system

Abstract

We provide the system. [Solution] A means for acquiring past meeting space usage information and future reservation information, and for pre-processing this data, A means for executing a machine learning algorithm to predict the demand for meeting spaces using the aforementioned preprocessed data, Means for generating a plan for dynamically arranging movable partitions and modular fixtures within a meeting space according to the predicted demand, Means for transmitting instructions to a device for reconfiguring the meeting space based on the generated plan, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, with the diversification of business activities and the transformation of work styles, the frequency of meetings and conferences has increased, and the efficient operation of meeting spaces in limited physical spaces has been demanded. However, since the layout of meeting spaces is fixed in many organizations, flexible changes according to demand cannot be made, resulting in waste of space and deterioration of the meeting environment. In order to solve this problem, means for accurately predicting the demand for meeting spaces and dynamically optimizing limited spaces are necessary.

Means for Solving the Problems

[0005] This invention has the function of acquiring past meeting space usage information and future reservation information, and predicting the demand for meeting spaces using a machine learning algorithm based on this data. Furthermore, it provides a system that generates an optimal meeting space layout plan according to the prediction results and automatically sends instructions for reconfiguring the space using movable partitions and modular equipment. This enables flexible operation of meeting spaces to meet demand and realizes efficient use of space.

[0006] "Past meeting space usage information" refers to data on the usage of meeting rooms and related spaces in the past, including the number of participants, frequency of use, and time of use.

[0007] "Future booking information" refers to booking data for meetings and events scheduled for the future, including information such as the planned date and time, the number of expected participants, and the required space size.

[0008] "Preprocessing" refers to the process of cleaning acquired data for use in prediction, standardizing its format, and imputing missing values, thereby preparing it for analysis.

[0009] A "machine learning algorithm" is a mathematical method for learning patterns from past data and predicting future demand; it is a program that uses various models to extract insights from data.

[0010] A "movable partition" refers to a divider or wall that can be moved or adjusted as needed within a meeting space, enabling flexible division of space.

[0011] "Modular fixtures" refer to furniture and equipment that can be rearranged and reconfigured, and are designed with mobility to enable efficient use of space according to usage conditions.

[0012] A "dynamic layout plan" is a plan for most efficiently allocating resources and space within a meeting space based on demand forecasts, and serves as a guideline for flexibly changing the space.

[0013] "Sending instructions" refers to the act of transmitting digital signals or commands to an automated system or person in charge to perform the operations necessary to reconfigure the meeting space. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This 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 the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system for achieving efficient operation of meeting spaces, and includes a method for demand forecasting using past and future meeting data, and for adjusting the dynamic layout using movable partitions and modular equipment. The processing of the program of this system is described below.

[0036] The server first collects past meeting usage information and future booking information from meeting room reservation systems and calendar applications. This data is then formatted to include usage date and time, number of participants, etc., for use in demand forecasting.

[0037] The server runs a machine learning algorithm based on pre-processed data to predict future meeting room demand. The predicted demand is specifically calculated as date, time, number of participants, and required space size. Based on this, a plan is generated to determine the optimal meeting room layout.

[0038] The terminal visualizes and notifies the user of the proposed layout changes received from the server. This proposed layout is designed with user convenience in mind and can be fine-tuned as needed. The user can review the presented layout, make any necessary adjustments, and then give final approval.

[0039] The approved layout is transmitted to the execution device via a terminal, and movable partitions and modular fixtures within the meeting space are moved accordingly. This allows for the immediate creation of an optimal meeting space tailored to the specific needs.

[0040] For example, if a large project meeting is needed at a specific time on a weekday, the server analyzes data from similar past meetings to predict the large space demand at that time. Based on this information, the server identifies the need for a large meeting space and proposes moving movable partitions to create a unified large space. If all users approve this proposal, the terminal sends an instruction, and the meeting room is arranged.

[0041] Thus, this invention aims to improve the efficiency of meeting management by providing a system that makes maximum use of limited physical space and can be flexibly modified in response to fluctuations in demand.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server retrieves past meeting usage data and future booking information from the meeting room reservation system and calendar application. This includes retrieving data using APIs or importing CSV files. The retrieved data is formatted to include date and time, number of participants, meeting duration, and other relevant information.

[0045] Step 2:

[0046] The server feeds pre-processed data into machine learning algorithms to predict future demand for meeting rooms. This prediction uses time series analysis and predictive models to calculate demand for specific dates and times, and records it in a database.

[0047] Step 3:

[0048] Based on the prediction results, the server automatically creates an optimal layout plan for moving movable partitions and modular equipment within the conference room. This plan aims for maximum space efficiency and is visualized in 3D as a virtual layout.

[0049] Step 4:

[0050] The terminal notifies the user of the created layout plan. The user can review the provided layout on the terminal screen and make adjustments as needed. Once adjustments are complete, the user gives final approval.

[0051] Step 5:

[0052] The terminal sends instruction signals to facility management staff or automated equipment to implement layout changes approved by the user. For example, partitions may be moved or equipment may be rearranged.

[0053] Step 6:

[0054] The server monitors whether the meeting space has been reconfigured as planned, using camera feeds and sensor data for verification. If an anomaly is detected, it issues an alert to prompt a quick response.

[0055] Step 7:

[0056] After the meeting ends, the terminal collects usage feedback from users and sends it to the server. This feedback is used as valuable data for improvement when planning future meetings.

[0057] (Example 1)

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

[0059] To achieve efficient use of shared spaces, it is necessary to flexibly adjust the space in response to fluctuating demand. However, conventional systems have challenges such as low prediction accuracy and the time-consuming manual process of reconfiguring the space.

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

[0061] In this invention, the server includes means for acquiring past collective space usage information and future reservation information and preprocessing these values; means for executing a machine learning method for predicting the demand for the collective space using the preprocessed values; and means for generating a plan for dynamically arranging movable compartments and prefabricated fixtures within the collective space according to the predicted demand. This enables rapid and efficient space utilization in response to demand.

[0062] "Gathering space usage information" refers to data regarding the usage history of a gathering place and future reservations for its use.

[0063] "Preprocessing" refers to processes such as data cleansing and formatting standardization that transform raw data into a format suitable for machine learning and analysis.

[0064] A "machine learning method" is a computational method that learns patterns and rules from large amounts of data to perform predictions and classifications.

[0065] "Demand forecasting" is the process of estimating the future need for services or goods based on past data.

[0066] A "movable partition" is a movable partition that can flexibly divide a physical space and easily change the layout of that space.

[0067] "Assembly-type equipment" refers to furniture and equipment whose configuration can be changed and rearranged according to its use and purpose.

[0068] "Dynamic arrangement" refers to flexibly changing equipment and spatial configurations according to the situation and conditions.

[0069] This invention is a system for achieving efficient operation of a collective space, combining data analysis and dynamic layout adjustment. Specifically, it consists of a server, terminals, and users.

[0070] Server Role

[0071] The server collects past and future reservation information for the shared space from the space's reservation system and calendar application. This data is preprocessed to a format suitable for machine learning models. Preprocessing includes standardizing date formats and removing unnecessary information. Based on this preprocessed data, the server uses machine learning algorithms—specifically, time series analysis and clustering techniques—to predict future demand.

[0072] Terminal role

[0073] The terminal presents the user with a layout plan generated based on demand forecast data calculated by the server. The presented layout is visualized through a graphical user interface (GUI) to make it easy for the user to understand. The terminal also provides an interface that allows for fine-tuning of the layout based on user feedback.

[0074] User roles

[0075] Users review the proposed layout through their terminal and approve or adjust it as needed. For example, when a user hosts a large meeting, the server analyzes data from similar past meetings and suggests a large space. Based on this suggestion, if the user approves the layout, the space is rearranged.

[0076] As a concrete example of implementing this system, let's consider a prompt. A prompt in the form of "Predict future meeting demand and suggest the optimal layout" is input to the generating AI model. Based on this prompt, the model derives the optimal arrangement of meeting spaces.

[0077] This allows the entire system to work together to support efficient space utilization and enables flexible layout adjustments in response to fluctuations in demand.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server collects past and future reservation information for shared spaces from shared space reservation systems and calendar applications. Input data includes meeting dates and times, number of participants, and purpose of use. The server processes this data, standardizing formats and filtering out unnecessary information to generate pre-processed data. The output is a dataset in a format suitable for machine learning processing.

[0081] Step 2:

[0082] The server runs a machine learning model using preprocessed data. Specifically, it uses a time series analysis algorithm to predict future demand for a shared space. The input for this step is the dataset generated in the previous step. The server analyzes the dataset, calculates predicted demand and space size, and provides the prediction results as output.

[0083] Step 3:

[0084] The server generates an optimal collective space layout plan based on predicted demand data. Specifically, it calculates the placement of movable compartments and prefabricated fixtures. The input for this step is the demand forecast results. The server outputs detailed layout instructions for dynamic placement and prepares them for transmission to the terminal.

[0085] Step 4:

[0086] The terminal visualizes and presents the layout plan received from the server to the user. The input is the layout proposal from the server. The terminal displays the information in a user-friendly format using a GUI and provides a visual layout preview as output. The user reviews, approves, or adjusts the layout based on this preview.

[0087] Step 5:

[0088] The user reviews the layout proposal presented through the terminal and makes adjustments as needed. The input is the layout proposal displayed on the terminal's GUI. The user makes adjustments and notifies the terminal of their final approval as output. This confirms the optimal layout setting.

[0089] Step 6:

[0090] The terminal transmits the approved layout proposal to the execution device and begins rearranging the collective space. The input is the final approved layout proposal from the user. Based on this information, the terminal issues instructions to the execution device, and the output is the implementation of the layout. Movable compartments are moved as needed, and modular fixtures are rearranged.

[0091] (Application Example 1)

[0092] 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."

[0093] In logistics centers and other work spaces, there is a need to maximize the use of limited physical space, respond flexibly to changes in demand, and ensure efficient inventory management and movement. However, conventional fixed layouts make it difficult to utilize space flexibly based on demand forecasts, hindering operational efficiency. To solve this problem, a system is needed that dynamically reconfigures space and instantly realizes the optimal inventory placement according to demand.

[0094] 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.

[0095] In this invention, the server includes means for acquiring past space usage information and future demand information and preprocessing this data; means for performing machine learning to predict space demand using the preprocessed data; and means for generating a plan for dynamically arranging movable partitions and modular components according to the predicted demand. This enables the creation of flexible workspaces in logistics centers and ensures effective arrangement of goods and movement routes in response to demand fluctuations.

[0096] "Past space usage information" refers to information about past item placement and usage history in logistics centers and similar facilities, and is used for demand forecasting and optimizing space layout.

[0097] "Future demand information" refers to information that indicates the future flow of goods and the scale of space required, and is data that is useful for reconfiguring space based on demand forecasts.

[0098] "Means for preprocessing" refers to a device or mechanism that performs processing to convert acquired data into a format suitable for machine learning algorithms.

[0099] "Machine learning" is a method of executing algorithms to predict future trends and patterns based on past data, and is used to forecast spatial demand.

[0100] A "movable partition" refers to a partition or wall that can flexibly change the physical division of a space, and is equipment that enables spatial reconfiguration according to demand.

[0101] "Modular components" refer to parts or devices that can be easily rearranged or reconfigured, and are used to increase the flexibility of spatial design.

[0102] A "control instruction" is a command or instruction that causes machinery and equipment to perform specific operations or actions based on a planned spatial layout.

[0103] A "work visual device" is a display device used to provide visual information to workers and is used to present planning information in real time.

[0104] An "autonomous mobile device" is a robot or mechanical device that moves automatically and places items according to a plan.

[0105] The system implementing this invention aims to efficiently utilize space in a logistics center. The server first acquires past space usage information and future demand information, and then performs preprocessing to convert this data into an appropriate format. As for the software to be used, Python is used for data analysis, and libraries such as Pandas and Matplotlib can be used for data preprocessing and visualization.

[0106] The server uses pre-processed data and machine learning libraries such as Scikit-learn and TENSORFLOW® to forecast demand. This allows for the understanding of future demand trends and the creation of spatial reconfiguration plans. The generated plans are then sent to the execution system as control instructions for the efficient placement of movable partitions and modular components.

[0107] The terminal visually displays planning information in real time to the work-related visual device worn by the worker. Since this visual device is expected to use Microsoft® HoloLens® or similar smart glasses, the worker can immediately understand and respond to appropriate instructions on-site. Furthermore, the robot, acting as an autonomous mobile device, uses ROS (Robot Operating System) to reconfigure the space according to the instructions.

