system

Abstract

We provide the system. [Solution] A detection means for monitoring the use of a reserved indoor space, An identification means that automatically identifies unused indoor spaces based on detected information, A notification means for notifying registered users of identified unused indoor spaces, A distribution method that utilizes a generative model to optimally allocate multiple unused indoor spaces and the number of people who wish to use them, A control mechanism that sends an alert to users who have made reservations without authorization and implements reservation restrictions for continued violations, A system that includes this.

Description

Technical Field

[0004] , , , , ,

[0005] , , , , ,

[0003] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern office environment, there is a demand for improving the reservation and utilization efficiency of indoor spaces. In particular, the existence of unutilized reserved indoor spaces creates a waste of facility resources and causes a loss of utilization opportunities for those who wish to use them. Also, for facility managers, unauthorized cancellations and management of usage situations are a major burden, so the development of a system that can efficiently solve these problems is an urgent need.

Means for Solving the Problems

[0005] This invention employs technology to monitor the usage status of reserved indoor spaces, automatically identifying unused spaces using sensors and devices. For spaces identified as unused, it quickly and automatically notifies registered users who have been waiting, providing them with an opportunity to use the space. Furthermore, AI technology appropriately matches multiple unused indoor spaces with users, achieving efficient utilization. In addition, it aims to maintain a better usage environment by issuing alerts in the event of no-shows and imposing reservation restrictions as a penalty for repeated violations. Through this entire process, the efficiency of indoor space utilization is significantly improved.

[0006] A "reserved indoor space" refers to a portion of a room or facility whose use has been registered in advance.

[0007] "Detection means for monitoring usage" refers to sensors or devices for monitoring the current usage status of an indoor space.

[0008] An "unused interior space" refers to a room that has been reserved but is not currently being used.

[0009] "Identification means" refers to devices or algorithms used to determine an unused state based on received data.

[0010] A "prospective user" is an individual or group who wishes to use the indoor space but has not yet done so.

[0011] A "notification means" is a means of communication used to inform a user of identified information.

[0012] "Distribution methods utilizing generative models" refer to methods that use artificial intelligence technology to appropriately match indoor spaces with prospective users.

[0013] A "reservation made without authorization" refers to an individual or organization that made a reservation but did not actually use the indoor space.

[0014] The "control means for transmitting an alert" is a function for transmitting a notification when an unauthorized cancellation occurs.

[0015] The "control means for implementing reservation restrictions" is a function for restricting new reservations of that individual or organization when a violation exceeds a certain standard.

Brief Description of the Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It 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 Example 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.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the 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 one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

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

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[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] The present invention provides a system for optimizing the use of indoor spaces in an office environment. This system includes a series of automated processes that monitor the usage status of reserved indoor spaces in real time, identify unused indoor spaces, and notify those on the waiting list. Furthermore, by using AI technology to optimally allocate multiple unused indoor spaces to those wishing to use them, efficient utilization can be achieved.

[0038] Subject: Server

[0039] The server is responsible for receiving various data from sensors installed in the indoor space. These sensors, such as motion sensors and door opening / closing sensors, monitor the usage status of the room. The received data is analyzed within the server, and if it is determined that a reserved space is not being used, the server proceeds to the next step using that information.

[0040] Subject: Server

[0041] The server uses information about unused indoor spaces to refer to a list of users who are on a waiting list. It then automatically sends notifications to these users. These notifications include the type, location, and available time of the indoor space, and are designed to encourage immediate use. This communication is conducted via email, messaging applications, and internal notification systems.

[0042] Subject: Server

[0043] Furthermore, the AI ​​system on the server acquires information on multiple unused indoor spaces and prospective users, and performs the most efficient and fair matching. This AI system calculates the optimal combination by considering pre-set criteria, such as the urgency of the request for use, the importance of the meeting, and the user's priority. Based on the results, it supports the effective use of indoor spaces by notifying prospective users.

[0044] Subject: Server

[0045] If a user fails to use the room without prior notice, the server will send an alert email to that user. This email will contain details of the violation and future precautions. Furthermore, if a user has a certain number of no-shows within a certain period, the server will take measures to restrict that user from booking meeting rooms. This restriction will prevent new bookings within a certain period and will be implemented by integrating with existing booking systems such as the Google Calendar API.

[0046] Thus, the system of the present invention, by combining sensors and AI technology, appropriately manages the usage status of reserved indoor spaces and promotes efficient use. As a result, it can contribute to improving productivity in the office environment.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server begins receiving data from sensors installed in the room. These sensors, such as motion sensors and door open / close sensors, continuously monitor the room's usage. The received data is sent to the server and prepared for analysis.

[0050] Step 2:

[0051] The server analyzes the received data to determine whether the room space is reserved but unused. If it is determined to be unused, it sets the "unused" flag in the database.

[0052] Step 3:

[0053] The server retrieves information on indoor spaces that have been flagged as "unused" and checks the list of users who have registered for the waiting list in advance. It then prepares to send notifications to the devices of the relevant users.

[0054] Step 4:

[0055] The server notifies users' devices of information about unused indoor spaces. The notification includes the location and availability time of the indoor space. The device then displays the notification to the user, facilitating their use.

[0056] Step 5:

[0057] The AI ​​model on the server retrieves all unused indoor spaces and potential users, and calculates the most efficient combination. This includes evaluations based on user-defined priorities and usage purposes.

[0058] Step 6:

[0059] The server assigns the most suitable indoor space to each user based on the AI ​​model's calculations and notifies the user's device accordingly. The user can then begin using the assigned indoor space according to this notification.

[0060] Step 7:

[0061] The server will send an alert email to the person who made the reservation if they cancel without notice. This email will remind the person about the importance of avoiding no-shows and encourage them to take measures to prevent recurrence.

[0062] Step 8:

[0063] The server will impose booking restrictions on a user if they have a certain number of no-shows. This will restrict their ability to make new bookings and limit their access to the booking system for a certain period.

[0064] (Example 1)

[0065] 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."

[0066] The efficiency of space utilization within companies and organizations can decrease when spaces are reserved but not actually used. Furthermore, repeated unauthorized cancellations can affect other users and lead to wasted resources. To address these challenges, more effective and equitable optimization of space utilization is necessary.

[0067] 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.

[0068] In this invention, the server includes acquisition means for monitoring the usage status of reserved partitions, analysis means for automatically determining unused partitions based on the acquired information, and notification means for notifying registered users of the determined unused partitions. This enables efficient use of resources by accurately monitoring partition usage and promptly encouraging their use as needed.

[0069] "Acquisition means" refers to a device or system that has the function of monitoring the usage status of a section and collecting relevant information.

[0070] "Analysis means" refers to a device or software that has the function of determining whether a compartment is unused based on the acquired information.

[0071] "Notification method" refers to a device or system that has the function of notifying registered users of the determined unused area information.

[0072] A "generative model" is an algorithm or program used to calculate the optimal allocation based on information about multiple unused plots and prospective users.

[0073] "Management measures" refer to devices or systems that have the function of issuing warnings for reservations that have not been used without permission, and implementing reservation restrictions if the violation continues.

[0074] A "detection device" is a sensor or device installed within a designated area and used to detect physical conditions or human movement.

[0075] An "information device" is an electronic device that is owned or used by a user and is capable of sending and receiving information.

[0076] The system of this invention is designed to maximize the efficiency of partition utilization within companies and organizations. This system mainly consists of servers, terminals, and users, and each element works closely together to achieve effective management and utilization of partitions.

[0077] The server collects data from motion sensors and door open / close sensors installed within the area. These sensors transmit data to the server via Bluetooth or Wi-Fi to monitor the area's usage. This data is centrally stored in a database for later analysis.

[0078] The server uses a generative AI model based on collected data to automatically determine unused parking spaces. The generative AI model employs machine learning algorithms such as the Scikit-learn library to compare past usage patterns with real-time data, resulting in highly accurate determinations. Based on these determinations, the server notifies registered users that unused parking spaces are available. These notifications are sent automatically via email or messaging apps, ensuring immediacy.

[0079] Furthermore, the server processes information on multiple unused plots and those wishing to use them to calculate the optimal matching. This ensures that plots are used in the most effective and fair way possible. The AI ​​performs this optimization using a scoring system that takes into account the urgency of the request and the importance of the plot.

[0080] For example, if meeting room A is reserved within the company, but the motion sensor does not detect any activity for more than an hour, the server will determine that the meeting room is unused and automatically notify user B, who is on the list of potential users, that "Meeting room A is now available."

[0081] Examples of prompt statements include:

[0082] "How can I use AI to optimize the booking status of unused meeting rooms in my office?"

[0083] There is.

[0084] Such a system not only allows users to utilize the space efficiently, but also enables the system to issue warnings to those who have made reservations but are not using them, and to impose reservation restrictions if necessary, thereby improving overall utilization efficiency.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The server acquires data from sensors within the area. These sensors include motion sensors and door open / close sensors. These sensors transmit data via Bluetooth or Wi-Fi. The input includes real-time data from the sensors. The server stores this data in a database and uses it to understand the current usage status of the area.

[0088] Step 2:

[0089] The server analyzes the data in the database to identify unused partitions. The usage data collected in step 1 is used as input. A generative AI model is used to process the data by comparing it with past usage patterns to determine if a partition is unused. The server outputs a list of partitions that it has determined to be unused.

[0090] Step 3:

[0091] The server compares information about unused sections with the list of prospective users. Based on the output list, the server notifies registered prospective users of available sections. The notification is delivered via email or messaging app and includes details about the location and time of available sections.

[0092] Step 4:

[0093] The server uses AI to process information on multiple unused partitions and those wishing to use them, and then makes the optimal allocation. Inputs include a list of unused partitions and priority information for those wishing to use them. Using a generative AI model, it calculates the optimized allocation by scoring based on urgency and importance, and then outputs the result to notify those wishing to use the partitions.

[0094] Step 5:

[0095] The server notifies users who have made reservations but have not used them without permission. Based on the data determined to be unused in Step 2, a warning email for unauthorized cancellation is sent to the user. If violations occur repeatedly during this process, the server will output a control signal in conjunction with the reservation system to impose reservation restrictions.

[0096] Through these steps, the system enables accurate monitoring and efficient partition reallocation, thereby improving utilization efficiency.

[0097] (Application Example 1)

[0098] 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."

[0099] In offices and commercial facilities, reserved indoor spaces are often left unused without permission, resulting in problems where those who wish to use the space are unable to do so. Furthermore, the inefficiency and fair allocation of space to users reduces the efficiency of facility management. There is a need to resolve these issues and improve the efficiency of indoor space utilization.

[0100] 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.

[0101] In this invention, the server includes detection means for monitoring the use of reserved indoor spaces, identification means for automatically identifying unused indoor spaces based on the detected information, and allocation means that utilize a generative model to optimally allocate multiple unused indoor spaces and users. This makes it possible to improve the efficiency of the facility through the optimal use of indoor spaces and to improve the convenience of users.

[0102] A "reserved indoor space" refers to a physical area that has been secured for use in advance by a user.

[0103] "Detection means" refers to a device or method for monitoring the usage status of an indoor space using sensors or other technologies.

[0104] "Identification means" refers to a process for determining the usage status of an indoor space based on detected data and identifying whether or not it is unused.

[0105] "Notification means" refers to a method or system for communicating information about an unused indoor space to those who wish to use it.

[0106] A "generative model" is a mathematical or algorithmic structure that utilizes AI technology to optimally match the user with the indoor space.

[0107] A "distribution method" is a method that uses a generative model to efficiently connect multiple indoor spaces with potential users.

[0108] A "control mechanism" is a function or process that imposes a penalty if a reservation is left unused without permission and restricts subsequent reservations.

[0109] "Private rooms in commercial facilities" refers to small spaces or rooms within commercial facilities such as shopping malls and stores that are used for specific purposes.

[0110] A "recommendation method" refers to an algorithm or system used to suggest available private rooms to users.

[0111] The system for implementing this invention facilitates the efficient use of private rooms within commercial facilities. Herein, we present the system's program, the hardware and software used, and specific examples.

[0112] First, the server monitors the usage status of individual rooms within the commercial facility in real time through sensors installed in those rooms. Motion sensors and door opening / closing sensors are used to accurately determine when a room is unused. The acquired data is sent to an analysis module within the server.

[0113] The server uses Python and Flask to analyze various data and identify unused private rooms. Firebase manages the data in real time and allocates rooms using AI as needed. Generative models are used to optimize allocation, taking into account the priorities and purposes of users who wish to use the rooms.

[0114] As a notification method, the server notifies users' devices of the current status via a smartphone app built with React Native. If there are unused private rooms, it sends availability information to those who wish to use them, encouraging their use. In addition, users who repeatedly cancel reservations without notice will receive an alert email and be penalized.

