system

Abstract

The system according to this embodiment aims to provide optimal reservation suggestions based on the usage history of meeting rooms. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a dialogue unit, and a learning unit. The collection unit collects the usage history of meeting rooms. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes optimal reservation suggestions based on the analysis results obtained by the analysis unit. The dialogue unit collects reservation preference information through dialogue with the user. The learning unit learns from the reservations proposed by the proposal unit and always makes optimal suggestions.

Description

Technical Field

[0004] ,

[0006] , , , , ,

[0005] , , , , ,

[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 the 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 is a problem that the reservation management of the meeting room is not efficiently performed and an optimal reservation proposal cannot be made.

[0005] The system according to the embodiment aims to make an optimal reservation proposal based on the usage history of the meeting room.

Means for Solving the Problems

[0006] <00000The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a dialogue unit, and a learning unit. The collection unit collects the usage history of meeting rooms. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes optimal reservation suggestions based on the analysis results obtained by the analysis unit. The dialogue unit collects reservation preference information through dialogue with the user. The learning unit learns from the reservations proposed by the proposal unit and always makes optimal suggestions. [Effects of the Invention]

[0007] The system according to this embodiment can make optimal reservation suggestions based on the usage history of meeting rooms. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 conference room reservation system according to an embodiment of the present invention is a system that collects and analyzes the usage history of conference rooms and makes optimal reservation suggestions. This system comprises a collection unit that collects the usage history of conference rooms, an analysis unit that analyzes the collected data, a suggestion unit that makes optimal reservation suggestions based on the analysis results, a dialogue unit that collects reservation preference information through dialogue with the user, and a learning unit that learns from suggested reservations and always makes optimal suggestions. For example, the conference room reservation system collects the usage history of conference rooms. The collection unit can collect input from users and past conference room usage records. Next, the conference room reservation system analyzes the collected data. The analysis unit can recognize patterns in the collected data and grasp the usage trends and peak times of each department. Next, the conference room reservation system makes optimal reservation suggestions based on the analysis results. The suggestion unit can make optimal reservation suggestions using generative AI. For example, the suggestion unit can suggest the use of available conference rooms. Furthermore, the conference room reservation system collects reservation preference information through dialogue with the user. The dialogue unit can collect reservation preference information through dialogue with the user. For example, the dialogue unit may include a feedback unit that collects reservation feedback. Finally, the conference room reservation system learns from suggested reservations and always makes optimal suggestions. The learning unit uses generative AI to learn from suggested reservations and always provide optimal suggestions. This enables the meeting room reservation system to efficiently collect, analyze, suggest, interact with, and learn from meeting room usage history.

[0029] The meeting room reservation system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a dialogue unit, and a learning unit. The collection unit collects the usage history of meeting rooms. The collection unit can, for example, collect user input and past meeting room usage records. The collection unit can, for example, obtain information entered by users when reserving meeting rooms and past meeting room usage records from a database. The collection unit can also collect meeting room usage status in real time using sensors. For example, the collection unit can collect meeting room usage status in real time using sensors installed on meeting room doors. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, recognize patterns in the collected data and grasp usage trends and peak times for each department. The analysis unit can, for example, recognize patterns in the collected data using data mining techniques. The analysis unit can also grasp usage trends and peak times for each department using machine learning algorithms. For example, the analysis unit can cluster the collected data and grasp usage trends for each department. The proposal unit makes optimal reservation suggestions based on the analysis results obtained by the analysis unit. The suggestion unit can make optimal reservation suggestions using generative AI. For example, the suggestion unit can suggest the most suitable meeting room based on the user's reservation preference information. The suggestion unit can also suggest the use of available meeting rooms. For example, the suggestion unit can suggest meeting rooms that have become available due to last-minute cancellations. The dialogue unit collects reservation preference information through dialogue with the user. The dialogue unit can engage in dialogue with the user using, for example, a chatbot. The dialogue unit can also engage in dialogue with the user using speech recognition technology. For example, the dialogue unit can collect the user's reservation preference information using speech recognition technology. The learning unit learns from the reservations suggested by the suggestion unit and always makes the best suggestions. The learning unit can learn from the suggested reservations using generative AI and always make the best suggestions. For example, the learning unit can optimize the suggestion algorithm based on past suggestion results. The learning unit can also improve the suggestion algorithm based on user feedback.For example, the learning unit can collect user feedback and improve the suggestion algorithm. This enables the meeting room reservation system according to the embodiment to efficiently collect, analyze, suggest, interact with, and learn from the meeting room usage history.

[0030] The data collection unit collects the usage history of meeting rooms. For example, it can collect user input and past meeting room usage records. Specifically, it can retrieve information entered by users when booking meeting rooms and past meeting room usage records from a database. This allows the data collection unit to understand user booking patterns and usage trends. Furthermore, the data collection unit can collect meeting room usage status in real time using sensors. For example, it can collect real-time usage status of meeting rooms using sensors installed on meeting room doors. This allows for accurate understanding of the actual usage status of meeting rooms. In addition, the data collection unit can collect environmental data such as temperature, humidity, and illuminance using environmental sensors within the meeting rooms. This allows for evaluation of meeting room comfort and the provision of an optimal environment for users. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department can recognize patterns in the collected data and understand usage trends and peak times for each department. Specifically, it can use data mining techniques to recognize patterns in the collected data. For example, it can analyze past meeting room usage records to understand usage trends on specific days of the week and time slots. The analysis department can also use machine learning algorithms to understand usage trends and peak times for each department. For example, it can cluster the collected data to understand usage trends for each department. This allows the analysis department to understand meeting room usage in detail and provide the basic data for making optimal reservation suggestions. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. For example, if a particular meeting room is booked an unusually large number of times, or if abnormal usage occurs during a specific time slot, the analysis department can detect this and notify the administrator. This allows the analysis department to not only understand 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 makes optimal reservation suggestions based on the analysis results obtained by the Analysis Department. The Proposal Department can make optimal reservation suggestions using generative AI. Specifically, it can suggest the most suitable meeting room based on the user's reservation request information. For example, if the user inputs information such as the desired date and time, number of participants, and required equipment, the Proposal Department will suggest the meeting room that best suits these conditions. The Proposal Department can also suggest the use of available meeting rooms. For example, it can suggest a meeting room that has become available due to a last-minute cancellation. This maximizes the efficiency of meeting room utilization and reduces wasted idle time. Furthermore, the Proposal Department can make individually customized suggestions based on the user's past usage history and feedback. For example, it can make optimal suggestions considering meeting rooms used by a particular user in the past and their preferred equipment. The Proposal Department can provide these suggestions to users quickly and accurately, improving user satisfaction. The Proposal Department can continuously learn and optimize its suggestion algorithm using generative AI. This allows the Proposal Department to always make suggestions based on the latest information and optimal algorithms, meeting the needs of users.