[0108] For example, the server can send a prompt to a generating AI model such as, "Based on next week's shipping data, please optimize the placement of goods within the logistics center according to the predicted demand," which can then automatically plan and execute the optimal placement. This enables flexible and efficient space management.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The server collects historical space usage information and future demand information from the logistics management system. Inputs include records of item placement and shipping history at various points in time, as well as future demand forecast data. This data is organized into the required format using the Pandas library. The output is a pre-processed dataset that is easy for machine learning algorithms to handle.

[0112] Step 2:

[0113] The server uses a Scikit-learn machine learning model to forecast demand based on pre-processed data. The input is a pre-processed dataset, which is used to predict future demand trends. The output is the forecast results, such as the predicted demand value and required space for each product.

[0114] Step 3:

[0115] The server uses the forecast results to plan the optimal placement of movable partitions and modular components. The input is the demand forecast values ​​obtained in step 2, and based on this, it generates a proposed spatial reconfiguration. The output is the placement plan for movable partitions and modular components.

[0116] Step 4:

[0117] The terminal displays the generated placement plan in real time on the worker's visual device. The input is the placement plan received from the server, and visualization is performed to present the data in a format that is easy for the worker to understand. The output is a visual instruction on the visual device worn by the worker.

[0118] Step 5:

[0119] The user begins work based on instructions from the terminal. Specifically, they adjust objects in the space according to information obtained through the visual device. This improves work efficiency.

[0120] Step 6:

[0121] The autonomous mobile device automatically adjusts the placement of movable partitions and items based on control instructions received from the terminal. The input is the placement plan from step 3, and the device operates to realize it. The output is the actual reconfigured physical space.

[0122] 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.

[0123] This invention is a system for achieving efficient operation of meeting spaces. It predicts the usage demand for meeting rooms and dynamically reconfigures the space to match that demand. At the same time, it recognizes the emotional state of users and reflects that information in the spatial design and environmental adjustments to provide an optimal meeting environment.

[0124] The server retrieves past usage data and future booking information from the meeting room reservation system and calendar application. This data is pre-processed by AI and used to predict future meeting demand. The server also runs an emotion recognition engine to analyze the user's emotional state in real time based on data acquired from cameras and microphones.

[0125] The terminal receives predictive data from the server and visualizes and presents proposed layout changes for the meeting space to the user. Based on this information, the user can make necessary adjustments and approve the final layout. Suggestions for space settings based on the user's emotions are also included; for example, the lighting can be softened if the user wants to relax.

[0126] Furthermore, the device sends emotional feedback from the user to the server, which is used to improve the emotion recognition algorithm. This allows the system to perform feedforward learning, enabling even more accurate emotion recognition and spatial design in subsequent uses.

[0127] For example, if a participant is fatigued during a project meeting, the emotion recognition engine will detect this situation, and the server will suggest a short break and adjust the lighting in the meeting space to create a relaxing environment. In this way, the present invention supports effective meeting management by automatically providing an appropriate environment according to the progress of the meeting and the state of the participants.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server retrieves past meeting usage information and future booking information from the meeting room reservation system and calendar application. The retrieved data is pre-processed into a format including date and time, number of participants, and meeting duration.

[0131] Step 2:

[0132] The server feeds pre-processed data into machine learning algorithms to predict future meeting demand and efficient space allocation. This prediction is made using time series analysis techniques that model demand trends.

[0133] Step 3:

[0134] The server analyzes users' emotions in real time using an emotion recognition engine via cameras and microphones in the meeting room. This generates indices such as each participant's stress level and concentration level.

[0135] Step 4:

[0136] The server creates an optimal layout plan for the meeting space based on predicted meeting demand and sentiment analysis results. This plan includes environmental elements such as the placement of movable partitions and lighting settings.

[0137] Step 5:

[0138] The terminal visually presents the generated layout change proposals and environment adjustment proposals to the user. The user reviews these, makes adjustments as needed, and approves the final change proposal.

[0139] Step 6:

[0140] The terminal transmits the approved layout proposal as instruction signals to the equipment necessary for reconfiguring the meeting space. This causes movable partitions and environmental control systems to automatically activate and perform the proposed arrangement and adjustments.

[0141] Step 7:

[0142] After the meeting ends, the server collects feedback from users regarding their impressions and usability, and uses this feedback to improve the emotion recognition engine and spatial placement algorithm. This feedback will contribute to improving the system's accuracy in future meetings.

[0143] (Example 2)

[0144] 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".

[0145] Modern meetings require flexible and efficient use of space to meet diverse needs, but currently, accurately predicting the demand for meeting space and setting up an environment that suits the emotions and state of participants is difficult. Furthermore, there is a lack of mechanisms to dynamically adjust the environment according to the progress of the meeting and the emotional state of the participants, which hinders the enhancement of participant productivity and comfort.

[0146] 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.

[0147] In this invention, the server includes means for acquiring past meeting space usage information and future reservation information and preprocessing this information; means for executing a machine learning algorithm to predict the demand for the meeting space using the preprocessed information; and means for analyzing information from a camera and an audio input device to recognize the emotional state of the user. This makes it possible to optimize the meeting space according to demand and to adjust the environment in real time to match the emotional state of the participants.

[0148] "Past meeting space usage information" refers to data regarding the reservation history and usage status of meeting rooms that have been used in the past.

[0149] "Future reservation information" refers to data regarding planned reservations for meeting rooms in the future.

[0150] "Preprocessing methods" refer to methods and techniques for transforming data into a format suitable for analysis or machine learning processing.

[0151] A "machine learning algorithm" is a technology that learns patterns from large amounts of data to make future predictions and classifications.

[0152] A "movable partition" is a wall or partition that can be rearranged flexibly within a meeting space.

[0153] "Modular equipment" refers to furniture and devices whose arrangement and configuration can be changed according to their intended use.

[0154] "Camera and audio input device" refers to a device for capturing and recording video and audio data.

[0155] "Means for recognizing a user's emotional state" refers to technologies for analyzing and judging a user's emotions from their facial expressions and statements.

[0156] "Means for generating control commands to adjust environmental conditions" refers to technology that generates commands to adjust environmental settings such as lighting and sound according to recognized emotional states.

[0157] "Means for transmitting instructions" refers to the function of transmitting instructions to devices that execute plans and control commands.

[0158] This invention is a system that supports the efficient operation of meeting rooms. The server acquires past meeting space usage data and future reservation information, and uses predetermined hardware and software to preprocess the data. Specifically, the server utilizes an API for data acquisition and uses a Python®-based library for data cleaning and analysis.

[0159] The server executes machine learning algorithms to predict future meeting demand. This utilizes AI technologies specialized in time-series forecasting, such as LSTM models. Furthermore, the server recognizes the user's emotional state in real time through cameras and voice input devices. OpenCV and dedicated voice analysis libraries are used for emotion recognition.

[0160] The terminal receives predictive data from the server and information based on the user's emotional state, and then visualizes and presents proposed layout changes for the meeting space to the user. This is done using 3D modeling software, providing information in a visually easy-to-understand format.

[0161] Users can make necessary adjustments based on the meeting space layout presented on their device and approve the final design. The device also suggests adjustments based on the user's emotional state; for example, if relaxation is needed, a soft lighting environment can be set through the lighting system.

[0162] As a concrete example, if a participant is feeling fatigued during a project meeting, the server uses emotion recognition technology to understand the situation. It then suggests a short break and adjusts the lighting in the meeting space to create a relaxing environment. In this way, the present invention makes it possible to provide an appropriate environment according to the emotional state of the participants and the progress of the meeting.

[0163] Here is an example of a prompt sentence for a generative AI model: "Please tell me how to predict the current meeting demand and suggest a spatial design that responds to the participants' emotions."

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The server retrieves past meeting space usage data and future reservation information from the meeting room reservation system and calendar application via API. The data retrieved as input is received in a data format such as JSON. Since this data cannot be used directly for machine learning, preprocessing is performed to remove unnecessary information and organize the necessary information. A formatted dataset is generated as output.

[0167] Step 2:

[0168] The server takes a pre-processed dataset as input and runs a machine learning algorithm. This process uses an LSTM model to predict future demand for meeting spaces. Specifically, it learns past usage patterns and performs calculations to predict demand for the next week or month. The output provides future demand forecast data.

[0169] Step 3:

[0170] The server acquires data in real time from cameras and audio input devices in the conference room and analyzes the user's emotional state. Facial image data and audio data are collected as input. This data is analyzed using OpenCV and speech analysis libraries to identify the user's emotions (e.g., joy, surprise, fatigue). The output is information about the recognized emotional state.

[0171] Step 4:

[0172] The terminal visualizes and presents proposed layout changes for the meeting space to the user, based on demand forecast data and emotional state information received from the server. Forecast data and emotional data are used as input, and 3D modeling software visualizes the actual layout changes within the meeting space. The output provides the user with a visual layout proposal.

[0173] Step 5:

[0174] The user makes adjustments based on the layout proposal viewed on the device and finalizes the design. A layout proposal is provided as input, and the user makes the necessary changes. This operation is performed via touchscreen or keyboard, and the final revised layout is generated as an approval instruction.

[0175] Step 6:

[0176] The terminal sends the user-approved final layout proposal to the server, and the actual environment adjustments begin. The server sends the generated control commands to the conference room's lighting system and sound equipment to adjust the environment. This brings the proposed environment to life, such as adjusting the conference room lighting to a relaxed state.

[0177] (Application Example 2)

[0178] 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".

[0179] There is a need to optimize space utilization efficiency and environmental control within factories to improve worker concentration and efficiency. However, conventional methods make it difficult to dynamically reconfigure spaces and adjust the environment in response to workers' emotional states, making it challenging to improve work efficiency in limited environments.

[0180] 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.

[0181] In this invention, the server includes means for acquiring past space utilization information and future reservation information and preprocessing this data; means for executing a machine learning algorithm for predicting space demand using the preprocessed data; means for generating a plan for dynamically arranging movable partitions and multipurpose equipment within the space according to the predicted demand; means for analyzing human emotional states in real time using emotion recognition technology; and means for adjusting the spatial environment based on the emotional state. As a result, factory workers are provided with an optimal work environment according to their emotional state at any given time, enabling efficient space utilization and improved work efficiency.

[0182] "Space demand forecasting" is a technology that predicts the need for space use within a specific environment based on past usage data and future reservation information.

[0183] A "movable partition" refers to a partition that can dynamically divide a space, and is a device that allows for flexible layout changes depending on the purpose.

[0184] "Multipurpose equipment" is a general term for equipment and devices whose arrangement and function can be changed according to various uses, and which helps to make efficient use of space.

[0185] "Emotion recognition technology" is a technology that analyzes data from cameras and microphones to identify a person's emotional state in real time.

[0186] "Spatial environment adjustment" refers to adjustments made to optimize lighting, sound, and layout within a space based on the user's emotional state, etc.

[0187] A system implementing this invention first uses a server to acquire past spatial usage information and future reservation information, and then uses a database management system and a machine learning platform to preprocess this data. This includes, for example, data organization using the Python Pandas library and training a demand forecasting model using TensorFlow.

[0188] The server predicts spatial demand based on the acquired data. Machine learning algorithms are executed using frameworks such as TensorFlow to accurately predict the demand for factories and workspaces.

[0189] Next, the server generates a plan for dynamically positioning movable partitions and multi-purpose fixtures within the space to match the predicted demand. This involves a process of visually designing the layout in conjunction with design software such as AutoCAD.

[0190] Meanwhile, the terminal visualizes and presents the layout of the generated space to the worker via smart glasses or other devices capable of AR display. Here, game development platforms such as Unity are used to simulate and execute the AR environment.

[0191] Furthermore, the server provides emotion recognition technology, using image processing libraries like OpenCV and speech analysis tools to analyze data from cameras and microphones to analyze the worker's emotional state in real time. Based on this, the server issues instructions to adjust the spatial environment. For example, the lighting system is adjusted in conjunction with the Hue bridge.

[0192] As a concrete example, during a design meeting for a new product in a factory, the server detects if a worker is losing focus. Based on this information, the server suggests relaxing the lighting and, through smart glasses, offers further suggestions for flexibly adjusting the layout.

[0193] An example of a prompt message would be: "Based on the employee's current emotional state and past data, please suggest the optimal spatial layout and environmental settings for the next 60-minute meeting."