[0115] For example, if a meeting room in a commercial facility suddenly becomes available, this system efficiently notifies other users, allowing them to utilize the time without wasting it. This ensures that specific-purpose spaces within the facility are used effectively.

[0116] As an example of a prompt sent to a generation AI model, you can use the following sentence: "Analyze the occupancy status of private rooms in the commercial facility this week and generate a list of recommended users for rooms that are not currently occupied. There are many occupants on weekends, especially in the afternoons."

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The server receives usage data from motion sensors and door sensors installed within the commercial facility. This data indicates whether each individual room is currently in use or unused. The server monitors this input data in real time and sets criteria for determining usage status. As a result, the server can identify which individual rooms are unused.

[0120] Step 2:

[0121] The server analyzes usage data and identifies information about private rooms that have been identified as unused. The input here is usage data obtained from sensors, and the output is identification information for unused private rooms. Through data analysis, it identifies private rooms that have not been used within a specific time frame and sets an unused flag.

[0122] Step 3:

[0123] The server uses information on unused private rooms to refer to a list of people who wish to use them and sends notifications to the terminals of those who wish to use them. It uses the list of people who wish to use the rooms and availability information as input and generates notification content as output. The notification content includes the type of private room, location, and available time, and is sent to the user's terminal via email or messaging app.

[0124] Step 4:

[0125] The server utilizes a generation AI model to calculate the optimal combination of multiple unused private rooms and applicants. It allocates rooms considering pre-set priority and urgency levels of applicants. Inputs include a list of users and information on unused rooms, and output is a recommended list of optimal matches. The AI ​​model optimizes allocation while considering fairness among users.

[0126] Step 5:

[0127] The server sends an alert email to the user if an unauthorized cancellation occurs. It uses the history of the unauthorized cancellation as input and generates the alert email as output. Furthermore, repeated violations will result in booking restrictions. The server will explain the restrictions to the user and encourage them to improve their future booking performance.

[0128] 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.

[0129] This invention combines a system for optimizing the reservation and use of indoor spaces with an emotion engine that recognizes user emotions, thereby enabling more effective and flexible utilization of indoor spaces. The system aims to improve utilization efficiency by using the emotion engine to evaluate the user's emotional state in real time and adjusting the allocation of indoor spaces based on the evaluation results.

[0130] Subject: Server

[0131] The server is equipped with an emotion engine and receives data from the user's device and sensing devices installed in the room. This data includes biometric information such as the user's voice, facial expressions, heart rate, and skin potential. The server comprehensively analyzes this data to estimate the user's stress level, satisfaction level, tension level, and other factors.

[0132] Subject: Server

[0133] The server flexibly adjusts the allocation of indoor spaces based on the estimated emotional state. For example, if a user is judged to have a high stress level, it can prioritize allocating a quiet indoor space to provide a calming environment. Furthermore, it facilitates appropriate communication by adjusting notification content and timing, taking into account the emotional state of the user.

[0134] Subject: User

[0135] Users can utilize an optimized room environment based on the evaluation results of the emotion engine. For example, if User A is determined to be very nervous before a meeting, the server can recommend a meeting room that provides a relaxing environment and send a notification to User A's terminal to automatically reserve that meeting room.

[0136] When implemented in this form, the present invention utilizes emotion recognition technology to go beyond mere physical allocation of indoor space and improve psychological comfort and user productivity. As a result, it is expected that overall work efficiency and ease of working in the office environment will improve.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The server receives biometric information such as voice, facial expressions, heart rate, and skin potential from the user's terminal or sensing devices installed in the room. This data is used to prepare for recognizing the user's current emotional state.

[0140] Step 2:

[0141] The server analyzes the received biometric information using an emotion engine to estimate the user's emotional state, including stress levels, satisfaction levels, and tension. This analysis helps to assess the user's current psychological state.

[0142] Step 3:

[0143] Based on the estimated emotional state, the server selects the most suitable room from a list of available indoor spaces. It prioritizes allocating quiet rooms to users experiencing high stress levels and comfortable spaces to users who need a relaxing environment.

[0144] Step 4:

[0145] The server notifies the user's terminal of the selected indoor space information. The notification includes the location of the assigned indoor space, the reservation time, and available characteristics (e.g., quietness, comfort), allowing the user to act accordingly.

[0146] Step 5:

[0147] Users check the information notified to their devices and utilize their assigned indoor space. During this time, additional monitoring of whether their emotional state has improved allows for feedback to be provided for future improvements.

[0148] Step 6:

[0149] The server stores all data from this usage process in a database, which is used to analyze user trends and make predictions. This improves the accuracy of allocations in subsequent uses and enables more appropriate use of space.

[0150] (Example 2)

[0151] 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".

[0152] In today's diverse office environments, there is a need to simultaneously achieve efficient use of indoor space and psychological comfort for users. However, current systems merely monitor the usage of reserved spaces and identify unused spaces, but they do not adequately consider the emotional state of users when allocating space. This has resulted in the challenge of not being able to maximize user productivity and comfort.

[0153] 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.

[0154] In this invention, the server includes sensing means for acquiring the user's biometric information, estimation means for analyzing the acquired biometric information and estimating the emotional state, and adjustment means for optimally allocating the indoor space based on the estimated emotional state. This enables the optimization of the indoor space according to the user's current emotional state, improving psychological comfort and increasing productivity in the overall office environment.

[0155] "Sensing means" refers to devices or methods for collecting a user's biometric information. Specifically, this includes acquiring data such as voice, facial expressions, heart rate, and skin potential.

[0156] An "estimation means" is a device or method for analyzing biometric information acquired by a sensing means to determine the user's emotional state. This allows for the estimation of stress levels, satisfaction levels, and tension levels.

[0157] A "regulating mechanism" refers to a device or method for optimally allocating the use of indoor space based on an estimated emotional state. It dynamically adjusts the allocation of space to provide an environment suitable for the user.

[0158] An "identification means" is a device or method that automatically identifies unused indoor space from collected data. This minimizes wasted space and enables efficient use.

[0159] "Notification means" refers to devices or methods for communicating reservation information of identified unused or optimized indoor spaces to prospective users or users.

[0160] A "distribution means" is a device or method that uses a generative model to optimally allocate multiple unused indoor spaces and prospective users. This maximizes the efficiency of indoor space utilization.

[0161] A "control device" is a device or method that issues an alert if the service is not used without permission and imposes reservation restrictions for continued violations. This helps to deter unauthorized use.

[0162] This invention is a system that evaluates the user's emotional state in real time and optimizes the allocation of indoor space. This system primarily functions with three components: a server, a terminal, and a user.

[0163] The server receives data from the user's terminal or from sensing devices installed in the room using sensing means. The sensing devices include microphones to collect sound, cameras to capture facial expressions, and wearable devices to measure heart rate. The data obtained is transmitted to the server as biometric information.

[0164] The server analyzes this biometric information by running it through a generative AI model as an estimation tool. This analysis uses algorithms to evaluate emotional states and estimate stress levels, satisfaction levels, and tension levels. Specific software examples include the use of commonly available AI platforms.

[0165] Based on the estimated emotional state, the server uses adjustment mechanisms to optimize the indoor space. This allows for space allocation tailored to the user's needs. For example, if a user's stress level is high and they need relaxation, a quiet space will be allocated.

[0166] The terminal is responsible for notifying users of recommended spaces and their reservation schedules. Information such as the availability of a specific space and reservation details are provided to the user's device in real time. This allows users to utilize the most suitable environment, both physically and psychologically.

[0167] For example, if the AI ​​determines that user A is experiencing high levels of stress during work, the server will assign the user to a quiet meeting room to help them concentrate. This information is immediately sent to user A's smartphone, and the user can use the meeting room at the designated time.

[0168] As an example of a prompt, inputting a question like, "Which room should be recommended to assign the optimal space to an employee with a high stress level in the office?" into the AI ​​model will enable more efficient and flexible use of space.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] Data collection

[0172] Subject: Server

[0173] The server receives biometric data from the user's terminal or sensing devices installed in the room. Inputs include data such as voice, facial expressions, heart rate, and skin potential. The server aggregates this data in one place to prepare for subsequent analysis. Specifically, it receives information from sensing devices sequentially and stores it in a database.

[0174] Step 2:

[0175] Data analysis and sentiment estimation

[0176] Subject: Server

[0177] The server uses the biometric information collected in Step 1 as input and performs analysis using a generative AI model. Specifically, it combines data from speech recognition, facial recognition, and heart rate measurement to estimate the user's stress level, tension level, and satisfaction level. The output represents the user's current emotional state as numerical or categorical data. The specific operation of emotion estimation involves analyzing the data through an AI algorithm and updating the emotional profile in real time.

[0178] Step 3:

[0179] Optimization of indoor space

[0180] Subject: Server

[0181] Using the emotional state data obtained in Step 2 as input, the server adjusts the allocation of indoor spaces. During this process, it consults a database beforehand to select the most suitable environment. Specifically, it automatically selects environments such as quiet rooms or relaxation rooms that correspond to the estimated emotional state. As output, information about the selected indoor spaces is registered in the reservation system.

[0182] Step 4:

[0183] Notifications and feedback

[0184] Subject: terminal

[0185] The terminal notifies the user of the indoor space information determined in step 3. The input is reservation status data sent from the server. Based on this information, the terminal displays details of available time slots and locations to the user. Specifically, it sends a push notification to the smartphone or PC so that the user can check it immediately. The output is that the notification to the user is complete and the user can use the indoor space appropriately.

[0186] Step 5:

[0187] User space utilization

[0188] Subject: User

[0189] Based on the notification received in step 4, the user utilizes the optimized indoor space. The input is notification information from the device. Specifically, the user moves to the designated time and place and uses that space to improve their psychological comfort. The output is feedback on the used space sent to the server, which is used to optimize the space for future use.

[0190] (Application Example 2)

[0191] 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".

[0192] In modern factory environments, the lack of appropriate work environments based on workers' emotional states leads to challenges such as decreased work efficiency and worker satisfaction. Furthermore, optimizing the work environment using emotion analysis is difficult.

[0193] 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.

[0194] In this invention, the server includes detection means for monitoring the use of reserved internal space, identification means for automatically extracting unused internal space based on the detected information, and control means for collecting and analyzing user emotional data using an emotional analysis engine and optimizing the work environment in real time. This enables the optimization of the work environment according to the emotional state of the worker, and is expected to improve productivity and satisfaction.

[0195] A "reserved internal space" is a specific location where usage is monitored, and where access rights are pre-configured by the user.

[0196] "Detection means" refers to means of monitoring the usage status of reserved interior spaces and collecting necessary information.

[0197] "Identification means" refers to a means for determining whether the internal space is unused based on the collected information, and for identifying the result.

[0198] "Communication means" refers to the means of transmitting information about identified, unused internal space to applicants.

[0199] An "emotion analysis engine" is software or a device that has the technology to analyze a worker's emotional state based on biological information.

[0200] "Control means" refers to methods for adjusting and optimizing the work environment based on data obtained through emotion analysis.

[0201] A "generative model" is an algorithm or system that processes user emotions and environmental data to generate instructions for optimal environmental adjustments.

[0202] This invention relates to a system that optimizes workspaces in a factory environment while considering the emotional state of workers. This system utilizes an emotion analysis engine to monitor the usage status of reserved internal spaces, identify unused spaces based on that information, and propose an optimal workspace.

[0203] The server collects data from sensing devices (cameras, microphones, biometric sensors, etc.) installed in the internal space and analyzes it in real time using AI services on the cloud (e.g., Google Cloud AI, Microsoft Azure AI). Based on the analysis results, emotional data of the workers is extracted, and their stress levels, satisfaction levels, and other states are estimated. Based on this information, an AI model proposes the optimal work environment for the workers (e.g., music selection, lighting adjustments, air conditioning settings), and the environment is immediately adjusted by the control system.

[0204] For example, if a server determines that a worker is experiencing stress, it will select relaxing music and optimize the volume settings. Similarly, if a worker is deemed fatigued, the system will adjust the ambient lighting to reduce the worker's burden. In this process, an example prompt given to the AI ​​model might be, "Generate an optimized work environment based on the following emotional data."

[0205] This system allows factory workers to work in an environment optimized to their emotional state, which is expected to improve productivity and working conditions.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The server collects biometric data (such as voice, facial expressions, heart rate, and skin potential) as input from sensing devices installed in the internal space. This data is digitized and stored in a database.

[0209] Step 2:

[0210] The server sends the collected data to an AI service in the cloud, where it is analyzed in real time by an emotion analysis engine. This analysis estimates the emotional state of the workers, such as their stress levels and satisfaction levels, from the input data.

