system

Abstract

Provide a system. 【Solution means】 Means for acquiring and aggregating reservation information in real time, Means for presenting the availability status of the space to the user based on the reservation information, Means for detecting a vacant reservation, confirming it with the reserving person, and automatically canceling the reservation if necessary, Means for analyzing the usage information of the space and recommending the optimal space, Means for analyzing the usage information and identifying the time periods with low space utilization rates and the causes of vacant reservations, Means for supporting the user to easily make reservations and cancellations for public facilities using an information terminal, Means for monitoring the real-time usage status and automatically canceling vacant reservations, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 a modern office environment, the reservation management of meeting rooms has become complicated, and problems such as the occurrence of empty reservations and the inability to use them when actually needed have occurred. In addition, since it takes a very large amount of man-hours and time to manually collect and analyze the reservation data of meeting rooms, the efficient operation of meeting rooms is hindered. The purpose of this invention is to realize the efficient operation of meeting rooms and eliminate meeting room refugees.

Means for Solving the Problems

[0005] By building a system that acquires and aggregates reservation data in real time, the availability of meeting rooms can be immediately displayed to users. Furthermore, by automating the detection of empty reservations and contacting the reservation holders, cancellations can be processed quickly as needed. The system also improves meeting room utilization efficiency by analyzing meeting room usage data and providing a means to recommend the most suitable meeting room. Further analysis of usage data allows for the identification of low-utilization time slots and the causes of empty reservations, enabling the optimization of meeting room operations. This simplifies meeting room management and creates a stress-free work environment.

[0006] "Reservation data" refers to information indicating the planned use of a meeting room, and includes information such as the date and time of the reservation, participants, and purpose.

[0007] An "empty reservation" refers to a reservation that is not actually used but remains in a reserved state, resulting in wasted resources.

[0008] "User" refers to an individual or group that reserves or uses a meeting room through the meeting room reservation system.

[0009] A "server" is a device that runs a meeting room reservation system and is part of a computer network used to collect, process, and store usage data.

[0010] "Usage data" refers to information regarding the usage status of meeting rooms, including past reservation history and usage frequency.

[0011] "Layout" refers to the design of the physical arrangement and design of a meeting room, optimized to meet the needs of the users.

[0012] "Real-time" refers to information being processed with virtually no delay and delivered simultaneously with real-world time.

[0013] "Automatic cancellation" refers to the process by which the system cancels a reservation without waiting for confirmation from the reservation holder or requiring any manual action.

[0014] "Recommendation" refers to the act of suggesting the most suitable meeting rooms and layouts based on usage data and user preferences.

[0015] "Analysis" refers to the process of evaluating usage patterns and problems based on data, and using that information to improve operations. [Brief explanation of the drawing]

[0016] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] 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.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 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.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] The 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.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a meeting room management system using an autonomous AI agent. It collects and analyzes reservation data in real time, detects and automatically cancels vacant reservations, and recommends the most suitable meeting room, thereby achieving efficient meeting room operation. The program's processing is described below in natural language.

[0038] The server retrieves various reservation data from the meeting room reservation system in real time. This data includes detailed information such as the date and time of the reservation, the number of participants, and the purpose of use. The server periodically checks this data and updates the integrated database to always have a grasp of the latest reservation status for the entire system. This makes it possible to provide accurate availability information to users' terminals.

[0039] The terminal provides an interface for users to access and displays meeting room availability in a visually easy-to-understand dashboard format. Users can browse this information, check availability, and select and reserve a meeting room that suits their needs.

[0040] The server simultaneously monitors reservation patterns and implements a process to detect potentially invalid reservations. For example, if no one enters or leaves a meeting room within a set time, the server automatically determines that the reservation is invalid and immediately sends a confirmation email to the person who made the reservation. If there is no response within a certain time, the reservation is automatically canceled, making it available to other users.

[0041] Based on a detailed analysis of usage data, the server provides information to optimize meeting rooms. If utilization is low during a specific time period, it analyzes the cause and proposes optimal improvement measures. This includes optimization through appropriate promotions and automated booking adjustments, for example. As a result, it prevents the consumption of wasted resources and enables a flexible response to demand.

[0042] This system makes it easy for users to select and book meeting rooms, reducing cumbersome manual operations. Furthermore, the entire system offers automated operation, resulting in stress-free and efficient meeting room management.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server retrieves reservation data from each meeting room reservation system at regular intervals. This data includes detailed information such as the reservation date and time, purpose of use, and number of participants for each meeting room. The server stores this data in an integrated database to create an overall reservation status.

[0046] Step 2:

[0047] The server analyzes the integrated reservation data to determine the current meeting room usage. This includes listing reserved meeting rooms and identifying available rooms. The analysis results are prepared as a dataset for the dashboard.

[0048] Step 3:

[0049] The terminal displays a dashboard through a user interface, allowing users to check the availability and reservation status of meeting rooms in real time. Based on this information, users can select and reserve meeting rooms.

[0050] Step 4:

[0051] Users reserve meeting rooms via their terminals. The information entered during the reservation process (date and time, number of people, purpose, etc.) is sent from the terminal to the server and registered in the server's integrated database.

[0052] Step 5:

[0053] The server monitors reservation data at regular intervals to check for the possibility of a vacant reservation. If a reservation is determined to be vacant, it automatically sends a confirmation email to the person who made the reservation, and if no reply is received, the reservation is automatically canceled.

[0054] Step 6:

[0055] The server analyzes usage patterns based on reservation data to identify time slots with low utilization rates and the causes of empty reservations. This allows it to suggest ways to optimize meeting rooms and improve the overall efficiency of the system.

[0056] Through this series of processes, efficient management and utilization of meeting rooms can be achieved, providing users with a more comfortable environment.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] Currently, many locations face the problem of cumbersome and inefficient management of space reservations for meetings and events. In particular, empty or underutilized time slots can occur, leading to wasted resources and reduced user convenience. To solve this problem, a system for automated reservation management and optimization is necessary.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for acquiring and integrating information in real time, means for presenting the availability status of the area to the user based on the information, and means for detecting empty appointments, confirming with the person who made the appointment, and automatically canceling the appointment if necessary. This makes it possible to efficiently manage the usage status of the area and maximize the utilization rate.

[0062] "Information" is a general term for data relating to users' activities and usage of domains.

[0063] "Area" refers to the specific physical or virtual space where reservations or use take place.

[0064] "Schedule" refers to information about the date, time, and details of when the user has planned to use the area.

[0065] "User" refers to an individual or organization that uses this system to reserve or manage areas.

[0066] A "server" refers to a computer system used to process and analyze information, and provides various services through a network.

[0067] "Integration" refers to the process of combining multiple different pieces of information and managing them as a single coherent whole.

[0068] "Presentation" means showing information in a way that is easy for users to understand.

[0069] "Detection" refers to the ability to automatically identify specific patterns or conditions.

[0070] This invention is implemented as an efficient domain management system utilizing an autonomous AI agent. A detailed explanation follows below.

[0071] The server first retrieves information in real time from the reservation system via the network. The hardware used is a high-performance computing system, and the software utilizes middleware specialized for data processing and big data analysis tools. Specifically, it aggregates detailed information such as the date, time, number of people, and purpose of each reservation and integrates it into a database.

[0072] The terminal functions as an interface for user access. A visually intuitive dashboard displays the availability of the area, allowing users to plan accordingly. The application used utilizes Vue.js or React for the frontend, enabling real-time updates.

[0073] The server further analyzes information patterns using an AI model to detect empty appointments. If there is any doubt, it sends a confirmation email to the person who made the reservation. If there is no response within a certain time, the system automatically cancels the reservation and makes the area available to another user.

[0074] Through this system, users can efficiently select and reserve areas. Specifically, they can check availability and receive recommendations for the most suitable area from AI. This eliminates unnecessary procedures and simplifies the operation.

[0075] As a concrete example, in the case of managing meeting rooms in a company, the system analyzes reservation data and detects that usage is low during the afternoon hours. As a result, the AI ​​can suggest specific promotions to encourage usage.

[0076] An example of a prompt sentence to input into the generating AI model is, "Explain the optimal reservation cancellation process in a meeting room management system using natural language." Using this prompt sentence, the AI ​​is designed to output the appropriate processing method in natural language.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server retrieves reservation information in real time via the network. Specifically, it obtains information such as reservation date and time, number of participants, and purpose from an API and uses this as input data. This data is then stored and organized in an integrated database to provide up-to-date information. The output is a structured reservation dataset.

[0080] Step 2:

[0081] The terminal provides a dashboard for users to check their reservation status. When a user accesses the terminal, availability data sent from the server is entered, and a list of available spaces is visually displayed on the dashboard. As output, the user can see the available spaces along with the date and time, and receives an interface for selection. Specifically, the UI is dynamically updated, and the user can proceed with the reservation process with clicks and taps.

[0082] Step 3:

[0083] The server continuously monitors booking data and uses an AI model to detect empty appointments. Past booking patterns and real-time data are provided as input, which the AI ​​analyzes to identify bookings that are likely unused. The output is a list of suspicious empty appointments. Specifically, the model flags bookings that exceed a certain threshold and are deemed highly unlikely to be actually used.

[0084] Step 4:

[0085] The server automatically sends a confirmation email to the booker if it suspects an appointment is vacant. The input includes the detected vacant appointment information and the booker's contact details. Based on this, a confirmation email is generated. The output is a customized message containing specific links encouraging the user to respond promptly. Specifically, if a response is not received within a certain time, the appointment is automatically canceled.

[0086] Step 5:

[0087] The server analyzes domain usage data and generates optimization information. Inputs include user reservation history, usage rates, and past usage trends. Based on this, data analysis tools are used to forecast demand and propose promotions. The output consists of specific measures and suggestions to improve usage efficiency, including strategies to maximize usage rates. Specific actions include, for example, the automated sending of promotional emails targeting specific time slots.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In the reservation management of spaces such as public facilities and meeting rooms, empty reservations sometimes occur, or spaces are not utilized efficiently. As a result, users have difficulty finding a suitable space, and the efficiency of the facility's operation itself can decrease. This invention aims to eliminate such inefficiencies in reservation management and enable users to reserve and utilize spaces smoothly.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for acquiring and aggregating reservation information in real time, means for presenting the availability of space to users based on the reservation information, and means for detecting vacant reservations, confirming with the reservation holder, and automatically canceling the reservation if necessary. As a result, users can easily find the space they need, and efficient use of space becomes possible.

[0093] "Reservation information" refers to detailed data that users register in advance for the use of a space or facility.

[0094] "Space" refers to a place used for a specific purpose, such as a conference room or a public facility.

[0095] "Usage information" refers to data related to the usage history and purpose of use of a space or facility.

[0096] "User" refers to an individual or group that intends to use a space or facility.

[0097] A "fake reservation" refers to a reservation made for a space or facility that is not actually going to be used.

[0098] "Equipment arrangement" refers to the placement and method of arranging furniture and equipment within a space.

[0099] An "information terminal" is an electronic device used for processing information, such as a smartphone or tablet.

[0100] A "server" refers to a computer system that processes and manages data and provides information.

[0101] To implement this invention, a reservation management system is central. First, the server uses cloud-based technologies such as AWS® Lambda, DynamoDB, and EC2 to acquire and aggregate reservation information in real time. This ensures that the latest reservation status is always stored and updated in the database.

[0102] The application, developed using React Native, is installed on the user's smartphone or tablet. This app allows users to visually check the availability of spaces. Furthermore, the application utilizes the Google® Maps API to provide a visually intuitive user interface.

[0103] The server uses an AI agent to automatically detect vacant reservations and send a confirmation notification to the reservation holder. If there is no response, the reservation is automatically canceled, and the space is made available to another user. This prevents the consumption of unnecessary resources.

[0104] Furthermore, the server analyzes usage data to identify unused time slots and the reasons for empty reservations. This utilizes a generative AI model to suggest optimal equipment placement based on usage patterns. This system allows users to reserve and utilize space efficiently and flexibly.

[0105] For example, if a citizen wants to use a city-owned public facility for a study group, they can use the app to instantly check availability and make a reservation. Even after the reservation is complete, the AI ​​automatically monitors for available slots, allowing for last-minute cancellations.

[0106] An example of a prompt might be, "Analyze the reservation status of public facilities in real time and use AI to propose optimization measures to ensure the most efficient and waste-free use." Through these specific prompts, the AI ​​can make more effective suggestions.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The server uses AWS Lambda to retrieve reservation information in real time and store it in DynamoDB. The input is reservation data from an external reservation system, and the output is the latest reservation information stored in DynamoDB. Specifically, it receives data such as the date and time of the reservation, the number of participants, and the purpose of use via an API, and stores this data in DynamoDB.

[0110] Step 2:

[0111] The device sends a reservation information request to the server based on the user's request. The input is the availability check request sent from the user's device, and the output is the availability of the space displayed on the device's screen. Specifically, an app developed with React Native uses the Google Maps API to visually display the available spaces for reservation.