[0194] This system supports efficient work within the factory by optimizing the spatial environment based on the emotions of the workers.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The server retrieves past space usage information and future reservation information. This input data is preprocessed using Pandas, imputing missing values ​​and formatting it into the required format. This prepares the data in a way that is easy for machine learning algorithms to handle.

[0198] Step 2:

[0199] The server runs a spatial demand forecasting model using TensorFlow based on preprocessed data. This model learns from past usage patterns and outputs future spatial demand as a numerical value. This makes it possible to predict how much space will be needed in the next timeframe.

[0200] Step 3:

[0201] The server uses AutoCAD to generate a layout plan for movable partitions and multi-purpose fixtures based on predicted space demand. This process designs the optimal layout while considering supply and demand forecasts and physical constraints. The generated plan is output as a layout drawing that is passed on to the next stage.

[0202] Step 4:

[0203] The device displays the generated layout in AR through smart glasses. At this stage, Unity is used to visually present the planned spatial arrangement to the user. This information intuitively shows the user what physical changes are necessary.

[0204] Step 5:

[0205] The server uses emotion recognition technology to analyze data acquired from cameras and microphones using OpenCV and other tools to recognize the user's emotional state in real time. If a specific emotional state (e.g., fatigue) is detected as a result of this analysis, it uses that as input to generate instructions for adjusting the environment.

[0206] Step 6:

[0207] The server sends commands to devices such as Hue bridges to adjust the spatial environment, including lighting and sound, based on emotional information. The specific action of this step is to put the proposed environmental settings into action.

[0208] Step 7:

[0209] Users work in a controlled environment and collect results and feedback. This results information is sent back to the server and used to improve the predictive model for future use. This allows the generative AI model to make more accurate predictions.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] This invention is a system for achieving efficient operation of meeting spaces, and includes a method for demand forecasting using past and future meeting data, and for adjusting the dynamic layout using movable partitions and modular equipment. The processing of the program of this system is described below.

[0227] The server first collects past meeting usage information and future booking information from meeting room reservation systems and calendar applications. This data is then formatted to include usage date and time, number of participants, etc., for use in demand forecasting.

[0228] The server runs a machine learning algorithm based on pre-processed data to predict future meeting room demand. The predicted demand is specifically calculated as date, time, number of participants, and required space size. Based on this, a plan is generated to determine the optimal meeting room layout.

[0229] The terminal visualizes and notifies the user of the proposed layout changes received from the server. This proposed layout is designed with user convenience in mind and can be fine-tuned as needed. The user can review the presented layout, make any necessary adjustments, and then give final approval.

[0230] The approved layout is transmitted to the execution device via a terminal, and movable partitions and modular fixtures within the meeting space are moved accordingly. This allows for the immediate creation of an optimal meeting space tailored to the specific needs.

[0231] For example, if a large project meeting is needed at a specific time on a weekday, the server analyzes data from similar past meetings to predict the large space demand at that time. Based on this information, the server identifies the need for a large meeting space and proposes moving movable partitions to create a unified large space. If all users approve this proposal, the terminal sends an instruction, and the meeting room is arranged.

[0232] Thus, this invention aims to improve the efficiency of meeting management by providing a system that makes maximum use of limited physical space and can be flexibly modified in response to fluctuations in demand.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server retrieves past meeting usage data and future booking information from the meeting room reservation system and calendar application. This includes retrieving data using APIs or importing CSV files. The retrieved data is formatted to include date and time, number of participants, meeting duration, and other relevant information.

[0236] Step 2:

[0237] The server feeds pre-processed data into machine learning algorithms to predict future demand for meeting rooms. This prediction uses time series analysis and predictive models to calculate demand for specific dates and times, and records it in a database.

[0238] Step 3:

[0239] Based on the prediction results, the server automatically creates an optimal layout plan for moving movable partitions and modular equipment within the conference room. This plan aims for maximum space efficiency and is visualized in 3D as a virtual layout.

[0240] Step 4:

[0241] The terminal notifies the user of the created layout plan. The user can review the provided layout on the terminal screen and make adjustments as needed. Once adjustments are complete, the user gives final approval.

[0242] Step 5:

[0243] The terminal sends instruction signals to facility management staff or automated equipment to implement layout changes approved by the user. For example, partitions may be moved or equipment may be rearranged.

[0244] Step 6:

[0245] The server monitors whether the meeting space has been reconfigured as planned, using camera feeds and sensor data for verification. If an anomaly is detected, it issues an alert to prompt a quick response.

[0246] Step 7:

[0247] After the meeting ends, the terminal collects usage feedback from users and sends it to the server. This feedback is used as valuable data for improvement when planning future meetings.

[0248] (Example 1)

[0249] 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."

[0250] To achieve efficient use of shared spaces, it is necessary to flexibly adjust the space in response to fluctuating demand. However, conventional systems have challenges such as low prediction accuracy and the time-consuming manual process of reconfiguring the space.

[0251] 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.

[0252] In this invention, the server includes means for acquiring past collective space usage information and future reservation information and preprocessing these values; means for executing a machine learning method for predicting the demand for the collective space using the preprocessed values; and means for generating a plan for dynamically arranging movable compartments and prefabricated fixtures within the collective space according to the predicted demand. This enables rapid and efficient space utilization in response to demand.

[0253] "Gathering space usage information" refers to data regarding the usage history of a gathering place and future reservations for its use.

[0254] "Preprocessing" refers to processes such as data cleansing and formatting standardization that transform raw data into a format suitable for machine learning and analysis.

[0255] A "machine learning method" is a computational method that learns patterns and rules from large amounts of data to perform predictions and classifications.

[0256] "Demand forecasting" is the process of estimating the future need for services or goods based on past data.

[0257] A "movable partition" is a movable partition that can flexibly divide a physical space and easily change the layout of that space.

[0258] "Assembly-type equipment" refers to furniture and equipment whose configuration can be changed and rearranged according to its use and purpose.

[0259] "Dynamic arrangement" refers to flexibly changing equipment and spatial configurations according to the situation and conditions.

[0260] This invention is a system for achieving efficient operation of a collective space, combining data analysis and dynamic layout adjustment. Specifically, it consists of a server, terminals, and users.

[0261] Server Role

[0262] The server collects past and future reservation information for the shared space from the space's reservation system and calendar application. This data is preprocessed to a format suitable for machine learning models. Preprocessing includes standardizing date formats and removing unnecessary information. Based on this preprocessed data, the server uses machine learning algorithms—specifically, time series analysis and clustering techniques—to predict future demand.

[0263] Terminal role

[0264] The terminal presents the user with a layout plan generated based on demand forecast data calculated by the server. The presented layout is visualized through a graphical user interface (GUI) to make it easy for the user to understand. The terminal also provides an interface that allows for fine-tuning of the layout based on user feedback.

[0265] User roles

[0266] Users review the proposed layout through their terminal and approve or adjust it as needed. For example, when a user hosts a large meeting, the server analyzes data from similar past meetings and suggests a large space. Based on this suggestion, if the user approves the layout, the space is rearranged.

[0267] As a concrete example of implementing this system, let's consider a prompt. A prompt in the form of "Predict future meeting demand and suggest the optimal layout" is input to the generating AI model. Based on this prompt, the model derives the optimal arrangement of meeting spaces.

[0268] This allows the entire system to work together to support efficient space utilization and enables flexible layout adjustments in response to fluctuations in demand.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The server collects past and future reservation information for shared spaces from shared space reservation systems and calendar applications. Input data includes meeting dates and times, number of participants, and purpose of use. The server processes this data, standardizing formats and filtering out unnecessary information to generate pre-processed data. The output is a dataset in a format suitable for machine learning processing.

[0272] Step 2:

[0273] The server runs a machine learning model using preprocessed data. Specifically, it uses a time series analysis algorithm to predict future demand for a shared space. The input for this step is the dataset generated in the previous step. The server analyzes the dataset, calculates predicted demand and space size, and provides the prediction results as output.

[0274] Step 3:

[0275] The server generates an optimal collective space layout plan based on predicted demand data. Specifically, it calculates the placement of movable compartments and prefabricated fixtures. The input for this step is the demand forecast results. The server outputs detailed layout instructions for dynamic placement and prepares them for transmission to the terminal.

[0276] Step 4:

[0277] The terminal visualizes and presents the layout plan received from the server to the user. The input is the layout proposal from the server. The terminal displays the information in a user-friendly format using a GUI and provides a visual layout preview as output. The user reviews, approves, or adjusts the layout based on this preview.

[0278] Step 5:

[0279] The user reviews the layout proposal presented through the terminal and makes adjustments as needed. The input is the layout proposal displayed on the terminal's GUI. The user makes adjustments and notifies the terminal of their final approval as output. This confirms the optimal layout setting.

[0280] Step 6:

[0281] The terminal sends the approved layout plan to the execution device and starts the rearrangement of the gathering space. The input is the finally approved layout plan from the user. Based on this information, the terminal instructs the execution device and realizes the layout as output. The movable partitions move as needed, and the modular equipment is rearranged.

[0282] (Application Example 1)

[0283] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0284] In a working space such as a logistics center, it is required to make the most of the limited physical space, flexibly respond to changes in demand, and ensure efficient goods management and traffic flow. However, with the conventional fixed layout, it is difficult to flexibly utilize the space according to demand prediction, and the efficiency of work is hindered. To solve this problem, a system that dynamically reconstructs the space and immediately realizes the optimal goods arrangement according to demand is necessary.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for acquiring past space usage information and future demand information and preprocessing these data, means for executing machine learning for predicting space demand using the preprocessed data, and means for generating a plan for dynamically arranging movable partitions and modular components according to the predicted demand. Thereby, a flexible working space can be constructed in the logistics center, and effective goods arrangement and traffic flow can be ensured according to demand fluctuations.

[0287] The "past space usage information" is information regarding past goods arrangements and usage histories in a logistics center or the like, and is data used for demand prediction and optimization of space layout.

[0288] "Future demand information" refers to information that indicates the future flow of goods and the scale of space required, and is data that is useful for reconfiguring space based on demand forecasts.

[0289] "Means for preprocessing" refers to a device or mechanism that performs processing to convert acquired data into a format suitable for machine learning algorithms.

[0290] "Machine learning" is a method of executing algorithms to predict future trends and patterns based on past data, and is used to forecast spatial demand.

[0291] A "movable partition" refers to a partition or wall that can flexibly change the physical division of a space, and is equipment that enables spatial reconfiguration according to demand.

[0292] "Modular components" refer to parts or devices that can be easily rearranged or reconfigured, and are used to increase the flexibility of spatial design.

[0293] A "control instruction" is a command or instruction that causes machinery and equipment to perform specific operations or actions based on a planned spatial layout.

[0294] A "work visual device" is a display device used to provide visual information to workers and is used to present planning information in real time.

[0295] An "autonomous mobile device" is a robot or mechanical device that moves automatically and places items according to a plan.

[0296] The system implementing this invention aims to efficiently utilize space in a logistics center. The server first acquires past space usage information and future demand information, and then performs preprocessing to convert this data into an appropriate format. As for the software to be used, Python is used for data analysis, and libraries such as Pandas and Matplotlib can be used for data preprocessing and visualization.

[0297] The server uses pre-processed data and machine learning libraries such as Scikit-learn and TensorFlow to forecast demand. This allows it to understand future demand trends and plan the spatial reconfiguration. The generated plan is then sent to the execution system as control instructions for efficiently arranging movable partitions and modular components.

[0298] The terminal visually displays planning information in real time to the work-related visual device worn by the worker. Since this visual device is expected to use Microsoft HoloLens or similar smart glasses, the worker can immediately understand and respond to appropriate instructions on-site. Furthermore, the robot, acting as an autonomous mobile device, uses ROS (Robot Operating System) to reconfigure the space according to the instructions.

[0299] For example, the server can send a prompt to a generating AI model such as, "Based on next week's shipping data, please optimize the placement of goods within the logistics center according to the predicted demand," which can then automatically plan and execute the optimal placement. This enables flexible and efficient space management.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The server collects historical space usage information and future demand information from the logistics management system. Inputs include records of item placement and shipping history at various points in time, as well as future demand forecast data. This data is organized into the required format using the Pandas library. The output is a pre-processed dataset that is easy for machine learning algorithms to handle.

[0303] Step 2:

[0304] The server uses a machine learning model of Scikit-learn to perform demand prediction based on preprocessed data. The input is the preprocessed dataset, and this is used to predict future demand trends. The output is the prediction results such as the demand prediction values for each product and the required space.