[0033] The dialogue unit collects reservation request information through dialogue with users. For example, the dialogue unit can interact with users using a chatbot. Specifically, when a user inputs reservation request information into the chatbot, the dialogue unit analyzes it and collects the necessary information. The dialogue unit can also interact with users using speech recognition technology. For example, when a user inputs reservation request information by voice, the dialogue unit analyzes it and collects the necessary information. This allows the dialogue unit to enable users to provide reservation request information easily and quickly. Furthermore, the dialogue unit can accurately understand user intent and provide appropriate responses using natural language processing technology. For example, even if a user uses ambiguous language, the dialogue unit can understand it and ask appropriate questions to extract the necessary information. The dialogue unit provides the information collected through these dialogues to the proposal and analysis units, which can then be used as basic data for making optimal reservation suggestions. This enables the dialogue unit to achieve smooth communication with users and improve the overall efficiency and accuracy of the system.

[0034] The learning unit learns from the reservations proposed by the proposal unit and always makes the optimal suggestions. The learning unit can learn from the proposed reservations using generative AI and always make the optimal suggestions. Specifically, it can optimize the suggestion algorithm based on past suggestion results. For example, it can improve the accuracy of the suggestion algorithm by comparing past suggestion results with the actual reservation status. The learning unit can also improve the suggestion algorithm based on user feedback. For example, if a user provides feedback evaluating their satisfaction with the suggested meeting room, the learning unit can improve the suggestion algorithm based on this. This allows the learning unit to make the optimal suggestions that meet the user's needs. Furthermore, the learning unit can continuously learn and optimize the suggestion algorithm using generative AI. This allows the learning unit to always make suggestions based on the latest information and the optimal algorithm, meeting the user's needs. The learning unit can provide these learning results to the proposal unit and the analysis unit, improving the accuracy and efficiency of the entire system. This allows the learning unit to improve the overall performance of the system and increase user satisfaction.

[0035] The proposal unit includes an adjustment unit to handle changes in reservation priority conditions or the addition of new meeting rooms. For example, the proposal unit can use the adjustment unit to make the best reservation proposal when reservation priority conditions change. For example, the proposal unit can use the adjustment unit to make the best reservation proposal when a user's reservation preference information changes. The proposal unit can also use the adjustment unit to make the best reservation proposal when a new meeting room is added. For example, the proposal unit can input the information of the newly added meeting room into the adjustment unit and make the best reservation proposal. This allows the proposal unit to flexibly respond to changes in reservation priority conditions and the addition of new meeting rooms.

[0036] The proposal department makes suggestions for the use of available meeting rooms. For example, the proposal department can suggest meeting rooms that have become available due to last-minute cancellations. For example, the proposal department can suggest meeting rooms that are not currently booked. Furthermore, the proposal department can suggest available meeting rooms based on the user's booking preferences. For example, the proposal department can suggest meeting rooms that are available at the time requested by the user. This allows the proposal department to efficiently suggest the use of available meeting rooms.

[0037] The dialogue unit includes a feedback unit that collects reservation feedback. The dialogue unit can collect reservation feedback, for example, through dialogue with the user. The dialogue unit can collect user feedback, for example, using a chatbot. The dialogue unit can also collect user feedback using speech recognition technology. For example, the dialogue unit can collect user feedback using speech recognition technology. This allows the dialogue unit to efficiently collect reservation feedback.

[0038] The data collection unit collects user input and past meeting room usage records. For example, the data collection unit can collect information entered by users when they reserve a meeting room. For example, it can collect information such as the desired date and time, the type of meeting room, and the number of participants entered by users when they reserve a meeting room. The data collection unit can also retrieve past meeting room usage records from a database. For example, the data collection unit can retrieve past meeting room usage records from a database and collect information such as the date and time of use, the purpose of use, and the participants. This allows the data collection unit to efficiently collect user input and past meeting room usage records.

[0039] The analysis department recognizes patterns in the collected data and understands usage trends and peak times for each department. For example, the analysis department can recognize patterns in the collected data using data mining techniques. For example, the analysis department can cluster the collected data to understand usage trends for each department. Furthermore, the analysis department can use machine learning algorithms to understand usage trends and peak times for each department. For example, the analysis department can understand usage trends and peak times for each department based on the collected data. This allows the analysis department to efficiently understand usage trends and peak times for each department.

[0040] The data collection unit analyzes the user's past meeting room usage history and selects the optimal collection method. For example, the data collection unit can prioritize collecting the history of meeting rooms that the user frequently uses. For example, the data collection unit can focus on collecting the history of meeting rooms that the user uses on specific days of the week or at specific times. The data collection unit can also analyze the user's past usage patterns and select the most efficient collection method. For example, the data collection unit can select the optimal collection method based on the user's past usage patterns. This enables efficient data collection by allowing the data collection unit to select the optimal collection method based on past usage history.

[0041] The data collection unit filters meeting room usage history based on the user's current projects and areas of interest. For example, the unit can prioritize collecting meeting room usage history related to the user's current projects. For example, the unit can filter and collect meeting room usage history related to the user's areas of interest. The unit can also collect relevant meeting room usage history based on projects the user has participated in in the past. For example, the unit can collect relevant meeting room usage history based on the user's past project history. This allows the unit to collect highly relevant data by filtering based on the user's current projects and areas of interest.

[0042] The data collection unit prioritizes collecting highly relevant data based on the user's geographical location when collecting meeting room usage history. For example, the data collection unit can prioritize collecting meeting room usage history for locations close to the user's current location. For example, the data collection unit can collect meeting room usage history related to places the user frequently visits. The data collection unit can also filter and collect highly relevant data based on the user's current geographical location. For example, the data collection unit can select the optimal collection method based on the user's geographical location. This enables efficient data collection by prioritizing the collection of highly relevant data based on geographical location information.

[0043] The data collection unit analyzes users' social media activity and collects relevant data when collecting meeting room usage history. For example, the data collection unit can collect meeting room usage history mentioned by users on social media. For example, the data collection unit can collect meeting room usage history related to topics of interest from users' social media activity. The data collection unit can also determine the optimal collection timing based on the time of day when users are active on social media. For example, the data collection unit can select the optimal collection method based on users' social media activity. This allows the data collection unit to collect highly relevant data by analyzing social media activity.

[0044] The analysis department adjusts the level of detail in its analysis based on the importance of the meeting room usage history. For example, it can perform a detailed analysis of meeting room usage history that is of high importance, and a concise analysis of meeting room usage history that is of low importance. The analysis department can also determine the priority of the analysis according to the importance of the meeting room usage history. For example, the analysis department can dynamically adjust the level of detail in the analysis based on the importance of the meeting room usage history. This allows the analysis department to perform efficient analysis by adjusting the level of detail in the analysis based on the importance of the meeting room usage history.

[0045] The analysis unit applies different analysis algorithms depending on the category of the meeting room during the analysis. For example, the analysis unit can perform a detailed usage pattern analysis for large meeting rooms. For example, the analysis unit can perform a concise usage pattern analysis for small meeting rooms. The analysis unit can also select the optimal analysis algorithm depending on the category of the meeting room. For example, the analysis unit can dynamically apply the optimal analysis algorithm based on the category of the meeting room. This enables efficient analysis by applying the optimal analysis algorithm according to the category of the meeting room.

[0046] The analysis department prioritizes analysis based on the submission date of meeting room usage records. For example, the analysis department can prioritize the analysis of recently submitted meeting room usage records. For example, the analysis department can postpone the analysis of older meeting room usage records. The analysis department can also dynamically adjust the analysis priority based on the submission date. For example, the analysis department can determine the analysis priority based on the submission date of the meeting room usage records. This allows the analysis department to perform efficient analysis by prioritizing analysis based on the submission date.