[0211] Step 3:

[0212] The server uses a generative AI model based on the estimated emotional state to generate prompts suggesting the optimal work environment. Specifically, it might form a prompt such as, "Please generate an optimized work environment based on the following emotional data."

[0213] Step 4:

[0214] Based on the generated prompts, the server uses control mechanisms to perform specific environmental adjustments (e.g., music playback, lighting adjustments, air conditioning settings, etc.). This output automatically sets up a work environment that matches the worker's emotional state.

[0215] Step 5:

[0216] Users can utilize the adjusted work environment to perform their tasks efficiently. User feedback and emotional changes are collected again by sensing devices, and the system is continuously optimized.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] The present invention provides a system for optimizing the use of indoor spaces in an office environment. This system includes a series of automated processes that monitor the usage status of reserved indoor spaces in real time, identify unused indoor spaces, and notify those on the waiting list. Furthermore, by using AI technology to optimally allocate multiple unused indoor spaces to those wishing to use them, efficient utilization can be achieved.

[0234] Subject: Server

[0235] The server is responsible for receiving various data from sensors installed in the indoor space. These sensors, such as motion sensors and door opening / closing sensors, monitor the usage status of the room. The received data is analyzed within the server, and if it is determined that a reserved space is not being used, the server proceeds to the next step using that information.

[0236] Subject: Server

[0237] The server uses information about unused indoor spaces to refer to a list of users who are on a waiting list. It then automatically sends notifications to these users. These notifications include the type, location, and available time of the indoor space, and are designed to encourage immediate use. This communication is conducted via email, messaging applications, and internal notification systems.

[0238] Subject: Server

[0239] Furthermore, the AI ​​system on the server acquires information on multiple unused indoor spaces and prospective users, and performs the most efficient and fair matching. This AI system calculates the optimal combination by considering pre-set criteria, such as the urgency of the request for use, the importance of the meeting, and the user's priority. Based on the results, it supports the effective use of indoor spaces by notifying prospective users.

[0240] Subject: Server

[0241] If a user fails to use the room without prior notice, the server will send an alert email to that user. This email will contain details of the violation and future precautions. Furthermore, if a user has a certain number of no-shows within a certain period, the server will take measures to restrict that user from booking meeting rooms. This restriction will prevent new bookings within a certain period and will be implemented by integrating with existing booking systems such as the Google Calendar API.

[0242] Thus, the system of the present invention, by combining sensors and AI technology, appropriately manages the usage status of reserved indoor spaces and promotes efficient use. As a result, it can contribute to improving productivity in the office environment.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] The server begins receiving data from sensors installed in the room. These sensors, such as motion sensors and door open / close sensors, continuously monitor the room's usage. The received data is sent to the server and prepared for analysis.

[0246] Step 2:

[0247] The server analyzes the received data to determine whether the room space is reserved but unused. If it is determined to be unused, it sets the "unused" flag in the database.

[0248] Step 3:

[0249] The server retrieves information on indoor spaces that have been flagged as "unused" and checks the list of users who have registered for the waiting list in advance. It then prepares to send notifications to the devices of the relevant users.

[0250] Step 4:

[0251] The server notifies users' devices of information about unused indoor spaces. The notification includes the location and availability time of the indoor space. The device then displays the notification to the user, facilitating their use.

[0252] Step 5:

[0253] The AI ​​model on the server retrieves all unused indoor spaces and potential users, and calculates the most efficient combination. This includes evaluations based on user-defined priorities and usage purposes.

[0254] Step 6:

[0255] The server assigns the most suitable indoor space to each user based on the AI ​​model's calculations and notifies the user's device accordingly. The user can then begin using the assigned indoor space according to this notification.

[0256] Step 7:

[0257] The server will send an alert email to the person who made the reservation if they cancel without notice. This email will remind the person about the importance of avoiding no-shows and encourage them to take measures to prevent recurrence.

[0258] Step 8:

[0259] The server will impose booking restrictions on a user if they have a certain number of no-shows. This will restrict their ability to make new bookings and limit their access to the booking system for a certain period.

[0260] (Example 1)

[0261] 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."

[0262] The efficiency of space utilization within companies and organizations can decrease when spaces are reserved but not actually used. Furthermore, repeated unauthorized cancellations can affect other users and lead to wasted resources. To address these challenges, more effective and equitable optimization of space utilization is necessary.

[0263] 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.

[0264] In this invention, the server includes acquisition means for monitoring the usage status of reserved partitions, analysis means for automatically determining unused partitions based on the acquired information, and notification means for notifying registered users of the determined unused partitions. This enables efficient use of resources by accurately monitoring partition usage and promptly encouraging their use as needed.

[0265] "Acquisition means" refers to a device or system that has the function of monitoring the usage status of a section and collecting relevant information.

[0266] "Analysis means" refers to a device or software that has the function of determining whether a compartment is unused based on the acquired information.

[0267] "Notification method" refers to a device or system that has the function of notifying registered users of the determined unused area information.

[0268] A "generative model" is an algorithm or program used to calculate the optimal allocation based on information about multiple unused plots and prospective users.

[0269] "Management measures" refer to devices or systems that have the function of issuing warnings for reservations that have not been used without permission, and implementing reservation restrictions if the violation continues.

[0270] A "detection device" is a sensor or device installed within a designated area and used to detect physical conditions or human movement.

[0271] An "information device" is an electronic device that is owned or used by a user and is capable of sending and receiving information.

[0272] The system of this invention is designed to maximize the efficiency of partition utilization within companies and organizations. This system mainly consists of servers, terminals, and users, and each element works closely together to achieve effective management and utilization of partitions.

[0273] The server collects data from motion sensors and door open / close sensors installed within the area. These sensors transmit data to the server via Bluetooth or Wi-Fi to monitor the area's usage. This data is centrally stored in a database for later analysis.

[0274] The server uses a generative AI model based on collected data to automatically determine unused parking spaces. The generative AI model employs machine learning algorithms such as the Scikit-learn library to compare past usage patterns with real-time data, resulting in highly accurate determinations. Based on these determinations, the server notifies registered users that unused parking spaces are available. These notifications are sent automatically via email or messaging apps, ensuring immediacy.

[0275] Furthermore, the server processes information on multiple unused plots and those wishing to use them to calculate the optimal matching. This ensures that plots are used in the most effective and fair way possible. The AI ​​performs this optimization using a scoring system that takes into account the urgency of the request and the importance of the plot.

[0276] For example, if meeting room A is reserved within the company, but the motion sensor does not detect any activity for more than an hour, the server will determine that the meeting room is unused and automatically notify user B, who is on the list of potential users, that "Meeting room A is now available."

[0277] Examples of prompt statements include:

[0278] "How can I use AI to optimize the booking status of unused meeting rooms in my office?"

[0279] There is.

[0280] With such a system, not only can users utilize the compartments without waste, but they can also issue warnings to non - using reservants and impose reservation restrictions if necessary, thereby improving the overall utilization efficiency.

[0281] The flow of the specific process in Example 1 will be described using FIG. 11.

[0282] Step 1:

[0283] The server acquires data from the sensors within the compartment. The sensors include a human presence sensor and a door opening / closing sensor. These sensors transmit data via Bluetooth or Wi - Fi. The input includes real - time data from the sensors. The server stores this data in the database and uses it to grasp the current usage status of the compartment.

[0284] Step 2:

[0285] The server analyzes the data in the database to identify unused compartments. The usage status data collected in Step 1 is used as the input. The generated AI model is utilized to perform data processing for comparison with past usage patterns and determination of unused status. The server outputs a list of compartments determined to be unused.

[0286] Step 3:

[0287] The server collates the information of the compartments determined to be unused with the list of potential users. The above - mentioned output list is input, and the server notifies the registered potential users of the available compartments. The notification is distributed via email or a messaging app and includes details of the location and time of the available compartments.

[0288] Step 4:

[0289] The server uses AI to process information on multiple unused partitions and those wishing to use them, and then makes the optimal allocation. Inputs include a list of unused partitions and priority information for those wishing to use them. Using a generative AI model, it calculates the optimized allocation by scoring based on urgency and importance, and then outputs the result to notify those wishing to use the partitions.

[0290] Step 5:

[0291] The server notifies users who have made reservations but have not used them without permission. Based on the data determined to be unused in Step 2, a warning email for unauthorized cancellation is sent to the user. If violations occur repeatedly during this process, the server will output a control signal in conjunction with the reservation system to impose reservation restrictions.

[0292] Through these steps, the system enables accurate monitoring and efficient partition reallocation, thereby improving utilization efficiency.

[0293] (Application Example 1)

[0294] 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."

[0295] In offices and commercial facilities, reserved indoor spaces are often left unused without permission, resulting in problems where those who wish to use the space are unable to do so. Furthermore, the inefficiency and fair allocation of space to users reduces the efficiency of facility management. There is a need to resolve these issues and improve the efficiency of indoor space utilization.

[0296] 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.

[0297] In this invention, the server includes detection means for monitoring the use of reserved indoor spaces, identification means for automatically identifying unused indoor spaces based on the detected information, and allocation means that utilize a generative model to optimally allocate multiple unused indoor spaces and users. This makes it possible to improve the efficiency of the facility through the optimal use of indoor spaces and to improve the convenience of users.

[0298] A "reserved indoor space" refers to a physical area that has been secured for use in advance by a user.

[0299] "Detection means" refers to a device or method for monitoring the usage status of an indoor space using sensors or other technologies.

[0300] "Identification means" refers to a process for determining the usage status of an indoor space based on detected data and identifying whether or not it is unused.

[0301] "Notification means" refers to a method or system for communicating information about an unused indoor space to those who wish to use it.

[0302] A "generative model" is a mathematical or algorithmic structure that utilizes AI technology to optimally match the user with the indoor space.

[0303] A "distribution method" is a method that uses a generative model to efficiently connect multiple indoor spaces with potential users.

[0304] A "control mechanism" is a function or process that imposes a penalty if a reservation is left unused without permission and restricts subsequent reservations.

[0305] "Private rooms in commercial facilities" refers to small spaces or rooms within commercial facilities such as shopping malls and stores that are used for specific purposes.

[0306] The "recommendation means" is an algorithm or system for proposing available private rooms to users.

[0307] The system for implementing this invention promotes the efficient use of private rooms within commercial facilities. Here, the program of the system, the hardware and software used, and specific examples are shown.

[0308] First, the server monitors the usage status inside the room in real time through sensors installed in the private rooms within the commercial facility. By using a presence sensor or a sensor that detects the opening and closing of the door, it is possible to accurately grasp the unused state. The acquired data is sent to the analysis module within the server.

[0309] The server analyzes various data using Python and Flask to identify unused private rooms. Firebase performs real-time management of the data, and distribution by AI is carried out as necessary. By utilizing the generation model, optimal distribution is implemented considering the priorities and usage purposes of the users who wish to use.

[0310] As a notification means, the server notifies the user's terminal of the current situation via a smartphone app using React Native. If there is an unused private room, the availability information is sent to the users who wish to use it to promote utilization. Also, alert emails are sent to users who continuously cancel without permission, and penalties are imposed.

[0311] As a specific example, for instance, when a meeting room within a commercial facility suddenly becomes vacant, this system efficiently notifies other users, enabling them to use it without wasting the vacant time. As a result, the specific-use spaces in the facility are effectively utilized.

[0312] As an example sentence when sending a prompt to the generation AI model, a sentence such as "Analyze the operating status of the private rooms within the commercial facility this week and generate a list of recommended users in case of unused rooms. There are many stayers on weekends, especially with a peak in the afternoon." can be utilized.

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The server receives usage data from motion sensors and door sensors installed within the commercial facility. This data indicates whether each individual room is currently in use or unused. The server monitors this input data in real time and sets criteria for determining usage status. As a result, the server can identify which individual rooms are unused.

[0316] Step 2:

[0317] The server analyzes usage data and identifies information about private rooms that have been identified as unused. The input here is usage data obtained from sensors, and the output is identification information for unused private rooms. Through data analysis, it identifies private rooms that have not been used within a specific time frame and sets an unused flag.

[0318] Step 3:

[0319] The server uses information on unused private rooms to refer to a list of people who wish to use them and sends notifications to the terminals of those who wish to use them. It uses the list of people who wish to use the rooms and availability information as input and generates notification content as output. The notification content includes the type of private room, location, and available time, and is sent to the user's terminal via email or messaging app.

[0320] Step 4:

[0321] The server utilizes a generation AI model to calculate the optimal combination of multiple unused private rooms and applicants. It allocates rooms considering pre-set priority and urgency levels of applicants. Inputs include a list of users and information on unused rooms, and output is a recommended list of optimal matches. The AI ​​model optimizes allocation while considering fairness among users.