[0305] Step 3:

[0306] The server uses the prediction results to plan the optimal placement of movable partitions and modular components. The input is the demand prediction value obtained in Step 2, and based on this, a space reconstruction plan is generated. The output is the placement plan for movable partitions and modular components.

[0307] Step 4:

[0308] The terminal presents the generated placement plan to the working visual device in real time. The input is the placement plan received from the server, and visualization is performed to present the data in a form that is easy for the operator to understand. The output is the visual instruction on the visual device worn by the operator.

[0309] Step 5:

[0310] The user starts the work based on the instructions from the terminal. Specifically, the user adjusts the items in the space according to the information obtained through the visual device. This improves the efficiency of the work.

[0311] Step 6:

[0312] The autonomous mobile device automatically adjusts the placement of movable partitions and items based on the control instructions received from the terminal. The input is the placement plan in Step 3, and it operates to realize this. The output is the actually reconstructed physical space.

[0313] 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.

[0314] This invention is a system for achieving efficient operation of meeting spaces. It predicts the usage demand for meeting rooms and dynamically reconfigures the space to match that demand. At the same time, it recognizes the emotional state of users and reflects that information in the spatial design and environmental adjustments to provide an optimal meeting environment.

[0315] The server retrieves past usage data and future booking information from the meeting room reservation system and calendar application. This data is pre-processed by AI and used to predict future meeting demand. The server also runs an emotion recognition engine to analyze the user's emotional state in real time based on data acquired from cameras and microphones.

[0316] The terminal receives predictive data from the server and visualizes and presents proposed layout changes for the meeting space to the user. Based on this information, the user can make necessary adjustments and approve the final layout. Suggestions for space settings based on the user's emotions are also included; for example, the lighting can be softened if the user wants to relax.

[0317] Furthermore, the device sends emotional feedback from the user to the server, which is used to improve the emotion recognition algorithm. This allows the system to perform feedforward learning, enabling even more accurate emotion recognition and spatial design in subsequent uses.

[0318] For example, if a participant is fatigued during a project meeting, the emotion recognition engine will detect this situation, and the server will suggest a short break and adjust the lighting in the meeting space to create a relaxing environment. In this way, the present invention supports effective meeting management by automatically providing an appropriate environment according to the progress of the meeting and the state of the participants.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The server retrieves past meeting usage information and future booking information from the meeting room reservation system and calendar application. The retrieved data is pre-processed into a format including date and time, number of participants, and meeting duration.

[0322] Step 2:

[0323] The server feeds pre-processed data into machine learning algorithms to predict future meeting demand and efficient space allocation. This prediction is made using time series analysis techniques that model demand trends.

[0324] Step 3:

[0325] The server analyzes users' emotions in real time using an emotion recognition engine via cameras and microphones in the meeting room. This generates indices such as each participant's stress level and concentration level.

[0326] Step 4:

[0327] The server creates an optimal layout plan for the meeting space based on predicted meeting demand and sentiment analysis results. This plan includes environmental elements such as the placement of movable partitions and lighting settings.

[0328] Step 5:

[0329] The terminal visually presents the generated layout change proposals and environment adjustment proposals to the user. The user reviews these, makes adjustments as needed, and approves the final change proposal.

[0330] Step 6:

[0331] The terminal transmits the approved layout proposal as instruction signals to the equipment necessary for reconfiguring the meeting space. This causes movable partitions and environmental control systems to automatically activate and perform the proposed arrangement and adjustments.

[0332] Step 7:

[0333] After the meeting ends, the server collects feedback from users regarding their impressions and usability, and uses this feedback to improve the emotion recognition engine and spatial placement algorithm. This feedback will contribute to improving the system's accuracy in future meetings.

[0334] (Example 2)

[0335] 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".

[0336] Modern meetings require flexible and efficient use of space to meet diverse needs, but currently, accurately predicting the demand for meeting space and setting up an environment that suits the emotions and state of participants is difficult. Furthermore, there is a lack of mechanisms to dynamically adjust the environment according to the progress of the meeting and the emotional state of the participants, which hinders the enhancement of participant productivity and comfort.

[0337] 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.

[0338] In this invention, the server includes means for acquiring past meeting space usage information and future reservation information and preprocessing this information; means for executing a machine learning algorithm to predict the demand for the meeting space using the preprocessed information; and means for analyzing information from a camera and an audio input device to recognize the emotional state of the user. This makes it possible to optimize the meeting space according to demand and to adjust the environment in real time to match the emotional state of the participants.

[0339] "Past meeting space usage information" refers to data regarding the reservation history and usage status of meeting rooms that have been used in the past.

[0340] "Future reservation information" refers to data regarding planned reservations for meeting rooms in the future.

[0341] "Preprocessing methods" refer to methods and techniques for transforming data into a format suitable for analysis or machine learning processing.

[0342] A "machine learning algorithm" is a technology that learns patterns from large amounts of data to make future predictions and classifications.

[0343] A "movable partition" is a wall or partition that can be rearranged flexibly within a meeting space.

[0344] "Modular equipment" refers to furniture and devices whose arrangement and configuration can be changed according to their intended use.

[0345] "Camera and audio input device" refers to a device for capturing and recording video and audio data.

[0346] "Means for recognizing a user's emotional state" refers to technologies for analyzing and judging a user's emotions from their facial expressions and statements.

[0347] "Means for generating control commands to adjust environmental conditions" refers to technology that generates commands to adjust environmental settings such as lighting and sound according to recognized emotional states.

[0348] "Means for transmitting instructions" refers to the function of transmitting instructions to devices that execute plans and control commands.

[0349] This invention is a system that supports the efficient operation of meeting rooms. The server acquires past meeting space usage data and future reservation information, and uses predetermined hardware and software to preprocess the data. Specifically, the server utilizes APIs for data acquisition and uses Python-based libraries for data cleaning and analysis.

[0350] The server executes machine learning algorithms to predict future meeting demand. This utilizes AI technologies specialized in time-series forecasting, such as LSTM models. Furthermore, the server recognizes the user's emotional state in real time through cameras and voice input devices. OpenCV and dedicated voice analysis libraries are used for emotion recognition.

[0351] The terminal receives predictive data from the server and information based on the user's emotional state, and then visualizes and presents proposed layout changes for the meeting space to the user. This is done using 3D modeling software, providing information in a visually easy-to-understand format.

[0352] Users can make necessary adjustments based on the meeting space layout presented on their device and approve the final design. The device also suggests adjustments based on the user's emotional state; for example, if relaxation is needed, a soft lighting environment can be set through the lighting system.

[0353] As a concrete example, if a participant is feeling fatigued during a project meeting, the server uses emotion recognition technology to understand the situation. It then suggests a short break and adjusts the lighting in the meeting space to create a relaxing environment. In this way, the present invention makes it possible to provide an appropriate environment according to the emotional state of the participants and the progress of the meeting.

[0354] Here is an example of a prompt sentence for a generative AI model: "Please tell me how to predict the current meeting demand and suggest a spatial design that responds to the participants' emotions."

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The server retrieves past meeting space usage data and future reservation information from the meeting room reservation system and calendar application via API. The data retrieved as input is received in a data format such as JSON. Since this data cannot be used directly for machine learning, preprocessing is performed to remove unnecessary information and organize the necessary information. A formatted dataset is generated as output.

[0358] Step 2:

[0359] The server takes a pre-processed dataset as input and runs a machine learning algorithm. This process uses an LSTM model to predict future demand for meeting spaces. Specifically, it learns past usage patterns and performs calculations to predict demand for the next week or month. The output provides future demand forecast data.

[0360] Step 3:

[0361] The server acquires data in real time from cameras and audio input devices in the conference room and analyzes the user's emotional state. Facial image data and audio data are collected as input. This data is analyzed using OpenCV and speech analysis libraries to identify the user's emotions (e.g., joy, surprise, fatigue). The output is information about the recognized emotional state.

[0362] Step 4:

[0363] The terminal visualizes and presents proposed layout changes for the meeting space to the user, based on demand forecast data and emotional state information received from the server. Forecast data and emotional data are used as input, and 3D modeling software visualizes the actual layout changes within the meeting space. The output provides the user with a visual layout proposal.

[0364] Step 5:

[0365] The user makes adjustments based on the layout proposal viewed on the device and finalizes the design. A layout proposal is provided as input, and the user makes the necessary changes. This operation is performed via touchscreen or keyboard, and the final revised layout is generated as an approval instruction.

[0366] Step 6:

[0367] The terminal sends the user-approved final layout proposal to the server, and the actual environment adjustments begin. The server sends the generated control commands to the conference room's lighting system and sound equipment to adjust the environment. This brings the proposed environment to life, such as adjusting the conference room lighting to a relaxed state.

[0368] (Application Example 2)

[0369] 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."

[0370] There is a need to optimize space utilization efficiency and environmental control within factories to improve worker concentration and efficiency. However, conventional methods make it difficult to dynamically reconfigure spaces and adjust the environment in response to workers' emotional states, making it challenging to improve work efficiency in limited environments.

[0371] 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.

[0372] In this invention, the server includes means for acquiring past space utilization information and future reservation information and preprocessing this data; means for executing a machine learning algorithm for predicting space demand using the preprocessed data; means for generating a plan for dynamically arranging movable partitions and multipurpose equipment within the space according to the predicted demand; means for analyzing human emotional states in real time using emotion recognition technology; and means for adjusting the spatial environment based on the emotional state. As a result, factory workers are provided with an optimal work environment according to their emotional state at any given time, enabling efficient space utilization and improved work efficiency.

[0373] "Space demand forecasting" is a technology that predicts the need for space use within a specific environment based on past usage data and future reservation information.

[0374] A "movable partition" refers to a partition that can dynamically divide a space, and is a device that allows for flexible layout changes depending on the purpose.

[0375] "Multipurpose equipment" is a general term for equipment and devices whose arrangement and function can be changed according to various uses, and which helps to make efficient use of space.

[0376] "Emotion recognition technology" is a technology that analyzes data from cameras and microphones to identify a person's emotional state in real time.

[0377] "Spatial environment adjustment" refers to adjustments made to optimize lighting, sound, and layout within a space based on the user's emotional state, etc.

[0378] A system implementing this invention first uses a server to acquire past spatial usage information and future reservation information, and then uses a database management system and a machine learning platform to preprocess this data. This includes, for example, data organization using the Python Pandas library and training a demand forecasting model using TensorFlow.

[0379] The server predicts spatial demand based on the acquired data. Machine learning algorithms are executed using frameworks such as TensorFlow to accurately predict the demand for factories and workspaces.

[0380] Next, the server generates a plan for dynamically positioning movable partitions and multi-purpose fixtures within the space to match the predicted demand. This involves a process of visually designing the layout in conjunction with design software such as AutoCAD.

[0381] Meanwhile, the terminal visualizes and presents the layout of the generated space to the worker via smart glasses or other devices capable of AR display. Here, game development platforms such as Unity are used to simulate and execute the AR environment.

[0382] Furthermore, the server provides emotion recognition technology, using image processing libraries like OpenCV and speech analysis tools to analyze data from cameras and microphones to analyze the worker's emotional state in real time. Based on this, the server issues instructions to adjust the spatial environment. For example, the lighting system is adjusted in conjunction with the Hue bridge.

[0383] As a concrete example, during a design meeting for a new product in a factory, the server detects if a worker is losing focus. Based on this information, the server suggests relaxing the lighting and, through smart glasses, offers further suggestions for flexibly adjusting the layout.

[0384] An example of a prompt message would be: "Based on the employee's current emotional state and past data, please suggest the optimal spatial layout and environmental settings for the next 60-minute meeting."

[0385] This system supports efficient work within the factory by optimizing the spatial environment based on the emotions of the workers.

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The server retrieves past space usage information and future reservation information. This input data is preprocessed using Pandas, imputing missing values ​​and formatting it into the required format. This prepares the data in a way that is easy for machine learning algorithms to handle.

[0389] Step 2:

[0390] The server runs a spatial demand forecasting model using TensorFlow based on preprocessed data. This model learns from past usage patterns and outputs future spatial demand as a numerical value. This makes it possible to predict how much space will be needed in the next timeframe.

[0391] Step 3:

[0392] The server uses AutoCAD to generate a layout plan for movable partitions and multi-purpose fixtures based on predicted space demand. This process designs the optimal layout while considering supply and demand forecasts and physical constraints. The generated plan is output as a layout drawing that is passed on to the next stage.