[0047] The analysis unit adjusts the order of analysis based on the relevance of the meeting rooms. For example, the analysis unit can prioritize the analysis of meeting room usage history that is highly relevant. For example, the analysis unit can postpone the analysis of meeting room usage history that is less relevant. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the meeting rooms. For example, the analysis unit can determine the order of analysis based on the relevance of the meeting rooms. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the meeting rooms.

[0048] The proposal department adjusts the level of detail in its proposals based on the importance of each meeting room. For example, it can provide detailed proposals for high-priority meeting rooms and concise proposals for lower-priority meeting rooms. The proposal department can also prioritize proposals based on the importance of each meeting room. For example, it can dynamically adjust the level of detail in proposals based on the importance of each meeting room. This allows the proposal department to make more efficient proposals by adjusting the level of detail based on the importance of each meeting room.

[0049] The proposal function applies different proposal algorithms depending on the category of the meeting room. For example, the proposal function can provide detailed usage suggestions for large meeting rooms, while providing concise suggestions for small meeting rooms. The proposal function can also select the optimal proposal algorithm based on the category of the meeting room. For example, the proposal function can dynamically apply the optimal proposal algorithm based on the category of the meeting room. This allows the proposal function to provide efficient suggestions by applying the most suitable proposal algorithm according to the category of the meeting room.

[0050] The proposal department prioritizes proposals based on the availability of meeting rooms. For example, the proposal department can prioritize proposals for available meeting rooms. For example, it can postpone proposals for meeting rooms that are fully booked. The proposal department can also dynamically adjust the priority of proposals based on meeting room availability. For example, the proposal department can determine the priority of proposals based on meeting room availability. This allows the proposal department to make efficient proposals by prioritizing proposals based on meeting room availability.

[0051] The proposal department adjusts the order of proposals based on the relevance of the meeting rooms. For example, the proposal department can prioritize proposals for highly relevant meeting rooms. For example, it can postpone proposals for less relevant meeting rooms. The proposal department can also dynamically adjust the order of proposals based on the relevance of the meeting rooms. For example, the proposal department can determine the order of proposals based on the relevance of the meeting rooms. This allows the proposal department to make efficient proposals by adjusting the order of proposals based on the relevance of the meeting rooms.

[0052] The dialogue unit selects the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can prioritize dialogue styles that the user has preferred in the past. For example, the dialogue unit can select the optimal dialogue method based on the user's past dialogue history. The dialogue unit can also avoid dialogue styles that the user has found unpleasant in the past. For example, the dialogue unit can select the optimal dialogue method based on the user's past dialogue history. In this way, the dialogue unit can select the optimal dialogue method by referring to past dialogue history.

[0053] The dialogue unit customizes the dialogue content based on the user's current situation during the conversation. For example, if the user is in a meeting, the dialogue unit can provide dialogue content related to the meeting. For example, if the user is traveling, the dialogue unit can provide dialogue content related to travel. Furthermore, if the user is taking a break, the dialogue unit can provide relaxing dialogue content. For example, the dialogue unit can dynamically customize the dialogue content based on the user's current situation. This allows the dialogue unit to provide more appropriate dialogue by customizing the dialogue content based on the current situation.

[0054] The dialogue unit selects the most appropriate dialogue method based on the user's geographical location information during a conversation. For example, if the user is in the office, the dialogue unit can provide dialogue content related to the office. For example, if the user is out of the office, the dialogue unit can provide dialogue content related to being out of the office. The dialogue unit can also select the most appropriate dialogue method based on the user's geographical location information. For example, the dialogue unit can dynamically select the most appropriate dialogue method based on the user's geographical location information. This allows the dialogue unit to select the most appropriate dialogue method based on geographical location information, enabling more appropriate conversations.

[0055] The dialogue unit analyzes the user's social media activity during a conversation and proposes dialogue content. For example, the dialogue unit can provide dialogue content related to topics the user has mentioned on social media. For example, the dialogue unit can propose dialogue content related to topics of interest based on the user's social media activity. The dialogue unit can also determine the optimal timing for a conversation based on the user's social media activity. For example, the dialogue unit can select the most appropriate dialogue content based on the user's social media activity. In this way, the dialogue unit can propose highly relevant dialogue content by analyzing social media activity.

[0056] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can select an algorithm that improves learning efficiency from past learning data. Furthermore, the learning unit can analyze past learning data and dynamically optimize the learning algorithm. For example, the learning unit can optimize the learning algorithm based on past learning data. In this way, the learning unit can optimize the learning algorithm by referring to past learning data.

[0057] The learning unit recognizes patterns in meeting room usage history during training to improve the accuracy of learning. For example, the learning unit can analyze patterns in meeting room usage history to improve the accuracy of learning. For example, the learning unit can select the optimal learning method based on patterns in meeting room usage history. Furthermore, the learning unit can recognize patterns in meeting room usage history and optimize the learning algorithm. For example, the learning unit can optimize the learning algorithm based on patterns in meeting room usage history. As a result, the learning unit can improve the accuracy of learning by recognizing patterns in meeting room usage history.

[0058] The learning unit weights the training data based on the submission date of the meeting room usage history during the learning process. For example, the learning unit can prioritize recently submitted meeting room usage history during learning. For example, the learning unit can reduce the weight of older meeting room usage history submissions. The learning unit can also dynamically adjust the weighting of the training data based on the submission date. For example, the learning unit can weight the training data based on the submission date of the meeting room usage history. This allows the learning unit to perform efficient learning by weighting the training data based on the submission date.

[0059] The learning unit improves the accuracy of its learning by referring to relevant literature in the conference room during the learning process. For example, the learning unit can improve the accuracy of its learning based on relevant literature in the conference room. For example, the learning unit can select the optimal learning method by referring to relevant literature in the conference room. Furthermore, the learning unit can analyze relevant literature in the conference room and optimize its learning algorithm. For example, the learning unit can optimize its learning algorithm based on relevant literature in the conference room. This allows the learning unit to improve the accuracy of its learning by referring to relevant literature.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit analyzes the user's past meeting room usage history and selects the optimal collection method. For example, the data collection unit can prioritize collecting the history of meeting rooms that the user frequently uses. For example, the data collection unit can focus on collecting the history of meeting rooms that the user uses on specific days of the week or at specific times. The data collection unit can also analyze the user's past usage patterns and select the most efficient collection method. For example, the data collection unit can select the optimal collection method based on the user's past usage patterns. This enables efficient data collection by allowing the data collection unit to select the optimal collection method based on past usage history.

[0062] The data collection unit filters meeting room usage history based on the user's current projects and areas of interest. For example, the unit can prioritize collecting meeting room usage history related to the user's current projects. For example, the unit can filter and collect meeting room usage history related to the user's areas of interest. The unit can also collect relevant meeting room usage history based on projects the user has participated in in the past. For example, the unit can collect relevant meeting room usage history based on the user's past project history. This allows the unit to collect highly relevant data by filtering based on the user's current projects and areas of interest.