[0112] Step 3:

[0113] The server uses an AI agent to analyze reservation data and detect empty reservations. The input is reservation information stored in DynamoDB, and the output is a list of reservations that have been determined to be empty. Specifically, it picks out reservations that have not seen any human activity within a certain period of time and marks them as empty reservations.

[0114] Step 4:

[0115] The server sends an email to users who have made reservations that are deemed invalid, and automatically cancels the reservation if no response is received. The input is a list of invalid reservations and user information, and the output is information on cancelled reservations. Specifically, an email is sent to the user, and if no reply is received within the set time, the reservation is deleted.

[0116] Step 5:

[0117] The server analyzes usage data and uses a generated AI model to propose optimal equipment placement and utilization promotion measures. The input is historical usage data, and the output is optimized suggestions. Specifically, data analysis identifies periods with low utilization rates, and simulations of efficient equipment placement are performed as improvement measures.

[0118] Step 6:

[0119] Users can check availability from their smartphones or tablets and make reservations as needed. The input is the user's reservation request, and the output is a reservation completion notification. Specifically, the user checks availability on the app and completes the reservation by pressing the reservation button for the space they want to use.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention combines an emotion engine with a meeting room reservation system to achieve flexible and effective meeting room management that responds to the user's emotional state. The program processing is as follows:

[0122] The server first acquires reservation data for each meeting room in real time and updates the integrated database. This allows for constant monitoring of reservation status and forms the foundation for efficient meeting room operation. Simultaneously, it analyzes usage data to calculate the optimal meeting room and schedule.

[0123] The terminal provides an interface for interaction between the user and the emotion engine. When a user makes a meeting room reservation, the terminal analyzes the user's facial expressions and tone of voice through the camera and microphone and sends the data to the emotion engine. Based on the received data, the emotion engine determines the user's emotional state and, taking that into consideration, suggests the most suitable meeting room and layout to the user.

[0124] Users select a suitable meeting room from the suggested options and complete the reservation. During this process, the meeting room's environment and atmosphere can be adjusted to suit the user's preferences and current mood. For example, a user experiencing stress might be recommended a meeting room with a relaxing environment.

[0125] The server continuously monitors booking status and usage patterns, and also performs processes to detect and automatically cancel vacant bookings. Furthermore, it utilizes data collected by the emotion engine to gain insights for optimizing meeting room operations.

[0126] Thus, meeting room management that takes user emotions into consideration goes beyond mere physical resource management, enabling the provision of services that meet users' psychological needs. This makes it possible to increase user satisfaction and provide a richer office environment. For example, for users who are feeling anxious, a meeting room with calming colors and acoustic design could be suggested to help them relax during the meeting.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The server retrieves reservation data for all meeting rooms in real time from each reservation system and stores it in an integrated database. This data includes detailed information on reserved meeting rooms, availability, and utilization rates.

[0130] Step 2:

[0131] The terminal displays the availability of meeting rooms in a dashboard format through the user interface. In addition, the terminal uses a camera and microphone to capture the user's facial expressions and voice, collecting emotional data in real time.

[0132] Step 3:

[0133] The device sends the acquired emotional data to the emotion engine, which analyzes the user's emotional state. Based on this data, the emotion engine identifies the user's current emotional state (e.g., stress, relaxation, excitement, etc.).

[0134] Step 4:

[0135] The server considers the emotional state obtained from the emotion engine to suggest the most suitable meeting room and environment settings. For example, if the user is feeling stressed, it will automatically recommend a meeting room with calming colors and soft lighting.

[0136] Step 5:

[0137] Users select the most suitable meeting room from the suggested options and make a reservation. This ensures that meeting room usage aligns with the user's emotional state.

[0138] Step 6:

[0139] The server monitors the reservation status, and if an empty reservation is detected, it automatically sends a confirmation email to the person who made the reservation. If a reply is not received within a certain time, the reservation is canceled. It also analyzes the collected data and generates suggestions for improving the operation of the meeting rooms.

[0140] This series of processes improves the efficiency of meeting room utilization and makes it possible to provide users with a comfortable and user-friendly office environment.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0143] In today's business environment, it is necessary to provide not only physical meeting rooms to users with diverse needs and emotional states, but also to create an optimal meeting environment tailored to the user's psychological state. However, conventional meeting room reservation systems do not take into account the user's emotional state when making suggestions or environmental adjustments, and therefore fail to sufficiently increase user satisfaction and productivity.

[0144] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0145] In this invention, the server includes means for acquiring and aggregating reservation data in real time, means for analyzing usage data of facilities and recommending the most suitable facilities, and means for analyzing the emotional state of users and proposing the most suitable facilities and layouts based on that analysis. This enables the provision of an optimal meeting room environment according to the emotional state of users, thereby improving user satisfaction and productivity.

[0146] "Reservation data" refers to information related to reservations made at the facility, including details of the person making the reservation, reservation time, and purpose of use.

[0147] "Facilities used" refers to places such as meeting rooms and multipurpose spaces that are used for a designated purpose.

[0148] "User" refers to an individual or group that reserves and uses a facility.

[0149] An "empty reservation" refers to a reservation that is not actually used and should be invalidated.

[0150] "Usage data" refers to data related to past and present use of facilities, including information that indicates user behavior and usage trends.

[0151] "Emotional state" refers to the user's psychological or emotional state, including their level of stress and relaxation.

[0152] "Environmental adjustment" refers to the process of modifying the physical or psychological environment within a facility to suit the user's emotional state and purpose of use.

[0153] One embodiment of this invention is to provide a reservation system for a facility with a management function that takes emotional states into consideration. Specific embodiments are described below.

[0154] The server acquires reservation data from multiple facilities in real time via the network and collects it in a central, integrated database. This data includes details about the reservation holder, reservation time, and purpose of use. The reservation data is efficiently managed using a database management system (e.g., MySQL®). Furthermore, the server uses data analysis tools and generative AI models (e.g., Python's scikit-learn library and TENSORFLOW®) to gain insights from past usage data and make recommendations to provide users with the most suitable facilities.

[0155] The terminal provides an interface for users to reserve facilities. Equipped with a camera and microphone, the terminal analyzes the user's emotional state by capturing their facial expressions and voice tone. This analysis uses emotion recognition software (e.g., IBM Watson®'s emotion analysis API), and the acquired emotional information is sent to a generative AI model. Based on the emotional state, the terminal has the function of suggesting the most suitable facilities and layouts for the user.

[0156] The user selects a facility from the suggested options and completes the reservation. The selected facility's environment is adjusted to suit the user's emotional state. For example, a user feeling stressed might be offered a facility with plenty of natural light to promote relaxation. Another example of a prompt might be, "Please suggest the optimal meeting room layout for a user who is feeling stressed." Through such prompts, the generative AI model can suggest the optimal environment adapted to the user's emotions.

[0157] This system design enables sophisticated facility management that meets the psychological needs of users, thereby increasing user satisfaction and efficiency.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The server periodically receives reservation data from each facility via the internet. The input is reservation data obtained from each facility's reservation system. When the server receives this data and saves it to the integrated database, it uses a database management system to perform data processing such as removing duplicate data and checking for consistency. The output is a centrally managed database with the latest reservation status recorded.

[0161] Step 2:

[0162] The server analyzes usage trends from accumulated reservation data. The input is historical reservation data accumulated in the past. The server uses machine learning algorithms to perform data calculations to identify frequently occurring usage times and popular facilities. The output is the result of the usage trend analysis, which is used as insights to make optimal recommendations.

[0163] Step 3:

[0164] The terminal captures the user's facial expressions and voice via camera and microphone when the user accesses the facility's reservation interface. The input consists of the user's facial image and audio data. The terminal uses emotion recognition software to analyze the data and process it to determine the user's emotional state. The output is an indicator of the user's emotional state.

[0165] Step 4:

[0166] The device proposes the most suitable facilities and layouts based on an analysis of the user's emotional state. The input consists of the user's emotional state and the results of an analysis of past usage trends. The device combines this data, uses a generative AI model to create prompts, and performs data calculations to propose the most appropriate options. The output is a list of facilities presented to the user.

[0167] Step 5:

[0168] The user selects the facility they wish to use from the presented options and completes the reservation. The input is the suggestions presented by the terminal. Once the user makes a selection, the reservation information is confirmed and sent to the server. The output is the confirmed reservation information, and the facility's environment may be adjusted based on this.

[0169] (Application Example 2)

[0170] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0171] In modern urban environments, meeting room booking and management are crucial for operational efficiency and optimal resource utilization. However, conventional booking systems often fail to consider user emotional states when making suggestions or optimizations, resulting in limited meeting efficiency and user experience. This can lead to unsatisfactory meeting environments, especially under stressful circumstances. A new system is needed to address these issues.

[0172] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0173] In this invention, the server includes means for acquiring and aggregating reservation data in real time; means for presenting the availability of meeting rooms to users based on the reservation data; means for detecting vacant reservations, confirming with the reservation holder, and automatically canceling the reservation if necessary; means for analyzing meeting room usage data and recommending the most suitable meeting room; means for analyzing usage data and identifying times of low meeting room utilization or causes of vacant reservations; and means for analyzing the emotional state of users and proposing the most suitable meeting space according to their emotions. This enables flexible and effective management of meeting rooms in accordance with the emotional state of users.

[0174] "Reservation data" refers to information related to meeting room reservations, including details such as the date and time of the reservation, the reservation holder's information, and the purpose of use.

[0175] "Acquiring and aggregating in real time" refers to the process by which the meeting room reservation system constantly receives, organizes, and retains the latest reservation information.

[0176] "Availability" refers to the status of a meeting room's availability within a specific time frame, providing information for users to determine whether or not they can use that meeting room.

[0177] A "fake reservation" refers to a reservation that is not used or is not actually intended to be used.

[0178] An "optimal meeting room" refers to a meeting room that best matches the needs and circumstances of the users, and is a space suitable for conducting efficient and effective meetings.

[0179] "User's emotional state" refers to the current psychological state and emotions of the user making a meeting room reservation, and is determined through analysis of facial expressions and voice.

[0180] "Proposing the optimal meeting space" is a process that takes into account the user's emotional state and other factors to suggest the most suitable meeting room and environment for the user.

[0181] The system for carrying out this invention is based on a network including servers, terminals, and an emotion engine.

[0182] The server acquires and aggregates reservation data in real time and uses this data to present meeting room availability to users. If an empty reservation is detected, it contacts the reservation holder for confirmation and automatically cancels the reservation if necessary. It also analyzes meeting room usage data to recommend the most suitable meeting room and identifies the causes of low utilization times and empty reservations. This entire process achieves high reliability and flexibility by utilizing cloud services. In actual operation, Amazon Web Services (AWS) DynamoDB and Google Cloud Platform (GCP) are sometimes used.

[0183] The device provides an interface with the user. This can take the form of a smartphone, smart glasses, or head-mounted display. The device uses a camera and microphone to record the user's facial expressions and voice, and sends the data to an emotion engine. The emotion engine performs emotion analysis using software such as OpenCV or DeepFace, and based on the results, suggests the most suitable meeting space to the user. This suggestion is made in real time, and the user can enjoy flexible choices that meet their psychological needs, such as being suggested a meeting room with a relaxing environment if they are feeling stressed.

[0184] As a concrete example, when a user uses the system to book a meeting, advanced information processing can be achieved by inputting a prompt message to the AI ​​model such as, "Please suggest the best meeting room option based on the user's current emotional state. For example, if the user needs to relax, what kind of environment would you suggest?"

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The server retrieves and aggregates meeting room reservation data in real time. The input data consists of reservation requests from users, and the server updates the reservation information in the database. This process ensures that the reservation status is kept up-to-date. The server collects reservations for each meeting room and processes the data into a format that users can access.

[0188] Step 2:

[0189] The device communicates with the user through its interface. Input consists of the user's facial expressions and voice, which are captured by the camera and microphone. The device converts this raw data into a format necessary for transmission to the emotion engine. When a user attempts to schedule a meeting, their emotional state is assessed through their facial expressions and tone of voice.

[0190] Step 3:

[0191] The emotion engine analyzes the user's emotional state based on facial expression and voice data received from the device. The input data consists of video and audio, and emotion recognition software performs data calculations to identify the emotional state. The analysis results output the user's current emotional state (e.g., relaxed, tense, stressed). OpenCV and DeepFace are used for this analysis.

[0192] Step 4:

[0193] The server receives the analysis results from the emotion engine and calculates the optimal meeting room and environment based on them. The input is data such as the user's emotional state and the availability of meeting rooms. The server uses this to run an optimization algorithm and outputs a list of optimal meeting rooms that match the user's emotional state. In this process, users who need to relax are provided with a calm environment.

[0194] Step 5:

[0195] The user receives a list of meeting room suggestions from the server and selects their preferred meeting room. The input is a list of suggestions from the server, from which the user makes a selection. Once the selection is complete, the information is sent to the server, and the reservation is confirmed. The output is the confirmed reservation information.