[0393] Step 4:

[0394] The device displays the generated layout in AR through smart glasses. At this stage, Unity is used to visually present the planned spatial arrangement to the user. This information intuitively shows the user what physical changes are necessary.

[0395] Step 5:

[0396] The server uses emotion recognition technology to analyze data acquired from cameras and microphones using OpenCV and other tools to recognize the user's emotional state in real time. If a specific emotional state (e.g., fatigue) is detected as a result of this analysis, it uses that as input to generate instructions for adjusting the environment.

[0397] Step 6:

[0398] The server sends commands to devices such as Hue bridges to adjust the spatial environment, including lighting and sound, based on emotional information. The specific action of this step is to put the proposed environmental settings into action.

[0399] Step 7:

[0400] Users work in a controlled environment and collect results and feedback. This results information is sent back to the server and used to improve the predictive model for future use. This allows the generative AI model to make more accurate predictions.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] [Third Embodiment]

[0405] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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".

[0417] This invention is a system for achieving efficient operation of meeting spaces, and includes a method for demand forecasting using past and future meeting data, and for adjusting the dynamic layout using movable partitions and modular equipment. The processing of the program of this system is described below.

[0418] The server first collects past meeting usage information and future booking information from meeting room reservation systems and calendar applications. This data is then formatted to include usage date and time, number of participants, etc., for use in demand forecasting.

[0419] The server runs a machine learning algorithm based on pre-processed data to predict future meeting room demand. The predicted demand is specifically calculated as date, time, number of participants, and required space size. Based on this, a plan is generated to determine the optimal meeting room layout.

[0420] The terminal visualizes and notifies the user of the proposed layout changes received from the server. This proposed layout is designed with user convenience in mind and can be fine-tuned as needed. The user can review the presented layout, make any necessary adjustments, and then give final approval.

[0421] The approved layout is transmitted to the execution device via a terminal, and movable partitions and modular fixtures within the meeting space are moved accordingly. This allows for the immediate creation of an optimal meeting space tailored to the specific needs.

[0422] For example, if a large project meeting is needed at a specific time on a weekday, the server analyzes data from similar past meetings to predict the large space demand at that time. Based on this information, the server identifies the need for a large meeting space and proposes moving movable partitions to create a unified large space. If all users approve this proposal, the terminal sends an instruction, and the meeting room is arranged.

[0423] Thus, this invention aims to improve the efficiency of meeting management by providing a system that makes maximum use of limited physical space and can be flexibly modified in response to fluctuations in demand.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] The server retrieves past meeting usage data and future booking information from the meeting room reservation system and calendar application. This includes retrieving data using APIs or importing CSV files. The retrieved data is formatted to include date and time, number of participants, meeting duration, and other relevant information.

[0427] Step 2:

[0428] The server feeds pre-processed data into machine learning algorithms to predict future demand for meeting rooms. This prediction uses time series analysis and predictive models to calculate demand for specific dates and times, and records it in a database.

[0429] Step 3:

[0430] Based on the prediction results, the server automatically creates an optimal layout plan for moving movable partitions and modular equipment within the conference room. This plan aims for maximum space efficiency and is visualized in 3D as a virtual layout.

[0431] Step 4:

[0432] The terminal notifies the user of the created layout plan. The user can review the provided layout on the terminal screen and make adjustments as needed. Once adjustments are complete, the user gives final approval.

[0433] Step 5:

[0434] The terminal sends instruction signals to facility management staff or automated equipment to implement layout changes approved by the user. For example, partitions may be moved or equipment may be rearranged.

[0435] Step 6:

[0436] The server monitors whether the meeting space has been reconfigured as planned, using camera feeds and sensor data for verification. If an anomaly is detected, it issues an alert to prompt a quick response.

[0437] Step 7:

[0438] After the meeting ends, the terminal collects usage feedback from users and sends it to the server. This feedback is used as valuable data for improvement when planning future meetings.

[0439] (Example 1)

[0440] 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."

[0441] To achieve efficient use of shared spaces, it is necessary to flexibly adjust the space in response to fluctuating demand. However, conventional systems have challenges such as low prediction accuracy and the time-consuming manual process of reconfiguring the space.

[0442] 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.

[0443] In this invention, the server includes means for acquiring past collective space usage information and future reservation information and preprocessing these values; means for executing a machine learning method for predicting the demand for the collective space using the preprocessed values; and means for generating a plan for dynamically arranging movable compartments and prefabricated fixtures within the collective space according to the predicted demand. This enables rapid and efficient space utilization in response to demand.

[0444] "Gathering space usage information" refers to data regarding the usage history of a gathering place and future reservations for its use.

[0445] "Preprocessing" refers to processes such as data cleansing and formatting standardization that transform raw data into a format suitable for machine learning and analysis.

[0446] A "machine learning method" is a computational method that learns patterns and rules from large amounts of data to perform predictions and classifications.

[0447] "Demand forecasting" is the process of estimating the future need for services or goods based on past data.

[0448] A "movable partition" is a movable partition that can flexibly divide a physical space and easily change the layout of that space.

[0449] "Assembly-type equipment" refers to furniture and equipment whose configuration can be changed and rearranged according to its use and purpose.

[0450] "Dynamic arrangement" refers to flexibly changing equipment and spatial configurations according to the situation and conditions.

[0451] This invention is a system for achieving efficient operation of a collective space, combining data analysis and dynamic layout adjustment. Specifically, it consists of a server, terminals, and users.

[0452] Server Role

[0453] The server collects past and future reservation information for the shared space from the space's reservation system and calendar application. This data is preprocessed to a format suitable for machine learning models. Preprocessing includes standardizing date formats and removing unnecessary information. Based on this preprocessed data, the server uses machine learning algorithms—specifically, time series analysis and clustering techniques—to predict future demand.

[0454] Terminal role

[0455] The terminal presents the user with a layout plan generated based on demand forecast data calculated by the server. The presented layout is visualized through a graphical user interface (GUI) to make it easy for the user to understand. The terminal also provides an interface that allows for fine-tuning of the layout based on user feedback.

[0456] User roles

[0457] Users review the proposed layout through their terminal and approve or adjust it as needed. For example, when a user hosts a large meeting, the server analyzes data from similar past meetings and suggests a large space. Based on this suggestion, if the user approves the layout, the space is rearranged.

[0458] As a concrete example of implementing this system, let's consider a prompt. A prompt in the form of "Predict future meeting demand and suggest the optimal layout" is input to the generating AI model. Based on this prompt, the model derives the optimal arrangement of meeting spaces.

[0459] This allows the entire system to work together to support efficient space utilization and enables flexible layout adjustments in response to fluctuations in demand.

[0460] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0461] Step 1:

[0462] The server collects past and future reservation information for shared spaces from shared space reservation systems and calendar applications. Input data includes meeting dates and times, number of participants, and purpose of use. The server processes this data, standardizing formats and filtering out unnecessary information to generate pre-processed data. The output is a dataset in a format suitable for machine learning processing.

[0463] Step 2:

[0464] The server runs a machine learning model using preprocessed data. Specifically, it uses a time series analysis algorithm to predict future demand for a shared space. The input for this step is the dataset generated in the previous step. The server analyzes the dataset, calculates predicted demand and space size, and provides the prediction results as output.

[0465] Step 3:

[0466] The server generates an optimal collective space layout plan based on predicted demand data. Specifically, it calculates the placement of movable compartments and prefabricated fixtures. The input for this step is the demand forecast results. The server outputs detailed layout instructions for dynamic placement and prepares them for transmission to the terminal.

[0467] Step 4:

[0468] The terminal visualizes and presents the layout plan received from the server to the user. The input is the layout proposal from the server. The terminal displays the information in a user-friendly format using a GUI and provides a visual layout preview as output. The user reviews, approves, or adjusts the layout based on this preview.

[0469] Step 5:

[0470] The user reviews the layout proposal presented through the terminal and makes adjustments as needed. The input is the layout proposal displayed on the terminal's GUI. The user makes adjustments and notifies the terminal of their final approval as output. This confirms the optimal layout setting.

[0471] Step 6:

[0472] The terminal transmits the approved layout proposal to the execution device and begins rearranging the collective space. The input is the final approved layout proposal from the user. Based on this information, the terminal issues instructions to the execution device, and the output is the implementation of the layout. Movable compartments are moved as needed, and modular fixtures are rearranged.

[0473] (Application Example 1)

[0474] 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."

[0475] In logistics centers and other work spaces, there is a need to maximize the use of limited physical space, respond flexibly to changes in demand, and ensure efficient inventory management and movement. However, conventional fixed layouts make it difficult to utilize space flexibly based on demand forecasts, hindering operational efficiency. To solve this problem, a system is needed that dynamically reconfigures space and instantly realizes the optimal inventory placement according to demand.

[0476] 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.

[0477] In this invention, the server includes means for acquiring past space usage information and future demand information and preprocessing this data; means for performing machine learning to predict space demand using the preprocessed data; and means for generating a plan for dynamically arranging movable partitions and modular components according to the predicted demand. This enables the creation of flexible workspaces in logistics centers and ensures effective arrangement of goods and movement routes in response to demand fluctuations.

[0478] "Past space usage information" refers to information about past item placement and usage history in logistics centers and similar facilities, and is used for demand forecasting and optimizing space layout.

[0479] "Future demand information" refers to information that indicates the future flow of goods and the scale of space required, and is data that is useful for reconfiguring space based on demand forecasts.

[0480] "Means for preprocessing" refers to a device or mechanism that performs processing to convert acquired data into a format suitable for machine learning algorithms.

[0481] "Machine learning" is a method of executing algorithms to predict future trends and patterns based on past data, and is used to forecast spatial demand.

[0482] A "movable partition" refers to a partition or wall that can flexibly change the physical division of a space, and is equipment that enables spatial reconfiguration according to demand.

[0483] "Modular components" refer to parts or devices that can be easily rearranged or reconfigured, and are used to increase the flexibility of spatial design.

[0484] A "control instruction" is a command or instruction that causes machinery and equipment to perform specific operations or actions based on a planned spatial layout.

[0485] A "work visual device" is a display device used to provide visual information to workers and is used to present planning information in real time.

[0486] An "autonomous mobile device" is a robot or mechanical device that moves automatically and places items according to a plan.

[0487] The system implementing this invention aims to efficiently utilize space in a logistics center. The server first acquires past space usage information and future demand information, and then performs preprocessing to convert this data into an appropriate format. As for the software to be used, Python is used for data analysis, and libraries such as Pandas and Matplotlib can be used for data preprocessing and visualization.

[0488] The server uses pre-processed data and machine learning libraries such as Scikit-learn and TensorFlow to forecast demand. This allows it to understand future demand trends and plan the spatial reconfiguration. The generated plan is then sent to the execution system as control instructions for efficiently arranging movable partitions and modular components.

[0489] The terminal visually displays planning information in real time to the work-related visual device worn by the worker. Since this visual device is expected to use Microsoft HoloLens or similar smart glasses, the worker can immediately understand and respond to appropriate instructions on-site. Furthermore, the robot, acting as an autonomous mobile device, uses ROS (Robot Operating System) to reconfigure the space according to the instructions.

[0490] For example, the server can send a prompt to a generating AI model such as, "Based on next week's shipping data, please optimize the placement of goods within the logistics center according to the predicted demand," which can then automatically plan and execute the optimal placement. This enables flexible and efficient space management.

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] The server collects historical space usage information and future demand information from the logistics management system. Inputs include records of item placement and shipping history at various points in time, as well as future demand forecast data. This data is organized into the required format using the Pandas library. The output is a pre-processed dataset that is easy for machine learning algorithms to handle.

[0494] Step 2:

[0495] The server uses a Scikit-learn machine learning model to forecast demand based on pre-processed data. The input is a pre-processed dataset, which is used to predict future demand trends. The output is the forecast results, such as the predicted demand value and required space for each product.

[0496] Step 3:

[0497] The server uses the forecast results to plan the optimal placement of movable partitions and modular components. The input is the demand forecast values ​​obtained in step 2, and based on this, it generates a proposed spatial reconfiguration. The output is the placement plan for movable partitions and modular components.

[0498] Step 4:

[0499] The terminal displays the generated placement plan in real time on the worker's visual device. The input is the placement plan received from the server, and visualization is performed to present the data in a format that is easy for the worker to understand. The output is a visual instruction on the visual device worn by the worker.

[0500] Step 5:

[0501] The user begins work based on instructions from the terminal. Specifically, they adjust objects in the space according to information obtained through the visual device. This improves work efficiency.