[0063] The data collection unit prioritizes collecting highly relevant data based on the user's geographical location when collecting meeting room usage history. For example, the data collection unit can prioritize collecting meeting room usage history for locations close to the user's current location. For example, the data collection unit can collect meeting room usage history related to places the user frequently visits. The data collection unit can also filter and collect highly relevant data based on the user's current geographical location. For example, the data collection unit can select the optimal collection method based on the user's geographical location. This enables efficient data collection by prioritizing the collection of highly relevant data based on geographical location information.

[0064] The data collection unit analyzes users' social media activity and collects relevant data when collecting meeting room usage history. For example, the data collection unit can collect meeting room usage history mentioned by users on social media. For example, the data collection unit can collect meeting room usage history related to topics of interest from users' social media activity. The data collection unit can also determine the optimal collection timing based on the time of day when users are active on social media. For example, the data collection unit can select the optimal collection method based on users' social media activity. This allows the data collection unit to collect highly relevant data by analyzing social media activity.

[0065] The analysis department adjusts the level of detail in its analysis based on the importance of the meeting room usage history. For example, it can perform a detailed analysis of meeting room usage history that is of high importance, and a concise analysis of meeting room usage history that is of low importance. The analysis department can also determine the priority of the analysis according to the importance of the meeting room usage history. For example, the analysis department can dynamically adjust the level of detail in the analysis based on the importance of the meeting room usage history. This allows the analysis department to perform efficient analysis by adjusting the level of detail in the analysis based on the importance of the meeting room usage history.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects the usage history of the meeting rooms. The collection unit can collect user input and past meeting room usage records. For example, it can retrieve information entered by users when booking meeting rooms and past meeting room usage records from a database. The collection unit can also collect meeting room usage status in real time using sensors. For example, it can collect meeting room usage status in real time using sensors installed on the meeting room doors. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department recognizes patterns in the collected data and understands the usage trends and peak times of each department. For example, they may use data mining techniques or machine learning algorithms to cluster the collected data and understand the usage trends of each department. Step 3: The Proposal Department makes optimal reservation suggestions based on the analysis results obtained by the Analysis Department. The Proposal Department uses a generation AI to suggest the most suitable meeting room based on the user's reservation preferences. It can also suggest the use of meeting rooms that have become available due to last-minute cancellations. Step 4: The dialogue unit collects reservation preference information through interaction with the user. The dialogue unit uses chatbots and speech recognition technology to interact with the user and collect reservation preference information. Step 5: The learning unit learns from the reservations proposed by the proposal unit and always makes the best suggestions. The learning unit uses generative AI to optimize and improve the suggestion algorithm based on past suggestion results and user feedback.

[0068] (Example of form 2) The conference room reservation system according to an embodiment of the present invention is a system that collects and analyzes the usage history of conference rooms and makes optimal reservation suggestions. This system comprises a collection unit that collects the usage history of conference rooms, an analysis unit that analyzes the collected data, a suggestion unit that makes optimal reservation suggestions based on the analysis results, a dialogue unit that collects reservation preference information through dialogue with the user, and a learning unit that learns from suggested reservations and always makes optimal suggestions. For example, the conference room reservation system collects the usage history of conference rooms. The collection unit can collect input from users and past conference room usage records. Next, the conference room reservation system analyzes the collected data. The analysis unit can recognize patterns in the collected data and grasp the usage trends and peak times of each department. Next, the conference room reservation system makes optimal reservation suggestions based on the analysis results. The suggestion unit can make optimal reservation suggestions using generative AI. For example, the suggestion unit can suggest the use of available conference rooms. Furthermore, the conference room reservation system collects reservation preference information through dialogue with the user. The dialogue unit can collect reservation preference information through dialogue with the user. For example, the dialogue unit may include a feedback unit that collects reservation feedback. Finally, the conference room reservation system learns from suggested reservations and always makes optimal suggestions. The learning unit uses generative AI to learn from suggested reservations and always provide optimal suggestions. This enables the meeting room reservation system to efficiently collect, analyze, suggest, interact with, and learn from meeting room usage history.

[0069] The meeting room reservation system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a dialogue unit, and a learning unit. The collection unit collects the usage history of meeting rooms. The collection unit can, for example, collect user input and past meeting room usage records. The collection unit can, for example, obtain information entered by users when reserving meeting rooms and past meeting room usage records from a database. The collection unit can also collect meeting room usage status in real time using sensors. For example, the collection unit can collect meeting room usage status in real time using sensors installed on meeting room doors. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, recognize patterns in the collected data and grasp usage trends and peak times for each department. The analysis unit can, for example, recognize patterns in the collected data using data mining techniques. The analysis unit can also grasp usage trends and peak times for each department using machine learning algorithms. For example, the analysis unit can cluster the collected data and grasp usage trends for each department. The proposal unit makes optimal reservation suggestions based on the analysis results obtained by the analysis unit. The suggestion unit can make optimal reservation suggestions using generative AI. For example, the suggestion unit can suggest the most suitable meeting room based on the user's reservation preference information. The suggestion unit can also suggest the use of available meeting rooms. For example, the suggestion unit can suggest meeting rooms that have become available due to last-minute cancellations. The dialogue unit collects reservation preference information through dialogue with the user. The dialogue unit can engage in dialogue with the user using, for example, a chatbot. The dialogue unit can also engage in dialogue with the user using speech recognition technology. For example, the dialogue unit can collect the user's reservation preference information using speech recognition technology. The learning unit learns from the reservations suggested by the suggestion unit and always makes the best suggestions. The learning unit can learn from the suggested reservations using generative AI and always make the best suggestions. For example, the learning unit can optimize the suggestion algorithm based on past suggestion results. The learning unit can also improve the suggestion algorithm based on user feedback.For example, the learning unit can collect user feedback and improve the suggestion algorithm. This enables the meeting room reservation system according to the embodiment to efficiently collect, analyze, suggest, interact with, and learn from the meeting room usage history.

[0070] The data collection unit collects the usage history of meeting rooms. For example, it can collect user input and past meeting room usage records. Specifically, it can retrieve information entered by users when booking meeting rooms and past meeting room usage records from a database. This allows the data collection unit to understand user booking patterns and usage trends. Furthermore, the data collection unit can collect meeting room usage status in real time using sensors. For example, it can collect real-time usage status of meeting rooms using sensors installed on meeting room doors. This allows for accurate understanding of the actual usage status of meeting rooms. In addition, the data collection unit can collect environmental data such as temperature, humidity, and illuminance using environmental sensors within the meeting rooms. This allows for evaluation of meeting room comfort and the provision of an optimal environment for users. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0071] The analysis department analyzes the data collected by the data collection department. For example, the analysis department can recognize patterns in the collected data and understand usage trends and peak times for each department. Specifically, it can use data mining techniques to recognize patterns in the collected data. For example, it can analyze past meeting room usage records to understand usage trends on specific days of the week and time slots. The analysis department can also use machine learning algorithms to understand usage trends and peak times for each department. For example, it can cluster the collected data to understand usage trends for each department. This allows the analysis department to understand meeting room usage in detail and provide the basic data for making optimal reservation suggestions. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. For example, if a particular meeting room is booked an unusually large number of times, or if abnormal usage occurs during a specific time slot, the analysis department can detect this and notify the administrator. This allows the analysis department to not only understand 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.