[0322] Step 5:

[0323] The server sends an alert email to the user if an unauthorized cancellation occurs. It uses the history of the unauthorized cancellation as input and generates the alert email as output. Furthermore, repeated violations will result in booking restrictions. The server will explain the restrictions to the user and encourage them to improve their future booking performance.

[0324] 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.

[0325] This invention combines a system for optimizing the reservation and use of indoor spaces with an emotion engine that recognizes user emotions, thereby enabling more effective and flexible utilization of indoor spaces. The system aims to improve utilization efficiency by using the emotion engine to evaluate the user's emotional state in real time and adjusting the allocation of indoor spaces based on the evaluation results.

[0326] Subject: Server

[0327] The server is equipped with an emotion engine and receives data from the user's device and sensing devices installed in the room. This data includes biometric information such as the user's voice, facial expressions, heart rate, and skin potential. The server comprehensively analyzes this data to estimate the user's stress level, satisfaction level, tension level, and other factors.

[0328] Subject: Server

[0329] The server flexibly adjusts the allocation of indoor spaces based on the estimated emotional state. For example, if a user is judged to have a high stress level, it can prioritize allocating a quiet indoor space to provide a calming environment. Furthermore, it facilitates appropriate communication by adjusting notification content and timing, taking into account the emotional state of the user.

[0330] Subject: User

[0331] Users can utilize an optimized room environment based on the evaluation results of the emotion engine. For example, if User A is determined to be very nervous before a meeting, the server can recommend a meeting room that provides a relaxing environment and send a notification to User A's terminal to automatically reserve that meeting room.

[0332] When implemented in this form, the present invention utilizes emotion recognition technology to go beyond mere physical allocation of indoor space and improve psychological comfort and user productivity. As a result, it is expected that overall work efficiency and ease of working in the office environment will improve.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] The server receives biometric information such as voice, facial expressions, heart rate, and skin potential from the user's terminal or sensing devices installed in the room. This data is used to prepare for recognizing the user's current emotional state.

[0336] Step 2:

[0337] The server analyzes the received biometric information using an emotion engine to estimate the user's emotional state, including stress levels, satisfaction levels, and tension. This analysis helps to assess the user's current psychological state.

[0338] Step 3:

[0339] Based on the estimated emotional state, the server selects the most suitable room from a list of available indoor spaces. It prioritizes allocating quiet rooms to users experiencing high stress levels and comfortable spaces to users who need a relaxing environment.

[0340] Step 4:

[0341] The server notifies the user's terminal of the selected indoor space information. The notification includes the location of the assigned indoor space, the reservation time, and available characteristics (e.g., quietness, comfort), allowing the user to act accordingly.

[0342] Step 5:

[0343] Users check the information notified to their devices and utilize their assigned indoor space. During this time, additional monitoring of whether their emotional state has improved allows for feedback to be provided for future improvements.

[0344] Step 6:

[0345] The server stores all data from this usage process in a database, which is used to analyze user trends and make predictions. This improves the accuracy of allocations in subsequent uses and enables more appropriate use of space.

[0346] (Example 2)

[0347] 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".

[0348] In today's diverse office environments, there is a need to simultaneously achieve efficient use of indoor space and psychological comfort for users. However, current systems merely monitor the usage of reserved spaces and identify unused spaces, but they do not adequately consider the emotional state of users when allocating space. This has resulted in the challenge of not being able to maximize user productivity and comfort.

[0349] 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.

[0350] In this invention, the server includes sensing means for acquiring the user's biometric information, estimation means for analyzing the acquired biometric information and estimating the emotional state, and adjustment means for optimally allocating the indoor space based on the estimated emotional state. This enables the optimization of the indoor space according to the user's current emotional state, improving psychological comfort and increasing productivity in the overall office environment.

[0351] "Sensing means" refers to devices or methods for collecting a user's biometric information. Specifically, this includes acquiring data such as voice, facial expressions, heart rate, and skin potential.

[0352] An "estimation means" is a device or method for analyzing biometric information acquired by a sensing means to determine the user's emotional state. This allows for the estimation of stress levels, satisfaction levels, and tension levels.

[0353] A "regulating mechanism" refers to a device or method for optimally allocating the use of indoor space based on an estimated emotional state. It dynamically adjusts the allocation of space to provide an environment suitable for the user.

[0354] An "identification means" is a device or method that automatically identifies unused indoor space from collected data. This minimizes wasted space and enables efficient use.

[0355] "Notification means" refers to devices or methods for communicating reservation information of identified unused or optimized indoor spaces to prospective users or users.

[0356] A "distribution means" is a device or method that uses a generative model to optimally allocate multiple unused indoor spaces and prospective users. This maximizes the efficiency of indoor space utilization.

[0357] A "control device" is a device or method that issues an alert if the service is not used without permission and imposes reservation restrictions for continued violations. This helps to deter unauthorized use.

[0358] This invention is a system that evaluates the user's emotional state in real time and optimizes the allocation of indoor space. This system primarily functions with three components: a server, a terminal, and a user.

[0359] The server receives data from the user's terminal or from sensing devices installed in the room using sensing means. The sensing devices include microphones to collect sound, cameras to capture facial expressions, and wearable devices to measure heart rate. The data obtained is transmitted to the server as biometric information.

[0360] The server analyzes this biometric information by running it through a generative AI model as an estimation tool. This analysis uses algorithms to evaluate emotional states and estimate stress levels, satisfaction levels, and tension levels. Specific software examples include the use of commonly available AI platforms.

[0361] Based on the estimated emotional state, the server uses adjustment mechanisms to optimize the indoor space. This allows for space allocation tailored to the user's needs. For example, if a user's stress level is high and they need relaxation, a quiet space will be allocated.

[0362] The terminal is responsible for notifying users of recommended spaces and their reservation schedules. Information on the availability of specific spaces and reservation details are provided to the user's device in real time. This allows users to utilize the most suitable environment, both physically and psychologically.

[0363] For example, if the AI ​​determines that user A is experiencing high levels of stress during work, the server will assign the user to a quiet meeting room to help them concentrate. This information is immediately sent to user A's smartphone, and the user can use the meeting room at the designated time.

[0364] As an example of a prompt, inputting a question like, "Which room should be recommended to assign the optimal space to an employee with a high stress level in the office?" into the AI ​​model will enable more efficient and flexible use of space.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] Data collection

[0368] Subject: Server

[0369] The server receives biometric data from the user's terminal or sensing devices installed in the room. Inputs include data such as voice, facial expressions, heart rate, and skin potential. The server aggregates this data in one place to prepare for subsequent analysis. Specifically, it receives information from sensing devices sequentially and stores it in a database.

[0370] Step 2:

[0371] Data analysis and sentiment estimation

[0372] Subject: Server

[0373] The server uses the biometric information collected in Step 1 as input and performs analysis using a generative AI model. Specifically, it combines data from speech recognition, facial recognition, and heart rate measurement to estimate the user's stress level, tension level, and satisfaction level. The output represents the user's current emotional state as numerical or categorical data. The specific operation of emotion estimation involves analyzing the data through an AI algorithm and updating the emotional profile in real time.

[0374] Step 3:

[0375] Optimization of indoor space

[0376] Subject: Server

[0377] Using the emotional state data obtained in Step 2 as input, the server adjusts the allocation of indoor spaces. During this process, it consults a database beforehand to select the most suitable environment. Specifically, it automatically selects environments such as quiet rooms or relaxation rooms that correspond to the estimated emotional state. As output, information about the selected indoor spaces is registered in the reservation system.

[0378] Step 4:

[0379] Notifications and feedback

[0380] Subject: terminal

[0381] The terminal notifies the user of the indoor space information determined in step 3. The input is reservation status data sent from the server. Based on this information, the terminal displays details of available time slots and locations to the user. Specifically, it sends a push notification to the smartphone or PC so that the user can check it immediately. The output is that the notification to the user is complete and the user can use the indoor space appropriately.

[0382] Step 5:

[0383] User space utilization

[0384] Subject: User

[0385] Based on the notification received in step 4, the user utilizes the optimized indoor space. The input is notification information from the device. Specifically, the user moves to the designated time and place and uses that space to improve their psychological comfort. The output is feedback on the used space sent to the server, which is used to optimize the space for future use.

[0386] (Application Example 2)

[0387] 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."

[0388] In modern factory environments, the lack of appropriate work environments based on workers' emotional states leads to challenges such as decreased work efficiency and worker satisfaction. Furthermore, optimizing the work environment using emotion analysis is difficult.

[0389] 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.

[0390] In this invention, the server includes detection means for monitoring the use of reserved internal space, identification means for automatically extracting unused internal space based on the detected information, and control means for collecting and analyzing user emotional data using an emotional analysis engine and optimizing the work environment in real time. This enables the optimization of the work environment according to the emotional state of the worker, and is expected to improve productivity and satisfaction.

[0391] A "reserved internal space" is a specific location where usage is monitored, and where access rights are pre-configured by the user.

[0392] "Detection means" refers to means of monitoring the usage status of reserved interior spaces and collecting necessary information.

[0393] "Identification means" refers to a means for determining whether the internal space is unused based on the collected information, and for identifying the result.

[0394] "Communication means" refers to the means of transmitting information about identified, unused internal space to applicants.

[0395] An "emotion analysis engine" is software or a device that has the technology to analyze a worker's emotional state based on biological information.

[0396] "Control means" refers to methods for adjusting and optimizing the work environment based on data obtained through emotion analysis.

[0397] A "generative model" is an algorithm or system that processes user emotions and environmental data to generate instructions for optimal environmental adjustments.

[0398] This invention relates to a system that optimizes workspaces in a factory environment while considering the emotional state of workers. This system utilizes an emotion analysis engine to monitor the usage status of reserved internal spaces, identify unused spaces based on that information, and propose an optimal workspace.

[0399] The server collects data from sensing devices (cameras, microphones, biometric sensors, etc.) installed in the internal space and analyzes it in real time using AI services on the cloud (e.g., Google Cloud AI, Microsoft Azure AI). Based on the analysis results, emotional data of the workers is extracted, and their stress levels, satisfaction levels, and other states are estimated. Based on this information, an AI model proposes the optimal work environment for the workers (e.g., music selection, lighting adjustments, air conditioning settings), and the environment is immediately adjusted by the control system.

[0400] For example, if a server determines that a worker is experiencing stress, it will select relaxing music and optimize the volume settings. Similarly, if a worker is deemed fatigued, the system will adjust the ambient lighting to reduce the worker's burden. In this process, an example prompt given to the AI ​​model might be, "Generate an optimized work environment based on the following emotional data."

[0401] This system allows factory workers to work in an environment optimized to their emotional state, which is expected to improve productivity and working conditions.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The server collects biometric data (such as voice, facial expressions, heart rate, and skin potential) as input from sensing devices installed in the internal space. This data is digitized and stored in a database.

[0405] Step 2:

[0406] The server sends the collected data to an AI service in the cloud, where it is analyzed in real time by an emotion analysis engine. This analysis estimates the emotional state of the workers, such as their stress levels and satisfaction levels, from the input data.

[0407] Step 3:

[0408] The server uses a generative AI model based on the estimated emotional state to generate prompts suggesting the optimal work environment. Specifically, it might form a prompt such as, "Please generate an optimized work environment based on the following emotional data."

[0409] Step 4:

[0410] Based on the generated prompts, the server uses control mechanisms to perform specific environmental adjustments (e.g., music playback, lighting adjustments, air conditioning settings, etc.). This output automatically sets up a work environment that matches the worker's emotional state.

[0411] Step 5:

[0412] Users can utilize the adjusted work environment to perform their tasks efficiently. User feedback and emotional changes are collected again by sensing devices, and the system is continuously optimized.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] [Third Embodiment]

[0417] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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".

[0429] The present invention provides a system for optimizing the use of indoor spaces in an office environment. This system includes a series of automated processes that monitor the usage status of reserved indoor spaces in real time, identify unused indoor spaces, and notify those on the waiting list. Furthermore, by using AI technology to optimally allocate multiple unused indoor spaces to those wishing to use them, efficient utilization can be achieved.

[0430] Subject: Server

[0431] The server is responsible for receiving various data from sensors installed in the indoor space. These sensors, such as motion sensors and door opening / closing sensors, monitor the usage status of the room. The received data is analyzed within the server, and if it is determined that a reserved space is not being used, the server proceeds to the next step using that information.

[0432] Subject: Server

[0433] The server uses information about unused indoor spaces to refer to a list of users who are on a waiting list. It then automatically sends notifications to these users. These notifications include the type, location, and available time of the indoor space, and are designed to encourage immediate use. This communication is conducted via email, messaging applications, and internal notification systems.

[0434] Subject: Server

[0435] Furthermore, the AI ​​system on the server acquires information on multiple unused indoor spaces and prospective users, and performs the most efficient and fair matching. This AI system calculates the optimal combination by considering pre-set criteria, such as the urgency of the request for use, the importance of the meeting, and the user's priority. Based on the results, it supports the effective use of indoor spaces by notifying prospective users.