[0196] Step 6:

[0197] The server continuously monitors reservation status, detecting empty reservations and automatically canceling them as needed. The input is continuously acquired reservation data, which is then analyzed to produce output that removes invalid reservations. This monitoring optimizes the overall operation of the meeting rooms.

[0198] 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.

[0199] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0205] 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.

[0206] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0207] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0208] 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.

[0209] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0210] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0211] The 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.

[0212] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0213] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0214] This invention is a meeting room management system using an autonomous AI agent. It collects and analyzes reservation data in real time, detects and automatically cancels vacant reservations, and recommends the most suitable meeting room, thereby achieving efficient meeting room operation. The program's processing is described below in natural language.

[0215] The server retrieves various reservation data from the meeting room reservation system in real time. This data includes detailed information such as the date and time of the reservation, the number of participants, and the purpose of use. The server periodically checks this data and updates the integrated database to always have a grasp of the latest reservation status for the entire system. This makes it possible to provide accurate availability information to users' terminals.

[0216] The terminal provides an interface for users to access and displays meeting room availability in a visually easy-to-understand dashboard format. Users can browse this information, check availability, and select and reserve a meeting room that suits their needs.

[0217] The server simultaneously monitors reservation patterns and implements a process to detect potentially invalid reservations. For example, if no one enters or leaves a meeting room within a set time, the server automatically determines that the reservation is invalid and immediately sends a confirmation email to the person who made the reservation. If there is no response within a certain time, the reservation is automatically canceled, making it available to other users.

[0218] Based on a detailed analysis of usage data, the server provides information to optimize meeting rooms. If utilization is low during a specific time period, it analyzes the cause and proposes optimal improvement measures. This includes optimization through appropriate promotions and automated booking adjustments, for example. As a result, it prevents the consumption of wasted resources and enables a flexible response to demand.

[0219] This system makes it easy for users to select and book meeting rooms, reducing cumbersome manual operations. Furthermore, the entire system offers automated operation, resulting in stress-free and efficient meeting room management.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The server retrieves reservation data from each meeting room reservation system at regular intervals. This data includes detailed information such as the reservation date and time, purpose of use, and number of participants for each meeting room. The server stores this data in an integrated database to create an overall reservation status.

[0223] Step 2:

[0224] The server analyzes the integrated reservation data to determine the current meeting room usage. This includes listing reserved meeting rooms and identifying available rooms. The analysis results are prepared as a dataset for the dashboard.

[0225] Step 3:

[0226] The terminal displays a dashboard through a user interface, allowing users to check the availability and reservation status of meeting rooms in real time. Based on this information, users can select and reserve meeting rooms.

[0227] Step 4:

[0228] Users reserve meeting rooms via their terminals. The information entered during the reservation process (date and time, number of people, purpose, etc.) is sent from the terminal to the server and registered in the server's integrated database.

[0229] Step 5:

[0230] The server monitors reservation data at regular intervals to check for the possibility of a vacant reservation. If a reservation is determined to be vacant, it automatically sends a confirmation email to the person who made the reservation, and if no reply is received, the reservation is automatically canceled.

[0231] Step 6:

[0232] The server analyzes usage patterns based on reservation data to identify time slots with low utilization rates and the causes of empty reservations. This allows it to suggest ways to optimize meeting rooms and improve the overall efficiency of the system.

[0233] Through this series of processes, efficient management and utilization of meeting rooms can be achieved, providing users with a more comfortable environment.

[0234] (Example 1)

[0235] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0236] Currently, many locations face the problem of cumbersome and inefficient management of space reservations for meetings and events. In particular, empty or underutilized time slots can occur, leading to wasted resources and reduced user convenience. To solve this problem, a system for automated reservation management and optimization is necessary.

[0237] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0238] In this invention, the server includes means for acquiring and integrating information in real time, means for presenting the availability status of the area to the user based on the information, and means for detecting empty appointments, confirming with the person who made the appointment, and automatically canceling the appointment if necessary. This makes it possible to efficiently manage the usage status of the area and maximize the utilization rate.

[0239] "Information" is a general term for data relating to users' activities and usage of domains.

[0240] "Area" refers to the specific physical or virtual space where reservations or use take place.

[0241] "Schedule" refers to information about the date, time, and details of when the user has planned to use the area.

[0242] "User" refers to an individual or organization that uses this system to reserve or manage areas.

[0243] A "server" refers to a computer system used to process and analyze information, and provides various services through a network.

[0244] "Integration" refers to the process of combining multiple different pieces of information and managing them as a single coherent whole.

[0245] "Presentation" means showing information in a way that is easy for users to understand.

[0246] "Detection" refers to the ability to automatically identify specific patterns or conditions.

[0247] This invention is implemented as an efficient domain management system utilizing an autonomous AI agent. A detailed explanation follows below.

[0248] The server first retrieves information in real time from the reservation system via the network. The hardware used is a high-performance computing system, and the software utilizes middleware specialized for data processing and big data analysis tools. Specifically, it aggregates detailed information such as the date, time, number of people, and purpose of each reservation and integrates it into a database.

[0249] The terminal functions as an interface for user access. A visually intuitive dashboard displays the availability of the area, allowing users to plan accordingly. The application used utilizes Vue.js or React for the frontend, enabling real-time updates.

[0250] The server further analyzes information patterns using an AI model to detect empty appointments. If there is any doubt, it sends a confirmation email to the person who made the reservation. If there is no response within a certain time, the system automatically cancels the reservation and makes the area available to another user.

[0251] Through this system, users can efficiently select and reserve areas. Specifically, they can check availability and receive recommendations for the most suitable area from AI. This eliminates unnecessary procedures and simplifies the operation.

[0252] As a concrete example, in the case of managing meeting rooms in a company, the system analyzes reservation data and detects that usage is low during the afternoon hours. As a result, the AI ​​can suggest specific promotions to encourage usage.

[0253] An example of a prompt sentence to input into the generating AI model is, "Explain the optimal reservation cancellation process in a meeting room management system using natural language." Using this prompt sentence, the AI ​​is designed to output the appropriate processing method in natural language.

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] The server retrieves reservation information in real time via the network. Specifically, it obtains information such as reservation date and time, number of participants, and purpose from an API and uses this as input data. This data is then stored and organized in an integrated database to provide up-to-date information. The output is a structured reservation dataset.

[0257] Step 2:

[0258] The terminal provides a dashboard for users to check their reservation status. When a user accesses the terminal, availability data sent from the server is entered, and a list of available spaces is visually displayed on the dashboard. As output, the user can see the available spaces along with the date and time, and receives an interface for selection. Specifically, the UI is dynamically updated, and the user can proceed with the reservation process with clicks and taps.

[0259] Step 3:

[0260] The server continuously monitors booking data and uses an AI model to detect empty appointments. Past booking patterns and real-time data are provided as input, which the AI ​​analyzes to identify bookings that are likely unused. The output is a list of suspicious empty appointments. Specifically, the model flags bookings that exceed a certain threshold and are deemed highly unlikely to be actually used.

[0261] Step 4:

[0262] The server automatically sends a confirmation email to the booker if it suspects an appointment is vacant. The input includes the detected vacant appointment information and the booker's contact details. Based on this, a confirmation email is generated. The output is a customized message containing specific links encouraging the user to respond promptly. Specifically, if a response is not received within a certain time, the appointment is automatically canceled.

[0263] Step 5:

[0264] The server analyzes domain usage data and generates optimization information. Inputs include user reservation history, usage rates, and past usage trends. Based on this, data analysis tools are used to forecast demand and propose promotions. The output consists of specific measures and suggestions to improve usage efficiency, including strategies to maximize usage rates. Specific actions include, for example, the automated sending of promotional emails targeting specific time slots.

[0265] (Application Example 1)

[0266] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0267] In the reservation management of spaces such as public facilities and meeting rooms, empty reservations sometimes occur, or spaces are not utilized efficiently. As a result, users have difficulty finding a suitable space, and the efficiency of the facility's operation itself can decrease. This invention aims to eliminate such inefficiencies in reservation management and enable users to reserve and utilize spaces smoothly.

[0268] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0269] In this invention, the server includes means for acquiring and aggregating reservation information in real time, means for presenting the availability of space to users based on the reservation information, and means for detecting vacant reservations, confirming with the reservation holder, and automatically canceling the reservation if necessary. As a result, users can easily find the space they need, and efficient use of space becomes possible.

[0270] "Reservation information" refers to detailed data that users register in advance for the use of a space or facility.

[0271] "Space" refers to a place used for a specific purpose, such as a conference room or a public facility.

[0272] "Usage information" refers to data related to the usage history and purpose of use of a space or facility.

[0273] "User" refers to an individual or group that intends to use a space or facility.

[0274] A "fake reservation" refers to a reservation made for a space or facility that is not actually going to be used.

[0275] "Equipment arrangement" refers to the placement and method of arranging furniture and equipment within a space.

[0276] An "information terminal" is an electronic device used for processing information, such as a smartphone or tablet.

[0277] A "server" refers to a computer system that processes and manages data and provides information.

[0278] To implement this invention, a reservation management system is central. First, the server uses cloud-based technologies such as AWS Lambda, DynamoDB, and EC2 to retrieve and aggregate reservation information in real time. This ensures that the latest reservation status is always stored and updated in the database.

[0279] The smartphones and tablets used as devices have applications developed with React Native installed. Users can use this app to visually check the availability of spaces. Furthermore, this application uses the Google Maps API to provide a visually intuitive user interface.

[0280] The server uses an AI agent to automatically detect vacant reservations and send a confirmation notification to the reservation holder. If there is no response, the reservation is automatically canceled, and the space is made available to another user. This prevents the consumption of unnecessary resources.

[0281] Furthermore, the server analyzes usage data to identify unused time slots and the reasons for empty reservations. This utilizes a generative AI model to suggest optimal equipment placement based on usage patterns. This system allows users to reserve and utilize space efficiently and flexibly.

[0282] As a specific example, when a citizen wants to use the city's public facilities for a study session, they can use an app to instantly check the availability status and proceed with the reservation. Even after the reservation is completed, the AI automatically monitors for empty reservations, so it can handle last-minute cancellations and the like.

[0283] As an example of a prompt sentence, an instruction such as "Analyze the reservation status of public facilities in real time and propose optimization measures using AI so that they can be used most efficiently without waste." can be considered. Through this specific prompt, the AI makes more effective proposals.

[0284] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The server uses AWS Lambda to obtain reservation information in real time and stores it in DynamoDB. At this time, the input is reservation data from an external reservation system, and the output is the latest reservation information stored in DynamoDB. Specifically, data such as the reservation date and time, the number of participants, and the purpose of use are received through an API and stored in DynamoDB.

[0287] Step 2:

[0288] Based on a request from the user, the terminal sends a request for reservation information to the server. The input is a request for checking the availability status sent from the user's terminal, and the output is the availability status of the space displayed on the terminal screen. Specifically, an app developed in React Native uses the Google Maps API to visually display reservable spaces.

[0289] Step 3:

[0290] The server uses an AI agent to analyze reservation data and detect empty reservations. The input is reservation information stored in DynamoDB, and the output is a list of reservations that have been determined to be empty. Specifically, it picks out reservations that have not seen any human activity within a certain period of time and marks them as empty reservations.

[0291] Step 4:

[0292] The server sends an email to users who have made reservations that are deemed invalid, and automatically cancels the reservation if no response is received. The input is a list of invalid reservations and user information, and the output is information on cancelled reservations. Specifically, an email is sent to the user, and if no reply is received within the set time, the reservation is deleted.

[0293] Step 5:

[0294] The server analyzes usage data and uses a generated AI model to propose optimal equipment placement and utilization promotion measures. The input is historical usage data, and the output is optimized suggestions. Specifically, data analysis identifies periods with low utilization rates, and simulations of efficient equipment placement are performed as improvement measures.

[0295] Step 6:

[0296] Users can check availability from their smartphones or tablets and make reservations as needed. The input is the user's reservation request, and the output is a reservation completion notification. Specifically, the user checks availability on the app and completes the reservation by pressing the reservation button for the space they want to use.

[0297] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0298] This invention combines an emotion engine with a meeting room reservation system to achieve flexible and effective meeting room management that responds to the user's emotional state. The program processing is as follows:

[0299] The server first acquires reservation data for each meeting room in real time and updates the integrated database. This allows for constant monitoring of reservation status and forms the foundation for efficient meeting room operation. Simultaneously, it analyzes usage data to calculate the optimal meeting room and schedule.

[0300] The terminal provides an interface for interaction between the user and the emotion engine. When a user makes a meeting room reservation, the terminal analyzes the user's facial expressions and tone of voice through the camera and microphone and sends the data to the emotion engine. Based on the received data, the emotion engine determines the user's emotional state and, taking that into consideration, suggests the most suitable meeting room and layout to the user.

[0301] Users select a suitable meeting room from the suggested options and complete the reservation. During this process, the meeting room's environment and atmosphere can be adjusted to suit the user's preferences and current mood. For example, a user experiencing stress might be recommended a meeting room with a relaxing environment.