[0072] The Proposal Department makes optimal reservation suggestions based on the analysis results obtained by the Analysis Department. The Proposal Department can make optimal reservation suggestions using generative AI. Specifically, it can suggest the most suitable meeting room based on the user's reservation request information. For example, if the user inputs information such as the desired date and time, number of participants, and required equipment, the Proposal Department will suggest the meeting room that best suits these conditions. The Proposal Department can also suggest the use of available meeting rooms. For example, it can suggest a meeting room that has become available due to a last-minute cancellation. This maximizes the efficiency of meeting room utilization and reduces wasted idle time. Furthermore, the Proposal Department can make individually customized suggestions based on the user's past usage history and feedback. For example, it can make optimal suggestions considering meeting rooms used by a particular user in the past and their preferred equipment. The Proposal Department can provide these suggestions to users quickly and accurately, improving user satisfaction. The Proposal Department can continuously learn and optimize its suggestion algorithm using generative AI. This allows the Proposal Department to always make suggestions based on the latest information and optimal algorithms, meeting the needs of users.

[0073] The dialogue unit collects reservation request information through dialogue with users. For example, the dialogue unit can interact with users using a chatbot. Specifically, when a user inputs reservation request information into the chatbot, the dialogue unit analyzes it and collects the necessary information. The dialogue unit can also interact with users using speech recognition technology. For example, when a user inputs reservation request information by voice, the dialogue unit analyzes it and collects the necessary information. This allows the dialogue unit to enable users to provide reservation request information easily and quickly. Furthermore, the dialogue unit can accurately understand user intent and provide appropriate responses using natural language processing technology. For example, even if a user uses ambiguous language, the dialogue unit can understand it and ask appropriate questions to extract the necessary information. The dialogue unit provides the information collected through these dialogues to the proposal and analysis units, which can then be used as basic data for making optimal reservation suggestions. This enables the dialogue unit to achieve smooth communication with users and improve the overall efficiency and accuracy of the system.

[0074] The learning unit learns from the reservations proposed by the proposal unit and always makes the optimal suggestions. The learning unit can learn from the proposed reservations using generative AI and always make the optimal suggestions. Specifically, it can optimize the suggestion algorithm based on past suggestion results. For example, it can improve the accuracy of the suggestion algorithm by comparing past suggestion results with the actual reservation status. The learning unit can also improve the suggestion algorithm based on user feedback. For example, if a user provides feedback evaluating their satisfaction with the suggested meeting room, the learning unit can improve the suggestion algorithm based on this. This allows the learning unit to make the optimal suggestions that meet the user's needs. Furthermore, the learning unit can continuously learn and optimize the suggestion algorithm using generative AI. This allows the learning unit to always make suggestions based on the latest information and the optimal algorithm, meeting the user's needs. The learning unit can provide these learning results to the proposal unit and the analysis unit, improving the accuracy and efficiency of the entire system. This allows the learning unit to improve the overall performance of the system and increase user satisfaction.

[0075] The proposal unit includes an adjustment unit to handle changes in reservation priority conditions or the addition of new meeting rooms. For example, the proposal unit can use the adjustment unit to make the best reservation proposal when reservation priority conditions change. For example, the proposal unit can use the adjustment unit to make the best reservation proposal when a user's reservation preference information changes. The proposal unit can also use the adjustment unit to make the best reservation proposal when a new meeting room is added. For example, the proposal unit can input the information of the newly added meeting room into the adjustment unit and make the best reservation proposal. This allows the proposal unit to flexibly respond to changes in reservation priority conditions and the addition of new meeting rooms.

[0076] The proposal department makes suggestions for the use of available meeting rooms. For example, the proposal department can suggest meeting rooms that have become available due to last-minute cancellations. For example, the proposal department can suggest meeting rooms that are not currently booked. Furthermore, the proposal department can suggest available meeting rooms based on the user's booking preferences. For example, the proposal department can suggest meeting rooms that are available at the time requested by the user. This allows the proposal department to efficiently suggest the use of available meeting rooms.

[0077] The dialogue unit includes a feedback unit that collects reservation feedback. The dialogue unit can collect reservation feedback, for example, through dialogue with the user. The dialogue unit can collect user feedback, for example, using a chatbot. The dialogue unit can also collect user feedback using speech recognition technology. For example, the dialogue unit can collect user feedback using speech recognition technology. This allows the dialogue unit to efficiently collect reservation feedback.

[0078] The data collection unit collects user input and past meeting room usage records. For example, the data collection unit can collect information entered by users when they reserve a meeting room. For example, it can collect information such as the desired date and time, the type of meeting room, and the number of participants entered by users when they reserve a meeting room. The data collection unit can also retrieve past meeting room usage records from a database. For example, the data collection unit can retrieve past meeting room usage records from a database and collect information such as the date and time of use, the purpose of use, and the participants. This allows the data collection unit to efficiently collect user input and past meeting room usage records.

[0079] The analysis department recognizes patterns in the collected data and understands usage trends and peak times for each department. For example, the analysis department can recognize patterns in the collected data using data mining techniques. For example, the analysis department can cluster the collected data to understand usage trends for each department. Furthermore, the analysis department can use machine learning algorithms to understand usage trends and peak times for each department. For example, the analysis department can understand usage trends and peak times for each department based on the collected data. This allows the analysis department to efficiently understand usage trends and peak times for each department.

[0080] The data collection unit estimates the user's emotions and adjusts the timing of meeting room usage history collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can refrain from collecting meeting room usage history and collect it when the user is relaxed. For example, if the user is busy, the data collection unit can collect meeting room usage history immediately after the meeting ends. Also, if the user is relaxed, the data collection unit can collect meeting room usage history in real time during the meeting. For example, the data collection unit monitors students' facial expressions in real time and immediately detects changes in emotion. This allows the data collection unit to adjust the collection timing according to the user's emotions, enabling more appropriate data collection. 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.

[0081] The data collection unit analyzes the user's past meeting room usage history and selects the optimal collection method. For example, the data collection unit can prioritize collecting the history of meeting rooms that the user frequently uses. For example, the data collection unit can focus on collecting the history of meeting rooms that the user uses on specific days of the week or at specific times. The data collection unit can also analyze the user's past usage patterns and select the most efficient collection method. For example, the data collection unit can select the optimal collection method based on the user's past usage patterns. This enables efficient data collection by allowing the data collection unit to select the optimal collection method based on past usage history.

[0082] The data collection unit filters meeting room usage history based on the user's current projects and areas of interest. For example, the unit can prioritize collecting meeting room usage history related to the user's current projects. For example, the unit can filter and collect meeting room usage history related to the user's areas of interest. The unit can also collect relevant meeting room usage history based on projects the user has participated in in the past. For example, the unit can collect relevant meeting room usage history based on the user's past project history. This allows the unit to collect highly relevant data by filtering based on the user's current projects and areas of interest.