[0436] Subject: Server

[0437] If a user fails to use the room without prior notice, the server will send an alert email to that user. This email will contain details of the violation and future precautions. Furthermore, if a user has a certain number of no-shows within a certain period, the server will take measures to restrict that user from booking meeting rooms. This restriction will prevent new bookings within a certain period and will be implemented by integrating with existing booking systems such as the Google Calendar API.

[0438] Thus, the system of the present invention, by combining sensors and AI technology, appropriately manages the usage status of reserved indoor spaces and promotes efficient use. As a result, it can contribute to improving productivity in the office environment.

[0439] The following describes the processing flow.

[0440] Step 1:

[0441] The server begins receiving data from sensors installed in the room. These sensors, such as motion sensors and door open / close sensors, continuously monitor the room's usage. The received data is sent to the server and prepared for analysis.

[0442] Step 2:

[0443] The server analyzes the received data to determine whether the room space is reserved but unused. If it is determined to be unused, it sets the "unused" flag in the database.

[0444] Step 3:

[0445] The server retrieves information on indoor spaces that have been flagged as "unused" and checks the list of users who have registered for the waiting list in advance. It then prepares to send notifications to the devices of the relevant users.

[0446] Step 4:

[0447] The server notifies users' devices of information about unused indoor spaces. The notification includes the location and availability time of the indoor space. The device then displays the notification to the user, facilitating their use.

[0448] Step 5:

[0449] The AI ​​model on the server retrieves all unused indoor spaces and potential users, and calculates the most efficient combination. This includes evaluations based on user-defined priorities and usage purposes.

[0450] Step 6:

[0451] The server assigns the most suitable indoor space to each user based on the AI ​​model's calculations and notifies the user's device accordingly. The user can then begin using the assigned indoor space according to this notification.

[0452] Step 7:

[0453] The server will send an alert email to the person who made the reservation if they cancel without notice. This email will remind the person about the importance of avoiding no-shows and encourage them to take measures to prevent recurrence.

[0454] Step 8:

[0455] The server will impose booking restrictions on a user if they have a certain number of no-shows. This will restrict their ability to make new bookings and limit their access to the booking system for a certain period.

[0456] (Example 1)

[0457] 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."

[0458] The efficiency of space utilization within companies and organizations can decrease when spaces are reserved but not actually used. Furthermore, repeated unauthorized cancellations can affect other users and lead to wasted resources. To address these challenges, more effective and equitable optimization of space utilization is necessary.

[0459] 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.

[0460] In this invention, the server includes acquisition means for monitoring the usage status of reserved partitions, analysis means for automatically determining unused partitions based on the acquired information, and notification means for notifying registered users of the determined unused partitions. This enables efficient use of resources by accurately monitoring partition usage and promptly encouraging their use as needed.

[0461] "Acquisition means" refers to a device or system that has the function of monitoring the usage status of a section and collecting relevant information.

[0462] "Analysis means" refers to a device or software that has the function of determining whether a compartment is unused based on the acquired information.

[0463] "Notification method" refers to a device or system that has the function of notifying registered users of the determined unused area information.

[0464] A "generative model" is an algorithm or program used to calculate the optimal allocation based on information about multiple unused plots and prospective users.

[0465] "Management measures" refer to devices or systems that have the function of issuing warnings for reservations that have not been used without permission, and implementing reservation restrictions if the violation continues.

[0466] A "detection device" is a sensor or device installed within a designated area and used to detect physical conditions or human movement.

[0467] An "information device" is an electronic device that is owned or used by a user and is capable of sending and receiving information.

[0468] The system of this invention is designed to maximize the efficiency of partition utilization within companies and organizations. This system mainly consists of servers, terminals, and users, and each element works closely together to achieve effective management and utilization of partitions.

[0469] The server collects data from motion sensors and door open / close sensors installed within the area. These sensors transmit data to the server via Bluetooth or Wi-Fi to monitor the area's usage. This data is centrally stored in a database for later analysis.

[0470] The server uses a generative AI model based on collected data to automatically determine unused parking spaces. The generative AI model employs machine learning algorithms such as the Scikit-learn library to compare past usage patterns with real-time data, resulting in highly accurate determinations. Based on these determinations, the server notifies registered users that unused parking spaces are available. These notifications are sent automatically via email or messaging apps, ensuring immediacy.

[0471] Furthermore, the server processes information on multiple unused plots and those wishing to use them to calculate the optimal matching. This ensures that plots are used in the most effective and fair way possible. The AI ​​performs this optimization using a scoring system that takes into account the urgency of the request and the importance of the plot.

[0472] For example, if meeting room A is reserved within the company, but the motion sensor does not detect any activity for more than an hour, the server will determine that the meeting room is unused and automatically notify user B, who is on the list of potential users, that "Meeting room A is now available."

[0473] Examples of prompt statements include:

[0474] "How can I use AI to optimize the booking status of unused meeting rooms in my office?"

[0475] There is.

[0476] Such a system not only allows users to utilize space efficiently, but also improves overall utilization efficiency by issuing warnings to those who have made reservations but are not using the space, and by imposing reservation restrictions if necessary.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The server acquires data from sensors within the area. These sensors include motion sensors and door open / close sensors. These sensors transmit data via Bluetooth or Wi-Fi. The input includes real-time data from the sensors. The server stores this data in a database and uses it to understand the current usage status of the area.

[0480] Step 2:

[0481] The server analyzes the data in the database to identify unused partitions. The usage data collected in step 1 is used as input. A generative AI model is used to process the data by comparing it with past usage patterns to determine if a partition is unused. The server outputs a list of partitions that it has determined to be unused.

[0482] Step 3:

[0483] The server compares information about unused sections with the list of prospective users. Based on the output list, the server notifies registered prospective users of available sections. The notification is delivered via email or messaging app and includes details about the location and time of available sections.

[0484] Step 4:

[0485] The server uses AI to process information on multiple unused partitions and those wishing to use them, and then makes the optimal allocation. Inputs include a list of unused partitions and priority information for those wishing to use them. Using a generative AI model, it calculates the optimized allocation by scoring based on urgency and importance, and then outputs the result to notify those wishing to use the partitions.

[0486] Step 5:

[0487] The server notifies users who have made reservations but have not used them without permission. Based on the data determined to be unused in Step 2, a warning email for unauthorized cancellation is sent to the user. If violations occur repeatedly during this process, the server will output a control signal in conjunction with the reservation system to impose reservation restrictions.

[0488] Through these steps, the system enables accurate monitoring and efficient partition reallocation, thereby improving utilization efficiency.

[0489] (Application Example 1)

[0490] 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."

[0491] In offices and commercial facilities, reserved indoor spaces are often left unused without permission, resulting in problems where those who wish to use the space are unable to do so. Furthermore, the inefficiency and fair allocation of space to users reduces the efficiency of facility management. There is a need to resolve these issues and improve the efficiency of indoor space utilization.

[0492] 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.

[0493] In this invention, the server includes detection means for monitoring the use of reserved indoor spaces, identification means for automatically identifying unused indoor spaces based on the detected information, and allocation means that utilize a generative model to optimally allocate multiple unused indoor spaces and users. This makes it possible to improve the efficiency of the facility through the optimal use of indoor spaces and to improve the convenience of users.

[0494] A "reserved indoor space" refers to a physical area that has been secured for use in advance by a user.

[0495] "Detection means" refers to a device or method for monitoring the usage status of an indoor space using sensors or other technologies.

[0496] "Identification means" refers to a process for determining the usage status of an indoor space based on detected data and identifying whether or not it is unused.

[0497] "Notification means" refers to a method or system for communicating information about an unused indoor space to those who wish to use it.

[0498] A "generative model" is a mathematical or algorithmic structure that utilizes AI technology to optimally match the user with the indoor space.

[0499] A "distribution method" is a method that uses a generative model to efficiently connect multiple indoor spaces with potential users.

[0500] A "control mechanism" is a function or process that imposes a penalty if a reservation is left unused without permission and restricts subsequent reservations.

[0501] "Private rooms in commercial facilities" refers to small spaces or rooms within commercial facilities such as shopping malls and stores that are used for specific purposes.

[0502] A "recommendation method" refers to an algorithm or system used to suggest available private rooms to users.

[0503] The system for implementing this invention facilitates the efficient use of private rooms within commercial facilities. Herein, we present the system's program, the hardware and software used, and specific examples.

[0504] First, the server monitors the usage status of individual rooms within the commercial facility in real time through sensors installed in those rooms. Motion sensors and door opening / closing sensors are used to accurately determine when a room is unused. The acquired data is sent to an analysis module within the server.

[0505] The server uses Python and Flask to analyze various data and identify unused private rooms. Firebase manages the data in real time, and AI-driven allocation is performed as needed. Generative models are used to optimize allocation, taking into account the priorities and purposes of users who wish to use the rooms.

[0506] As a notification method, the server notifies users' devices of the current status via a smartphone app built with React Native. If there are unused private rooms, it sends availability information to those who wish to use them, encouraging their use. In addition, users who repeatedly cancel reservations without notice will receive an alert email and be penalized.

[0507] For example, if a meeting room in a commercial facility suddenly becomes available, this system efficiently notifies other users, allowing them to utilize the time without wasting it. This ensures that specific-purpose spaces within the facility are used effectively.

[0508] As an example of a prompt sent to a generation AI model, you can use the following sentence: "Analyze the occupancy status of private rooms in the commercial facility this week and generate a list of recommended users for rooms that are not currently occupied. There are many occupants on weekends, especially in the afternoon."

[0509] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0510] Step 1:

[0511] The server receives usage data from motion sensors and door sensors installed within the commercial facility. This data indicates whether each individual room is currently in use or unused. The server monitors this input data in real time and sets criteria for determining usage status. As a result, the server can identify which individual rooms are unused.

[0512] Step 2:

[0513] The server analyzes usage data and identifies information about private rooms that have been identified as unused. The input here is usage data obtained from sensors, and the output is identification information for unused private rooms. Through data analysis, it identifies private rooms that have not been used within a specific time frame and sets an unused flag.

[0514] Step 3:

[0515] The server uses information on unused private rooms to refer to a list of people who wish to use them and sends notifications to the terminals of those who wish to use them. It uses the list of people who wish to use the rooms and availability information as input and generates notification content as output. The notification content includes the type of private room, location, and available time, and is sent to the user's terminal via email or messaging app.

[0516] Step 4:

[0517] The server utilizes a generation AI model to calculate the optimal combination of multiple unused private rooms and applicants. It allocates rooms considering pre-set priority and urgency levels of applicants. Inputs include a list of users and information on unused rooms, and output is a recommended list of optimal matches. The AI ​​model optimizes allocation while considering fairness among users.

[0518] Step 5:

[0519] The server sends an alert email to the user if an unauthorized cancellation occurs. It uses the history of the unauthorized cancellation as input and generates the alert email as output. Furthermore, repeated violations will result in booking restrictions. The server will explain the restrictions to the user and encourage them to improve their future booking performance.

[0520] 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.

[0521] This invention combines a system for optimizing the reservation and use of indoor spaces with an emotion engine that recognizes user emotions, thereby enabling more effective and flexible utilization of indoor spaces. The system aims to improve utilization efficiency by using the emotion engine to evaluate the user's emotional state in real time and adjusting the allocation of indoor spaces based on the evaluation results.

[0522] Subject: Server

[0523] The server is equipped with an emotion engine and receives data from the user's device and sensing devices installed in the room. This data includes biometric information such as the user's voice, facial expressions, heart rate, and skin potential. The server comprehensively analyzes this data to estimate the user's stress level, satisfaction level, tension level, and other factors.

[0524] Subject: Server

[0525] The server flexibly adjusts the allocation of indoor spaces based on the estimated emotional state. For example, if a user is judged to have a high stress level, it can prioritize allocating a quiet indoor space to provide a calming environment. Furthermore, it facilitates appropriate communication by adjusting notification content and timing, taking into account the emotional state of the user.

[0526] Subject: User

[0527] Users can utilize an optimized room environment based on the evaluation results of the emotion engine. For example, if User A is determined to be very nervous before a meeting, the server can recommend a meeting room that provides a relaxing environment and send a notification to User A's terminal to automatically reserve that meeting room.

[0528] When implemented in this form, the present invention utilizes emotion recognition technology to go beyond mere physical allocation of indoor space and improve psychological comfort and user productivity. As a result, it is expected that overall work efficiency and ease of working in the office environment will improve.

[0529] The following describes the processing flow.

[0530] Step 1:

[0531] The server receives biometric information such as voice, facial expressions, heart rate, and skin potential from the user's terminal or sensing devices installed in the room. This data is used to prepare for recognizing the user's current emotional state.