[0502] Step 6:

[0503] The autonomous mobile device automatically adjusts the placement of movable partitions and items based on control instructions received from the terminal. The input is the placement plan from step 3, and the device operates to realize it. The output is the actual reconfigured physical space.

[0504] 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.

[0505] This invention is a system for achieving efficient operation of meeting spaces. It predicts the usage demand for meeting rooms and dynamically reconfigures the space to match that demand. At the same time, it recognizes the emotional state of users and reflects that information in the spatial design and environmental adjustments to provide an optimal meeting environment.

[0506] The server retrieves past usage data and future booking information from the meeting room reservation system and calendar application. This data is pre-processed by AI and used to predict future meeting demand. The server also runs an emotion recognition engine to analyze the user's emotional state in real time based on data acquired from cameras and microphones.

[0507] The terminal receives predictive data from the server and visualizes and presents proposed layout changes for the meeting space to the user. Based on this information, the user can make necessary adjustments and approve the final layout. Suggestions for space settings based on the user's emotions are also included; for example, the lighting can be softened if the user wants to relax.

[0508] Furthermore, the device sends emotional feedback from the user to the server, which is used to improve the emotion recognition algorithm. This allows the system to perform feedforward learning, enabling even more accurate emotion recognition and spatial design in subsequent uses.

[0509] For example, if a participant is fatigued during a project meeting, the emotion recognition engine will detect this situation, and the server will suggest a short break and adjust the lighting in the meeting space to create a relaxing environment. In this way, the present invention supports effective meeting management by automatically providing an appropriate environment according to the progress of the meeting and the state of the participants.

[0510] The following describes the processing flow.

[0511] Step 1:

[0512] The server retrieves past meeting usage information and future booking information from the meeting room reservation system and calendar application. The retrieved data is pre-processed into a format including date and time, number of participants, and meeting duration.

[0513] Step 2:

[0514] The server feeds pre-processed data into machine learning algorithms to predict future meeting demand and efficient space allocation. This prediction is made using time series analysis techniques that model demand trends.

[0515] Step 3:

[0516] The server analyzes users' emotions in real time using an emotion recognition engine via cameras and microphones in the meeting room. This generates indices such as each participant's stress level and concentration level.

[0517] Step 4:

[0518] The server creates an optimal layout plan for the meeting space based on predicted meeting demand and sentiment analysis results. This plan includes environmental elements such as the placement of movable partitions and lighting settings.

[0519] Step 5:

[0520] The terminal visually presents the generated layout change proposals and environment adjustment proposals to the user. The user reviews these, makes adjustments as needed, and approves the final change proposal.

[0521] Step 6:

[0522] The terminal transmits the approved layout proposal as instruction signals to the equipment necessary for reconfiguring the meeting space. This causes movable partitions and environmental control systems to automatically activate and perform the proposed arrangement and adjustments.

[0523] Step 7:

[0524] After the meeting ends, the server collects feedback from users regarding their impressions and usability, and uses this feedback to improve the emotion recognition engine and spatial placement algorithm. This feedback will contribute to improving the system's accuracy in future meetings.

[0525] (Example 2)

[0526] 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."

[0527] Modern meetings require flexible and efficient use of space to meet diverse needs, but currently, accurately predicting the demand for meeting space and setting up an environment that suits the emotions and state of participants is difficult. Furthermore, there is a lack of mechanisms to dynamically adjust the environment according to the progress of the meeting and the emotional state of the participants, which hinders the enhancement of participant productivity and comfort.

[0528] 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.

[0529] In this invention, the server includes means for acquiring past meeting space usage information and future reservation information and preprocessing this information; means for executing a machine learning algorithm to predict the demand for the meeting space using the preprocessed information; and means for analyzing information from a camera and an audio input device to recognize the emotional state of the user. This makes it possible to optimize the meeting space according to demand and to adjust the environment in real time to match the emotional state of the participants.

[0530] "Past meeting space usage information" refers to data regarding the reservation history and usage status of meeting rooms that have been used in the past.

[0531] "Future reservation information" refers to data regarding planned reservations for meeting rooms in the future.

[0532] "Preprocessing methods" refer to methods and techniques for transforming data into a format suitable for analysis or machine learning processing.

[0533] A "machine learning algorithm" is a technology that learns patterns from large amounts of data to make future predictions and classifications.

[0534] A "movable partition" is a wall or partition that can be rearranged flexibly within a meeting space.

[0535] "Modular equipment" refers to furniture and devices whose arrangement and configuration can be changed according to their intended use.

[0536] "Camera and audio input device" refers to a device for capturing and recording video and audio data.

[0537] "Means for recognizing a user's emotional state" refers to technologies for analyzing and judging a user's emotions from their facial expressions and statements.

[0538] "Means for generating control commands to adjust environmental conditions" refers to technology that generates commands to adjust environmental settings such as lighting and sound according to recognized emotional states.

[0539] "Means for transmitting instructions" refers to the function of transmitting instructions to devices that execute plans and control commands.

[0540] This invention is a system that supports the efficient operation of meeting rooms. The server acquires past meeting space usage data and future reservation information, and uses predetermined hardware and software to preprocess the data. Specifically, the server utilizes APIs for data acquisition and uses Python-based libraries for data cleaning and analysis.

[0541] The server executes machine learning algorithms to predict future meeting demand. This utilizes AI technologies specialized in time-series forecasting, such as LSTM models. Furthermore, the server recognizes the user's emotional state in real time through cameras and voice input devices. OpenCV and dedicated voice analysis libraries are used for emotion recognition.

[0542] The terminal receives predictive data from the server and information based on the user's emotional state, and then visualizes and presents proposed layout changes for the meeting space to the user. This is done using 3D modeling software, providing information in a visually easy-to-understand format.

[0543] Users can make necessary adjustments based on the meeting space layout presented on their device and approve the final design. The device also suggests adjustments based on the user's emotional state; for example, if relaxation is needed, a soft lighting environment can be set through the lighting system.

[0544] As a concrete example, if a participant is feeling fatigued during a project meeting, the server uses emotion recognition technology to understand the situation. It then suggests a short break and adjusts the lighting in the meeting space to create a relaxing environment. In this way, the present invention makes it possible to provide an appropriate environment according to the emotional state of the participants and the progress of the meeting.

[0545] Here is an example of a prompt sentence for a generative AI model: "Please tell me how to predict the current meeting demand and suggest a spatial design that responds to the participants' emotions."

[0546] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0547] Step 1:

[0548] The server retrieves past meeting space usage data and future reservation information from the meeting room reservation system and calendar application via API. The data retrieved as input is received in a data format such as JSON. Since this data cannot be used directly for machine learning, preprocessing is performed to remove unnecessary information and organize the necessary information. A formatted dataset is generated as output.

[0549] Step 2:

[0550] The server takes a pre-processed dataset as input and runs a machine learning algorithm. This process uses an LSTM model to predict future demand for meeting spaces. Specifically, it learns past usage patterns and performs calculations to predict demand for the next week or month. The output provides future demand forecast data.

[0551] Step 3:

[0552] The server acquires data in real time from cameras and audio input devices in the conference room and analyzes the user's emotional state. Facial image data and audio data are collected as input. This data is analyzed using OpenCV and speech analysis libraries to identify the user's emotions (e.g., joy, surprise, fatigue). The output is information about the recognized emotional state.

[0553] Step 4:

[0554] The terminal visualizes and presents proposed layout changes for the meeting space to the user, based on demand forecast data and emotional state information received from the server. Forecast data and emotional data are used as input, and 3D modeling software visualizes the actual layout changes within the meeting space. The output provides the user with a visual layout proposal.

[0555] Step 5:

[0556] The user makes adjustments based on the layout proposal viewed on the device and finalizes the design. A layout proposal is provided as input, and the user makes the necessary changes. This operation is performed via touchscreen or keyboard, and the final revised layout is generated as an approval instruction.

[0557] Step 6:

[0558] The terminal sends the user-approved final layout proposal to the server, and the actual environment adjustments begin. The server sends the generated control commands to the conference room's lighting system and sound equipment to adjust the environment. This brings the proposed environment to life, such as adjusting the conference room lighting to a relaxed state.

[0559] (Application Example 2)

[0560] 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."

[0561] There is a need to optimize space utilization efficiency and environmental control within factories to improve worker concentration and efficiency. However, conventional methods make it difficult to dynamically reconfigure spaces and adjust the environment in response to workers' emotional states, making it challenging to improve work efficiency in limited environments.

[0562] 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.

[0563] In this invention, the server includes means for acquiring past space utilization information and future reservation information and preprocessing this data; means for executing a machine learning algorithm for predicting space demand using the preprocessed data; means for generating a plan for dynamically arranging movable partitions and multipurpose equipment within the space according to the predicted demand; means for analyzing human emotional states in real time using emotion recognition technology; and means for adjusting the spatial environment based on the emotional state. As a result, factory workers are provided with an optimal work environment according to their emotional state at any given time, enabling efficient space utilization and improved work efficiency.

[0564] "Space demand forecasting" is a technology that predicts the need for space use within a specific environment based on past usage data and future reservation information.

[0565] A "movable partition" refers to a partition that can dynamically divide a space, and is a device that allows for flexible layout changes depending on the purpose.

[0566] "Multipurpose equipment" is a general term for equipment and devices whose arrangement and function can be changed according to various uses, and which helps to make efficient use of space.

[0567] "Emotion recognition technology" is a technology that analyzes data from cameras and microphones to identify a person's emotional state in real time.

[0568] "Spatial environment adjustment" refers to adjustments made to optimize lighting, sound, and layout within a space based on the user's emotional state, etc.

[0569] A system implementing this invention first uses a server to acquire past spatial usage information and future reservation information, and then uses a database management system and a machine learning platform to preprocess this data. This includes, for example, data organization using the Python Pandas library and training a demand forecasting model using TensorFlow.

[0570] The server predicts spatial demand based on the acquired data. Machine learning algorithms are executed using frameworks such as TensorFlow to accurately predict the demand for factories and workspaces.

[0571] Next, the server generates a plan for dynamically positioning movable partitions and multi-purpose fixtures within the space to match the predicted demand. This involves a process of visually designing the layout in conjunction with design software such as AutoCAD.

[0572] Meanwhile, the terminal visualizes and presents the layout of the generated space to the worker via smart glasses or other devices capable of AR display. Here, game development platforms such as Unity are used to simulate and execute the AR environment.

[0573] Furthermore, the server provides emotion recognition technology, using image processing libraries like OpenCV and speech analysis tools to analyze data from cameras and microphones to analyze the worker's emotional state in real time. Based on this, the server issues instructions to adjust the spatial environment. For example, the lighting system is adjusted in conjunction with the Hue bridge.

[0574] As a concrete example, during a design meeting for a new product in a factory, the server detects if a worker is losing focus. Based on this information, the server suggests relaxing the lighting and, through smart glasses, offers further suggestions for flexibly adjusting the layout.

[0575] An example of a prompt message would be: "Based on the employee's current emotional state and past data, please suggest the optimal spatial layout and environmental settings for the next 60-minute meeting."

[0576] This system supports efficient work within the factory by optimizing the spatial environment based on the emotions of the workers.

[0577] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0578] Step 1:

[0579] The server retrieves past space usage information and future reservation information. This input data is preprocessed using Pandas, imputing missing values ​​and formatting it into the required format. This prepares the data in a way that is easy for machine learning algorithms to handle.

[0580] Step 2:

[0581] The server runs a spatial demand forecasting model using TensorFlow based on preprocessed data. This model learns from past usage patterns and outputs future spatial demand as a numerical value. This makes it possible to predict how much space will be needed in the next timeframe.

[0582] Step 3:

[0583] The server uses AutoCAD to generate a layout plan for movable partitions and multi-purpose fixtures based on predicted space demand. This process designs the optimal layout while considering supply and demand forecasts and physical constraints. The generated plan is output as a layout drawing that is passed on to the next stage.

[0584] Step 4:

[0585] The device displays the generated layout in AR through smart glasses. At this stage, Unity is used to visually present the planned spatial arrangement to the user. This information intuitively shows the user what physical changes are necessary.

[0586] Step 5:

[0587] The server uses emotion recognition technology to analyze data acquired from cameras and microphones using OpenCV and other tools to recognize the user's emotional state in real time. If a specific emotional state (e.g., fatigue) is detected as a result of this analysis, it uses that as input to generate instructions for adjusting the environment.