[0083] The data collection unit estimates the user's emotions and prioritizes the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone the collection of less important data. For example, if the user is relaxed, the data collection unit can prioritize the collection of detailed data. Also, if the user is in a hurry, the data collection unit can prioritize the collection of highly important data. For example, the data collection unit can dynamically adjust the priority of the data to be collected according to the user's emotional state. This enables efficient data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The data collection unit prioritizes collecting highly relevant data based on the user's geographical location when collecting meeting room usage history. For example, the data collection unit can prioritize collecting meeting room usage history for locations close to the user's current location. For example, the data collection unit can collect meeting room usage history related to places the user frequently visits. The data collection unit can also filter and collect highly relevant data based on the user's current geographical location. For example, the data collection unit can select the optimal collection method based on the user's geographical location. This enables efficient data collection by prioritizing the collection of highly relevant data based on geographical location information.

[0085] The data collection unit analyzes users' social media activity and collects relevant data when collecting meeting room usage history. For example, the data collection unit can collect meeting room usage history mentioned by users on social media. For example, the data collection unit can collect meeting room usage history related to topics of interest from users' social media activity. The data collection unit can also determine the optimal collection timing based on the time of day when users are active on social media. For example, the data collection unit can select the optimal collection method based on users' social media activity. This allows the data collection unit to collect highly relevant data by analyzing social media activity.

[0086] The analysis unit estimates the user's emotions and adjusts the data analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can employ a concise and to-the-point analysis method. If the user is relaxed, for example, the analysis unit can perform a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can employ an analysis method that provides results quickly. For example, the analysis unit can dynamically adjust the data analysis method according to the user's emotional state. This allows the analysis unit to perform more appropriate analysis by adjusting the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The analysis department adjusts the level of detail in its analysis based on the importance of the meeting room usage history. For example, it can perform a detailed analysis of meeting room usage history that is of high importance, and a concise analysis of meeting room usage history that is of low importance. The analysis department can also determine the priority of the analysis according to the importance of the meeting room usage history. For example, the analysis department can dynamically adjust the level of detail in the analysis based on the importance of the meeting room usage history. This allows the analysis department to perform efficient analysis by adjusting the level of detail in the analysis based on the importance of the meeting room usage history.

[0088] The analysis unit applies different analysis algorithms depending on the category of the meeting room during the analysis. For example, the analysis unit can perform a detailed usage pattern analysis for large meeting rooms. For example, the analysis unit can perform a concise usage pattern analysis for small meeting rooms. The analysis unit can also select the optimal analysis algorithm depending on the category of the meeting room. For example, the analysis unit can dynamically apply the optimal analysis algorithm based on the category of the meeting room. This enables efficient analysis by applying the optimal analysis algorithm according to the category of the meeting room.

[0089] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. For example, the analysis unit can dynamically adjust the display method of the analysis results according to the user's emotional state. This allows the analysis unit to provide a more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The analysis department prioritizes analysis based on the submission date of meeting room usage records. For example, the analysis department can prioritize the analysis of recently submitted meeting room usage records. For example, the analysis department can postpone the analysis of older meeting room usage records. The analysis department can also dynamically adjust the analysis priority based on the submission date. For example, the analysis department can determine the analysis priority based on the submission date of the meeting room usage records. This allows the analysis department to perform efficient analysis by prioritizing analysis based on the submission date.

[0091] The analysis unit adjusts the order of analysis based on the relevance of the meeting rooms. For example, the analysis unit can prioritize the analysis of meeting room usage history that is highly relevant. For example, the analysis unit can postpone the analysis of meeting room usage history that is less relevant. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the meeting rooms. For example, the analysis unit can determine the order of analysis based on the relevance of the meeting rooms. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the meeting rooms.

[0092] The suggestion unit estimates the user's emotions and adjusts the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide concise and to-the-point suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that are easy to understand quickly. For instance, the suggestion unit can dynamically adjust the way it presents suggestions according to the user's emotional state. This allows the suggestion unit to provide more appropriate suggestions by adjusting the presentation of suggestions 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.

[0093] The proposal department adjusts the level of detail in its proposals based on the importance of each meeting room. For example, it can provide detailed proposals for high-priority meeting rooms and concise proposals for lower-priority meeting rooms. The proposal department can also prioritize proposals based on the importance of each meeting room. For example, it can dynamically adjust the level of detail in proposals based on the importance of each meeting room. This allows the proposal department to make more efficient proposals by adjusting the level of detail based on the importance of each meeting room.

[0094] The proposal function applies different proposal algorithms depending on the category of the meeting room. For example, the proposal function can provide detailed usage suggestions for large meeting rooms, while providing concise suggestions for small meeting rooms. The proposal function can also select the optimal proposal algorithm based on the category of the meeting room. For example, the proposal function can dynamically apply the optimal proposal algorithm based on the category of the meeting room. This allows the proposal function to provide efficient suggestions by applying the most suitable proposal algorithm according to the category of the meeting room.

[0095] The suggestion unit estimates the user's emotions and adjusts the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that can be quickly understood. For example, the suggestion unit can dynamically adjust the length of suggestions according to the user's emotional state. This allows the suggestion unit to provide more appropriate suggestions by adjusting the length of suggestions 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.

[0096] The proposal department prioritizes proposals based on the availability of meeting rooms. For example, the proposal department can prioritize proposals for available meeting rooms. For example, it can postpone proposals for meeting rooms that are fully booked. The proposal department can also dynamically adjust the priority of proposals based on meeting room availability. For example, the proposal department can determine the priority of proposals based on meeting room availability. This allows the proposal department to make efficient proposals by prioritizing proposals based on meeting room availability.

[0097] The proposal department adjusts the order of proposals based on the relevance of the meeting rooms. For example, the proposal department can prioritize proposals for highly relevant meeting rooms. For example, it can postpone proposals for less relevant meeting rooms. The proposal department can also dynamically adjust the order of proposals based on the relevance of the meeting rooms. For example, the proposal department can determine the order of proposals based on the relevance of the meeting rooms. This allows the proposal department to make efficient proposals by adjusting the order of proposals based on the relevance of the meeting rooms.

[0098] The dialogue unit estimates the user's emotions and adjusts the dialogue method based on the estimated emotions. For example, if the user is stressed, the dialogue unit can engage in dialogue in a calm tone. For example, if the user is relaxed, the dialogue unit can engage in dialogue in a friendly tone. Furthermore, if the user is in a hurry, the dialogue unit can engage in quick and concise dialogue. For example, the dialogue unit can dynamically adjust the dialogue method according to the user's emotional state. This allows the dialogue unit to engage in more appropriate dialogue by adjusting the dialogue method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The dialogue unit selects the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can prioritize dialogue styles that the user has preferred in the past. For example, the dialogue unit can select the optimal dialogue method based on the user's past dialogue history. The dialogue unit can also avoid dialogue styles that the user has found unpleasant in the past. For example, the dialogue unit can select the optimal dialogue method based on the user's past dialogue history. In this way, the dialogue unit can select the optimal dialogue method by referring to past dialogue history.

[0100] The dialogue unit customizes the dialogue content based on the user's current situation during the conversation. For example, if the user is in a meeting, the dialogue unit can provide dialogue content related to the meeting. For example, if the user is traveling, the dialogue unit can provide dialogue content related to travel. Furthermore, if the user is taking a break, the dialogue unit can provide relaxing dialogue content. For example, the dialogue unit can dynamically customize the dialogue content based on the user's current situation. This allows the dialogue unit to provide more appropriate dialogue by customizing the dialogue content based on the current situation.

