the AI. This allows the user to reserve a meeting room without any effort. For example, the user simply confirms the meeting room and time proposed by the AI ​​and approves it to complete the reservation. This mechanism prevents duplicate reservations and unnecessary reservations. Because AI optimizes booking status in real time, the efficiency of meeting room utilization is maximized. For example, by effectively utilizing the time slots when meeting rooms are not in use, unnecessary bookings can be reduced. In addition, by analyzing the user's meeting patterns and history, the AI ​​can suggest the optimal meeting room and time. This allows users to hold meetings efficiently. For example, users who hold meetings frequently can be offered regularly available meeting rooms, saving them the trouble of booking. In this way, using an AI agent can streamline meeting room booking and promote appropriate use. In today's world, where the efficient use of meeting rooms is urgently needed due to the spread of remote work and the hybridization of offices, this invention is extremely useful. As a result, the meeting room booking system can streamline meeting room booking and promote appropriate use.

[0029] The meeting room reservation system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a reservation unit. The data collection unit collects the user's reservation history and meeting patterns. The user's reservation history includes, but is not limited to, past reservation dates and times, reserved meeting rooms, and reservation frequency. The data collection unit collects past reservation data to understand the frequency of meetings and participant trends. The data collection unit can also analyze meeting frequency and participant trends in order to collect meeting patterns. For example, the data collection unit can understand the tendency for meetings to be concentrated on specific days of the week or time slots. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes, for example, the data collected to understand the tendency for meetings to be concentrated on specific days of the week or time slots. The analysis unit can also analyze meeting frequency and participant trends based on the data collected. For example, the analysis unit analyzes past reservation data to understand meeting frequency and participant trends. The proposal unit proposes the optimal meeting room and time based on the analysis results obtained by the analysis unit. The suggestion unit proposes the most suitable meeting room and time based, for example, on the current reservation status and the user's reservation history. The suggestion unit can also regularly suggest available meeting rooms to users who frequently hold meetings. For example, the suggestion unit can make suggestions for effectively utilizing time slots when meeting rooms are available. The reservation unit reserves the meeting room and time suggested by the suggestion unit. For example, the reservation unit can automatically reserve the suggested meeting room and time. The reservation unit can also be configured so that the reservation is completed simply by the user's approval. For example, the reservation unit can confirm the suggested meeting room and time, and the reservation is completed simply by the user's approval. As a result, the meeting room reservation system according to this embodiment can streamline meeting room reservations and promote appropriate use.

[0030] The data collection unit collects user reservation history and meeting patterns. User reservation history includes, but is not limited to, past reservation dates and times, reserved meeting rooms, and reservation frequency. Specifically, the data collection unit collects detailed data such as which meeting rooms users have reserved in the past and at what times, how often they hold meetings, the number of participants, and the purpose of the meetings. This allows the unit to understand the user's meeting room usage trends and patterns. The data collection unit can also analyze meeting frequency and participant trends to collect meeting patterns. For example, it analyzes past reservation data to understand the tendency for meetings to be concentrated on specific days of the week or time slots. Furthermore, the data collection unit collects information such as the job titles and departments of meeting participants, as well as the purpose and content of the meetings, allowing for a more detailed understanding of meeting patterns. This enables the data collection unit to understand the user's meeting room usage trends and patterns and collect data to provide to the analysis and proposal departments. The data collection unit centrally manages this data and can link with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, based on the collected data, the analysis unit can identify trends in the concentration of meetings on specific days of the week or time slots. Specifically, it analyzes the frequency of meetings and participant trends based on past reservation data. For instance, the analysis unit can identify trends in the concentration of meetings on specific days of the week or time slots and provide information to optimize meeting room usage. The analysis unit can also analyze the frequency of meetings and participant trends. For example, it can identify the frequency of meetings and participant trends based on past reservation data. Furthermore, the analysis unit can use AI to analyze data and predict user behavior patterns and meeting trends. For example, the AI ​​can predict which days of the week and time slots a particular user tends to hold meetings on based on past reservation data and suggest the optimal meeting room and time. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The Proposal Department proposes the optimal meeting room and time based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes the optimal meeting room and time based on the current reservation status and the user's reservation history. Specifically, the Proposal Department proposes the optimal meeting room and time based on the data provided by the Analysis Department, taking into account the user's past reservation history and meeting patterns. For example, it can propose regularly available meeting rooms to users who frequently hold meetings. The Proposal Department makes suggestions for effectively utilizing the time slots when meeting rooms are available. For example, if there is a tendency for meetings to be concentrated on specific days or times, the Proposal Department will propose holding meetings during those times if meeting rooms are available. In addition, the Proposal Department can propose the optimal meeting room by considering conditions such as the facilities and capacity of the meeting room according to the user's needs. In this way, the Proposal Department can propose the optimal meeting room and time to the user and improve the efficiency of meeting room utilization. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, it can evaluate user satisfaction with the proposed meeting rooms and times and improve the suggestions. In this way, the Proposal Department can propose the optimal meeting room and time to the user and improve the efficiency of meeting room utilization.

[0033] The reservation department reserves meeting rooms and times proposed by the proposal department. For example, the reservation department can automatically reserve proposed meeting rooms and times. Specifically, based on the optimal meeting room and time provided by the proposal department, the reservation department automatically performs the reservation process. The reservation department can also enable a system where reservations are completed simply by the user's approval. For example, the user simply reviews the proposed meeting room and time and approves it to complete the reservation. This allows the reservation department to save user effort and book meeting rooms quickly and efficiently. Furthermore, the reservation department can manage reservation status in real time and flexibly respond to changes and cancellations. For example, if a user wants to change a reservation, the reservation department will propose a new meeting room and time and quickly update the reservation. The reservation department can also constantly monitor the reservation status and make appropriate adjustments to prevent overlapping or conflicting reservations. This allows the reservation department to provide users with quick and efficient meeting room reservations and improve the efficiency of meeting room utilization. Additionally, the reservation department can collect user feedback and continuously improve the accuracy and effectiveness of the reservation system. For example, it can evaluate user satisfaction with the ease of the reservation process and the certainty of reservations, and use this feedback to improve the reservation system. This allows the reservation department to provide users with quick and efficient meeting room reservations, thereby improving the efficiency of meeting room utilization.

[0034] The suggestion unit can propose the optimal meeting room and time based on the current reservation status and the user's reservation history. For example, the suggestion unit can propose the optimal meeting room and time based on the current reservation status. It can also propose the optimal meeting room and time based on the user's reservation history. For example, the suggestion unit can grasp the current reservation status in real time and propose the optimal meeting room and time. The user's reservation history includes, but is not limited to, past reservation dates and times, reserved meeting rooms, and reservation frequency. This allows the suggestion unit to propose the optimal meeting room and time based on the current reservation status and the user's reservation history. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can propose the optimal meeting room and time using an AI model that takes the current reservation status and the user's reservation history as input and outputs the optimal meeting room and time.

[0035] The reservation department can automatically reserve the proposed meeting room and time. For example, the reservation department can automatically reserve the proposed meeting room and time. Alternatively, the reservation department can make it so that the reservation is completed simply by the user's approval. For example, the reservation department can review the proposed meeting room and time, and the reservation is completed simply by the user's approval. This reduces the effort required for reservations by automatically reserving the proposed meeting room and time. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can automatically reserve the proposed meeting room and time using an AI model that takes the proposed meeting room and time as input and outputs a reservation completion.