[0302] The server continuously monitors booking status and usage patterns, and also performs processes to detect and automatically cancel vacant bookings. Furthermore, it utilizes data collected by the emotion engine to gain insights for optimizing meeting room operations.

[0303] Thus, meeting room management that takes user emotions into consideration goes beyond mere physical resource management, enabling the provision of services that meet users' psychological needs. This makes it possible to increase user satisfaction and provide a richer office environment. For example, for users who are feeling anxious, a meeting room with calming colors and acoustic design could be suggested to help them relax during the meeting.

[0304] The following is an explanation of the processing flow.

[0305] Step 1:

[0306] The server retrieves the reservation data of all meeting rooms from each reservation system in real time and stores it in the integrated database. This data includes detailed information on reserved meeting rooms, availability, and utilization rate.

[0307] Step 2:

[0308] The terminal displays the availability of meeting rooms in the form of a dashboard through the user interface. In addition, the terminal uses a camera and a microphone to capture the user's expression and voice, and collects emotion data in real time.

[0309] Step 3:

[0310] The terminal sends the acquired emotion data to the emotion engine to analyze the user's emotional state. The emotion engine identifies the current emotional state of the user (e.g., stress, relaxation, excitement, etc.) based on the data.

[0311] Step 4:

[0312] The server proposes the optimal meeting room and environmental settings considering the emotional state obtained from the emotion engine. For example, when the user is in a tense state, it automatically recommends a meeting room with a calming color tone and gentle lighting.

[0313] Step 5:

[0314] The user selects the most suitable one from the proposed meeting rooms and makes a reservation. This realizes the use of meeting rooms according to the user's emotional state.

[0315] Step 6:

[0316] The server monitors the reservation status, and if an empty reservation is detected, it automatically sends a confirmation email to the person who made the reservation. If a reply is not received within a certain time, the reservation is canceled. It also analyzes the collected data and generates suggestions for improving the operation of the meeting rooms.

[0317] This series of processes improves the efficiency of meeting room utilization and makes it possible to provide users with a comfortable and user-friendly office environment.

[0318] (Example 2)

[0319] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0320] In today's business environment, it is necessary to provide not only physical meeting rooms to users with diverse needs and emotional states, but also to create an optimal meeting environment tailored to the user's psychological state. However, conventional meeting room reservation systems do not take into account the user's emotional state when making suggestions or environmental adjustments, and therefore fail to sufficiently increase user satisfaction and productivity.

[0321] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0322] In this invention, the server includes means for acquiring and aggregating reservation data in real time, means for analyzing usage data of facilities and recommending the most suitable facilities, and means for analyzing the emotional state of users and proposing the most suitable facilities and layouts based on that analysis. This enables the provision of an optimal meeting room environment according to the emotional state of users, thereby improving user satisfaction and productivity.

[0323] "Reservation data" refers to information related to reservations made at the facility, including details of the person making the reservation, reservation time, and purpose of use.

[0324] "Facilities used" refers to places such as meeting rooms and multipurpose spaces that are used for a designated purpose.

[0325] "User" refers to an individual or group that reserves and uses a facility.

[0326] An "empty reservation" refers to a reservation that is not actually used and should be invalidated.

[0327] "Usage data" refers to data related to past and present use of facilities, including information that indicates user behavior and usage trends.

[0328] "Emotional state" refers to the user's psychological or emotional state, including their level of stress and relaxation.

[0329] "Environmental adjustment" refers to the process of modifying the physical or psychological environment within a facility to suit the user's emotional state and purpose of use.

[0330] One embodiment of this invention is to provide a reservation system for a facility with a management function that takes emotional states into consideration. Specific embodiments are described below.

[0331] The server acquires reservation data from multiple facilities in real time via the network and collects it in a central, integrated database. This data includes details about the reservation holder, reservation time, and purpose of use. The reservation data is efficiently managed using a database management system (e.g., MySQL). Furthermore, the server uses data analysis tools and generative AI models (e.g., Python's scikit-learn library and TensorFlow) to gain insights from past usage data and make recommendations to provide users with the most suitable facilities.

[0332] The terminal provides an interface for users to reserve facilities. Equipped with a camera and microphone, the terminal analyzes the user's emotional state by capturing their facial expressions and voice tone. This analysis utilizes emotion recognition software (e.g., IBM Watson's emotion analysis API), and the acquired emotional information is sent to a generative AI model. Based on the emotional state, the terminal has the function of suggesting the most suitable facilities and layouts for the user.

[0333] The user selects a facility from the suggested options and completes the reservation. The selected facility's environment is adjusted to suit the user's emotional state. For example, a user feeling stressed might be offered a facility with plenty of natural light to promote relaxation. Another example of a prompt might be, "Please suggest the optimal meeting room layout for a user who is feeling stressed." Through such prompts, the generative AI model can suggest the optimal environment adapted to the user's emotions.

[0334] This system design enables sophisticated facility management that meets the psychological needs of users, thereby increasing user satisfaction and efficiency.

[0335] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0336] Step 1:

[0337] The server periodically receives reservation data from each facility via the internet. The input is reservation data obtained from each facility's reservation system. When the server receives this data and saves it to the integrated database, it uses a database management system to perform data processing such as removing duplicate data and checking for consistency. The output is a centrally managed database with the latest reservation status recorded.

[0338] Step 2:

[0339] The server analyzes usage trends from accumulated reservation data. The input is historical reservation data accumulated in the past. The server uses machine learning algorithms to perform data calculations to identify frequently occurring usage times and popular facilities. The output is the result of the usage trend analysis, which is used as insights to make optimal recommendations.

[0340] Step 3:

[0341] The terminal captures the user's facial expressions and voice via camera and microphone when the user accesses the facility's reservation interface. The input consists of the user's facial image and audio data. The terminal uses emotion recognition software to analyze the data and process it to determine the user's emotional state. The output is an indicator of the user's emotional state.

[0342] Step 4:

[0343] The device proposes the most suitable facilities and layouts based on an analysis of the user's emotional state. The input consists of the user's emotional state and the results of an analysis of past usage trends. The device combines this data, uses a generative AI model to create prompts, and performs data calculations to propose the most appropriate options. The output is a list of facilities presented to the user.

[0344] Step 5:

[0345] The user selects the facility they wish to use from the presented options and completes the reservation. The input is the suggestions presented by the terminal. Once the user makes a selection, the reservation information is confirmed and sent to the server. The output is the confirmed reservation information, and the facility's environment may be adjusted based on this.

[0346] (Application Example 2)

[0347] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0348] In modern urban environments, meeting room booking and management are crucial for operational efficiency and optimal resource utilization. However, conventional booking systems often fail to consider user emotional states when making suggestions or optimizations, resulting in limited meeting efficiency and user experience. This can lead to unsatisfactory meeting environments, especially under stressful circumstances. A new system is needed to address these issues.

[0349] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0350] In this invention, the server includes means for acquiring and aggregating reservation data in real time; means for presenting the availability of meeting rooms to users based on the reservation data; means for detecting vacant reservations, confirming with the reservation holder, and automatically canceling the reservation if necessary; means for analyzing meeting room usage data and recommending the most suitable meeting room; means for analyzing usage data and identifying times of low meeting room utilization or causes of vacant reservations; and means for analyzing the emotional state of users and proposing the most suitable meeting space according to their emotions. This enables flexible and effective management of meeting rooms in accordance with the emotional state of users.

[0351] "Reservation data" refers to information related to meeting room reservations, including details such as the date and time of the reservation, the reservation holder's information, and the purpose of use.

[0352] "Acquiring and aggregating in real time" refers to the process by which the meeting room reservation system constantly receives, organizes, and retains the latest reservation information.

[0353] "Availability" refers to the status of a meeting room's availability within a specific time frame, providing information for users to determine whether or not they can use that meeting room.

[0354] A "fake reservation" refers to a reservation that is not used or is not actually intended to be used.

[0355] An "optimal meeting room" refers to a meeting room that best matches the needs and circumstances of the users, and is a space suitable for conducting efficient and effective meetings.

[0356] "User's emotional state" refers to the current psychological state and emotions of the user making a meeting room reservation, and is determined through analysis of facial expressions and voice.

[0357] "Proposing the optimal meeting space" is a process that takes into account the user's emotional state and other factors to suggest the most suitable meeting room and environment for the user.

[0358] The system for carrying out this invention is based on a network including servers, terminals, and an emotion engine.

[0359] The server acquires and aggregates reservation data in real time and uses this data to present meeting room availability to users. If an empty reservation is detected, it contacts the reservation holder for confirmation and automatically cancels the reservation if necessary. It also analyzes meeting room usage data to recommend the most suitable meeting room and identifies the causes of low utilization times and empty reservations. This entire process achieves high reliability and flexibility by utilizing cloud services. In actual operation, Amazon Web Services (AWS) DynamoDB and Google Cloud Platform (GCP) are sometimes used.

[0360] The device provides an interface with the user. This can take the form of a smartphone, smart glasses, or head-mounted display. The device uses a camera and microphone to record the user's facial expressions and voice, and sends the data to an emotion engine. The emotion engine performs emotion analysis using software such as OpenCV or DeepFace, and based on the results, suggests the most suitable meeting space to the user. This suggestion is made in real time, and the user can enjoy flexible choices that meet their psychological needs, such as being suggested a meeting room with a relaxing environment if they are feeling stressed.

[0361] As a concrete example, when a user uses the system to book a meeting, advanced information processing can be achieved by inputting a prompt message to the AI ​​model such as, "Please suggest the best meeting room option based on the user's current emotional state. For example, if the user needs to relax, what kind of environment would you suggest?"

[0362] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0363] Step 1:

[0364] The server retrieves and aggregates meeting room reservation data in real time. The input data consists of reservation requests from users, and the server updates the reservation information in the database. This process ensures that the reservation status is kept up-to-date. The server collects reservations for each meeting room and processes the data into a format that users can access.

[0365] Step 2:

[0366] The device communicates with the user through its interface. Input consists of the user's facial expressions and voice, which are captured by the camera and microphone. The device converts this raw data into a format necessary for transmission to the emotion engine. When a user attempts to schedule a meeting, their emotional state is assessed through their facial expressions and tone of voice.

[0367] Step 3:

[0368] The emotion engine analyzes the user's emotional state based on facial expression and voice data received from the device. The input data consists of video and audio, and emotion recognition software performs data calculations to identify the emotional state. The analysis results output the user's current emotional state (e.g., relaxed, tense, stressed). OpenCV and DeepFace are used for this analysis.

[0369] Step 4:

[0370] The server receives the analysis results from the emotion engine and calculates the optimal meeting room and environment based on them. The input is data such as the user's emotional state and the availability of meeting rooms. The server uses this to run an optimization algorithm and outputs a list of optimal meeting rooms that match the user's emotional state. In this process, users who need to relax are provided with a calm environment.

[0371] Step 5:

[0372] The user receives a list of meeting room suggestions from the server and selects their preferred meeting room. The input is a list of suggestions from the server, from which the user makes a selection. Once the selection is complete, the information is sent to the server, and the reservation is confirmed. The output is the confirmed reservation information.

[0373] Step 6:

[0374] The server continuously monitors reservation status, detecting empty reservations and automatically canceling them as needed. The input is continuously acquired reservation data, which is then analyzed to produce output that removes invalid reservations. This monitoring optimizes the overall operation of the meeting rooms.

[0375] 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.

[0376] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0377] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0378] [Third Embodiment]

[0379] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0380] 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.

[0381] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0382] 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.

[0383] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0384] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0385] 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.

[0386] 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.

[0387] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0388] The 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.

[0389] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0390] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0391] This invention is a meeting room management system using an autonomous AI agent. It collects and analyzes reservation data in real time, detects and automatically cancels vacant reservations, and recommends the most suitable meeting room, thereby achieving efficient meeting room operation. The program's processing is described below in natural language.

[0392] The server retrieves various reservation data from the meeting room reservation system in real time. This data includes detailed information such as the date and time of the reservation, the number of participants, and the purpose of use. The server periodically checks this data and updates the integrated database to always have a grasp of the latest reservation status for the entire system. This makes it possible to provide accurate availability information to users' terminals.

[0393] The terminal provides an interface for users to access and displays meeting room availability in a visually easy-to-understand dashboard format. Users can browse this information, check availability, and select and reserve a meeting room that suits their needs.

[0394] The server simultaneously monitors reservation patterns and implements a process to detect potentially invalid reservations. For example, if no one enters or leaves a meeting room within a set time, the server automatically determines that the reservation is invalid and immediately sends a confirmation email to the person who made the reservation. If there is no response within a certain time, the reservation is automatically canceled, making it available to other users.

[0395] Based on a detailed analysis of usage data, the server provides information to optimize meeting rooms. If utilization is low during a specific time period, it analyzes the cause and proposes optimal improvement measures. This includes optimization through appropriate promotions and automated booking adjustments, for example. As a result, it prevents the consumption of wasted resources and enables a flexible response to demand.

[0396] This system makes it easy for users to select and book meeting rooms, reducing cumbersome manual operations. Furthermore, the entire system offers automated operation, resulting in stress-free and efficient meeting room management.