[0532] Step 2:

[0533] The server analyzes the received biometric information using an emotion engine to estimate the user's emotional state, including stress levels, satisfaction levels, and tension. This analysis helps to assess the user's current psychological state.

[0534] Step 3:

[0535] Based on the estimated emotional state, the server selects the most suitable room from a list of available indoor spaces. It prioritizes allocating quiet rooms to users experiencing high stress levels and comfortable spaces to users who need a relaxing environment.

[0536] Step 4:

[0537] The server notifies the user's terminal of the selected indoor space information. The notification includes the location of the assigned indoor space, the reservation time, and available characteristics (e.g., quietness, comfort), allowing the user to act accordingly.

[0538] Step 5:

[0539] Users check the information notified to their devices and utilize their assigned indoor space. During this time, additional monitoring of whether their emotional state has improved allows for feedback to be provided for future improvements.

[0540] Step 6:

[0541] The server stores all data from this usage process in a database, which is used to analyze user trends and make predictions. This improves the accuracy of allocations in subsequent uses and enables more appropriate use of space.

[0542] (Example 2)

[0543] 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."

[0544] In today's diverse office environments, there is a need to simultaneously achieve efficient use of indoor space and psychological comfort for users. However, current systems merely monitor the usage of reserved spaces and identify unused spaces, but they do not adequately consider the emotional state of users when allocating space. This has resulted in the challenge of not being able to maximize user productivity and comfort.

[0545] 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.

[0546] In this invention, the server includes sensing means for acquiring the user's biometric information, estimation means for analyzing the acquired biometric information and estimating the emotional state, and adjustment means for optimally allocating the indoor space based on the estimated emotional state. This enables the optimization of the indoor space according to the user's current emotional state, improving psychological comfort and increasing productivity in the overall office environment.

[0547] "Sensing means" refers to devices or methods for collecting a user's biometric information. Specifically, this includes acquiring data such as voice, facial expressions, heart rate, and skin potential.

[0548] An "estimation means" is a device or method for analyzing biometric information acquired by a sensing means to determine the user's emotional state. This allows for the estimation of stress levels, satisfaction levels, and tension levels.

[0549] A "regulating mechanism" refers to a device or method for optimally allocating the use of indoor space based on an estimated emotional state. It dynamically adjusts the allocation of space to provide an environment suitable for the user.

[0550] An "identification means" is a device or method that automatically identifies unused indoor space from collected data. This minimizes wasted space and enables efficient use.

[0551] "Notification means" refers to devices or methods for communicating reservation information of identified unused or optimized indoor spaces to prospective users or users.

[0552] A "distribution means" is a device or method that uses a generative model to optimally allocate multiple unused indoor spaces and prospective users. This maximizes the efficiency of indoor space utilization.

[0553] A "control device" is a device or method that issues an alert if the service is not used without permission and imposes reservation restrictions for continued violations. This helps to deter unauthorized use.

[0554] This invention is a system that evaluates the user's emotional state in real time and optimizes the allocation of indoor space. This system primarily functions with three components: a server, a terminal, and a user.

[0555] The server receives data from the user's terminal or from sensing devices installed in the room using sensing means. The sensing devices include microphones to collect sound, cameras to capture facial expressions, and wearable devices to measure heart rate. The data obtained is transmitted to the server as biometric information.

[0556] The server analyzes this biometric information by running it through a generative AI model as an estimation tool. This analysis uses algorithms to evaluate emotional states and estimate stress levels, satisfaction levels, and tension levels. Specific software examples include the use of commonly available AI platforms.

[0557] Based on the estimated emotional state, the server uses adjustment mechanisms to optimize the indoor space. This allows for space allocation tailored to the user's needs. For example, if a user's stress level is high and they need relaxation, a quiet space will be allocated.

[0558] The terminal is responsible for notifying users of recommended spaces and their reservation schedules. Information on the availability of specific spaces and reservation details are provided to the user's device in real time. This allows users to utilize the most suitable environment, both physically and psychologically.

[0559] For example, if the AI ​​determines that user A is experiencing high levels of stress during work, the server will assign the user to a quiet meeting room to help them concentrate. This information is immediately sent to user A's smartphone, and the user can use the meeting room at the designated time.

[0560] As an example of a prompt, inputting a question like, "Which room should be recommended to assign the optimal space to an employee with a high stress level in the office?" into the AI ​​model will enable more efficient and flexible use of space.

[0561] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0562] Step 1:

[0563] Data collection

[0564] Subject: Server

[0565] The server receives biometric data from the user's terminal or sensing devices installed in the room. Inputs include data such as voice, facial expressions, heart rate, and skin potential. The server aggregates this data in one place to prepare for subsequent analysis. Specifically, it receives information from sensing devices sequentially and stores it in a database.

[0566] Step 2:

[0567] Data analysis and sentiment estimation

[0568] Subject: Server

[0569] The server uses the biometric information collected in Step 1 as input and performs analysis using a generative AI model. Specifically, it combines data from speech recognition, facial recognition, and heart rate measurement to estimate the user's stress level, tension level, and satisfaction level. The output represents the user's current emotional state as numerical or categorical data. The specific operation of emotion estimation involves analyzing the data through an AI algorithm and updating the emotional profile in real time.

[0570] Step 3:

[0571] Optimization of indoor space

[0572] Subject: Server

[0573] Using the emotional state data obtained in Step 2 as input, the server adjusts the allocation of indoor spaces. During this process, it consults a database beforehand to select the most suitable environment. Specifically, it automatically selects environments such as quiet rooms or relaxation rooms that correspond to the estimated emotional state. As output, information about the selected indoor spaces is registered in the reservation system.

[0574] Step 4:

[0575] Notifications and feedback

[0576] Subject: terminal

[0577] The terminal notifies the user of the indoor space information determined in step 3. The input is reservation status data sent from the server. Based on this information, the terminal displays details of available time slots and locations to the user. Specifically, it sends a push notification to the smartphone or PC so that the user can check it immediately. The output is that the notification to the user is complete and the user can use the indoor space appropriately.

[0578] Step 5:

[0579] User space utilization

[0580] Subject: User

[0581] Based on the notification received in step 4, the user utilizes the optimized indoor space. The input is notification information from the device. Specifically, the user moves to the designated time and place and uses that space to improve their psychological comfort. The output is feedback on the used space sent to the server, which is used to optimize the space for future use.

[0582] (Application Example 2)

[0583] 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."

[0584] In modern factory environments, the lack of appropriate work environments based on workers' emotional states leads to challenges such as decreased work efficiency and worker satisfaction. Furthermore, optimizing the work environment using emotion analysis is difficult.

[0585] 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.

[0586] In this invention, the server includes detection means for monitoring the use of reserved internal space, identification means for automatically extracting unused internal space based on the detected information, and control means for collecting and analyzing user emotional data using an emotional analysis engine and optimizing the work environment in real time. This enables the optimization of the work environment according to the emotional state of the worker, and is expected to improve productivity and satisfaction.

[0587] A "reserved internal space" is a specific location where usage is monitored, and where access rights are pre-configured by the user.

[0588] "Detection means" refers to means of monitoring the usage status of reserved interior spaces and collecting necessary information.

[0589] "Identification means" refers to a means for determining whether the internal space is unused based on the collected information, and for identifying the result.

[0590] "Communication means" refers to the means of transmitting information about identified, unused internal space to applicants.

[0591] An "emotion analysis engine" is software or a device that has the technology to analyze a worker's emotional state based on biological information.

[0592] "Control means" refers to methods for adjusting and optimizing the work environment based on data obtained through emotion analysis.

[0593] A "generative model" is an algorithm or system that processes user emotions and environmental data to generate instructions for optimal environmental adjustments.

[0594] This invention relates to a system that optimizes workspaces in a factory environment while considering the emotional state of workers. This system utilizes an emotion analysis engine to monitor the usage status of reserved internal spaces, identify unused spaces based on that information, and propose an optimal workspace.

[0595] The server collects data from sensing devices (cameras, microphones, biometric sensors, etc.) installed in the internal space and analyzes it in real time using AI services on the cloud (e.g., Google Cloud AI, Microsoft Azure AI). Based on the analysis results, emotional data of the workers is extracted, and their stress levels, satisfaction levels, and other states are estimated. Based on this information, an AI model proposes the optimal work environment for the workers (e.g., music selection, lighting adjustments, air conditioning settings), and the environment is immediately adjusted by the control system.

[0596] For example, if a server determines that a worker is experiencing stress, it will select relaxing music and optimize the volume settings. Similarly, if a worker is deemed fatigued, the system will adjust the ambient lighting to reduce the worker's burden. In this process, an example prompt given to the AI ​​model might be, "Generate an optimized work environment based on the following emotional data."

[0597] This system allows factory workers to work in an environment optimized to their emotional state, which is expected to improve productivity and working conditions.

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] The server collects biometric data (such as voice, facial expressions, heart rate, and skin potential) as input from sensing devices installed in the internal space. This data is digitized and stored in a database.

[0601] Step 2:

[0602] The server sends the collected data to an AI service in the cloud, where it is analyzed in real time by an emotion analysis engine. This analysis estimates the emotional state of the workers, such as their stress levels and satisfaction levels, from the input data.

[0603] Step 3:

[0604] The server uses a generative AI model based on the estimated emotional state to generate prompts suggesting the optimal work environment. Specifically, it might form a prompt such as, "Please generate an optimized work environment based on the following emotional data."

[0605] Step 4:

[0606] Based on the generated prompts, the server uses control mechanisms to perform specific environmental adjustments (e.g., music playback, lighting adjustments, air conditioning settings, etc.). This output automatically sets up a work environment that matches the worker's emotional state.

[0607] Step 5:

[0608] Users can utilize the adjusted work environment to perform their tasks efficiently. User feedback and emotional changes are collected again by sensing devices, and the system is continuously optimized.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] [Fourth Embodiment]

[0613] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0614] 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.

[0615] 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).

[0616] 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.

[0617] 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.

[0618] 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).

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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".

[0626] The present invention provides a system for optimizing the use of indoor spaces in an office environment. This system includes a series of automated processes that monitor the usage status of reserved indoor spaces in real time, identify unused indoor spaces, and notify those on the waiting list. Furthermore, by using AI technology to optimally allocate multiple unused indoor spaces to those wishing to use them, efficient utilization can be achieved.

[0627] Subject: Server

[0628] The server is responsible for receiving various data from sensors installed in the indoor space. These sensors, such as motion sensors and door opening / closing sensors, monitor the usage status of the room. The received data is analyzed within the server, and if it is determined that a reserved space is not being used, the server proceeds to the next step using that information.

[0629] Subject: Server

[0630] The server uses information about unused indoor spaces to refer to a list of users who are on a waiting list. It then automatically sends notifications to these users. These notifications include the type, location, and available time of the indoor space, and are designed to encourage immediate use. This communication is conducted via email, messaging applications, and internal notification systems.

[0631] Subject: Server

[0632] Furthermore, the AI ​​system on the server acquires information on multiple unused indoor spaces and prospective users, and performs the most efficient and fair matching. This AI system calculates the optimal combination by considering pre-set criteria, such as the urgency of the request for use, the importance of the meeting, and the user's priority. Based on the results, it supports the effective use of indoor spaces by notifying prospective users.

[0633] Subject: Server

[0634] If a user fails to use the room without prior notice, the server will send an alert email to that user. This email will contain details of the violation and future precautions. Furthermore, if a user has a certain number of no-shows within a certain period, the server will take measures to restrict that user from booking meeting rooms. This restriction will prevent new bookings within a certain period and will be implemented by integrating with existing booking systems such as the Google Calendar API.

[0635] Thus, the system of the present invention, by combining sensors and AI technology, appropriately manages the usage status of reserved indoor spaces and promotes efficient use. As a result, it can contribute to improving productivity in the office environment.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] The server begins receiving data from sensors installed in the room. These sensors, such as motion sensors and door open / close sensors, continuously monitor the room's usage. The received data is sent to the server and prepared for analysis.

[0639] Step 2:

[0640] The server analyzes the received data to determine whether the room space is reserved but unused. If it is determined to be unused, it sets the "unused" flag in the database.

[0641] Step 3:

[0642] The server retrieves information on indoor spaces that have been flagged as "unused" and checks the list of users who have registered for the waiting list in advance. It then prepares to send notifications to the devices of the relevant users.

[0643] Step 4:

[0644] The server notifies users' devices of information about unused indoor spaces. The notification includes the location and availability time of the indoor space. The device then displays the notification to the user, facilitating their use.

[0645] Step 5:

[0646] The AI ​​model on the server retrieves all unused indoor spaces and potential users, and calculates the most efficient combination. This includes evaluations based on user-defined priorities and usage purposes.