[0036] The data collection unit can collect past reservation data, meeting frequency, participant trends, and more. For example, the data collection unit can collect past reservation data to understand meeting frequency and participant trends. The data collection unit can also analyze meeting frequency and participant trends to collect meeting patterns. For example, the data collection unit can identify trends where meetings are concentrated on specific days of the week or time slots. This allows for more accurate analysis by collecting past reservation data, meeting frequency, and participant trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect past reservation data, meeting frequency, and participant trends using an AI model that takes past reservation data, meeting frequency, and participant trends as input and outputs the collected results.

[0037] The analysis unit can analyze the collected data to identify trends in meeting scheduling, such as the concentration of meetings on specific days of the week or time slots. For example, the analysis unit can use the collected data to identify trends in meeting scheduling. The analysis unit can also analyze meeting frequency and participant trends based on the collected data. For example, the analysis unit can use past booking data to identify meeting frequency and participant trends. This allows the system to identify trends in meeting scheduling on specific days of the week and time slots, enabling it to suggest the most suitable meeting room and time. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that takes the collected data as input and outputs trends in meeting scheduling on specific days of the week or time slots to analyze the collected data.

[0038] The suggestion department can regularly suggest available meeting rooms to users who frequently hold meetings. For example, the suggestion department can regularly suggest available meeting rooms to users who frequently hold meetings. The suggestion department can also make suggestions for effectively utilizing the time slots when meeting rooms are available. For example, the suggestion department can suggest the optimal meeting room and time based on the time slots when meeting rooms are available. This reduces the effort required for reservations by regularly suggesting available meeting rooms to users who frequently hold meetings. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can use an AI model that takes the reservation history of users who frequently hold meetings as input and outputs regularly available meeting rooms to regularly suggest available meeting rooms to users who frequently hold meetings.

[0039] The reservation system can be designed so that reservations are completed simply by the user's approval. For example, the reservation system can review the suggested meeting room and time, and the reservation is completed simply by the user's approval. The reservation system can also automatically reserve the suggested meeting room and time. For example, the reservation system can automatically reserve the suggested meeting room and time, and the reservation is completed simply by the user's approval. This reduces the effort required for reservations by allowing users to complete reservations simply by their approval. Some or all of the above processes in the reservation system may be performed using AI, or not. For example, the reservation system can use an AI model that takes the suggested meeting room and time as input and outputs a reservation completion, so that reservations are completed simply by the user's approval.

[0040] The proposal department can make suggestions for effectively utilizing the time slots when meeting rooms are unavailable. For example, the proposal department can suggest the optimal meeting room and time based on the available time slots. The proposal department can also regularly suggest available meeting rooms to users who frequently hold meetings. For example, the proposal department can monitor the availability of meeting rooms in real time and suggest the optimal meeting room and time. This maximizes the efficiency of meeting room utilization by effectively utilizing the time slots when meeting rooms are unavailable. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can use an AI model that takes the available time slots when meeting rooms are available as input and outputs the optimal meeting room and time to make suggestions for effectively utilizing the time slots when meeting rooms are unavailable.

[0041] The data collection unit can collect meeting objectives and importance in addition to past reservation data. For example, the data collection unit can have users input the meeting objectives and collect that information. It can also have users evaluate the meeting importance and collect that information. For example, the data collection unit can automatically estimate the meeting objectives and importance and collect that information. By collecting meeting objectives and importance, more accurate analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can use an AI model that takes past reservation data, meeting objectives, and importance as inputs and outputs the collection results to collect meeting objectives and importance in addition to past reservation data.

[0042] The data collection unit can collect information on the job titles and departments of meeting participants and evaluate the importance of the meeting. For example, the data collection unit can collect information on the job titles of meeting participants and evaluate the importance. The data collection unit can also collect information on the departments of meeting participants and evaluate the importance. For example, the data collection unit can combine information on the job titles and departments of meeting participants to evaluate the importance. In this way, the importance of a meeting can be evaluated by collecting information on the job titles and departments of meeting participants. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information on the job titles and departments of meeting participants and evaluate the importance of a meeting using an AI model that takes information on the job titles and departments of meeting participants as input and outputs a result that evaluates importance.

[0043] The data collection unit can collect schedule information of meeting participants and use it as data to suggest the optimal meeting time. For example, the data collection unit can automatically collect schedule information of meeting participants from a calendar. Alternatively, the data collection unit can collect schedule information by having meeting participants input it. For example, the data collection unit can collect schedule information of meeting participants via an API. By collecting schedule information of meeting participants in this way, it can be used as data to suggest the optimal meeting time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes schedule information of meeting participants as input and outputs the collection results to collect schedule information of meeting participants and use it as data to suggest the optimal meeting time.

[0044] The data collection unit can collect past meeting attendance rates of meeting participants and evaluate the importance of the meeting. For example, the data collection unit can collect past meeting attendance rates of meeting participants from a database. Alternatively, the data collection unit can collect past meeting attendance rates from meeting participants by having them input this information. For example, the data collection unit can collect past meeting attendance rates of meeting participants via an API. This allows the importance of a meeting to be evaluated by collecting past meeting attendance rates of meeting participants. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect past meeting attendance rates of meeting participants and evaluate the importance of a meeting using an AI model that takes past meeting attendance rates of meeting participants as input and outputs results evaluating importance.

[0045] The analysis unit can perform analyses based on the collected data, tailored to the purpose and importance of each meeting. For example, the analysis unit can perform analyses to evaluate importance based on the purpose of each meeting. The analysis unit can also determine the priority of analyses based on the importance of each meeting. For example, the analysis unit can perform a comprehensive analysis by combining the purpose and importance of each meeting. This allows for more accurate analysis results by performing analyses tailored to the purpose and importance of each meeting. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that takes the collected data as input and outputs analysis results tailored to the purpose and importance of each meeting, to perform analyses based on the collected data, tailored to the purpose and importance of each meeting.

[0046] The analysis unit can perform analyses to evaluate the importance of a meeting based on the job titles and departmental information of the meeting participants. For example, the analysis unit can perform an analysis to evaluate importance based on the job titles of the meeting participants. The analysis unit can also perform an analysis to evaluate importance based on the departmental information of the meeting participants. For example, the analysis unit can perform an analysis to evaluate importance by combining the job titles and departmental information of the meeting participants. This allows for more accurate analysis results by evaluating the importance of a meeting based on the job titles and departmental information of the meeting participants. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that takes the job titles and departmental information of the meeting participants as input and outputs an analysis result that evaluates importance to perform an analysis to evaluate the importance of a meeting based on the job titles and departmental information of the meeting participants.

[0047] The analysis unit can analyze the schedule information of meeting participants based on the collected data and propose the optimal meeting time. For example, the analysis unit can analyze the schedule information of meeting participants and propose the optimal meeting time. The analysis unit can also evaluate the importance of a meeting based on the schedule information of meeting participants. For example, the analysis unit can combine the schedule information of meeting participants to propose the optimal meeting time. In this way, the analysis unit can propose the optimal meeting time by analyzing the schedule information of meeting participants. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that takes the schedule information of meeting participants as input and outputs the optimal meeting time to analyze the schedule information of meeting participants based on the collected data and propose the optimal meeting time.