[0588] Step 6:

[0589] The server sends commands to devices such as Hue bridges to adjust the spatial environment, including lighting and sound, based on emotional information. The specific action of this step is to put the proposed environmental settings into action.

[0590] Step 7:

[0591] Users work in a controlled environment and collect results and feedback. This results information is sent back to the server and used to improve the predictive model for future use. This allows the generative AI model to make more accurate predictions.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] [Fourth Embodiment]

[0596] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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".

[0609] This invention is a system for achieving efficient operation of meeting spaces, and includes a method for demand forecasting using past and future meeting data, and for adjusting the dynamic layout using movable partitions and modular equipment. The processing of the program of this system is described below.

[0610] The server first collects past meeting usage information and future booking information from meeting room reservation systems and calendar applications. This data is then formatted to include usage date and time, number of participants, etc., for use in demand forecasting.

[0611] The server runs a machine learning algorithm based on pre-processed data to predict future meeting room demand. The predicted demand is specifically calculated as date, time, number of participants, and required space size. Based on this, a plan is generated to determine the optimal meeting room layout.

[0612] The terminal visualizes and notifies the user of the proposed layout changes received from the server. This proposed layout is designed with user convenience in mind and can be fine-tuned as needed. The user can review the presented layout, make any necessary adjustments, and then give final approval.

[0613] The approved layout is transmitted to the execution device via a terminal, and movable partitions and modular fixtures within the meeting space are moved accordingly. This allows for the immediate creation of an optimal meeting space tailored to the specific needs.

[0614] For example, if a large project meeting is needed at a specific time on a weekday, the server analyzes data from similar past meetings to predict the large space demand at that time. Based on this information, the server identifies the need for a large meeting space and proposes moving movable partitions to create a unified large space. If all users approve this proposal, the terminal sends an instruction, and the meeting room is arranged.

[0615] Thus, this invention aims to improve the efficiency of meeting management by providing a system that makes maximum use of limited physical space and can be flexibly modified in response to fluctuations in demand.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] The server retrieves past meeting usage data and future booking information from the meeting room reservation system and calendar application. This includes retrieving data using APIs or importing CSV files. The retrieved data is formatted to include date and time, number of participants, meeting duration, and other relevant information.

[0619] Step 2:

[0620] The server feeds pre-processed data into machine learning algorithms to predict future demand for meeting rooms. This prediction uses time series analysis and predictive models to calculate demand for specific dates and times, and records it in a database.

[0621] Step 3:

[0622] Based on the prediction results, the server automatically creates an optimal layout plan for moving movable partitions and modular equipment within the conference room. This plan aims for maximum space efficiency and is visualized in 3D as a virtual layout.

[0623] Step 4:

[0624] The terminal notifies the user of the created layout plan. The user can review the provided layout on the terminal screen and make adjustments as needed. Once adjustments are complete, the user gives final approval.

[0625] Step 5:

[0626] The terminal sends instruction signals to facility management staff or automated equipment to implement layout changes approved by the user. For example, partitions may be moved or equipment may be rearranged.

[0627] Step 6:

[0628] The server monitors whether the meeting space has been reconfigured as planned, using camera feeds and sensor data for verification. If an anomaly is detected, it issues an alert to prompt a quick response.

[0629] Step 7:

[0630] After the meeting ends, the terminal collects usage feedback from users and sends it to the server. This feedback is used as valuable data for improvement when planning future meetings.

[0631] (Example 1)

[0632] 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".

[0633] To achieve efficient use of shared spaces, it is necessary to flexibly adjust the space in response to fluctuating demand. However, conventional systems have challenges such as low prediction accuracy and the time-consuming manual process of reconfiguring the space.

[0634] 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.

[0635] In this invention, the server includes means for acquiring past collective space usage information and future reservation information and preprocessing these values; means for executing a machine learning method for predicting the demand for the collective space using the preprocessed values; and means for generating a plan for dynamically arranging movable compartments and prefabricated fixtures within the collective space according to the predicted demand. This enables rapid and efficient space utilization in response to demand.

[0636] "Gathering space usage information" refers to data regarding the usage history of a gathering place and future reservations for its use.

[0637] "Preprocessing" refers to processes such as data cleansing and formatting standardization that transform raw data into a format suitable for machine learning and analysis.

[0638] A "machine learning method" is a computational method that learns patterns and rules from large amounts of data to perform predictions and classifications.

[0639] "Demand forecasting" is the process of estimating the future need for services or goods based on past data.

[0640] A "movable partition" is a movable partition that can flexibly divide a physical space and easily change the layout of that space.

[0641] "Assembly-type equipment" refers to furniture and equipment whose configuration can be changed and rearranged according to its use and purpose.

[0642] "Dynamic arrangement" refers to flexibly changing equipment and spatial configurations according to the situation and conditions.

[0643] This invention is a system for achieving efficient operation of a collective space, combining data analysis and dynamic layout adjustment. Specifically, it consists of a server, terminals, and users.

[0644] Server Role

[0645] The server collects past and future reservation information for the shared space from the space's reservation system and calendar application. This data is preprocessed to a format suitable for machine learning models. Preprocessing includes standardizing date formats and removing unnecessary information. Based on this preprocessed data, the server uses machine learning algorithms—specifically, time series analysis and clustering techniques—to predict future demand.

[0646] Terminal role

[0647] The terminal presents the user with a layout plan generated based on demand forecast data calculated by the server. The presented layout is visualized through a graphical user interface (GUI) to make it easy for the user to understand. The terminal also provides an interface that allows for fine-tuning of the layout based on user feedback.

[0648] User roles

[0649] Users review the proposed layout through their terminal and approve or adjust it as needed. For example, when a user hosts a large meeting, the server analyzes data from similar past meetings and suggests a large space. Based on this suggestion, if the user approves the layout, the space is rearranged.

[0650] As a concrete example of implementing this system, let's consider a prompt. A prompt in the form of "Predict future meeting demand and suggest the optimal layout" is input to the generating AI model. Based on this prompt, the model derives the optimal arrangement of meeting spaces.

[0651] This allows the entire system to work together to support efficient space utilization and enables flexible layout adjustments in response to fluctuations in demand.

[0652] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0653] Step 1:

[0654] The server collects past and future reservation information for shared spaces from shared space reservation systems and calendar applications. Input data includes meeting dates and times, number of participants, and purpose of use. The server processes this data, standardizing formats and filtering out unnecessary information to generate pre-processed data. The output is a dataset in a format suitable for machine learning processing.

[0655] Step 2:

[0656] The server runs a machine learning model using preprocessed data. Specifically, it uses a time series analysis algorithm to predict future demand for a shared space. The input for this step is the dataset generated in the previous step. The server analyzes the dataset, calculates predicted demand and space size, and provides the prediction results as output.

[0657] Step 3:

[0658] The server generates an optimal collective space layout plan based on predicted demand data. Specifically, it calculates the placement of movable compartments and prefabricated fixtures. The input for this step is the demand forecast results. The server outputs detailed layout instructions for dynamic placement and prepares them for transmission to the terminal.

[0659] Step 4:

[0660] The terminal visualizes and presents the layout plan received from the server to the user. The input is the layout proposal from the server. The terminal displays the information in a user-friendly format using a GUI and provides a visual layout preview as output. The user reviews, approves, or adjusts the layout based on this preview.

[0661] Step 5:

[0662] The user reviews the layout proposal presented through the terminal and makes adjustments as needed. The input is the layout proposal displayed on the terminal's GUI. The user makes adjustments and notifies the terminal of their final approval as output. This confirms the optimal layout setting.

[0663] Step 6:

[0664] The terminal transmits the approved layout proposal to the execution device and begins rearranging the collective space. The input is the final approved layout proposal from the user. Based on this information, the terminal issues instructions to the execution device, and the output is the implementation of the layout. Movable compartments are moved as needed, and modular fixtures are rearranged.

[0665] (Application Example 1)

[0666] 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".

[0667] In logistics centers and other work spaces, there is a need to maximize the use of limited physical space, respond flexibly to changes in demand, and ensure efficient inventory management and movement. However, conventional fixed layouts make it difficult to utilize space flexibly based on demand forecasts, hindering operational efficiency. To solve this problem, a system is needed that dynamically reconfigures space and instantly realizes the optimal inventory placement according to demand.

[0668] 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.

[0669] In this invention, the server includes means for acquiring past space usage information and future demand information and preprocessing this data; means for performing machine learning to predict space demand using the preprocessed data; and means for generating a plan for dynamically arranging movable partitions and modular components according to the predicted demand. This enables the creation of flexible workspaces in logistics centers and ensures effective arrangement of goods and movement routes in response to demand fluctuations.

[0670] "Past space usage information" refers to information about past item placement and usage history in logistics centers and similar facilities, and is used for demand forecasting and optimizing space layout.

[0671] "Future demand information" refers to information that indicates the future flow of goods and the scale of space required, and is data that is useful for reconfiguring space based on demand forecasts.

[0672] "Means for preprocessing" refers to a device or mechanism that performs processing to convert acquired data into a format suitable for machine learning algorithms.

[0673] "Machine learning" is a method of executing algorithms to predict future trends and patterns based on past data, and is used to forecast spatial demand.

[0674] A "movable partition" refers to a partition or wall that can flexibly change the physical division of a space, and is equipment that enables spatial reconfiguration according to demand.

[0675] "Modular components" refer to parts or devices that can be easily rearranged or reconfigured, and are used to increase the flexibility of spatial design.

[0676] A "control instruction" is a command or instruction that causes machinery and equipment to perform specific operations or actions based on a planned spatial layout.

[0677] A "work visual device" is a display device used to provide visual information to workers and is used to present planning information in real time.

[0678] An "autonomous mobile device" is a robot or mechanical device that moves automatically and places items according to a plan.

[0679] The system implementing this invention aims to efficiently utilize space in a logistics center. The server first acquires past space usage information and future demand information, and then performs preprocessing to convert this data into an appropriate format. As for the software to be used, Python is used for data analysis, and libraries such as Pandas and Matplotlib can be used for data preprocessing and visualization.

[0680] The server uses pre-processed data and machine learning libraries such as Scikit-learn and TensorFlow to forecast demand. This allows it to understand future demand trends and plan the spatial reconfiguration. The generated plan is then sent to the execution system as control instructions for efficiently arranging movable partitions and modular components.

[0681] The terminal visually displays planning information in real time to the work-related visual device worn by the worker. Since this visual device is expected to use Microsoft HoloLens or similar smart glasses, the worker can immediately understand and respond to appropriate instructions on-site. Furthermore, the robot, acting as an autonomous mobile device, uses ROS (Robot Operating System) to reconfigure the space according to the instructions.

[0682] For example, the server can send a prompt to a generating AI model such as, "Based on next week's shipping data, please optimize the placement of goods within the logistics center according to the predicted demand," which can then automatically plan and execute the optimal placement. This enables flexible and efficient space management.

[0683] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0684] Step 1:

[0685] The server collects historical space usage information and future demand information from the logistics management system. Inputs include records of item placement and shipping history at various points in time, as well as future demand forecast data. This data is organized into the required format using the Pandas library. The output is a pre-processed dataset that is easy for machine learning algorithms to handle.

[0686] Step 2:

[0687] The server uses a Scikit-learn machine learning model to forecast demand based on pre-processed data. The input is a pre-processed dataset, which is used to predict future demand trends. The output is the forecast results, such as the predicted demand value and required space for each product.

[0688] Step 3:

[0689] The server uses the forecast results to plan the optimal placement of movable partitions and modular components. The input is the demand forecast values ​​obtained in step 2, and based on this, it generates a proposed spatial reconfiguration. The output is the placement plan for movable partitions and modular components.

[0690] Step 4:

[0691] The terminal displays the generated placement plan in real time on the worker's visual device. The input is the placement plan received from the server, and visualization is performed to present the data in a format that is easy for the worker to understand. The output is a visual instruction on the visual device worn by the worker.

[0692] Step 5:

[0693] The user begins work based on instructions from the terminal. Specifically, they adjust objects in the space according to information obtained through the visual device. This improves work efficiency.

[0694] Step 6:

[0695] The autonomous mobile device automatically adjusts the placement of movable partitions and items based on control instructions received from the terminal. The input is the placement plan from step 3, and the device operates to realize it. The output is the actual reconfigured physical space.

[0696] 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.