[0397] The following describes the processing flow.

[0398] Step 1:

[0399] The server retrieves reservation data from each meeting room reservation system at regular intervals. This data includes detailed information such as the reservation date and time, purpose of use, and number of participants for each meeting room. The server stores this data in an integrated database to create an overall reservation status.

[0400] Step 2:

[0401] The server analyzes the integrated reservation data to determine the current meeting room usage. This includes listing reserved meeting rooms and identifying available rooms. The analysis results are prepared as a dataset for the dashboard.

[0402] Step 3:

[0403] The terminal displays a dashboard through a user interface, allowing users to check the availability and reservation status of meeting rooms in real time. Based on this information, users can select and reserve meeting rooms.

[0404] Step 4:

[0405] Users reserve meeting rooms via their terminals. The information entered during the reservation process (date and time, number of people, purpose, etc.) is sent from the terminal to the server and registered in the server's integrated database.

[0406] Step 5:

[0407] The server monitors reservation data at regular intervals to check for the possibility of a vacant reservation. If a reservation is determined to be vacant, it automatically sends a confirmation email to the person who made the reservation, and if no reply is received, the reservation is automatically canceled.

[0408] Step 6:

[0409] The server analyzes usage patterns based on reservation data to identify time slots with low utilization rates and the causes of empty reservations. This allows it to suggest ways to optimize meeting rooms and improve the overall efficiency of the system.

[0410] Through this series of processes, efficient management and utilization of meeting rooms can be achieved, providing users with a more comfortable environment.

[0411] (Example 1)

[0412] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0413] Currently, many locations face the problem of cumbersome and inefficient management of space reservations for meetings and events. In particular, empty or underutilized time slots can occur, leading to wasted resources and reduced user convenience. To solve this problem, a system for automated reservation management and optimization is necessary.

[0414] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0415] In this invention, the server includes means for acquiring and integrating information in real time, means for presenting the availability status of the area to the user based on the information, and means for detecting empty appointments, confirming with the person who made the appointment, and automatically canceling the appointment if necessary. This makes it possible to efficiently manage the usage status of the area and maximize the utilization rate.

[0416] "Information" is a general term for data relating to users' activities and usage of domains.

[0417] "Area" refers to the specific physical or virtual space where reservations or use take place.

[0418] "Schedule" refers to information about the date, time, and details of when the user has planned to use the area.

[0419] "User" refers to an individual or organization that uses this system to reserve or manage areas.

[0420] A "server" refers to a computer system used to process and analyze information, and provides various services through a network.

[0421] "Integration" refers to the process of combining multiple different pieces of information and managing them as a single coherent whole.

[0422] "Presentation" means showing information in a way that is easy for users to understand.

[0423] "Detection" refers to the ability to automatically identify specific patterns or conditions.

[0424] This invention is implemented as an efficient domain management system utilizing an autonomous AI agent. A detailed explanation follows below.

[0425] The server first retrieves information in real time from the reservation system via the network. The hardware used is a high-performance computing system, and the software utilizes middleware specialized for data processing and big data analysis tools. Specifically, it aggregates detailed information such as the date, time, number of people, and purpose of each reservation and integrates it into a database.

[0426] The terminal functions as an interface for user access. A visually intuitive dashboard displays the availability of the area, allowing users to plan accordingly. The application used utilizes Vue.js or React for the frontend, enabling real-time updates.

[0427] The server further analyzes information patterns using an AI model to detect empty appointments. If there is any doubt, it sends a confirmation email to the person who made the reservation. If there is no response within a certain time, the system automatically cancels the reservation and makes the area available to another user.

[0428] Through this system, users can efficiently select and reserve areas. Specifically, they can check availability and receive recommendations for the most suitable area from AI. This eliminates unnecessary procedures and simplifies the operation.

[0429] As a concrete example, in the case of managing meeting rooms in a company, the system analyzes reservation data and detects that usage is low during the afternoon hours. As a result, the AI ​​can suggest specific promotions to encourage usage.

[0430] An example of a prompt sentence to input into the generating AI model is, "Explain the optimal reservation cancellation process in a meeting room management system using natural language." Using this prompt sentence, the AI ​​is designed to output the appropriate processing method in natural language.

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1:

[0433] The server retrieves reservation information in real time via the network. Specifically, it obtains information such as reservation date and time, number of participants, and purpose from an API and uses this as input data. This data is then stored and organized in an integrated database to provide up-to-date information. The output is a structured reservation dataset.

[0434] Step 2:

[0435] The terminal provides a dashboard for users to check their reservation status. When a user accesses the terminal, availability data sent from the server is entered, and a list of available spaces is visually displayed on the dashboard. As output, the user can see the available spaces along with the date and time, and receives an interface for selection. Specifically, the UI is dynamically updated, and the user can proceed with the reservation process with clicks and taps.

[0436] Step 3:

[0437] The server continuously monitors booking data and uses an AI model to detect empty appointments. Past booking patterns and real-time data are provided as input, which the AI ​​analyzes to identify bookings that are likely unused. The output is a list of suspicious empty appointments. Specifically, the model flags bookings that exceed a certain threshold and are deemed highly unlikely to be actually used.

[0438] Step 4:

[0439] The server automatically sends a confirmation email to the booker if it suspects an appointment is vacant. The input includes the detected vacant appointment information and the booker's contact details. Based on this, a confirmation email is generated. The output is a customized message containing specific links encouraging the user to respond promptly. Specifically, if a response is not received within a certain time, the appointment is automatically canceled.

[0440] Step 5:

[0441] The server analyzes domain usage data and generates optimization information. Inputs include user reservation history, usage rates, and past usage trends. Based on this, data analysis tools are used to forecast demand and propose promotions. The output consists of specific measures and suggestions to improve usage efficiency, including strategies to maximize usage rates. Specific actions include, for example, the automated sending of promotional emails targeting specific time slots.

[0442] (Application Example 1)

[0443] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0444] In the reservation management of spaces such as public facilities and meeting rooms, empty reservations sometimes occur, or spaces are not utilized efficiently. As a result, users have difficulty finding a suitable space, and the efficiency of the facility's operation itself can decrease. This invention aims to eliminate such inefficiencies in reservation management and enable users to reserve and utilize spaces smoothly.

[0445] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0446] In this invention, the server includes means for acquiring and aggregating reservation information in real time, means for presenting the availability of space to users based on the reservation information, and means for detecting vacant reservations, confirming with the reservation holder, and automatically canceling the reservation if necessary. As a result, users can easily find the space they need, and efficient use of space becomes possible.

[0447] "Reservation information" refers to detailed data that users register in advance for the use of a space or facility.

[0448] "Space" refers to a place used for a specific purpose, such as a conference room or a public facility.

[0449] "Usage information" refers to data related to the usage history and purpose of use of a space or facility.

[0450] "User" refers to an individual or group that intends to use a space or facility.

[0451] A "fake reservation" refers to a reservation made for a space or facility that is not actually going to be used.

[0452] "Equipment arrangement" refers to the placement and method of arranging furniture and equipment within a space.

[0453] An "information terminal" is an electronic device used for processing information, such as a smartphone or tablet.

[0454] A "server" refers to a computer system that processes and manages data and provides information.

[0455] To implement this invention, a reservation management system is central. First, the server uses cloud-based technologies such as AWS Lambda, DynamoDB, and EC2 to retrieve and aggregate reservation information in real time. This ensures that the latest reservation status is always stored and updated in the database.

[0456] The smartphones and tablets used as devices have applications developed with React Native installed. Users can use this app to visually check the availability of spaces. Furthermore, this application uses the Google Maps API to provide a visually intuitive user interface.

[0457] The server uses an AI agent to automatically detect vacant reservations and send a confirmation notification to the reservation holder. If there is no response, the reservation is automatically canceled, and the space is made available to another user. This prevents the consumption of unnecessary resources.

[0458] Furthermore, the server analyzes usage data to identify unused time slots and the reasons for empty reservations. This utilizes a generative AI model to suggest optimal equipment placement based on usage patterns. This system allows users to reserve and utilize space efficiently and flexibly.

[0459] For example, if a citizen wants to use a city-owned public facility for a study group, they can use the app to instantly check availability and make a reservation. Even after the reservation is complete, the AI ​​automatically monitors for available slots, allowing for last-minute cancellations.

[0460] An example of a prompt might be, "Analyze the reservation status of public facilities in real time and use AI to propose optimization measures to ensure the most efficient and waste-free use." Through these specific prompts, the AI ​​can make more effective suggestions.

[0461] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0462] Step 1:

[0463] The server uses AWS Lambda to retrieve reservation information in real time and store it in DynamoDB. The input is reservation data from an external reservation system, and the output is the latest reservation information stored in DynamoDB. Specifically, it receives data such as the date and time of the reservation, the number of participants, and the purpose of use via an API, and stores this data in DynamoDB.

[0464] Step 2:

[0465] The device sends a reservation information request to the server based on the user's request. The input is the availability check request sent from the user's device, and the output is the availability of the space displayed on the device's screen. Specifically, an app developed with React Native uses the Google Maps API to visually display the available spaces for reservation.

[0466] Step 3:

[0467] The server uses an AI agent to analyze reservation data and detect empty reservations. The input is reservation information stored in DynamoDB, and the output is a list of reservations that have been determined to be empty. Specifically, it picks out reservations that have not seen any human activity within a certain period of time and marks them as empty reservations.

[0468] Step 4:

[0469] The server sends an email to users who have made reservations that are deemed invalid, and automatically cancels the reservation if no response is received. The input is a list of invalid reservations and user information, and the output is information on cancelled reservations. Specifically, an email is sent to the user, and if no reply is received within the set time, the reservation is deleted.

[0470] Step 5:

[0471] The server analyzes usage data and uses a generated AI model to propose optimal equipment placement and utilization promotion measures. The input is historical usage data, and the output is optimized suggestions. Specifically, data analysis identifies periods with low utilization rates, and simulations of efficient equipment placement are performed as improvement measures.

[0472] Step 6:

[0473] Users can check availability from their smartphones or tablets and make reservations as needed. The input is the user's reservation request, and the output is a reservation completion notification. Specifically, the user checks availability on the app and completes the reservation by pressing the reservation button for the space they want to use.

[0474] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0475] This invention combines an emotion engine with a meeting room reservation system to achieve flexible and effective meeting room management that responds to the user's emotional state. The program processing is as follows:

[0476] The server first acquires reservation data for each meeting room in real time and updates the integrated database. This allows for constant monitoring of reservation status and forms the foundation for efficient meeting room operation. Simultaneously, it analyzes usage data to calculate the optimal meeting room and schedule.

[0477] The terminal provides an interface for interaction between the user and the emotion engine. When a user makes a meeting room reservation, the terminal analyzes the user's facial expressions and tone of voice through the camera and microphone and sends the data to the emotion engine. Based on the received data, the emotion engine determines the user's emotional state and, taking that into consideration, suggests the most suitable meeting room and layout to the user.

[0478] Users select a suitable meeting room from the suggested options and complete the reservation. During this process, the meeting room's environment and atmosphere can be adjusted to suit the user's preferences and current mood. For example, a user experiencing stress might be recommended a meeting room with a relaxing environment.

[0479] The server continuously monitors booking status and usage patterns, and also performs processes to detect and automatically cancel vacant bookings. Furthermore, it utilizes data collected by the emotion engine to gain insights for optimizing meeting room operations.

[0480] Thus, meeting room management that takes user emotions into consideration goes beyond mere physical resource management, enabling the provision of services that meet users' psychological needs. This makes it possible to increase user satisfaction and provide a richer office environment. For example, for users who are feeling anxious, a meeting room with calming colors and acoustic design could be suggested to help them relax during the meeting.

[0481] The following describes the processing flow.

[0482] Step 1:

[0483] The server retrieves reservation data for all meeting rooms in real time from each reservation system and stores it in an integrated database. This data includes detailed information on reserved meeting rooms, availability, and utilization rates.

[0484] Step 2:

[0485] The terminal displays the availability of meeting rooms in a dashboard format through the user interface. In addition, the terminal uses a camera and microphone to capture the user's facial expressions and voice, collecting emotional data in real time.

[0486] Step 3:

[0487] The device sends the acquired emotional data to the emotion engine, which analyzes the user's emotional state. Based on this data, the emotion engine identifies the user's current emotional state (e.g., stress, relaxation, excitement, etc.).

[0488] Step 4:

[0489] The server considers the emotional state obtained from the emotion engine to suggest the most suitable meeting room and environment settings. For example, if the user is feeling stressed, it will automatically recommend a meeting room with calming colors and soft lighting.

[0490] Step 5:

[0491] Users select the most suitable meeting room from the suggested options and make a reservation. This ensures that meeting room usage aligns with the user's emotional state.

[0492] Step 6:

[0493] The server monitors the reservation status, and if an empty reservation is detected, it automatically sends a confirmation email to the person who made the reservation. If a reply is not received within a certain time, the reservation is canceled. It also analyzes the collected data and generates suggestions for improving the operation of the meeting rooms.