[0048] The analysis unit can analyze the past meeting attendance rates of meeting participants based on the collected data and evaluate the importance of the meeting. For example, the analysis unit can analyze the past meeting attendance rates of meeting participants and evaluate the importance. The analysis unit can also evaluate the importance of a meeting based on the past meeting attendance rates of meeting participants. For example, the analysis unit can evaluate the importance by combining the past meeting attendance rates of meeting participants. In this way, the importance of a meeting can be evaluated by analyzing the past meeting attendance rates of meeting participants. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can use an AI model that takes the past meeting attendance rates of meeting participants as input and outputs an analysis result that evaluates importance, to analyze the past meeting attendance rates of meeting participants based on the collected data and evaluate the importance of the meeting.

[0049] The proposal unit can suggest the optimal meeting room and time based on the purpose and importance of the meeting, based on the analysis results. For example, the proposal unit can suggest the optimal meeting room and time based on the purpose of the meeting, based on the analysis results. The proposal unit can also suggest the optimal meeting room and time based on the importance of the meeting, based on the analysis results. For example, the proposal unit can suggest the optimal meeting room and time by combining the purpose and importance of the meeting, based on the analysis results. This improves the efficiency of meetings by suggesting the optimal meeting room and time based on the purpose and importance of the meeting. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can use an AI model that takes the analysis results as input and outputs the optimal meeting room and time to suggest the optimal meeting room and time based on the purpose and importance of the meeting, based on the analysis results.

[0050] The proposal department can suggest the optimal meeting room and time based on the job titles and departmental information of the meeting participants. For example, the proposal department can suggest the optimal meeting room and time based on the job titles of the meeting participants. It can also suggest the optimal meeting room and time based on the departmental information of the meeting participants. For example, the proposal department can suggest the optimal meeting room and time by combining the job titles and departmental information of the meeting participants. This improves the efficiency of meetings by suggesting the optimal meeting room and time based on the job titles and departmental information of the meeting participants. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can suggest the optimal meeting room and time based on the job titles and departmental information of the meeting participants using an AI model that takes the job titles and departmental information of the meeting participants as input and outputs the optimal meeting room and time.

[0051] The proposal unit can propose an optimal meeting time that takes into account the schedule information of the meeting participants, based on the analysis results. For example, the proposal unit can propose an optimal meeting time that takes into account the schedule information of the meeting participants, based on the analysis results. The proposal unit can also propose an optimal meeting time based on the schedule information of the meeting participants, based on the analysis results. For example, the proposal unit can propose an optimal meeting time that takes into account the schedule information of the meeting participants, thereby improving the efficiency of the meeting. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can use an AI model that takes the analysis results as input and outputs an optimal meeting time to propose an optimal meeting time that takes into account the schedule information of the meeting participants, based on the analysis results.

[0052] The proposal unit can suggest the optimal meeting room and time based on the analysis results, taking into account the past meeting attendance rates of the meeting participants. For example, the proposal unit can suggest the optimal meeting room and time based on the analysis results, taking into account the past meeting attendance rates of the meeting participants. Alternatively, the proposal unit can suggest the optimal meeting room and time based on the analysis results, taking into account the past meeting attendance rates of the meeting participants. This improves the efficiency of meetings by suggesting the optimal meeting room and time that takes into account the past meeting attendance rates of the meeting participants. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can use an AI model that takes the analysis results as input and outputs the optimal meeting room and time to suggest the optimal meeting room and time based on the analysis results, taking into account the past meeting attendance rates of the meeting participants.

[0053] The reservation system can consider the purpose and importance of a meeting when automatically booking a proposed meeting room and time. For example, the reservation system can prioritize booking meetings of high importance by considering their purpose. Alternatively, the reservation system can evaluate the importance of meetings and prioritize booking those of high importance. For example, the reservation system can automatically book the optimal meeting room and time by combining the purpose and importance of the meeting. This allows for prioritizing the booking of more important meetings by considering their purpose and importance. Some or all of the above processing in the reservation system may be performed using AI, or not. For example, the reservation system can consider the purpose and importance of a meeting when automatically booking a proposed meeting room and time using an AI model that takes the purpose and importance of a meeting as input and outputs the optimal meeting room and time.

[0054] The reservation department can consider the job titles and departmental information of meeting participants when automatically reserving a proposed meeting room and time. For example, the reservation department can automatically reserve the optimal meeting room and time by considering the job titles of the meeting participants. Alternatively, the reservation department can automatically reserve the optimal meeting room and time by considering the departmental information of the meeting participants. For example, the reservation department can automatically reserve the optimal meeting room and time by combining the job titles and departmental information of the meeting participants. This allows for the reservation of a more appropriate meeting room and time by considering the job titles and departmental information of the meeting participants. Some or all of the above processing in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can consider the job titles and departmental information of meeting participants when automatically reserving a proposed meeting room and time using an AI model that takes the job titles and departmental information of meeting participants as input and outputs the optimal meeting room and time.

[0055] The reservation system can consider the schedule information of meeting participants when automatically reserving a proposed meeting room and time. For example, the reservation system can automatically reserve the optimal meeting room and time by considering the schedule information of meeting participants. Alternatively, the reservation system can automatically reserve the optimal meeting room and time based on the schedule information of meeting participants. For example, the reservation system can automatically reserve the optimal meeting room and time by combining the schedule information of meeting participants. This allows for the reservation of a more appropriate meeting room and time by considering the schedule information of meeting participants. Some or all of the above processing in the reservation system may be performed using AI, for example, or without AI. For example, the reservation system can consider the schedule information of meeting participants when automatically reserving a proposed meeting room and time using an AI model that takes the schedule information of meeting participants as input and outputs the optimal meeting room and time.

[0056] The reservation system can consider the past attendance rates of meeting participants when automatically reserving a proposed meeting room and time. For example, the reservation system can automatically reserve the optimal meeting room and time by considering the past attendance rates of meeting participants. Alternatively, the reservation system can automatically reserve the optimal meeting room and time based on the past attendance rates of meeting participants. For example, the reservation system can automatically reserve the optimal meeting room and time by combining the past attendance rates of meeting participants. This allows for the reservation of a more appropriate meeting room and time by considering the past attendance rates of meeting participants. Some or all of the above processing in the reservation system may be performed using AI, for example, or without AI. For example, the reservation system can consider the past attendance rates of meeting participants when automatically reserving a proposed meeting room and time using an AI model that takes the past attendance rates of meeting participants as input and outputs the optimal meeting room and time.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The meeting room reservation system can also include a notification function. This function can send notifications to all participants once a meeting reservation is complete. For example, it can send detailed meeting information to participants via email, SMS, or in-app notifications. The notification function can also send reminders as the meeting start time approaches. For instance, it can send a reminder 10 minutes before the meeting to ensure participants arrive on time. Furthermore, the notification function can quickly notify participants if there are any changes or cancellations to the meeting. This ensures participants always have access to the latest meeting information, improving meeting efficiency.

[0059] The meeting room reservation system can also include a feedback section. This feedback section can collect feedback from participants after the meeting. For example, it can have participants input their evaluations of the meeting's content and progress, and collect this information. The feedback section can also collect opinions on the meeting room's facilities and environment. For example, it can collect evaluations of the meeting room's cleanliness and ease of use of its facilities. Furthermore, the feedback section can analyze the collected feedback and use it to improve the meeting room's usage. This can increase user satisfaction with the meeting rooms.