[0647] Step 6:

[0648] The server assigns the most suitable indoor space to each user based on the AI ​​model's calculations and notifies the user's device accordingly. The user can then begin using the assigned indoor space according to this notification.

[0649] Step 7:

[0650] The server will send an alert email to the person who made the reservation if they cancel without notice. This email will remind the person about the importance of avoiding no-shows and encourage them to take measures to prevent recurrence.

[0651] Step 8:

[0652] The server will impose booking restrictions on a user if they have a certain number of no-shows. This will restrict their ability to make new bookings and limit their access to the booking system for a certain period.

[0653] (Example 1)

[0654] 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".

[0655] The efficiency of space utilization within companies and organizations can decrease when spaces are reserved but not actually used. Furthermore, repeated unauthorized cancellations can affect other users and lead to wasted resources. To address these challenges, more effective and equitable optimization of space utilization is necessary.

[0656] 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.

[0657] In this invention, the server includes acquisition means for monitoring the usage status of reserved partitions, analysis means for automatically determining unused partitions based on the acquired information, and notification means for notifying registered users of the determined unused partitions. This enables efficient use of resources by accurately monitoring partition usage and promptly encouraging their use as needed.

[0658] "Acquisition means" refers to a device or system that has the function of monitoring the usage status of a section and collecting relevant information.

[0659] "Analysis means" refers to a device or software that has the function of determining whether a compartment is unused based on the acquired information.

[0660] "Notification method" refers to a device or system that has the function of notifying registered users of the determined unused area information.

[0661] A "generative model" is an algorithm or program used to calculate the optimal allocation based on information about multiple unused plots and prospective users.

[0662] "Management measures" refer to devices or systems that have the function of issuing warnings for reservations that have not been used without permission, and implementing reservation restrictions if the violation continues.

[0663] A "detection device" is a sensor or device installed within a designated area and used to detect physical conditions or human movement.

[0664] An "information device" is an electronic device that is owned or used by a user and is capable of sending and receiving information.

[0665] The system of this invention is designed to maximize the efficiency of partition utilization within companies and organizations. This system mainly consists of servers, terminals, and users, and each element works closely together to achieve effective management and utilization of partitions.

[0666] The server collects data from motion sensors and door open / close sensors installed within the area. These sensors transmit data to the server via Bluetooth or Wi-Fi to monitor the area's usage. This data is centrally stored in a database for later analysis.

[0667] The server uses a generative AI model based on collected data to automatically determine unused parking spaces. The generative AI model employs machine learning algorithms such as the Scikit-learn library to compare past usage patterns with real-time data, resulting in highly accurate determinations. Based on these determinations, the server notifies registered users that unused parking spaces are available. These notifications are sent automatically via email or messaging apps, ensuring immediacy.

[0668] Furthermore, the server processes information on multiple unused plots and those wishing to use them to calculate the optimal matching. This ensures that plots are used in the most effective and fair way possible. The AI ​​performs this optimization using a scoring system that takes into account the urgency of the request and the importance of the plot.

[0669] For example, if meeting room A is reserved within the company, but the motion sensor does not detect any activity for more than an hour, the server will determine that the meeting room is unused and automatically notify user B, who is on the list of potential users, that "Meeting room A is now available."

[0670] Examples of prompt statements include:

[0671] "How can I use AI to optimize the booking status of unused meeting rooms in my office?"

[0672] There is.

[0673] Such a system not only allows users to utilize space efficiently, but also improves overall utilization efficiency by issuing warnings to those who have made reservations but are not using the space, and by imposing reservation restrictions if necessary.

[0674] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0675] Step 1:

[0676] The server acquires data from sensors within the area. These sensors include motion sensors and door open / close sensors. These sensors transmit data via Bluetooth or Wi-Fi. The input includes real-time data from the sensors. The server stores this data in a database and uses it to understand the current usage status of the area.

[0677] Step 2:

[0678] The server analyzes the data in the database to identify unused partitions. The usage data collected in step 1 is used as input. A generative AI model is used to process the data by comparing it with past usage patterns to determine if a partition is unused. The server outputs a list of partitions that it has determined to be unused.

[0679] Step 3:

[0680] The server compares information about unused sections with the list of prospective users. Based on the output list, the server notifies registered prospective users of available sections. The notification is delivered via email or messaging app and includes details about the location and time of available sections.

[0681] Step 4:

[0682] The server uses AI to process information on multiple unused partitions and those wishing to use them, and then makes the optimal allocation. Inputs include a list of unused partitions and priority information for those wishing to use them. Using a generative AI model, it calculates the optimized allocation by scoring based on urgency and importance, and then outputs the result to notify those wishing to use the partitions.

[0683] Step 5:

[0684] The server notifies users who have made reservations but have not used them without permission. Based on the data determined to be unused in Step 2, a warning email for unauthorized cancellation is sent to the user. If violations occur repeatedly during this process, the server will output a control signal in conjunction with the reservation system to impose reservation restrictions.

[0685] Through these steps, the system enables accurate monitoring and efficient partition reallocation, thereby improving utilization efficiency.

[0686] (Application Example 1)

[0687] 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".

[0688] In offices and commercial facilities, reserved indoor spaces are often left unused without permission, resulting in problems where those who wish to use the space are unable to do so. Furthermore, the inefficiency and fair allocation of space to users reduces the efficiency of facility management. There is a need to resolve these issues and improve the efficiency of indoor space utilization.

[0689] 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.

[0690] In this invention, the server includes detection means for monitoring the use of reserved indoor spaces, identification means for automatically identifying unused indoor spaces based on the detected information, and allocation means that utilize a generative model to optimally allocate multiple unused indoor spaces and users. This makes it possible to improve the efficiency of the facility through the optimal use of indoor spaces and to improve the convenience of users.

[0691] A "reserved indoor space" refers to a physical area that has been secured for use in advance by a user.

[0692] "Detection means" refers to a device or method for monitoring the usage status of an indoor space using sensors or other technologies.

[0693] "Identification means" refers to a process for determining the usage status of an indoor space based on detected data and identifying whether or not it is unused.

[0694] "Notification means" refers to a method or system for communicating information about an unused indoor space to those who wish to use it.

[0695] A "generative model" is a mathematical or algorithmic structure that utilizes AI technology to optimally match the user with the indoor space.

[0696] A "distribution method" is a method that uses a generative model to efficiently connect multiple indoor spaces with potential users.

[0697] A "control mechanism" is a function or process that imposes a penalty if a reservation is left unused without permission and restricts subsequent reservations.

[0698] "Private rooms in commercial facilities" refers to small spaces or rooms within commercial facilities such as shopping malls and stores that are used for specific purposes.

[0699] A "recommendation method" refers to an algorithm or system used to suggest available private rooms to users.

[0700] The system for implementing this invention facilitates the efficient use of private rooms within commercial facilities. Herein, we present the system's program, the hardware and software used, and specific examples.

[0701] First, the server monitors the usage status of individual rooms within the commercial facility in real time through sensors installed in those rooms. Motion sensors and door opening / closing sensors are used to accurately determine when a room is unused. The acquired data is sent to an analysis module within the server.

[0702] The server uses Python and Flask to analyze various data and identify unused private rooms. Firebase manages the data in real time, and AI-driven allocation is performed as needed. Generative models are used to optimize allocation, taking into account the priorities and purposes of users who wish to use the rooms.

[0703] As a notification method, the server notifies users' devices of the current status via a smartphone app built with React Native. If there are unused private rooms, it sends availability information to those who wish to use them, encouraging their use. In addition, users who repeatedly cancel reservations without notice will receive an alert email and be penalized.

[0704] For example, if a meeting room in a commercial facility suddenly becomes available, this system efficiently notifies other users, allowing them to utilize the time without wasting it. This ensures that specific-purpose spaces within the facility are used effectively.

[0705] As an example of a prompt sent to a generation AI model, you can use the following sentence: "Analyze the occupancy status of private rooms in the commercial facility this week and generate a list of recommended users for rooms that are not currently occupied. There are many occupants on weekends, especially in the afternoon."

[0706] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0707] Step 1:

[0708] The server receives usage data from motion sensors and door sensors installed within the commercial facility. This data indicates whether each individual room is currently in use or unused. The server monitors this input data in real time and sets criteria for determining usage status. As a result, the server can identify which individual rooms are unused.

[0709] Step 2:

[0710] The server analyzes usage data and identifies information about private rooms that have been identified as unused. The input here is usage data obtained from sensors, and the output is identification information for unused private rooms. Through data analysis, it identifies private rooms that have not been used within a specific time frame and sets an unused flag.

[0711] Step 3:

[0712] The server uses information on unused private rooms to refer to a list of people who wish to use them and sends notifications to the terminals of those who wish to use them. It uses the list of people who wish to use the rooms and availability information as input and generates notification content as output. The notification content includes the type of private room, location, and available time, and is sent to the user's terminal via email or messaging app.

[0713] Step 4:

[0714] The server utilizes a generation AI model to calculate the optimal combination of multiple unused private rooms and applicants. It allocates rooms considering pre-set priority and urgency levels of applicants. Inputs include a list of users and information on unused rooms, and output is a recommended list of optimal matches. The AI ​​model optimizes allocation while considering fairness among users.

[0715] Step 5:

[0716] The server sends an alert email to the user if an unauthorized cancellation occurs. It uses the history of the unauthorized cancellation as input and generates the alert email as output. Furthermore, repeated violations will result in booking restrictions. The server will explain the restrictions to the user and encourage them to improve their future booking performance.

[0717] 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.

[0718] This invention combines a system for optimizing the reservation and use of indoor spaces with an emotion engine that recognizes user emotions, thereby enabling more effective and flexible utilization of indoor spaces. The system aims to improve utilization efficiency by using the emotion engine to evaluate the user's emotional state in real time and adjusting the allocation of indoor spaces based on the evaluation results.

[0719] Subject: Server

[0720] The server is equipped with an emotion engine and receives data from the user's device and sensing devices installed in the room. This data includes biometric information such as the user's voice, facial expressions, heart rate, and skin potential. The server comprehensively analyzes this data to estimate the user's stress level, satisfaction level, tension level, and other factors.

[0721] Subject: Server

[0722] The server flexibly adjusts the allocation of indoor spaces based on the estimated emotional state. For example, if a user is judged to have a high stress level, it can prioritize allocating a quiet indoor space to provide a calming environment. Furthermore, it facilitates appropriate communication by adjusting notification content and timing, taking into account the emotional state of the user.

[0723] Subject: User

[0724] Users can utilize an optimized room environment based on the evaluation results of the emotion engine. For example, if User A is determined to be very nervous before a meeting, the server can recommend a meeting room that provides a relaxing environment and send a notification to User A's terminal to automatically reserve that meeting room.

[0725] When implemented in this form, the present invention utilizes emotion recognition technology to go beyond mere physical allocation of indoor space and improve psychological comfort and user productivity. As a result, it is expected that overall work efficiency and ease of working in the office environment will improve.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The server receives biometric information such as voice, facial expressions, heart rate, and skin potential from the user's terminal or sensing devices installed in the room. This data is used to prepare for recognizing the user's current emotional state.

[0729] Step 2:

[0730] The server analyzes the received biometric information using an emotion engine to estimate the user's emotional state, including stress levels, satisfaction levels, and tension. This analysis helps to assess the user's current psychological state.

[0731] Step 3:

[0732] Based on the estimated emotional state, the server selects the most suitable room from a list of available indoor spaces. It prioritizes allocating quiet rooms to users experiencing high stress levels and comfortable spaces to users who need a relaxing environment.

[0733] Step 4:

[0734] The server notifies the user's terminal of the selected indoor space information. The notification includes the location of the assigned indoor space, the reservation time, and available characteristics (e.g., quietness, comfort), allowing the user to act accordingly.

[0735] Step 5:

[0736] Users check the information notified to their devices and utilize their assigned indoor space. During this time, additional monitoring of whether their emotional state has improved allows for feedback to be provided for future improvements.

[0737] Step 6:

[0738] The server stores all data from this usage process in a database, which is used to analyze user trends and make predictions. This improves the accuracy of allocations in subsequent uses and enables more appropriate use of space.

[0739] (Example 2)

[0740] 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".

[0741] In today's diverse office environments, there is a need to simultaneously achieve efficient use of indoor space and psychological comfort for users. However, current systems merely monitor the usage of reserved spaces and identify unused spaces, but they do not adequately consider the emotional state of users when allocating space. This has resulted in the challenge of not being able to maximize user productivity and comfort.

[0742] 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.

[0743] In this invention, the server includes sensing means for acquiring the user's biometric information, estimation means for analyzing the acquired biometric information and estimating the emotional state, and adjustment means for optimally allocating the indoor space based on the estimated emotional state. This enables the optimization of the indoor space according to the user's current emotional state, improving psychological comfort and increasing productivity in the overall office environment.