[0494] This series of processes improves the efficiency of meeting room utilization and makes it possible to provide users with a comfortable and user-friendly office environment.

[0495] (Example 2)

[0496] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0497] In today's business environment, it is necessary to provide not only physical meeting rooms to users with diverse needs and emotional states, but also to create an optimal meeting environment tailored to the user's psychological state. However, conventional meeting room reservation systems do not take into account the user's emotional state when making suggestions or environmental adjustments, and therefore fail to sufficiently increase user satisfaction and productivity.

[0498] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0499] In this invention, the server includes means for acquiring and aggregating reservation data in real time, means for analyzing usage data of facilities and recommending the most suitable facilities, and means for analyzing the emotional state of users and proposing the most suitable facilities and layouts based on that analysis. This enables the provision of an optimal meeting room environment according to the emotional state of users, thereby improving user satisfaction and productivity.

[0500] "Reservation data" refers to information related to reservations made at the facility, including details of the person making the reservation, reservation time, and purpose of use.

[0501] "Facilities used" refers to places such as meeting rooms and multipurpose spaces that are used for a designated purpose.

[0502] "User" refers to an individual or group that reserves and uses a facility.

[0503] An "empty reservation" refers to a reservation that is not actually used and should be invalidated.

[0504] "Usage data" refers to data related to past and present use of facilities, including information that indicates user behavior and usage trends.

[0505] "Emotional state" refers to the user's psychological or emotional state, including their level of stress and relaxation.

[0506] "Environmental adjustment" refers to the process of modifying the physical or psychological environment within a facility to suit the user's emotional state and purpose of use.

[0507] One embodiment of this invention is to provide a reservation system for a facility with a management function that takes emotional states into consideration. Specific embodiments are described below.

[0508] The server acquires reservation data from multiple facilities in real time via the network and collects it in a central, integrated database. This data includes details about the reservation holder, reservation time, and purpose of use. The reservation data is efficiently managed using a database management system (e.g., MySQL). Furthermore, the server uses data analysis tools and generative AI models (e.g., Python's scikit-learn library and TensorFlow) to gain insights from past usage data and make recommendations to provide users with the most suitable facilities.

[0509] The terminal provides an interface for users to reserve facilities. Equipped with a camera and microphone, the terminal analyzes the user's emotional state by capturing their facial expressions and voice tone. This analysis utilizes emotion recognition software (e.g., IBM Watson's emotion analysis API), and the acquired emotional information is sent to a generative AI model. Based on the emotional state, the terminal has the function of suggesting the most suitable facilities and layouts for the user.

[0510] The user selects a facility from the suggested options and completes the reservation. The selected facility's environment is adjusted to suit the user's emotional state. For example, a user feeling stressed might be offered a facility with plenty of natural light to promote relaxation. Another example of a prompt might be, "Please suggest the optimal meeting room layout for a user who is feeling stressed." Through such prompts, the generative AI model can suggest the optimal environment adapted to the user's emotions.

[0511] This system design enables sophisticated facility management that meets the psychological needs of users, thereby increasing user satisfaction and efficiency.

[0512] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0513] Step 1:

[0514] The server periodically receives reservation data from each facility via the internet. The input is reservation data obtained from each facility's reservation system. When the server receives this data and saves it to the integrated database, it uses a database management system to perform data processing such as removing duplicate data and checking for consistency. The output is a centrally managed database with the latest reservation status recorded.

[0515] Step 2:

[0516] The server analyzes usage trends from accumulated reservation data. The input is historical reservation data accumulated in the past. The server uses machine learning algorithms to perform data calculations to identify frequently occurring usage times and popular facilities. The output is the result of the usage trend analysis, which is used as insights to make optimal recommendations.

[0517] Step 3:

[0518] The terminal captures the user's facial expressions and voice via camera and microphone when the user accesses the facility's reservation interface. The input consists of the user's facial image and audio data. The terminal uses emotion recognition software to analyze the data and process it to determine the user's emotional state. The output is an indicator of the user's emotional state.

[0519] Step 4:

[0520] The device proposes the most suitable facilities and layouts based on an analysis of the user's emotional state. The input consists of the user's emotional state and the results of an analysis of past usage trends. The device combines this data, uses a generative AI model to create prompts, and performs data calculations to propose the most appropriate options. The output is a list of facilities presented to the user.

[0521] Step 5:

[0522] The user selects the facility they wish to use from the presented options and completes the reservation. The input is the suggestions presented by the terminal. Once the user makes a selection, the reservation information is confirmed and sent to the server. The output is the confirmed reservation information, and the facility's environment may be adjusted based on this.

[0523] (Application Example 2)

[0524] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0525] In modern urban environments, meeting room booking and management are crucial for operational efficiency and optimal resource utilization. However, conventional booking systems often fail to consider user emotional states when making suggestions or optimizations, resulting in limited meeting efficiency and user experience. This can lead to unsatisfactory meeting environments, especially under stressful circumstances. A new system is needed to address these issues.

[0526] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0527] In this invention, the server includes means for acquiring and aggregating reservation data in real time; means for presenting the availability of meeting rooms to users based on the reservation data; means for detecting vacant reservations, confirming with the reservation holder, and automatically canceling the reservation if necessary; means for analyzing meeting room usage data and recommending the most suitable meeting room; means for analyzing usage data and identifying times of low meeting room utilization or causes of vacant reservations; and means for analyzing the emotional state of users and proposing the most suitable meeting space according to their emotions. This enables flexible and effective management of meeting rooms in accordance with the emotional state of users.

[0528] "Reservation data" refers to information related to meeting room reservations, including details such as the date and time of the reservation, the reservation holder's information, and the purpose of use.

[0529] "Acquiring and aggregating in real time" refers to the process by which the meeting room reservation system constantly receives, organizes, and retains the latest reservation information.

[0530] "Availability" refers to the status of a meeting room's availability within a specific time frame, providing information for users to determine whether or not they can use that meeting room.

[0531] A "fake reservation" refers to a reservation that is not used or is not actually intended to be used.

[0532] An "optimal meeting room" refers to a meeting room that best matches the needs and circumstances of the users, and is a space suitable for conducting efficient and effective meetings.

[0533] "User's emotional state" refers to the current psychological state and emotions of the user making a meeting room reservation, and is determined through analysis of facial expressions and voice.

[0534] "Proposing the optimal meeting space" is a process that takes into account the user's emotional state and other factors to suggest the most suitable meeting room and environment for the user.

[0535] The system for carrying out this invention is based on a network including servers, terminals, and an emotion engine.

[0536] The server acquires and aggregates reservation data in real time and uses this data to present meeting room availability to users. If an empty reservation is detected, it contacts the reservation holder for confirmation and automatically cancels the reservation if necessary. It also analyzes meeting room usage data to recommend the most suitable meeting room and identifies the causes of low utilization times and empty reservations. This entire process achieves high reliability and flexibility by utilizing cloud services. In actual operation, Amazon Web Services (AWS) DynamoDB and Google Cloud Platform (GCP) are sometimes used.

[0537] The device provides an interface with the user. This can take the form of a smartphone, smart glasses, or head-mounted display. The device uses a camera and microphone to record the user's facial expressions and voice, and sends the data to an emotion engine. The emotion engine performs emotion analysis using software such as OpenCV or DeepFace, and based on the results, suggests the most suitable meeting space to the user. This suggestion is made in real time, and the user can enjoy flexible choices that meet their psychological needs, such as being suggested a meeting room with a relaxing environment if they are feeling stressed.

[0538] As a concrete example, when a user uses the system to book a meeting, advanced information processing can be achieved by inputting a prompt message to the AI ​​model such as, "Please suggest the best meeting room option based on the user's current emotional state. For example, if the user needs to relax, what kind of environment would you suggest?"

[0539] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0540] Step 1:

[0541] The server retrieves and aggregates meeting room reservation data in real time. The input data consists of reservation requests from users, and the server updates the reservation information in the database. This process ensures that the reservation status is kept up-to-date. The server collects reservations for each meeting room and processes the data into a format that users can access.

[0542] Step 2:

[0543] The device communicates with the user through its interface. Input consists of the user's facial expressions and voice, which are captured by the camera and microphone. The device converts this raw data into a format necessary for transmission to the emotion engine. When a user attempts to schedule a meeting, their emotional state is assessed through their facial expressions and tone of voice.

[0544] Step 3:

[0545] The emotion engine analyzes the user's emotional state based on facial expression and voice data received from the device. The input data consists of video and audio, and emotion recognition software performs data calculations to identify the emotional state. The analysis results output the user's current emotional state (e.g., relaxed, tense, stressed). OpenCV and DeepFace are used for this analysis.

[0546] Step 4:

[0547] The server receives the analysis results from the emotion engine and calculates the optimal meeting room and environment based on them. The input is data such as the user's emotional state and the availability of meeting rooms. The server uses this to run an optimization algorithm and outputs a list of optimal meeting rooms that match the user's emotional state. In this process, users who need to relax are provided with a calm environment.

[0548] Step 5:

[0549] The user receives a list of meeting room suggestions from the server and selects their preferred meeting room. The input is a list of suggestions from the server, from which the user makes a selection. Once the selection is complete, the information is sent to the server, and the reservation is confirmed. The output is the confirmed reservation information.

[0550] Step 6:

[0551] The server continuously monitors reservation status, detecting empty reservations and automatically canceling them as needed. The input is continuously acquired reservation data, which is then analyzed to produce output that removes invalid reservations. This monitoring optimizes the overall operation of the meeting rooms.

[0552] 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.

[0553] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0554] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0555] [Fourth Embodiment]

[0556] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0557] 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.

[0558] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0559] 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.

[0560] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0561] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0562] 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.

[0563] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0564] 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.

[0565] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0566] The 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.

[0567] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0568] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0569] This invention is a meeting room management system using an autonomous AI agent. It collects and analyzes reservation data in real time, detects and automatically cancels vacant reservations, and recommends the most suitable meeting room, thereby achieving efficient meeting room operation. The program's processing is described below in natural language.

[0570] The server retrieves various reservation data from the meeting room reservation system in real time. This data includes detailed information such as the date and time of the reservation, the number of participants, and the purpose of use. The server periodically checks this data and updates the integrated database to always have a grasp of the latest reservation status for the entire system. This makes it possible to provide accurate availability information to users' terminals.

[0571] The terminal provides an interface for users to access and displays meeting room availability in a visually easy-to-understand dashboard format. Users can browse this information, check availability, and select and reserve a meeting room that suits their needs.

[0572] The server simultaneously monitors reservation patterns and implements a process to detect potentially invalid reservations. For example, if no one enters or leaves a meeting room within a set time, the server automatically determines that the reservation is invalid and immediately sends a confirmation email to the person who made the reservation. If there is no response within a certain time, the reservation is automatically canceled, making it available to other users.

[0573] Based on a detailed analysis of usage data, the server provides information to optimize meeting rooms. If utilization is low during a specific time period, it analyzes the cause and proposes optimal improvement measures. This includes optimization through appropriate promotions and automated booking adjustments, for example. As a result, it prevents the consumption of wasted resources and enables a flexible response to demand.

[0574] This system makes it easy for users to select and book meeting rooms, reducing cumbersome manual operations. Furthermore, the entire system offers automated operation, resulting in stress-free and efficient meeting room management.

[0575] The following describes the processing flow.

[0576] Step 1:

[0577] The server retrieves reservation data from each meeting room reservation system at regular intervals. This data includes detailed information such as the reservation date and time, purpose of use, and number of participants for each meeting room. The server stores this data in an integrated database to create an overall reservation status.

[0578] Step 2:

[0579] The server analyzes the integrated reservation data to determine the current meeting room usage. This includes listing reserved meeting rooms and identifying available rooms. The analysis results are prepared as a dataset for the dashboard.

[0580] Step 3:

[0581] The terminal displays a dashboard through a user interface, allowing users to check the availability and reservation status of meeting rooms in real time. Based on this information, users can select and reserve meeting rooms.

[0582] Step 4:

[0583] Users reserve meeting rooms via their terminals. The information entered during the reservation process (date and time, number of people, purpose, etc.) is sent from the terminal to the server and registered in the server's integrated database.

[0584] Step 5:

[0585] The server monitors reservation data at regular intervals to check for the possibility of a vacant reservation. If a reservation is determined to be vacant, it automatically sends a confirmation email to the person who made the reservation, and if no reply is received, the reservation is automatically canceled.

[0586] Step 6:

[0587] The server analyzes usage patterns based on reservation data to identify time slots with low utilization rates and the causes of empty reservations. This allows it to suggest ways to optimize meeting rooms and improve the overall efficiency of the system.

[0588] Through this series of processes, efficient management and utilization of meeting rooms can be achieved, providing users with a more comfortable environment.

[0589] (Example 1)

[0590] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0591] Currently, many locations face the problem of cumbersome and inefficient management of space reservations for meetings and events. In particular, empty or underutilized time slots can occur, leading to wasted resources and reduced user convenience. To solve this problem, a system for automated reservation management and optimization is necessary.