[0060] The meeting room reservation system can also include a statistics section. This section can statistically analyze meeting room usage and provide this information to users. For example, it can analyze meeting room usage frequency and reservation trends, displaying them as graphs and charts. Furthermore, the statistics section can analyze the utilization rate of specific meeting rooms and peak reservation times. For instance, it can analyze whether meeting room usage is concentrated on specific days or times. Additionally, based on the analysis results, the statistics section can offer suggestions to improve meeting room utilization efficiency. This allows for a better understanding of meeting room usage and promotes more efficient use.

[0061] The meeting room reservation system can also include a customization section. This customization section allows users to customize meeting room reservation settings according to their needs. For example, it can prioritize meeting room reservations for specific user groups. It can also customize meeting room reservation times and usage conditions. For instance, it can restrict reservations to specific time slots. Furthermore, the customization section can flexibly modify meeting room reservation settings based on user feedback. This enables flexible meeting room reservations tailored to user needs.

[0062] The meeting room reservation system can also include a security section. This security section provides functions to protect meeting room reservation information and participant information. For example, the security section can restrict which users can access reservation information. It can also encrypt reservation information and record access logs. For instance, the security section can encrypt and store reservation information to prevent unauthorized access. Furthermore, the security section can perform user authentication and access control. This allows for the secure management of meeting room reservation information and participant information.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects user booking history and meeting patterns. For example, it collects data such as past booking dates and times, booked meeting rooms, and booking frequency to understand meeting frequency and participant trends. It also collects data on trends where meetings are concentrated on specific days of the week or time slots. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it analyzes past reservation data to understand trends such as the concentration of meetings on specific days of the week or time slots, as well as the frequency and participant trends of meetings. Step 3: The proposal department proposes the optimal meeting room and time based on the analysis results obtained by the analysis department. For example, it proposes the optimal meeting room and time based on the current reservation status and the user's reservation history, and suggests ways to effectively utilize the time slots when meeting rooms are available. Step 4: The booking department reserves the meeting room and time proposed by the proposal department. For example, the proposed meeting room and time are automatically reserved, and the reservation is completed simply by the user's approval.

[0065] (Example of form 2) The meeting room reservation system according to an embodiment of the present invention is an AI agent for streamlining meeting room reservations and promoting appropriate use. This meeting room reservation system learns the user's reservation history and meeting patterns and optimizes the reservation status in real time. For example, the AI ​​learns the user's reservation history and meeting patterns. In this process, the AI ​​analyzes past reservation data, meeting frequency, participant trends, etc. For example, if there is a tendency for meetings to be concentrated on specific days of the week or time slots, the AI ​​can grasp that pattern. Next, the AI ​​optimizes the reservation status in real time. Based on the current reservation status and the user's reservation history, the AI ​​proposes the most appropriate meeting room and time. For example, if meeting room A is already reserved, the AI ​​proposes meeting room B. Also, if the user wishes to hold a meeting during a specific time slot, the AI ​​proposes an available meeting room during that time slot. Furthermore, the meeting room reservation system automatically reserves the meeting room and time proposed by the AI. This allows the user to reserve a meeting room without any effort. For example, the user simply confirms the meeting room and time proposed by the AI ​​and approves it to complete the reservation. This mechanism prevents duplicate reservations and unnecessary reservations. Because AI optimizes booking status in real time, the efficiency of meeting room utilization is maximized. For example, by effectively utilizing the time slots when meeting rooms are not in use, unnecessary bookings can be reduced. In addition, by analyzing the user's meeting patterns and history, the AI ​​can suggest the optimal meeting room and time. This allows users to hold meetings efficiently. For example, users who hold meetings frequently can be offered regularly available meeting rooms, saving them the trouble of booking. In this way, using an AI agent can streamline meeting room booking and promote appropriate use. In today's world, where the efficient use of meeting rooms is urgently needed due to the spread of remote work and the hybridization of offices, this invention is extremely useful. As a result, the meeting room booking system can streamline meeting room booking and promote appropriate use.

[0066] The meeting room reservation system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a reservation unit. The data collection unit collects the user's reservation history and meeting patterns. The user's reservation history includes, but is not limited to, past reservation dates and times, reserved meeting rooms, and reservation frequency. The data collection unit collects past reservation data to understand the frequency of meetings and participant trends. The data collection unit can also analyze meeting frequency and participant trends in order to collect meeting patterns. For example, the data collection unit can understand the tendency for meetings to be concentrated on specific days of the week or time slots. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes, for example, the data collected to understand the tendency for meetings to be concentrated on specific days of the week or time slots. The analysis unit can also analyze meeting frequency and participant trends based on the data collected. For example, the analysis unit analyzes past reservation data to understand meeting frequency and participant trends. The proposal unit proposes the optimal meeting room and time based on the analysis results obtained by the analysis unit. The suggestion unit proposes the most suitable meeting room and time based, for example, on the current reservation status and the user's reservation history. The suggestion unit can also regularly suggest available meeting rooms to users who frequently hold meetings. For example, the suggestion unit can make suggestions for effectively utilizing time slots when meeting rooms are available. The reservation unit reserves the meeting room and time suggested by the suggestion unit. For example, the reservation unit can automatically reserve the suggested meeting room and time. The reservation unit can also be configured so that the reservation is completed simply by the user's approval. For example, the reservation unit can confirm the suggested meeting room and time, and the reservation is completed simply by the user's approval. As a result, the meeting room reservation system according to this embodiment can streamline meeting room reservations and promote appropriate use.

[0067] The data collection unit collects user reservation history and meeting patterns. User reservation history includes, but is not limited to, past reservation dates and times, reserved meeting rooms, and reservation frequency. Specifically, the data collection unit collects detailed data such as which meeting rooms users have reserved in the past and at what times, how often they hold meetings, the number of participants, and the purpose of the meetings. This allows the unit to understand the user's meeting room usage trends and patterns. The data collection unit can also analyze meeting frequency and participant trends to collect meeting patterns. For example, it analyzes past reservation data to understand the tendency for meetings to be concentrated on specific days of the week or time slots. Furthermore, the data collection unit collects information such as the job titles and departments of meeting participants, as well as the purpose and content of the meetings, allowing for a more detailed understanding of meeting patterns. This enables the data collection unit to understand the user's meeting room usage trends and patterns and collect data to provide to the analysis and proposal departments. The data collection unit centrally manages this data and can link with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0068] The analysis unit analyzes the data collected by the data collection unit. For example, based on the collected data, the analysis unit can identify trends in the concentration of meetings on specific days of the week or time slots. Specifically, it analyzes the frequency of meetings and participant trends based on past reservation data. For instance, the analysis unit can identify trends in the concentration of meetings on specific days of the week or time slots and provide information to optimize meeting room usage. The analysis unit can also analyze the frequency of meetings and participant trends. For example, it can identify the frequency of meetings and participant trends based on past reservation data. Furthermore, the analysis unit can use AI to analyze data and predict user behavior patterns and meeting trends. For example, the AI ​​can predict which days of the week and time slots a particular user tends to hold meetings on based on past reservation data and suggest the optimal meeting room and time. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0069] The Proposal Department proposes the optimal meeting room and time based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes the optimal meeting room and time based on the current reservation status and the user's reservation history. Specifically, the Proposal Department proposes the optimal meeting room and time based on the data provided by the Analysis Department, taking into account the user's past reservation history and meeting patterns. For example, it can propose regularly available meeting rooms to users who frequently hold meetings. The Proposal Department makes suggestions for effectively utilizing the time slots when meeting rooms are available. For example, if there is a tendency for meetings to be concentrated on specific days or times, the Proposal Department will propose holding meetings during those times if meeting rooms are available. In addition, the Proposal Department can propose the optimal meeting room by considering conditions such as the facilities and capacity of the meeting room according to the user's needs. In this way, the Proposal Department can propose the optimal meeting room and time to the user and improve the efficiency of meeting room utilization. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, it can evaluate user satisfaction with the proposed meeting rooms and times and improve the suggestions. In this way, the Proposal Department can propose the optimal meeting room and time to the user and improve the efficiency of meeting room utilization.