[0101] The dialogue unit estimates the user's emotions and prioritizes conversations based on those emotions. For example, if the user is stressed, the dialogue unit can prioritize high-priority conversations. For example, if the user is relaxed, the dialogue unit can prioritize detailed conversations. The dialogue unit can also conduct conversations quickly if the user is in a hurry. For example, the dialogue unit can dynamically adjust conversation priorities according to the user's emotional state. This allows the dialogue unit to conduct more appropriate conversations by prioritizing conversations according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The dialogue unit selects the most appropriate dialogue method based on the user's geographical location information during a conversation. For example, if the user is in the office, the dialogue unit can provide dialogue content related to the office. For example, if the user is out of the office, the dialogue unit can provide dialogue content related to being out of the office. The dialogue unit can also select the most appropriate dialogue method based on the user's geographical location information. For example, the dialogue unit can dynamically select the most appropriate dialogue method based on the user's geographical location information. This allows the dialogue unit to select the most appropriate dialogue method based on geographical location information, enabling more appropriate conversations.

[0103] The dialogue unit analyzes the user's social media activity during a conversation and proposes dialogue content. For example, the dialogue unit can provide dialogue content related to topics the user has mentioned on social media. For example, the dialogue unit can propose dialogue content related to topics of interest based on the user's social media activity. The dialogue unit can also determine the optimal timing for a conversation based on the user's social media activity. For example, the dialogue unit can select the most appropriate dialogue content based on the user's social media activity. In this way, the dialogue unit can propose highly relevant dialogue content by analyzing social media activity.

[0104] The learning unit estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is stressed, the learning unit can select concise and to-the-point training data. If the user is relaxed, for example, the learning unit can select detailed training data. Also, if the user is in a hurry, the learning unit can select data that allows for rapid learning. For example, the learning unit can dynamically adjust the selection of training data according to the user's emotional state. This allows the learning unit to perform more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can select an algorithm that improves learning efficiency from past learning data. Furthermore, the learning unit can analyze past learning data and dynamically optimize the learning algorithm. For example, the learning unit can optimize the learning algorithm based on past learning data. In this way, the learning unit can optimize the learning algorithm by referring to past learning data.

[0106] The learning unit recognizes patterns in meeting room usage history during training to improve the accuracy of learning. For example, the learning unit can analyze patterns in meeting room usage history to improve the accuracy of learning. For example, the learning unit can select the optimal learning method based on patterns in meeting room usage history. Furthermore, the learning unit can recognize patterns in meeting room usage history and optimize the learning algorithm. For example, the learning unit can optimize the learning algorithm based on patterns in meeting room usage history. As a result, the learning unit can improve the accuracy of learning by recognizing patterns in meeting room usage history.

[0107] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency. For example, if the user is relaxed, the learning unit can increase the learning frequency. The learning unit can also adjust the learning frequency if the user is in a hurry. For example, the learning unit can dynamically adjust the learning frequency according to the user's emotional state. This allows the learning unit to perform more appropriate learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The learning unit weights the training data based on the submission date of the meeting room usage history during the learning process. For example, the learning unit can prioritize recently submitted meeting room usage history during learning. For example, the learning unit can reduce the weight of older meeting room usage history submissions. The learning unit can also dynamically adjust the weighting of the training data based on the submission date. For example, the learning unit can weight the training data based on the submission date of the meeting room usage history. This allows the learning unit to perform efficient learning by weighting the training data based on the submission date.

[0109] The learning unit improves the accuracy of its learning by referring to relevant literature in the conference room during the learning process. For example, the learning unit can improve the accuracy of its learning based on relevant literature in the conference room. For example, the learning unit can select the optimal learning method by referring to relevant literature in the conference room. Furthermore, the learning unit can analyze relevant literature in the conference room and optimize its learning algorithm. For example, the learning unit can optimize its learning algorithm based on relevant literature in the conference room. This allows the learning unit to improve the accuracy of its learning by referring to relevant literature.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The suggestion unit estimates the user's emotions and adjusts the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide concise and to-the-point suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that are easy to understand quickly. For instance, the suggestion unit can dynamically adjust the way it presents suggestions according to the user's emotional state. This allows the suggestion unit to provide more appropriate suggestions by adjusting the presentation of suggestions 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.

[0112] The data collection unit estimates the user's emotions and adjusts the timing of meeting room usage history collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can refrain from collecting meeting room usage history and collect it when the user is relaxed. For example, if the user is busy, the data collection unit can collect meeting room usage history immediately after the meeting ends. Also, if the user is relaxed, the data collection unit can collect meeting room usage history in real time during the meeting. For example, the data collection unit monitors students' facial expressions in real time and immediately detects changes in emotion. This allows the data collection unit to adjust the collection timing according to the user's emotions, enabling more appropriate data collection. 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.

[0113] The analysis unit estimates the user's emotions and adjusts the data analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can employ a concise and to-the-point analysis method. If the user is relaxed, for example, the analysis unit can perform a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can employ an analysis method that provides results quickly. For example, the analysis unit can dynamically adjust the data analysis method according to the user's emotional state. This allows the analysis unit to perform more appropriate analysis by adjusting the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The dialogue unit estimates the user's emotions and adjusts the dialogue method based on the estimated emotions. For example, if the user is stressed, the dialogue unit can engage in dialogue in a calm tone. For example, if the user is relaxed, the dialogue unit can engage in dialogue in a friendly tone. Furthermore, if the user is in a hurry, the dialogue unit can engage in quick and concise dialogue. For example, the dialogue unit can dynamically adjust the dialogue method according to the user's emotional state. This allows the dialogue unit to engage in more appropriate dialogue by adjusting the dialogue method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The learning unit estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is stressed, the learning unit can select concise and to-the-point training data. If the user is relaxed, for example, the learning unit can select detailed training data. Also, if the user is in a hurry, the learning unit can select data that allows for rapid learning. For example, the learning unit can dynamically adjust the selection of training data according to the user's emotional state. This allows the learning unit to perform more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The data collection unit analyzes the user's past meeting room usage history and selects the optimal collection method. For example, the data collection unit can prioritize collecting the history of meeting rooms that the user frequently uses. For example, the data collection unit can focus on collecting the history of meeting rooms that the user uses on specific days of the week or at specific times. The data collection unit can also analyze the user's past usage patterns and select the most efficient collection method. For example, the data collection unit can select the optimal collection method based on the user's past usage patterns. This enables efficient data collection by allowing the data collection unit to select the optimal collection method based on past usage history.

[0117] The data collection unit filters meeting room usage history based on the user's current projects and areas of interest. For example, the unit can prioritize collecting meeting room usage history related to the user's current projects. For example, the unit can filter and collect meeting room usage history related to the user's areas of interest. The unit can also collect relevant meeting room usage history based on projects the user has participated in in the past. For example, the unit can collect relevant meeting room usage history based on the user's past project history. This allows the unit to collect highly relevant data by filtering based on the user's current projects and areas of interest.