[0592] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0593] In this invention, the server includes means for acquiring and integrating information in real time, means for presenting the availability status of the area to the user based on the information, and means for detecting empty appointments, confirming with the person who made the appointment, and automatically canceling the appointment if necessary. This makes it possible to efficiently manage the usage status of the area and maximize the utilization rate.

[0594] "Information" is a general term for data relating to users' activities and usage of domains.

[0595] "Area" refers to the specific physical or virtual space where reservations or use take place.

[0596] "Schedule" refers to information about the date, time, and details of when the user has planned to use the area.

[0597] "User" refers to an individual or organization that uses this system to reserve or manage areas.

[0598] A "server" refers to a computer system used to process and analyze information, and provides various services through a network.

[0599] "Integration" refers to the process of combining multiple different pieces of information and managing them as a single coherent whole.

[0600] "Presentation" means showing information in a way that is easy for users to understand.

[0601] "Detection" refers to the ability to automatically identify specific patterns or conditions.

[0602] This invention is implemented as an efficient domain management system utilizing an autonomous AI agent. A detailed explanation follows below.

[0603] The server first retrieves information in real time from the reservation system via the network. The hardware used is a high-performance computing system, and the software utilizes middleware specialized for data processing and big data analysis tools. Specifically, it aggregates detailed information such as the date, time, number of people, and purpose of each reservation and integrates it into a database.

[0604] The terminal functions as an interface for user access. A visually intuitive dashboard displays the availability of the area, allowing users to plan accordingly. The application used utilizes Vue.js or React for the frontend, enabling real-time updates.

[0605] The server further analyzes information patterns using an AI model to detect empty appointments. If there is any doubt, it sends a confirmation email to the person who made the reservation. If there is no response within a certain time, the system automatically cancels the reservation and makes the area available to another user.

[0606] Through this system, users can efficiently select and reserve areas. Specifically, they can check availability and receive recommendations for the most suitable area from AI. This eliminates unnecessary procedures and simplifies the operation.

[0607] As a concrete example, in the case of managing meeting rooms in a company, the system analyzes reservation data and detects that usage is low during the afternoon hours. As a result, the AI ​​can suggest specific promotions to encourage usage.

[0608] An example of a prompt sentence to input into the generating AI model is, "Explain the optimal reservation cancellation process in a meeting room management system using natural language." Using this prompt sentence, the AI ​​is designed to output the appropriate processing method in natural language.

[0609] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0610] Step 1:

[0611] The server retrieves reservation information in real time via the network. Specifically, it obtains information such as reservation date and time, number of participants, and purpose from an API and uses this as input data. This data is then stored and organized in an integrated database to provide up-to-date information. The output is a structured reservation dataset.

[0612] Step 2:

[0613] The terminal provides a dashboard for users to check their reservation status. When a user accesses the terminal, availability data sent from the server is entered, and a list of available spaces is visually displayed on the dashboard. As output, the user can see the available spaces along with the date and time, and receives an interface for selection. Specifically, the UI is dynamically updated, and the user can proceed with the reservation process with clicks and taps.

[0614] Step 3:

[0615] The server continuously monitors booking data and uses an AI model to detect empty appointments. Past booking patterns and real-time data are provided as input, which the AI ​​analyzes to identify bookings that are likely unused. The output is a list of suspicious empty appointments. Specifically, the model flags bookings that exceed a certain threshold and are deemed highly unlikely to be actually used.

[0616] Step 4:

[0617] The server automatically sends a confirmation email to the booker if it suspects an appointment is vacant. The input includes the detected vacant appointment information and the booker's contact details. Based on this, a confirmation email is generated. The output is a customized message containing specific links encouraging the user to respond promptly. Specifically, if a response is not received within a certain time, the appointment is automatically canceled.

[0618] Step 5:

[0619] The server analyzes domain usage data and generates optimization information. Inputs include user reservation history, usage rates, and past usage trends. Based on this, data analysis tools are used to forecast demand and propose promotions. The output consists of specific measures and suggestions to improve usage efficiency, including strategies to maximize usage rates. Specific actions include, for example, the automated sending of promotional emails targeting specific time slots.

[0620] (Application Example 1)

[0621] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0622] In the reservation management of spaces such as public facilities and meeting rooms, empty reservations sometimes occur, or spaces are not utilized efficiently. As a result, users have difficulty finding a suitable space, and the efficiency of the facility's operation itself can decrease. This invention aims to eliminate such inefficiencies in reservation management and enable users to reserve and utilize spaces smoothly.

[0623] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0624] In this invention, the server includes means for acquiring and aggregating reservation information in real time, means for presenting the availability of space to users based on the reservation information, and means for detecting vacant reservations, confirming with the reservation holder, and automatically canceling the reservation if necessary. As a result, users can easily find the space they need, and efficient use of space becomes possible.

[0625] "Reservation information" refers to detailed data that users register in advance for the use of a space or facility.

[0626] "Space" refers to a place used for a specific purpose, such as a conference room or a public facility.

[0627] "Usage information" refers to data related to the usage history and purpose of use of a space or facility.

[0628] "User" refers to an individual or group that intends to use a space or facility.

[0629] A "fake reservation" refers to a reservation made for a space or facility that is not actually going to be used.

[0630] "Equipment arrangement" refers to the placement and method of arranging furniture and equipment within a space.

[0631] An "information terminal" is an electronic device used for processing information, such as a smartphone or tablet.

[0632] A "server" refers to a computer system that processes and manages data and provides information.

[0633] To implement this invention, a reservation management system is central. First, the server uses cloud-based technologies such as AWS Lambda, DynamoDB, and EC2 to retrieve and aggregate reservation information in real time. This ensures that the latest reservation status is always stored and updated in the database.

[0634] The smartphones and tablets used as devices have applications developed with React Native installed. Users can use this app to visually check the availability of spaces. Furthermore, this application uses the Google Maps API to provide a visually intuitive user interface.

[0635] The server uses an AI agent to automatically detect vacant reservations and send a confirmation notification to the reservation holder. If there is no response, the reservation is automatically canceled, and the space is made available to another user. This prevents the consumption of unnecessary resources.

[0636] Furthermore, the server analyzes usage data to identify unused time slots and the reasons for empty reservations. This utilizes a generative AI model to suggest optimal equipment placement based on usage patterns. This system allows users to reserve and utilize space efficiently and flexibly.

[0637] For example, if a citizen wants to use a city-owned public facility for a study group, they can use the app to instantly check availability and make a reservation. Even after the reservation is complete, the AI ​​automatically monitors for available slots, allowing for last-minute cancellations.

[0638] An example of a prompt might be, "Analyze the reservation status of public facilities in real time and use AI to propose optimization measures to ensure the most efficient and waste-free use." Through these specific prompts, the AI ​​can make more effective suggestions.

[0639] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0640] Step 1:

[0641] The server uses AWS Lambda to retrieve reservation information in real time and store it in DynamoDB. The input is reservation data from an external reservation system, and the output is the latest reservation information stored in DynamoDB. Specifically, it receives data such as the date and time of the reservation, the number of participants, and the purpose of use via an API, and stores this data in DynamoDB.

[0642] Step 2:

[0643] The device sends a reservation information request to the server based on the user's request. The input is the availability check request sent from the user's device, and the output is the availability of the space displayed on the device's screen. Specifically, an app developed with React Native uses the Google Maps API to visually display the available spaces for reservation.

[0644] Step 3:

[0645] The server uses an AI agent to analyze reservation data and detect empty reservations. The input is reservation information stored in DynamoDB, and the output is a list of reservations that have been determined to be empty. Specifically, it picks out reservations that have not seen any human activity within a certain period of time and marks them as empty reservations.

[0646] Step 4:

[0647] The server sends an email to users who have made reservations that are deemed invalid, and automatically cancels the reservation if no response is received. The input is a list of invalid reservations and user information, and the output is information on cancelled reservations. Specifically, an email is sent to the user, and if no reply is received within the set time, the reservation is deleted.

[0648] Step 5:

[0649] The server analyzes usage data and uses a generated AI model to propose optimal equipment placement and utilization promotion measures. The input is historical usage data, and the output is optimized suggestions. Specifically, data analysis identifies periods with low utilization rates, and simulations of efficient equipment placement are performed as improvement measures.

[0650] Step 6:

[0651] Users can check availability from their smartphones or tablets and make reservations as needed. The input is the user's reservation request, and the output is a reservation completion notification. Specifically, the user checks availability on the app and completes the reservation by pressing the reservation button for the space they want to use.

[0652] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0653] This invention combines an emotion engine with a meeting room reservation system to achieve flexible and effective meeting room management that responds to the user's emotional state. The program processing is as follows:

[0654] The server first acquires reservation data for each meeting room in real time and updates the integrated database. This allows for constant monitoring of reservation status and forms the foundation for efficient meeting room operation. Simultaneously, it analyzes usage data to calculate the optimal meeting room and schedule.

[0655] The terminal provides an interface for interaction between the user and the emotion engine. When a user makes a meeting room reservation, the terminal analyzes the user's facial expressions and tone of voice through the camera and microphone and sends the data to the emotion engine. Based on the received data, the emotion engine determines the user's emotional state and, taking that into consideration, suggests the most suitable meeting room and layout to the user.

[0656] Users select a suitable meeting room from the suggested options and complete the reservation. During this process, the meeting room's environment and atmosphere can be adjusted to suit the user's preferences and current mood. For example, a user experiencing stress might be recommended a meeting room with a relaxing environment.

[0657] The server continuously monitors booking status and usage patterns, and also performs processes to detect and automatically cancel vacant bookings. Furthermore, it utilizes data collected by the emotion engine to gain insights for optimizing meeting room operations.

[0658] Thus, meeting room management that takes user emotions into consideration goes beyond mere physical resource management, enabling the provision of services that meet users' psychological needs. This makes it possible to increase user satisfaction and provide a richer office environment. For example, for users who are feeling anxious, a meeting room with calming colors and acoustic design could be suggested to help them relax during the meeting.

[0659] The following describes the processing flow.

[0660] Step 1:

[0661] The server retrieves reservation data for all meeting rooms in real time from each reservation system and stores it in an integrated database. This data includes detailed information on reserved meeting rooms, availability, and utilization rates.

[0662] Step 2:

[0663] The terminal displays the availability of meeting rooms in a dashboard format through the user interface. In addition, the terminal uses a camera and microphone to capture the user's facial expressions and voice, collecting emotional data in real time.

[0664] Step 3:

[0665] The device sends the acquired emotional data to the emotion engine, which analyzes the user's emotional state. Based on this data, the emotion engine identifies the user's current emotional state (e.g., stress, relaxation, excitement, etc.).

[0666] Step 4:

[0667] The server considers the emotional state obtained from the emotion engine to suggest the most suitable meeting room and environment settings. For example, if the user is feeling stressed, it will automatically recommend a meeting room with calming colors and soft lighting.

[0668] Step 5:

[0669] Users select the most suitable meeting room from the suggested options and make a reservation. This ensures that meeting room usage aligns with the user's emotional state.

[0670] Step 6:

[0671] The server monitors the reservation status, and if an empty reservation is detected, it automatically sends a confirmation email to the person who made the reservation. If a reply is not received within a certain time, the reservation is canceled. It also analyzes the collected data and generates suggestions for improving the operation of the meeting rooms.

[0672] This series of processes improves the efficiency of meeting room utilization and makes it possible to provide users with a comfortable and user-friendly office environment.

[0673] (Example 2)

[0674] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0675] In today's business environment, it is necessary to provide not only physical meeting rooms to users with diverse needs and emotional states, but also to create an optimal meeting environment tailored to the user's psychological state. However, conventional meeting room reservation systems do not take into account the user's emotional state when making suggestions or environmental adjustments, and therefore fail to sufficiently increase user satisfaction and productivity.

[0676] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0677] In this invention, the server includes means for acquiring and aggregating reservation data in real time, means for analyzing usage data of facilities and recommending the most suitable facilities, and means for analyzing the emotional state of users and proposing the most suitable facilities and layouts based on that analysis. This enables the provision of an optimal meeting room environment according to the emotional state of users, thereby improving user satisfaction and productivity.

[0678] "Reservation data" refers to information related to reservations made at the facility, including details of the person making the reservation, reservation time, and purpose of use.

[0679] "Facilities used" refers to places such as meeting rooms and multipurpose spaces that are used for a designated purpose.

[0680] "User" refers to an individual or group that reserves and uses a facility.

[0681] An "empty reservation" refers to a reservation that is not actually used and should be invalidated.

[0682] "Usage data" refers to data related to past and present use of facilities, including information that indicates user behavior and usage trends.

[0683] "Emotional state" refers to the user's psychological or emotional state, including their level of stress and relaxation.

[0684] "Environmental adjustment" refers to the process of modifying the physical or psychological environment within a facility to suit the user's emotional state and purpose of use.

[0685] One embodiment of this invention is to provide a reservation system for a facility with a management function that takes emotional states into consideration. Specific embodiments are described below.