[0070] The reservation department reserves meeting rooms and times proposed by the proposal department. For example, the reservation department can automatically reserve proposed meeting rooms and times. Specifically, based on the optimal meeting room and time provided by the proposal department, the reservation department automatically performs the reservation process. The reservation department can also enable a system where reservations are completed simply by the user's approval. For example, the user simply reviews the proposed meeting room and time and approves it to complete the reservation. This allows the reservation department to save user effort and book meeting rooms quickly and efficiently. Furthermore, the reservation department can manage reservation status in real time and flexibly respond to changes and cancellations. For example, if a user wants to change a reservation, the reservation department will propose a new meeting room and time and quickly update the reservation. The reservation department can also constantly monitor the reservation status and make appropriate adjustments to prevent overlapping or conflicting reservations. This allows the reservation department to provide users with quick and efficient meeting room reservations and improve the efficiency of meeting room utilization. Additionally, the reservation department can collect user feedback and continuously improve the accuracy and effectiveness of the reservation system. For example, it can evaluate user satisfaction with the ease of the reservation process and the certainty of reservations, and use this feedback to improve the reservation system. This allows the reservation department to provide users with quick and efficient meeting room reservations, thereby improving the efficiency of meeting room utilization.

[0071] The suggestion unit can propose the optimal meeting room and time based on the current reservation status and the user's reservation history. For example, the suggestion unit can propose the optimal meeting room and time based on the current reservation status. It can also propose the optimal meeting room and time based on the user's reservation history. For example, the suggestion unit can grasp the current reservation status in real time and propose the optimal meeting room and time. The user's reservation history includes, but is not limited to, past reservation dates and times, reserved meeting rooms, and reservation frequency. This allows the suggestion unit to propose the optimal meeting room and time based on the current reservation status and the user's reservation history. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can propose the optimal meeting room and time using an AI model that takes the current reservation status and the user's reservation history as input and outputs the optimal meeting room and time.

[0072] The reservation department can automatically reserve the proposed meeting room and time. For example, the reservation department can automatically reserve the proposed meeting room and time. Alternatively, the reservation department can make it so that the reservation is completed simply by the user's approval. For example, the reservation department can review the proposed meeting room and time, and the reservation is completed simply by the user's approval. This reduces the effort required for reservations by automatically reserving the proposed meeting room and time. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can automatically reserve the proposed meeting room and time using an AI model that takes the proposed meeting room and time as input and outputs a reservation completion.

[0073] The data collection unit can collect past reservation data, meeting frequency, participant trends, and more. For example, the data collection unit can collect past reservation data to understand meeting frequency and participant trends. The data collection unit can also analyze meeting frequency and participant trends to collect meeting patterns. For example, the data collection unit can identify trends where meetings are concentrated on specific days of the week or time slots. This allows for more accurate analysis by collecting past reservation data, meeting frequency, and participant trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect past reservation data, meeting frequency, and participant trends using an AI model that takes past reservation data, meeting frequency, and participant trends as input and outputs the collected results.

[0074] The analysis unit can analyze the collected data to identify trends in meeting scheduling, such as the concentration of meetings on specific days of the week or time slots. For example, the analysis unit can use the collected data to identify trends in meeting scheduling. The analysis unit can also analyze meeting frequency and participant trends based on the collected data. For example, the analysis unit can use past booking data to identify meeting frequency and participant trends. This allows the system to identify trends in meeting scheduling on specific days of the week and time slots, enabling it to suggest the most suitable meeting room and time. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that takes the collected data as input and outputs trends in meeting scheduling on specific days of the week or time slots to analyze the collected data.

[0075] The suggestion department can regularly suggest available meeting rooms to users who frequently hold meetings. For example, the suggestion department can regularly suggest available meeting rooms to users who frequently hold meetings. The suggestion department can also make suggestions for effectively utilizing the time slots when meeting rooms are available. For example, the suggestion department can suggest the optimal meeting room and time based on the time slots when meeting rooms are available. This reduces the effort required for reservations by regularly suggesting available meeting rooms to users who frequently hold meetings. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can use an AI model that takes the reservation history of users who frequently hold meetings as input and outputs regularly available meeting rooms to regularly suggest available meeting rooms to users who frequently hold meetings.

[0076] The reservation system can be designed so that reservations are completed simply by the user's approval. For example, the reservation system can review the suggested meeting room and time, and the reservation is completed simply by the user's approval. The reservation system can also automatically reserve the suggested meeting room and time. For example, the reservation system can automatically reserve the suggested meeting room and time, and the reservation is completed simply by the user's approval. This reduces the effort required for reservations by allowing users to complete reservations simply by their approval. Some or all of the above processes in the reservation system may be performed using AI, or not. For example, the reservation system can use an AI model that takes the suggested meeting room and time as input and outputs a reservation completion, so that reservations are completed simply by the user's approval.

[0077] The proposal department can make suggestions for effectively utilizing the time slots when meeting rooms are unavailable. For example, the proposal department can suggest the optimal meeting room and time based on the available time slots. The proposal department can also regularly suggest available meeting rooms to users who frequently hold meetings. For example, the proposal department can monitor the availability of meeting rooms in real time and suggest the optimal meeting room and time. This maximizes the efficiency of meeting room utilization by effectively utilizing the time slots when meeting rooms are unavailable. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can use an AI model that takes the available time slots when meeting rooms are available as input and outputs the optimal meeting room and time to make suggestions for effectively utilizing the time slots when meeting rooms are unavailable.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect the data when the user is relaxed. Alternatively, if the user is relaxed, the data collection unit can immediately collect the data and perform rapid analysis. For example, if the user is in a hurry, the data collection unit can advance the collection timing to quickly collect the data. By adjusting the timing of data collection according to the user's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can adjust the timing of data collection based on the user's emotions using an AI model that takes user emotion data as input and outputs the collection timing.

[0079] The data collection unit can collect meeting objectives and importance in addition to past reservation data. For example, the data collection unit can have users input the meeting objectives and collect that information. It can also have users evaluate the meeting importance and collect that information. For example, the data collection unit can automatically estimate the meeting objectives and importance and collect that information. By collecting meeting objectives and importance, more accurate analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can use an AI model that takes past reservation data, meeting objectives, and importance as inputs and outputs the collection results to collect meeting objectives and importance in addition to past reservation data.

[0080] The data collection unit can collect information on the job titles and departments of meeting participants and evaluate the importance of the meeting. For example, the data collection unit can collect information on the job titles of meeting participants and evaluate the importance. The data collection unit can also collect information on the departments of meeting participants and evaluate the importance. For example, the data collection unit can combine information on the job titles and departments of meeting participants to evaluate the importance. In this way, the importance of a meeting can be evaluated by collecting information on the job titles and departments of meeting participants. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information on the job titles and departments of meeting participants and evaluate the importance of a meeting using an AI model that takes information on the job titles and departments of meeting participants as input and outputs a result that evaluates importance.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. Conversely, if the user is relaxed, the data collection unit can collect all data equally. For example, if the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This allows for the priority of collecting more important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can determine the priority of data to collect based on the user's emotions using an AI model that takes user emotion data as input and outputs the priority of data to collect.