[0744] "Sensing means" refers to devices or methods for collecting a user's biometric information. Specifically, this includes acquiring data such as voice, facial expressions, heart rate, and skin potential.

[0745] An "estimation means" is a device or method for analyzing biometric information acquired by a sensing means to determine the user's emotional state. This allows for the estimation of stress levels, satisfaction levels, and tension levels.

[0746] A "regulating mechanism" refers to a device or method for optimally allocating the use of indoor space based on an estimated emotional state. It dynamically adjusts the allocation of space to provide an environment suitable for the user.

[0747] An "identification means" is a device or method that automatically identifies unused indoor space from collected data. This minimizes wasted space and enables efficient use.

[0748] "Notification means" refers to devices or methods for communicating reservation information of identified unused or optimized indoor spaces to prospective users or users.

[0749] A "distribution means" is a device or method that uses a generative model to optimally allocate multiple unused indoor spaces and prospective users. This maximizes the efficiency of indoor space utilization.

[0750] A "control device" is a device or method that issues an alert if the service is not used without permission and imposes reservation restrictions for continued violations. This helps to deter unauthorized use.

[0751] This invention is a system that evaluates the user's emotional state in real time and optimizes the allocation of indoor space. This system primarily functions with three components: a server, a terminal, and a user.

[0752] The server receives data from the user's terminal or from sensing devices installed in the room using sensing means. The sensing devices include microphones to collect sound, cameras to capture facial expressions, and wearable devices to measure heart rate. The data obtained is transmitted to the server as biometric information.

[0753] The server analyzes this biometric information by running it through a generative AI model as an estimation tool. This analysis uses algorithms to evaluate emotional states and estimate stress levels, satisfaction levels, and tension levels. Specific software examples include the use of commonly available AI platforms.

[0754] Based on the estimated emotional state, the server uses adjustment mechanisms to optimize the indoor space. This allows for space allocation tailored to the user's needs. For example, if a user's stress level is high and they need relaxation, a quiet space will be allocated.

[0755] The terminal is responsible for notifying users of recommended spaces and their reservation schedules. Information on the availability of specific spaces and reservation details are provided to the user's device in real time. This allows users to utilize the most suitable environment, both physically and psychologically.

[0756] For example, if the AI ​​determines that user A is experiencing high levels of stress during work, the server will assign the user to a quiet meeting room to help them concentrate. This information is immediately sent to user A's smartphone, and the user can use the meeting room at the designated time.

[0757] As an example of a prompt, inputting a question like, "Which room should be recommended to assign the optimal space to an employee with a high stress level in the office?" into the AI ​​model will enable more efficient and flexible use of space.

[0758] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0759] Step 1:

[0760] Data collection

[0761] Subject: Server

[0762] The server receives biometric data from the user's terminal or sensing devices installed in the room. Inputs include data such as voice, facial expressions, heart rate, and skin potential. The server aggregates this data in one place to prepare for subsequent analysis. Specifically, it receives information from sensing devices sequentially and stores it in a database.

[0763] Step 2:

[0764] Data analysis and sentiment estimation

[0765] Subject: Server

[0766] The server uses the biometric information collected in Step 1 as input and performs analysis using a generative AI model. Specifically, it combines data from speech recognition, facial recognition, and heart rate measurement to estimate the user's stress level, tension level, and satisfaction level. The output represents the user's current emotional state as numerical or categorical data. The specific operation of emotion estimation involves analyzing the data through an AI algorithm and updating the emotional profile in real time.

[0767] Step 3:

[0768] Optimization of indoor space

[0769] Subject: Server

[0770] Using the emotional state data obtained in Step 2 as input, the server adjusts the allocation of indoor spaces. During this process, it consults a database beforehand to select the most suitable environment. Specifically, it automatically selects environments such as quiet rooms or relaxation rooms that correspond to the estimated emotional state. As output, information about the selected indoor spaces is registered in the reservation system.

[0771] Step 4:

[0772] Notifications and feedback

[0773] Subject: terminal

[0774] The terminal notifies the user of the indoor space information determined in step 3. The input is reservation status data sent from the server. Based on this information, the terminal displays details of available time slots and locations to the user. Specifically, it sends a push notification to the smartphone or PC so that the user can check it immediately. The output is that the notification to the user is complete and the user can use the indoor space appropriately.

[0775] Step 5:

[0776] User space utilization

[0777] Subject: User

[0778] Based on the notification received in step 4, the user utilizes the optimized indoor space. The input is notification information from the device. Specifically, the user moves to the designated time and place and uses that space to improve their psychological comfort. The output is feedback on the used space sent to the server, which is used to optimize the space for future use.

[0779] (Application Example 2)

[0780] 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".

[0781] In modern factory environments, the lack of appropriate work environments based on workers' emotional states leads to challenges such as decreased work efficiency and worker satisfaction. Furthermore, optimizing the work environment using emotion analysis is difficult.

[0782] 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.

[0783] In this invention, the server includes detection means for monitoring the use of reserved internal space, identification means for automatically extracting unused internal space based on the detected information, and control means for collecting and analyzing user emotional data using an emotional analysis engine and optimizing the work environment in real time. This enables the optimization of the work environment according to the emotional state of the worker, and is expected to improve productivity and satisfaction.

[0784] A "reserved internal space" is a specific location where usage is monitored, and where access rights are pre-configured by the user.

[0785] "Detection means" refers to means of monitoring the usage status of reserved interior spaces and collecting necessary information.

[0786] "Identification means" refers to a means for determining whether the internal space is unused based on the collected information, and for identifying the result.

[0787] "Communication means" refers to the means of transmitting information about identified, unused internal space to applicants.

[0788] An "emotion analysis engine" is software or a device that has the technology to analyze a worker's emotional state based on biological information.

[0789] "Control means" refers to methods for adjusting and optimizing the work environment based on data obtained through emotion analysis.

[0790] A "generative model" is an algorithm or system that processes user emotions and environmental data to generate instructions for optimal environmental adjustments.

[0791] This invention relates to a system that optimizes workspaces in a factory environment while considering the emotional state of workers. This system utilizes an emotion analysis engine to monitor the usage status of reserved internal spaces, identify unused spaces based on that information, and propose an optimal workspace.

[0792] The server collects data from sensing devices (cameras, microphones, biometric sensors, etc.) installed in the internal space and analyzes it in real time using AI services on the cloud (e.g., Google Cloud AI, Microsoft Azure AI). Based on the analysis results, emotional data of the workers is extracted, and their stress levels, satisfaction levels, and other states are estimated. Based on this information, an AI model proposes the optimal work environment for the workers (e.g., music selection, lighting adjustments, air conditioning settings), and the environment is immediately adjusted by the control system.

[0793] For example, if a server determines that a worker is experiencing stress, it will select relaxing music and optimize the volume settings. Similarly, if a worker is deemed fatigued, the system will adjust the ambient lighting to reduce the worker's burden. In this process, an example prompt given to the AI ​​model might be, "Generate an optimized work environment based on the following emotional data."

[0794] This system allows factory workers to work in an environment optimized to their emotional state, which is expected to improve productivity and working conditions.

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The server collects biometric data (such as voice, facial expressions, heart rate, and skin potential) as input from sensing devices installed in the internal space. This data is digitized and stored in a database.

[0798] Step 2:

[0799] The server sends the collected data to an AI service in the cloud, where it is analyzed in real time by an emotion analysis engine. This analysis estimates the emotional state of the workers, such as their stress levels and satisfaction levels, from the input data.

[0800] Step 3:

[0801] The server uses a generative AI model based on the estimated emotional state to generate prompts suggesting the optimal work environment. Specifically, it might form a prompt such as, "Please generate an optimized work environment based on the following emotional data."

[0802] Step 4:

[0803] Based on the generated prompts, the server uses control mechanisms to perform specific environmental adjustments (e.g., music playback, lighting adjustments, air conditioning settings, etc.). This output automatically sets up a work environment that matches the worker's emotional state.

[0804] Step 5:

[0805] Users can utilize the adjusted work environment to perform their tasks efficiently. User feedback and emotional changes are collected again by sensing devices, and the system is continuously optimized.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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."

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] The following is further disclosed regarding the embodiments described above.

[0828] (Claim 1)

[0829] A detection means for monitoring the use of a reserved indoor space,

[0830] An identification means that automatically identifies unused indoor spaces based on detected information,

[0831] A notification means for notifying registered users of identified unused indoor spaces,

[0832] A distribution method that utilizes a generative model to optimally allocate multiple unused indoor spaces and the number of people who wish to use them,

[0833] A control mechanism that sends an alert to users who have made reservations without authorization and implements reservation restrictions for continued violations,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, wherein the identification means analyzes data from a sensing device arranged in the indoor space to determine whether the device is unused.

[0837] (Claim 3)

[0838] The system according to claim 1, wherein the notification means automatically transmits information to a recording device of a user.

[0839] "Example 1"

[0840] (Claim 1)

[0841] A means for monitoring the usage status of reserved plots,

[0842] An analysis means that automatically determines unused sections based on acquired information,

[0843] A means of notifying registered users of the determined unused plots,

[0844] A means of allocation that utilizes a generative model to optimally allocate multiple unused sections and users,

[0845] A management system that sends warnings to users who have made reservations without authorization and imposes reservation restrictions for continued violations,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, wherein the analysis means analyzes information from detection devices placed within the compartment to determine whether it is unused.

[0849] (Claim 3)

[0850] The system according to claim 1, wherein the notification means automatically transmits information to the information device of the person wishing to use the service.

[0851] "Application Example 1"

[0852] (Claim 1)

[0853] A detection means for monitoring the use of a reserved indoor space,

[0854] An identification means that automatically identifies unused indoor spaces based on detected information,

[0855] A notification means for notifying registered users of identified unused indoor spaces,

[0856] A distribution method that utilizes a generative model to optimally allocate multiple unused indoor spaces and the number of people who wish to use them,

[0857] A control mechanism that sends an alert to users who have made reservations without authorization and implements reservation restrictions for continued violations,

[0858] A recommendation system that allows real-time monitoring of the availability of private rooms in commercial facilities and suggests the most suitable room to those wishing to use it,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, wherein the identification means analyzes data from a sensing device arranged in the indoor space to determine whether the device is unused.

[0862] (Claim 3)

[0863] The system according to claim 1, wherein the notification means automatically transmits information to a recording device of a user.

[0864] "Example 2 of combining an emotion engine"

[0865] (Claim 1)

[0866] A sensing means for acquiring the user's biometric information,

[0867] An estimation method that analyzes acquired biometric information to estimate emotional state,

[0868] An adjustment means for optimally allocating indoor space based on estimated emotional state,

[0869] A means of notifying users of the reservation status of an optimized indoor space,

[0870] An identification method that uses a generative model to automatically identify unused indoor spaces and notifies those who wish to use them,

[0871] A notification method to inform registered users of unused indoor space,

[0872] A distribution method using a generative model that optimally allocates multiple unused indoor spaces and users,

[0873] A control mechanism that sends an alert to users who have made reservations without authorization and implements reservation restrictions for continued violations,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, wherein the estimation means analyzes biological information from a sensing device to determine the emotional state.

[0877] (Claim 3)

[0878] The system according to claim 1, wherein the notification means automatically transmits optimized reservation information to the user's recording device.

[0879] "Application example 2 when combining with an emotional engine"

[0880] (Claim 1)

[0881] A detection means for monitoring the use of reserved interior space,

[0882] An identification means that automatically extracts unused internal space based on detected information,

[0883] A communication means for transmitting the extracted unused internal space to registered users,

[0884] A control means for optimizing the work environment in real time by collecting and analyzing user emotional data using an emotion analysis engine,

[0885] An adjustment means that utilizes a generative model that generates instructions for adjusting the work environment based on the user's state estimated from biological information,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, wherein the identification means determines the unused status by analyzing data from a sensing device installed in the internal space.

[0889] (Claim 3)

[0890] The system according to claim 1, wherein the communication means automatically transmits information to an information recording device of the applicant. [Explanation of symbols]

[0891] 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 detection means for monitoring the use of a reserved indoor space, An identification means that automatically identifies unused indoor spaces based on detected information, A notification means for notifying registered users of identified unused indoor spaces, A distribution method that utilizes a generative model to optimally allocate multiple unused indoor spaces and the number of people who wish to use them, A control mechanism that sends an alert to users who have made reservations without authorization and implements reservation restrictions for continued violations, A system that includes this.

2. The system according to claim 1, wherein the identification means analyzes data from a sensing device arranged in the indoor space to determine whether the device is unused.

3. The system according to claim 1, wherein the notification means automatically transmits information to a recording device of a user.

Images