[0686] The server acquires reservation data from multiple facilities in real time via the network and collects it in a central, integrated database. This data includes details about the reservation holder, reservation time, and purpose of use. The reservation data is efficiently managed using a database management system (e.g., MySQL). Furthermore, the server uses data analysis tools and generative AI models (e.g., Python's scikit-learn library and TensorFlow) to gain insights from past usage data and make recommendations to provide users with the most suitable facilities.

[0687] The terminal provides an interface for users to reserve facilities. Equipped with a camera and microphone, the terminal analyzes the user's emotional state by capturing their facial expressions and voice tone. This analysis utilizes emotion recognition software (e.g., IBM Watson's emotion analysis API), and the acquired emotional information is sent to a generative AI model. Based on the emotional state, the terminal has the function of suggesting the most suitable facilities and layouts for the user.

[0688] The user selects a facility from the suggested options and completes the reservation. The selected facility's environment is adjusted to suit the user's emotional state. For example, a user feeling stressed might be offered a facility with plenty of natural light to promote relaxation. Another example of a prompt might be, "Please suggest the optimal meeting room layout for a user who is feeling stressed." Through such prompts, the generative AI model can suggest the optimal environment adapted to the user's emotions.

[0689] This system design enables sophisticated facility management that meets the psychological needs of users, thereby increasing user satisfaction and efficiency.

[0690] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0691] Step 1:

[0692] The server periodically receives reservation data from each facility via the internet. The input is reservation data obtained from each facility's reservation system. When the server receives this data and saves it to the integrated database, it uses a database management system to perform data processing such as removing duplicate data and checking for consistency. The output is a centrally managed database with the latest reservation status recorded.

[0693] Step 2:

[0694] The server analyzes usage trends from accumulated reservation data. The input is historical reservation data accumulated in the past. The server uses machine learning algorithms to perform data calculations to identify frequently occurring usage times and popular facilities. The output is the result of the usage trend analysis, which is used as insights to make optimal recommendations.

[0695] Step 3:

[0696] The terminal captures the user's facial expressions and voice via camera and microphone when the user accesses the facility's reservation interface. The input consists of the user's facial image and audio data. The terminal uses emotion recognition software to analyze the data and process it to determine the user's emotional state. The output is an indicator of the user's emotional state.

[0697] Step 4:

[0698] The device proposes the most suitable facilities and layouts based on an analysis of the user's emotional state. The input consists of the user's emotional state and the results of an analysis of past usage trends. The device combines this data, uses a generative AI model to create prompts, and performs data calculations to propose the most appropriate options. The output is a list of facilities presented to the user.

[0699] Step 5:

[0700] The user selects the facility they wish to use from the presented options and completes the reservation. The input is the suggestions presented by the terminal. Once the user makes a selection, the reservation information is confirmed and sent to the server. The output is the confirmed reservation information, and the facility's environment may be adjusted based on this.

[0701] (Application Example 2)

[0702] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0703] In modern urban environments, meeting room booking and management are crucial for operational efficiency and optimal resource utilization. However, conventional booking systems often fail to consider user emotional states when making suggestions or optimizations, resulting in limited meeting efficiency and user experience. This can lead to unsatisfactory meeting environments, especially under stressful circumstances. A new system is needed to address these issues.

[0704] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0705] In this invention, the server includes means for acquiring and aggregating reservation data in real time; means for presenting the availability of meeting rooms to users based on the reservation data; means for detecting vacant reservations, confirming with the reservation holder, and automatically canceling the reservation if necessary; means for analyzing meeting room usage data and recommending the most suitable meeting room; means for analyzing usage data and identifying times of low meeting room utilization or causes of vacant reservations; and means for analyzing the emotional state of users and proposing the most suitable meeting space according to their emotions. This enables flexible and effective management of meeting rooms in accordance with the emotional state of users.

[0706] "Reservation data" refers to information related to meeting room reservations, including details such as the date and time of the reservation, the reservation holder's information, and the purpose of use.

[0707] "Acquiring and aggregating in real time" refers to the process by which the meeting room reservation system constantly receives, organizes, and retains the latest reservation information.

[0708] "Availability" refers to the status of a meeting room's availability within a specific time frame, providing information for users to determine whether or not they can use that meeting room.

[0709] A "fake reservation" refers to a reservation that is not used or is not actually intended to be used.

[0710] An "optimal meeting room" refers to a meeting room that best matches the needs and circumstances of the users, and is a space suitable for conducting efficient and effective meetings.

[0711] "User's emotional state" refers to the current psychological state and emotions of the user making a meeting room reservation, and is determined through analysis of facial expressions and voice.

[0712] "Proposing the optimal meeting space" is a process that takes into account the user's emotional state and other factors to suggest the most suitable meeting room and environment for the user.

[0713] The system for carrying out this invention is based on a network including servers, terminals, and an emotion engine.

[0714] The server acquires and aggregates reservation data in real time and uses this data to present meeting room availability to users. If an empty reservation is detected, it contacts the reservation holder for confirmation and automatically cancels the reservation if necessary. It also analyzes meeting room usage data to recommend the most suitable meeting room and identifies the causes of low utilization times and empty reservations. This entire process achieves high reliability and flexibility by utilizing cloud services. In actual operation, Amazon Web Services (AWS) DynamoDB and Google Cloud Platform (GCP) are sometimes used.

[0715] The device provides an interface with the user. This can take the form of a smartphone, smart glasses, or head-mounted display. The device uses a camera and microphone to record the user's facial expressions and voice, and sends the data to an emotion engine. The emotion engine performs emotion analysis using software such as OpenCV or DeepFace, and based on the results, suggests the most suitable meeting space to the user. This suggestion is made in real time, and the user can enjoy flexible choices that meet their psychological needs, such as being suggested a meeting room with a relaxing environment if they are feeling stressed.

[0716] As a concrete example, when a user uses the system to book a meeting, advanced information processing can be achieved by inputting a prompt message to the AI ​​model such as, "Please suggest the best meeting room option based on the user's current emotional state. For example, if the user needs to relax, what kind of environment would you suggest?"

[0717] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0718] Step 1:

[0719] The server retrieves and aggregates meeting room reservation data in real time. The input data consists of reservation requests from users, and the server updates the reservation information in the database. This process ensures that the reservation status is kept up-to-date. The server collects reservations for each meeting room and processes the data into a format that users can access.

[0720] Step 2:

[0721] The device communicates with the user through its interface. Input consists of the user's facial expressions and voice, which are captured by the camera and microphone. The device converts this raw data into a format necessary for transmission to the emotion engine. When a user attempts to schedule a meeting, their emotional state is assessed through their facial expressions and tone of voice.

[0722] Step 3:

[0723] The emotion engine analyzes the user's emotional state based on facial expression and voice data received from the device. The input data consists of video and audio, and emotion recognition software performs data calculations to identify the emotional state. The analysis results output the user's current emotional state (e.g., relaxed, tense, stressed). OpenCV and DeepFace are used for this analysis.

[0724] Step 4:

[0725] The server receives the analysis results from the emotion engine and calculates the optimal meeting room and environment based on them. The input is data such as the user's emotional state and the availability of meeting rooms. The server uses this to run an optimization algorithm and outputs a list of optimal meeting rooms that match the user's emotional state. In this process, users who need to relax are provided with a calm environment.

[0726] Step 5:

[0727] The user receives a list of meeting room suggestions from the server and selects their preferred meeting room. The input is a list of suggestions from the server, from which the user makes a selection. Once the selection is complete, the information is sent to the server, and the reservation is confirmed. The output is the confirmed reservation information.

[0728] Step 6:

[0729] The server continuously monitors reservation status, detecting empty reservations and automatically canceling them as needed. The input is continuously acquired reservation data, which is then analyzed to produce output that removes invalid reservations. This monitoring optimizes the overall operation of the meeting rooms.

[0730] 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.

[0731] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0732] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0733] 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.

[0734] Figure 9 shows an 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.

[0735] 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.

[0736] 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.

[0737] 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, motorcycles, etc., 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, for example, based 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.

[0738] 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."

[0739] 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.

[0740] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0741] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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 the like 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.

[0750] 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 as being incorporated by reference.

[0751] The following is further disclosed regarding the embodiments described above.

[0752] (Claim 1)

[0753] A method for acquiring and aggregating reservation data in real time,

[0754] A means of showing users the availability of meeting rooms based on reservation data,

[0755] A method to detect vacant reservations, contact the reservation holder for confirmation, and automatically cancel the reservation if necessary,

[0756] A method for recommending the most suitable meeting room by analyzing meeting room usage data,

[0757] By analyzing usage data, we can identify the times of day when meeting room utilization is low and the reasons for empty bookings.

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, further comprising means for suggesting the optimal meeting room layout based on the meeting room reservation status.

[0761] (Claim 3)

[0762] The system according to claim 1, further comprising means for approving reservation changes from users and automatically adjusting meeting room reservations.

[0763] "Example 1"

[0764] (Claim 1)

[0765] A means of acquiring and integrating information in real time,

[0766] A means of presenting the availability status of an area to the user based on information,

[0767] A means to detect empty appointments, confirm with the person scheduled, and automatically cancel the appointment if necessary,

[0768] A method for analyzing domain usage information and recommending the optimal domain,

[0769] A means of analyzing usage information to identify the causes of low utilization rates or empty schedules in a domain,

[0770] A means of visually presenting information using a terminal and providing a user-selectable interface,

[0771] A system that includes this.

[0772] (Claim 2)

[0773] The system according to claim 1, further comprising means for proposing the optimal arrangement of areas based on the information settings.

[0774] (Claim 3)

[0775] The system according to claim 1, further comprising means for approving setting changes from the user and automatically adjusting the area settings.

[0776] "Application Example 1"

[0777] (Claim 1)

[0778] A method for acquiring and aggregating reservation information in real time,

[0779] A means of showing users the availability of space based on reservation information,

[0780] A method to detect vacant reservations, contact the reservation holder for confirmation, and automatically cancel the reservation if necessary,

[0781] A method for analyzing space utilization information and recommending the optimal space,

[0782] A means of analyzing usage data to identify times of day with low space utilization and the causes of empty bookings,

[0783] A means to support users in easily making and canceling reservations for public facilities using information terminals,

[0784] A method to monitor usage in real time and automatically cancel empty reservations,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, further comprising means for proposing the optimal equipment layout based on the reservation status of a public space.

[0788] (Claim 3)

[0789] The system according to claim 1, further comprising means for approving reservation changes from users and automatically adjusting space reservations.

[0790] "Example 2 of combining an emotion engine"

[0791] (Claim 1)

[0792] A method for acquiring and aggregating reservation data in real time,

[0793] A means of showing users the availability of facilities based on reservation data,

[0794] A method to detect vacant reservations, contact the reservation holder for confirmation, and automatically cancel the reservation if necessary,

[0795] A method for recommending the most suitable facility by analyzing usage data of the facilities used,

[0796] By analyzing usage data, we can identify the time slots with low utilization rates for facilities and the causes of vacant bookings.

[0797] A means of analyzing the emotional state of users and proposing the optimal facilities and layout based on that analysis,

[0798] Means for adjusting the environment of the facility according to the emotional state of the user,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, further comprising means for proposing the optimal layout of the facilities to be used based on the reservation status of the facilities to be used.

[0802] (Claim 3)

[0803] The system according to claim 1, further comprising means for approving reservation changes from users and automatically adjusting reservations for facilities to be used.

[0804] "Application example 2 when combining with an emotional engine"

[0805] (Claim 1)

[0806] A method for acquiring and aggregating reservation data in real time,

[0807] A means of presenting the availability of meeting rooms to users based on reservation data,

[0808] A method to detect vacant reservations, contact the reservation holder for confirmation, and automatically cancel the reservation if necessary,

[0809] A method for recommending the most suitable meeting room by analyzing meeting room usage data,

[0810] By analyzing usage data, we can identify the times of day when meeting room utilization is low and the reasons for empty bookings.

[0811] A means of analyzing the emotional state of users and proposing the optimal meeting space according to those emotions,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, further comprising means for suggesting the optimal meeting room environment based on the meeting room reservation status.

[0815] (Claim 3)

[0816] The system according to claim 1, further comprising means for approving reservation changes from users and automatically adjusting meeting room reservations. [Explanation of Symbols]

[0817] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A method for acquiring and aggregating reservation information in real time, A means of showing users the availability of space based on reservation information, A method to detect vacant reservations, contact the reservation holder for confirmation, and automatically cancel the reservation if necessary, A method for analyzing space utilization information and recommending the optimal space, A means of analyzing usage data to identify times of day with low space utilization and the causes of empty bookings, A means to support users in easily making and canceling reservations for public facilities using information terminals, A method to monitor usage in real time and automatically cancel empty reservations, A system that includes this.

2. The system according to claim 1, further comprising means for proposing the optimal equipment layout based on the reservation status of public spaces.

3. The system according to claim 1, further comprising means for approving reservation changes from users and automatically adjusting space reservations.

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