[0082] The data collection unit can collect schedule information of meeting participants and use it as data to suggest the optimal meeting time. For example, the data collection unit can automatically collect schedule information of meeting participants from a calendar. Alternatively, the data collection unit can collect schedule information by having meeting participants input it. For example, the data collection unit can collect schedule information of meeting participants via an API. By collecting schedule information of meeting participants in this way, it can be used as data to suggest the optimal meeting time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes schedule information of meeting participants as input and outputs the collection results to collect schedule information of meeting participants and use it as data to suggest the optimal meeting time.

[0083] The data collection unit can collect past meeting attendance rates of meeting participants and evaluate the importance of the meeting. For example, the data collection unit can collect past meeting attendance rates of meeting participants from a database. Alternatively, the data collection unit can collect past meeting attendance rates from meeting participants by having them input this information. For example, the data collection unit can collect past meeting attendance rates of meeting participants via an API. This allows the importance of a meeting to be evaluated by collecting past meeting attendance rates of meeting participants. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect past meeting attendance rates of meeting participants and evaluate the importance of a meeting using an AI model that takes past meeting attendance rates of meeting participants as input and outputs results evaluating importance.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can simplify the analysis algorithm and produce results quickly. Conversely, if the user is relaxed, the analysis unit can perform a detailed analysis and produce highly accurate results. For example, if the user is in a hurry, the analysis unit can speed up the analysis algorithm and produce results quickly. In this way, by adjusting the analysis algorithm according to the user's emotions, more appropriate analysis results can be obtained. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the analysis algorithm based on the user's emotions using an AI model that takes user emotion data as input and outputs the result of adjusting the analysis algorithm.

[0085] The analysis unit can perform analyses based on the collected data, tailored to the purpose and importance of each meeting. For example, the analysis unit can perform analyses to evaluate importance based on the purpose of each meeting. The analysis unit can also determine the priority of analyses based on the importance of each meeting. For example, the analysis unit can perform a comprehensive analysis by combining the purpose and importance of each meeting. This allows for more accurate analysis results by performing analyses tailored to the purpose and importance of each meeting. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that takes the collected data as input and outputs analysis results tailored to the purpose and importance of each meeting, to perform analyses based on the collected data, tailored to the purpose and importance of each meeting.

[0086] The analysis unit can perform analyses to evaluate the importance of a meeting based on the job titles and departmental information of the meeting participants. For example, the analysis unit can perform an analysis to evaluate importance based on the job titles of the meeting participants. The analysis unit can also perform an analysis to evaluate importance based on the departmental information of the meeting participants. For example, the analysis unit can perform an analysis to evaluate importance by combining the job titles and departmental information of the meeting participants. This allows for more accurate analysis results by evaluating the importance of a meeting based on the job titles and departmental information of the meeting participants. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that takes the job titles and departmental information of the meeting participants as input and outputs an analysis result that evaluates importance to perform an analysis to evaluate the importance of a meeting based on the job titles and departmental information of the meeting participants.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is in a hurry, the analysis unit can provide a concise display method. This allows for a more appropriate display method by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can adjust the display method of the analysis results based on the user's emotions using an AI model that takes user emotion data as input and outputs results that adjust the display method of the analysis results.

[0088] The analysis unit can analyze the schedule information of meeting participants based on the collected data and propose the optimal meeting time. For example, the analysis unit can analyze the schedule information of meeting participants and propose the optimal meeting time. The analysis unit can also evaluate the importance of a meeting based on the schedule information of meeting participants. For example, the analysis unit can combine the schedule information of meeting participants to propose the optimal meeting time. In this way, the analysis unit can propose the optimal meeting time by analyzing the schedule information of meeting participants. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that takes the schedule information of meeting participants as input and outputs the optimal meeting time to analyze the schedule information of meeting participants based on the collected data and propose the optimal meeting time.

[0089] The analysis unit can analyze the past meeting attendance rates of meeting participants based on the collected data and evaluate the importance of the meeting. For example, the analysis unit can analyze the past meeting attendance rates of meeting participants and evaluate the importance. The analysis unit can also evaluate the importance of a meeting based on the past meeting attendance rates of meeting participants. For example, the analysis unit can evaluate the importance by combining the past meeting attendance rates of meeting participants. In this way, the importance of a meeting can be evaluated by analyzing the past meeting attendance rates of meeting participants. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can use an AI model that takes the past meeting attendance rates of meeting participants as input and outputs an analysis result that evaluates importance, to analyze the past meeting attendance rates of meeting participants based on the collected data and evaluate the importance of the meeting.

[0090] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit will present simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also present suggestions that include detailed information. For example, if the user is in a hurry, the suggestion unit will present concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can adjust the way suggestions are presented based on the user's emotions using an AI model that takes user emotion data as input and outputs results that adjust the way suggestions are presented.

[0091] The proposal unit can suggest the optimal meeting room and time based on the purpose and importance of the meeting, based on the analysis results. For example, the proposal unit can suggest the optimal meeting room and time based on the purpose of the meeting, based on the analysis results. The proposal unit can also suggest the optimal meeting room and time based on the importance of the meeting, based on the analysis results. For example, the proposal unit can suggest the optimal meeting room and time by combining the purpose and importance of the meeting, based on the analysis results. This improves the efficiency of meetings by suggesting the optimal meeting room and time based on the purpose and importance of the meeting. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can use an AI model that takes the analysis results as input and outputs the optimal meeting room and time to suggest the optimal meeting room and time based on the purpose and importance of the meeting, based on the analysis results.

[0092] The proposal department can suggest the optimal meeting room and time based on the job titles and departmental information of the meeting participants. For example, the proposal department can suggest the optimal meeting room and time based on the job titles of the meeting participants. It can also suggest the optimal meeting room and time based on the departmental information of the meeting participants. For example, the proposal department can suggest the optimal meeting room and time by combining the job titles and departmental information of the meeting participants. This improves the efficiency of meetings by suggesting the optimal meeting room and time based on the job titles and departmental information of the meeting participants. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can suggest the optimal meeting room and time based on the job titles and departmental information of the meeting participants using an AI model that takes the job titles and departmental information of the meeting participants as input and outputs the optimal meeting room and time.

[0093] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize high-importance suggestions. Conversely, if the user is relaxed, the suggestion unit can distribute all suggestions equally. For example, if the user is in a hurry, the suggestion unit will prioritize suggestions that can be acted upon quickly. This allows for prioritizing more important suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can determine the priority of suggestions based on the user's emotions using an AI model that takes user emotion data as input and outputs results for determining the priority of suggestions.

[0094] The proposal unit can propose an optimal meeting time that takes into account the schedule information of the meeting participants, based on the analysis results. For example, the proposal unit can propose an optimal meeting time that takes into account the schedule information of the meeting participants, based on the analysis results. The proposal unit can also propose an optimal meeting time based on the schedule information of the meeting participants, based on the analysis results. For example, the proposal unit can propose an optimal meeting time that takes into account the schedule information of the meeting participants, thereby improving the efficiency of the meeting. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can use an AI model that takes the analysis results as input and outputs an optimal meeting time to propose an optimal meeting time that takes into account the schedule information of the meeting participants, based on the analysis results.