[0118] The data collection unit prioritizes collecting highly relevant data based on the user's geographical location when collecting meeting room usage history. For example, the data collection unit can prioritize collecting meeting room usage history for locations close to the user's current location. For example, the data collection unit can collect meeting room usage history related to places the user frequently visits. The data collection unit can also filter and collect highly relevant data based on the user's current geographical location. For example, the data collection unit can select the optimal collection method based on the user's geographical location. This enables efficient data collection by prioritizing the collection of highly relevant data based on geographical location information.

[0119] The data collection unit analyzes users' social media activity and collects relevant data when collecting meeting room usage history. For example, the data collection unit can collect meeting room usage history mentioned by users on social media. For example, the data collection unit can collect meeting room usage history related to topics of interest from users' social media activity. The data collection unit can also determine the optimal collection timing based on the time of day when users are active on social media. For example, the data collection unit can select the optimal collection method based on users' social media activity. This allows the data collection unit to collect highly relevant data by analyzing social media activity.

[0120] The analysis department adjusts the level of detail in its analysis based on the importance of the meeting room usage history. For example, it can perform a detailed analysis of meeting room usage history that is of high importance, and a concise analysis of meeting room usage history that is of low importance. The analysis department can also determine the priority of the analysis according to the importance of the meeting room usage history. For example, the analysis department can dynamically adjust the level of detail in the analysis based on the importance of the meeting room usage history. This allows the analysis department to perform efficient analysis by adjusting the level of detail in the analysis based on the importance of the meeting room usage history.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit collects the usage history of the meeting rooms. The collection unit can collect user input and past meeting room usage records. For example, it can retrieve information entered by users when booking meeting rooms and past meeting room usage records from a database. The collection unit can also collect meeting room usage status in real time using sensors. For example, it can collect meeting room usage status in real time using sensors installed on the meeting room doors. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department recognizes patterns in the collected data and understands the usage trends and peak times of each department. For example, they may use data mining techniques or machine learning algorithms to cluster the collected data and understand the usage trends of each department. Step 3: The Proposal Department makes optimal reservation suggestions based on the analysis results obtained by the Analysis Department. The Proposal Department uses a generation AI to suggest the most suitable meeting room based on the user's reservation preferences. It can also suggest the use of meeting rooms that have become available due to last-minute cancellations. Step 4: The dialogue unit collects reservation preference information through interaction with the user. The dialogue unit uses chatbots and speech recognition technology to interact with the user and collect reservation preference information. Step 5: The learning unit learns from the reservations proposed by the proposal unit and always makes the best suggestions. The learning unit uses generative AI to optimize and improve the suggestion algorithm based on past suggestion results and user feedback.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, dialogue unit, and learning unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect real-time data on the usage status of meeting rooms using the camera 42 and sensors of the smart device 14. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to understand usage trends and peak times. The proposal unit makes optimal reservation suggestions using the specific processing unit 290 of the data processing unit 12 and presents them to the user through the display 40A of the smart device 14. The dialogue unit interacts with the user using the microphone 38B of the smart device 14 and collects reservation preference information. The learning unit learns the reservations proposed by the specific processing unit 290 of the data processing unit 12 and always makes optimal suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, dialogue unit, and learning unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect real-time information on the usage status of meeting rooms using the camera 42 and sensors of the smart glasses 214. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to understand usage trends and peak times. The proposal unit makes optimal reservation suggestions using the specific processing unit 290 of the data processing unit 12 and presents them to the user through the display of the smart glasses 214. The dialogue unit interacts with the user using the microphone 238 of the smart glasses 214 and collects reservation preference information. The learning unit learns the reservations proposed by the specific processing unit 290 of the data processing unit 12 and always makes optimal suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, dialogue unit, and learning unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect real-time information on the usage status of meeting rooms using the camera 42 and sensors of the headset terminal 314. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to understand usage trends and peak times. The proposal unit makes optimal reservation suggestions using the specific processing unit 290 of the data processing unit 12 and presents them to the user through the display 343 of the headset terminal 314. The dialogue unit interacts with the user using the microphone 238 of the headset terminal 314 and collects reservation preference information. The learning unit learns the reservations proposed by the specific processing unit 290 of the data processing unit 12 and always makes optimal suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, dialogue unit, and learning unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit can collect real-time data on the usage status of meeting rooms using the camera 42 and sensors of the robot 414. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to understand usage trends and peak times. The proposal unit makes optimal reservation suggestions using the specific processing unit 290 of the data processing unit 12 and presents them to the user through the display of the robot 414. The dialogue unit interacts with the user using the microphone 238 of the robot 414 and collects reservation preference information. The learning unit learns the reservations proposed by the specific processing unit 290 of the data processing unit 12 and always makes optimal suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection department that collects the usage history of meeting rooms, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit makes the optimal reservation proposal, A dialogue unit that collects reservation request information through interaction with users, The system includes a learning unit that learns the reservations proposed by the proposal unit and always makes the optimal proposal. A system characterized by the following features. (Note 2) The aforementioned proposal section is, It includes an adjustment unit to handle changes in reservation priority conditions or the addition of new meeting rooms. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose using the available meeting rooms. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned dialogue unit, It includes a feedback section for collecting reservation feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect user input and past meeting room usage records. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is Recognize patterns in the collected data and understand usage trends and peak times in each department. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of meeting room usage history collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze users' past meeting room usage history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting meeting room usage history, filter it based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates 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 11) The aforementioned collection unit is When collecting meeting room usage history, the system prioritizes collecting highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting meeting room usage history, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the meeting room usage history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of the meeting room. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, the priority of the analysis will be determined based on when the meeting room usage history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of the meeting rooms. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the meeting room. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the meeting room. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, prioritize it based on the availability of meeting rooms. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the meeting rooms. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way it interacts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dialogue unit, During a conversation, the system selects the optimal conversation method by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dialogue unit, During conversations, the dialogue content is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned dialogue unit, During interaction, the system selects the optimal interaction method based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and suggests conversation topics. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned learning unit, During the learning process, patterns in meeting room usage history are recognized to improve the accuracy of the learning. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned learning unit, During training, the training data is weighted based on when the meeting room usage history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned learning unit, During learning, refer to relevant literature in the conference room to improve the accuracy of your learning. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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 collection department that collects the usage history of meeting rooms, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit makes the optimal reservation proposal, A dialogue unit that collects reservation request information through interaction with users, The system includes a learning unit that learns the reservations proposed by the aforementioned proposal unit and always makes the optimal proposal. A system characterized by the following features.

2. The aforementioned proposal section is, It includes an adjustment unit to handle changes in reservation priority conditions or the addition of new meeting rooms. The system according to feature 1.

3. The aforementioned proposal section is, We propose using the available meeting rooms. The system according to feature 1.

4. The aforementioned dialogue unit, It includes a feedback section for collecting reservation feedback. The system according to feature 1.

5. The aforementioned collection unit is Collect user input and past meeting room usage records. The system according to feature 1.

6. The aforementioned analysis unit is Recognize patterns in the collected data and understand usage trends and peak times in each department. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze users' past meeting room usage history to select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting meeting room usage history, filter it based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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