[0095] The proposal unit can suggest the optimal meeting room and time based on the analysis results, taking into account the past meeting attendance rates of the meeting participants. For example, the proposal unit can suggest the optimal meeting room and time based on the analysis results, taking into account the past meeting attendance rates of the meeting participants. Alternatively, the proposal unit can suggest the optimal meeting room and time based on the analysis results, taking into account the past meeting attendance rates of the meeting participants. This improves the efficiency of meetings by suggesting the optimal meeting room and time that takes into account the past meeting attendance rates of the meeting participants. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can use an AI model that takes the analysis results as input and outputs the optimal meeting room and time to suggest the optimal meeting room and time based on the analysis results, taking into account the past meeting attendance rates of the meeting participants.

[0096] The reservation unit can estimate the user's emotions and adjust the reservation confirmation method based on the estimated emotions. For example, if the user is stressed, the reservation unit can provide a simple and highly visible confirmation method. If the user is relaxed, the reservation unit can also provide a confirmation method that includes detailed information. For example, if the user is in a hurry, the reservation unit can provide a concise confirmation method. In this way, by adjusting the reservation confirmation method according to the user's emotions, a more appropriate confirmation method can be provided. 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 reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can adjust the reservation confirmation method based on the user's emotions using an AI model that takes user emotion data as input and outputs a result that adjusts the reservation confirmation method.

[0097] The reservation system can consider the purpose and importance of a meeting when automatically booking a proposed meeting room and time. For example, the reservation system can prioritize booking meetings of high importance by considering their purpose. Alternatively, the reservation system can evaluate the importance of meetings and prioritize booking those of high importance. For example, the reservation system can automatically book the optimal meeting room and time by combining the purpose and importance of the meeting. This allows for prioritizing the booking of more important meetings by considering their purpose and importance. Some or all of the above processing in the reservation system may be performed using AI, or not. For example, the reservation system can consider the purpose and importance of a meeting when automatically booking a proposed meeting room and time using an AI model that takes the purpose and importance of a meeting as input and outputs the optimal meeting room and time.

[0098] The reservation department can consider the job titles and departmental information of meeting participants when automatically reserving a proposed meeting room and time. For example, the reservation department can automatically reserve the optimal meeting room and time by considering the job titles of the meeting participants. Alternatively, the reservation department can automatically reserve the optimal meeting room and time by considering the departmental information of the meeting participants. For example, the reservation department can automatically reserve the optimal meeting room and time by combining the job titles and departmental information of the meeting participants. This allows for the reservation of a more appropriate meeting room and time by considering the job titles and departmental information of the meeting participants. Some or all of the above processing in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can consider the job titles and departmental information of meeting participants when automatically reserving a proposed meeting room and time using an AI model that takes the job titles and departmental information of meeting participants as input and outputs the optimal meeting room and time.

[0099] The reservation system can estimate the user's emotions and determine reservation priorities based on those emotions. For example, if the user is stressed, the reservation system will prioritize high-priority reservations. Conversely, if the user is relaxed, the reservation system can allocate all reservations equally. For example, if the user is in a hurry, the reservation system will prioritize reservations that can be executed quickly. This allows for prioritizing more important reservations by determining reservation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation system may be performed using AI or not. For example, the reservation system can determine reservation priorities based on the user's emotions using an AI model that takes user emotion data as input and outputs results for determining reservation priorities.

[0100] The reservation system can consider the schedule information of meeting participants when automatically reserving a proposed meeting room and time. For example, the reservation system can automatically reserve the optimal meeting room and time by considering the schedule information of meeting participants. Alternatively, the reservation system can automatically reserve the optimal meeting room and time based on the schedule information of meeting participants. For example, the reservation system can automatically reserve the optimal meeting room and time by combining the schedule information of meeting participants. This allows for the reservation of a more appropriate meeting room and time by considering the schedule information of meeting participants. Some or all of the above processing in the reservation system may be performed using AI, for example, or without AI. For example, the reservation system can consider the schedule information of meeting participants when automatically reserving a proposed meeting room and time using an AI model that takes the schedule information of meeting participants as input and outputs the optimal meeting room and time.

[0101] The reservation system can consider the past attendance rates of meeting participants when automatically reserving a proposed meeting room and time. For example, the reservation system can automatically reserve the optimal meeting room and time by considering the past attendance rates of meeting participants. Alternatively, the reservation system can automatically reserve the optimal meeting room and time based on the past attendance rates of meeting participants. For example, the reservation system can automatically reserve the optimal meeting room and time by combining the past attendance rates of meeting participants. This allows for the reservation of a more appropriate meeting room and time by considering the past attendance rates of meeting participants. Some or all of the above processing in the reservation system may be performed using AI, for example, or without AI. For example, the reservation system can consider the past attendance rates of meeting participants when automatically reserving a proposed meeting room and time using an AI model that takes the past attendance rates of meeting participants as input and outputs the optimal meeting room and time.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The meeting room reservation system can also include a notification function. This function can send notifications to all participants once a meeting reservation is complete. For example, it can send detailed meeting information to participants via email, SMS, or in-app notifications. The notification function can also send reminders as the meeting start time approaches. For instance, it can send a reminder 10 minutes before the meeting to ensure participants arrive on time. Furthermore, the notification function can quickly notify participants if there are any changes or cancellations to the meeting. This ensures participants always have access to the latest meeting information, improving meeting efficiency.

[0104] The meeting room reservation system can also include a feedback section. This feedback section can collect feedback from participants after the meeting. For example, it can have participants input their evaluations of the meeting's content and progress, and collect this information. The feedback section can also collect opinions on the meeting room's facilities and environment. For example, it can collect evaluations of the meeting room's cleanliness and ease of use of its facilities. Furthermore, the feedback section can analyze the collected feedback and use it to improve the meeting room's usage. This can increase user satisfaction with the meeting rooms.

[0105] The meeting room reservation system can also include a statistics section. This section can statistically analyze meeting room usage and provide this information to users. For example, it can analyze meeting room usage frequency and reservation trends, displaying them as graphs and charts. Furthermore, the statistics section can analyze the utilization rate of specific meeting rooms and peak reservation times. For instance, it can analyze whether meeting room usage is concentrated on specific days or times. Additionally, based on the analysis results, the statistics section can offer suggestions to improve meeting room utilization efficiency. This allows for a better understanding of meeting room usage and promotes more efficient use.

[0106] The meeting room reservation system can also include a customization section. This customization section allows users to customize meeting room reservation settings according to their needs. For example, it can prioritize meeting room reservations for specific user groups. It can also customize meeting room reservation times and usage conditions. For instance, it can restrict reservations to specific time slots. Furthermore, the customization section can flexibly modify meeting room reservation settings based on user feedback. This enables flexible meeting room reservations tailored to user needs.

[0107] The meeting room reservation system can also include a security section. This security section provides functions to protect meeting room reservation information and participant information. For example, the security section can restrict which users can access reservation information. It can also encrypt reservation information and record access logs. For instance, the security section can encrypt and store reservation information to prevent unauthorized access. Furthermore, the security section can perform user authentication and access control. This allows for the secure management of meeting room reservation information and participant information.

[0108] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, it can delay the suggestion until the user is relaxed. Conversely, if the user is relaxed, the suggestion function can immediately make a suggestion and quickly proceed with the booking. For example, if the user is in a hurry, the suggestion function can advance the suggestion to make it more prompt. By adjusting the timing of suggestions according to the user's emotions, suggestions can be made at a more appropriate time.

[0109] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible notification method. Conversely, if the user is relaxed, the analysis unit can provide a notification method that includes detailed information. For example, if the user is in a hurry, it can provide a concise notification method. By adjusting the notification method of the analysis results according to the user's emotions, a more appropriate notification method can be provided.

[0110] The suggestion function can estimate the user's emotions and adjust the content of the suggestions based on those emotions. For example, if the user is stressed, it will provide simple and easy-to-understand suggestions. Conversely, if the user is relaxed, the suggestion function can provide suggestions that include detailed information. For example, if the user is in a hurry, the suggestion function will provide concise suggestions. By adjusting the content of suggestions according to the user's emotions, it is possible to provide more appropriate suggestions.

[0111] The reservation system can estimate the user's emotions and adjust the reservation cancellation method based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-understand cancellation method. Conversely, if the user is relaxed, it can provide a more detailed cancellation method. For example, if the user is in a hurry, it can provide a concise cancellation method. This allows for a more appropriate cancellation method to be provided by adjusting the reservation cancellation method according to the user's emotions.

[0112] The suggestion function can estimate the user's emotions and adjust the frequency of suggestions based on those emotions. For example, if the user is stressed, it can reduce the frequency of suggestions and offer suggestions when the user is relaxed. Conversely, if the user is relaxed, the suggestion function can increase the frequency of suggestions to expedite the booking process. For example, if the user is in a hurry, the suggestion function can increase the frequency of suggestions to expedite the process. By adjusting the frequency of suggestions according to the user's emotions, suggestions can be offered at a more appropriate frequency.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects user booking history and meeting patterns. For example, it collects data such as past booking dates and times, booked meeting rooms, and booking frequency to understand meeting frequency and participant trends. It also collects data on trends where meetings are concentrated on specific days of the week or time slots. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it analyzes past reservation data to understand trends such as the concentration of meetings on specific days of the week or time slots, as well as the frequency and participant trends of meetings. Step 3: The proposal department proposes the optimal meeting room and time based on the analysis results obtained by the analysis department. For example, it proposes the optimal meeting room and time based on the current reservation status and the user's reservation history, and suggests ways to effectively utilize the time slots when meeting rooms are available. Step 4: The booking department reserves the meeting room and time proposed by the proposal department. For example, the proposed meeting room and time are automatically reserved, and the reservation is completed simply by the user's approval.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and reservation unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects the user's reservation history and meeting patterns using the camera 42 and microphone 38B of the smart device 14, and processes the data with the control unit 46A. The analysis unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 to understand the meeting patterns. The proposal unit proposes the optimal meeting room and time based on the analysis results with the specific processing unit 290 of the data processing unit 12. The reservation unit can automatically reserve the meeting room and time proposed by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[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 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.

[0123] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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 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.

[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 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.

[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 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.

[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 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and reservation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's reservation history and meeting patterns using the camera 42 and microphone 238 of the smart glasses 214, and processes the data with the control unit 46A. The analysis unit analyzes the collected data with the identification processing unit 290 of the data processing unit 12 to understand the meeting patterns. The proposal unit proposes the optimal meeting room and time based on the analysis results with the identification processing unit 290 of the data processing unit 12. The reservation unit can automatically reserve the meeting room and time proposed by the control unit 46A of the smart glasses 214. 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] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[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 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.

[0139] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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 (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).

[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] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and reservation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects the user's reservation history and meeting patterns using the camera 42 and microphone 238 of the headset terminal 314, and processes the data with the control unit 46A. The analysis unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 to understand the meeting patterns. The proposal unit proposes the optimal meeting room and time based on the analysis results with the specific processing unit 290 of the data processing unit 12. The reservation unit can automatically reserve the meeting room and time proposed by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and reservation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects the user's reservation history and meeting patterns using the camera 42 and microphone 238 of the robot 414, and processes the data with the control unit 46A. The analysis unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 to understand the meeting patterns. The proposal unit proposes the optimal meeting room and time based on the analysis results with the specific processing unit 290 of the data processing unit 12. The reservation unit can automatically reserve the meeting room and time proposed by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A data collection unit that collects user reservation history and meeting patterns, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal meeting room and time, The system comprises a reservation unit for reserving meeting rooms and times proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We will suggest the most suitable meeting room and time based on the current booking status and the user's booking history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reservation section is, The suggested meeting room and time will be automatically booked. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect past booking data, meeting frequency, participant trends, and more. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, By analyzing the collected data, we can identify trends in the concentration of meetings on specific days of the week or time slots. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, For users who hold meetings frequently, we will regularly suggest available meeting rooms. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reservation section is, The reservation will be completed simply by the user's approval. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, We propose ways to effectively utilize the time slots when meeting rooms are not in use. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of reservation data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is In addition to past booking data, we collect information on the purpose and importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Collect information on the job titles and departments of meeting participants and evaluate the importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is We collect scheduling information from meeting participants and use it as data to suggest the optimal meeting time. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Collect past meeting attendance rates of meeting participants to assess the importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Based on the collected data, analysis is performed according to the purpose and importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, We will perform an analysis to evaluate the importance of a meeting based on the job titles and departmental information of the meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, Based on the collected data, the system analyzes the schedule information of meeting participants and proposes the optimal meeting time. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, Based on the collected data, we analyze the past meeting attendance rates of meeting participants and evaluate the importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Based on the analysis results, we will propose the optimal meeting room and time according to the purpose and importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, Based on the job titles and departmental information of the meeting participants, we will suggest the most suitable meeting room and time. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, Based on the analysis results, we propose the optimal meeting time, taking into account the schedule information of the meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, Based on the analysis results, we propose the optimal meeting room and time, taking into account the past meeting attendance rates of the meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reservation section is, The system estimates the user's emotions and adjusts the reservation confirmation method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reservation section is, When automatically booking a suggested meeting room and time, the purpose and importance of the meeting will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reservation section is, When automatically booking a suggested meeting room and time, the system takes into account the job titles and departments of the meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reservation section is, When automatically booking a suggested meeting room and time, the system takes into account the scheduling information of the meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reservation section is, When automatically booking a suggested meeting room and time, the system takes into account the past meeting attendance rate of the meeting participants. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 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 data collection unit that collects user reservation history and meeting patterns, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal meeting room and time, The system comprises a reservation unit for reserving meeting rooms and times proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, We will suggest the most suitable meeting room and time based on the current booking status and the user's booking history. The system according to feature 1.

3. The aforementioned reservation section is, The suggested meeting room and time will be automatically booked. The system according to feature 1.

4. The aforementioned collection unit is Collect past booking data, meeting frequency, participant trends, and more. The system according to feature 1.

5. The aforementioned analysis unit, By analyzing the collected data, we can identify trends in the concentration of meetings on specific days of the week or time slots. The system according to feature 1.

6. The aforementioned proposal section is, For users who hold meetings frequently, we will regularly suggest available meeting rooms. The system according to feature 1.

7. The aforementioned reservation section is, The reservation will be completed simply by the user's approval. The system according to feature 1.

8. The aforementioned proposal section is, We propose ways to effectively utilize the time slots when meeting rooms are not in use. The system according to feature 1.

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