[0697] This invention is a system for achieving efficient operation of meeting spaces. It predicts the usage demand for meeting rooms and dynamically reconfigures the space to match that demand. At the same time, it recognizes the emotional state of users and reflects that information in the spatial design and environmental adjustments to provide an optimal meeting environment.

[0698] The server retrieves past usage data and future booking information from the meeting room reservation system and calendar application. This data is pre-processed by AI and used to predict future meeting demand. The server also runs an emotion recognition engine to analyze the user's emotional state in real time based on data acquired from cameras and microphones.

[0699] The terminal receives predictive data from the server and visualizes and presents proposed layout changes for the meeting space to the user. Based on this information, the user can make necessary adjustments and approve the final layout. Suggestions for space settings based on the user's emotions are also included; for example, the lighting can be softened if the user wants to relax.

[0700] Furthermore, the device sends emotional feedback from the user to the server, which is used to improve the emotion recognition algorithm. This allows the system to perform feedforward learning, enabling even more accurate emotion recognition and spatial design in subsequent uses.

[0701] For example, if a participant is fatigued during a project meeting, the emotion recognition engine will detect this situation, and the server will suggest a short break and adjust the lighting in the meeting space to create a relaxing environment. In this way, the present invention supports effective meeting management by automatically providing an appropriate environment according to the progress of the meeting and the state of the participants.

[0702] The following describes the processing flow.

[0703] Step 1:

[0704] The server retrieves past meeting usage information and future booking information from the meeting room reservation system and calendar application. The retrieved data is pre-processed into a format including date and time, number of participants, and meeting duration.

[0705] Step 2:

[0706] The server feeds pre-processed data into machine learning algorithms to predict future meeting demand and efficient space allocation. This prediction is made using time series analysis techniques that model demand trends.

[0707] Step 3:

[0708] The server analyzes users' emotions in real time using an emotion recognition engine via cameras and microphones in the meeting room. This generates indices such as each participant's stress level and concentration level.

[0709] Step 4:

[0710] The server creates an optimal layout plan for the meeting space based on predicted meeting demand and sentiment analysis results. This plan includes environmental elements such as the placement of movable partitions and lighting settings.

[0711] Step 5:

[0712] The terminal visually presents the generated layout change proposals and environment adjustment proposals to the user. The user reviews these, makes adjustments as needed, and approves the final change proposal.

[0713] Step 6:

[0714] The terminal transmits the approved layout proposal as a signal to the equipment necessary for reconfiguring the meeting space. This causes movable partitions and environmental control systems to automatically activate and perform the proposed arrangement and adjustments.

[0715] Step 7:

[0716] The server collects feedback from users regarding their impressions and usability after the meeting ends, and uses this feedback to improve the emotion recognition engine and spatial placement algorithm. This feedback will contribute to improving the system's accuracy in future meetings.

[0717] (Example 2)

[0718] 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".

[0719] Modern meetings require flexible and efficient use of space to meet diverse needs, but currently, accurately predicting the demand for meeting space and setting up an environment that suits the emotions and state of participants is difficult. Furthermore, there is a lack of mechanisms to dynamically adjust the environment according to the progress of the meeting and the emotional state of the participants, which hinders the enhancement of participant productivity and comfort.

[0720] 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.

[0721] In this invention, the server includes means for acquiring past meeting space usage information and future reservation information and preprocessing this information; means for executing a machine learning algorithm to predict the demand for the meeting space using the preprocessed information; and means for analyzing information from a camera and an audio input device to recognize the emotional state of the user. This makes it possible to optimize the meeting space according to demand and to adjust the environment in real time to match the emotional state of the participants.

[0722] "Past meeting space usage information" refers to data regarding the reservation history and usage status of meeting rooms that have been used in the past.

[0723] "Future reservation information" refers to data regarding planned reservations for meeting rooms in the future.

[0724] "Preprocessing methods" refer to methods and techniques for transforming data into a format suitable for analysis or machine learning processing.

[0725] A "machine learning algorithm" is a technology that learns patterns from large amounts of data to make future predictions and classifications.

[0726] A "movable partition" is a wall or partition that can be rearranged flexibly within a meeting space.

[0727] "Modular equipment" refers to furniture and devices whose arrangement and configuration can be changed according to their intended use.

[0728] "Camera and audio input device" refers to a device for capturing and recording video and audio data.

[0729] "Means for recognizing a user's emotional state" refers to technologies for analyzing and judging a user's emotions from their facial expressions and statements.

[0730] "Means for generating control commands to adjust environmental conditions" refers to technology that generates commands to adjust environmental settings such as lighting and sound according to recognized emotional states.

[0731] "Means for transmitting instructions" refers to the function of transmitting instructions to devices that execute plans and control commands.

[0732] This invention is a system that supports the efficient operation of meeting rooms. The server acquires past meeting space usage data and future reservation information, and uses predetermined hardware and software to preprocess the data. Specifically, the server utilizes APIs for data acquisition and uses Python-based libraries for data cleaning and analysis.

[0733] The server executes machine learning algorithms to predict future meeting demand. This utilizes AI technologies specialized in time-series forecasting, such as LSTM models. Furthermore, the server recognizes the user's emotional state in real time through cameras and voice input devices. OpenCV and dedicated voice analysis libraries are used for emotion recognition.

[0734] The terminal receives predictive data from the server and information based on the user's emotional state, and then visualizes and presents proposed layout changes for the meeting space to the user. This is done using 3D modeling software, providing information in a visually easy-to-understand format.

[0735] Users can make necessary adjustments based on the meeting space layout presented on their device and approve the final design. The device also suggests adjustments based on the user's emotional state; for example, if relaxation is needed, a soft lighting environment can be set through the lighting system.

[0736] As a concrete example, if a participant is feeling fatigued during a project meeting, the server uses emotion recognition technology to understand the situation. It then suggests a short break and adjusts the lighting in the meeting space to create a relaxing environment. In this way, the present invention makes it possible to provide an appropriate environment according to the emotional state of the participants and the progress of the meeting.

[0737] Here is an example of a prompt sentence for a generative AI model: "Please tell me how to predict the current meeting demand and suggest a spatial design that responds to the participants' emotions."

[0738] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0739] Step 1:

[0740] The server retrieves past meeting space usage data and future reservation information from the meeting room reservation system and calendar application via API. The data retrieved as input is received in a data format such as JSON. Since this data cannot be used directly for machine learning, preprocessing is performed to remove unnecessary information and organize the necessary information. A formatted dataset is generated as output.

[0741] Step 2:

[0742] The server takes a pre-processed dataset as input and runs a machine learning algorithm. This process uses an LSTM model to predict future demand for meeting spaces. Specifically, it learns past usage patterns and performs calculations to predict demand for the next week or month. The output provides future demand forecast data.

[0743] Step 3:

[0744] The server acquires data in real time from cameras and audio input devices in the conference room and analyzes the user's emotional state. Facial image data and audio data are collected as input. This data is analyzed using OpenCV and speech analysis libraries to identify the user's emotions (e.g., joy, surprise, fatigue). The output is information about the recognized emotional state.

[0745] Step 4:

[0746] The terminal visualizes and presents proposed layout changes for the meeting space to the user, based on demand forecast data and emotional state information received from the server. Forecast data and emotional data are used as input, and 3D modeling software visualizes the actual layout changes within the meeting space. The output provides the user with a visual layout proposal.

[0747] Step 5:

[0748] The user makes adjustments based on the layout proposal viewed on the device and finalizes the design. A layout proposal is provided as input, and the user makes the necessary changes. This operation is performed via touchscreen or keyboard, and the final revised layout is generated as an approval instruction.

[0749] Step 6:

[0750] The terminal sends the user-approved final layout proposal to the server, and the actual environment adjustments begin. The server sends the generated control commands to the conference room's lighting system and sound equipment to adjust the environment. This brings the proposed environment to life, such as adjusting the conference room lighting to a relaxed state.

[0751] (Application Example 2)

[0752] 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".

[0753] There is a need to optimize space utilization efficiency and environmental control within factories to improve worker concentration and efficiency. However, conventional methods make it difficult to dynamically reconfigure spaces and adjust the environment in response to workers' emotional states, making it challenging to improve work efficiency in limited environments.

[0754] 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.

[0755] In this invention, the server includes means for acquiring past space utilization information and future reservation information and preprocessing this data; means for executing a machine learning algorithm for predicting space demand using the preprocessed data; means for generating a plan for dynamically arranging movable partitions and multipurpose equipment within the space according to the predicted demand; means for analyzing human emotional states in real time using emotion recognition technology; and means for adjusting the spatial environment based on the emotional state. As a result, factory workers are provided with an optimal work environment according to their emotional state at any given time, enabling efficient space utilization and improved work efficiency.

[0756] "Space demand forecasting" is a technology that predicts the need for space use within a specific environment based on past usage data and future reservation information.

[0757] A "movable partition" refers to a partition that can dynamically divide a space, and is a device that allows for flexible layout changes depending on the purpose.

[0758] "Multipurpose equipment" is a general term for equipment and devices whose arrangement and function can be changed according to various uses, and which helps to make efficient use of space.

[0759] "Emotion recognition technology" is a technology that analyzes data from cameras and microphones to identify a person's emotional state in real time.

[0760] "Spatial environment adjustment" refers to adjustments made to optimize lighting, sound, and layout within a space based on the user's emotional state, etc.

[0761] A system implementing this invention first uses a server to acquire past spatial usage information and future reservation information, and then uses a database management system and a machine learning platform to preprocess this data. This includes, for example, data organization using the Python Pandas library and training a demand forecasting model using TensorFlow.

[0762] The server predicts spatial demand based on the acquired data. Machine learning algorithms are executed using frameworks such as TensorFlow to accurately predict the demand for factories and workspaces.

[0763] Next, the server generates a plan for dynamically positioning movable partitions and multi-purpose fixtures within the space to match the predicted demand. This involves a process of visually designing the layout in conjunction with design software such as AutoCAD.

[0764] Meanwhile, the terminal visualizes and presents the layout of the generated space to the worker via smart glasses or other devices capable of AR display. Here, game development platforms such as Unity are used to simulate and execute the AR environment.

[0765] Furthermore, the server provides emotion recognition technology, using image processing libraries like OpenCV and speech analysis tools to analyze data from cameras and microphones to analyze the worker's emotional state in real time. Based on this, the server issues instructions to adjust the spatial environment. For example, the lighting system is adjusted in conjunction with the Hue bridge.

[0766] As a concrete example, during a design meeting for a new product in a factory, the server detects if a worker is losing focus. Based on this information, the server suggests relaxing the lighting and, through smart glasses, offers further suggestions for flexibly adjusting the layout.

[0767] An example of a prompt message would be: "Based on the employee's current emotional state and past data, please suggest the optimal spatial layout and environmental settings for the next 60-minute meeting."

[0768] This system supports efficient work within the factory by optimizing the spatial environment based on the emotions of the workers.

[0769] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0770] Step 1:

[0771] The server retrieves past space usage information and future reservation information. This input data is preprocessed using Pandas, imputing missing values ​​and formatting it into the required format. This prepares the data in a way that is easy for machine learning algorithms to handle.

[0772] Step 2:

[0773] The server runs a spatial demand forecasting model using TensorFlow based on preprocessed data. This model learns from past usage patterns and outputs future spatial demand as a numerical value. This makes it possible to predict how much space will be needed in the next timeframe.

[0774] Step 3:

[0775] The server uses AutoCAD to generate a layout plan for movable partitions and multi-purpose fixtures based on predicted space demand. This process designs the optimal layout while considering supply and demand forecasts and physical constraints. The generated plan is output as a layout drawing that is passed on to the next stage.

[0776] Step 4:

[0777] The device displays the generated layout in AR through smart glasses. At this stage, Unity is used to visually present the planned spatial arrangement to the user. This information intuitively shows the user what physical changes are necessary.

[0778] Step 5:

[0779] The server uses emotion recognition technology to analyze data acquired from cameras and microphones using OpenCV and other tools to recognize the user's emotional state in real time. If a specific emotional state (e.g., fatigue) is detected as a result of this analysis, it uses that as input to generate instructions for adjusting the environment.

[0780] Step 6:

[0781] The server sends commands to devices such as Hue bridges to adjust the spatial environment, including lighting and sound, based on emotional information. The specific action of this step is to put the proposed environmental settings into action.

[0782] Step 7:

[0783] Users work in a controlled environment and collect results and feedback. This results information is sent back to the server and used to improve the predictive model for future use. This allows the generative AI model to make more accurate predictions.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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."

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] The following is further disclosed regarding the embodiments described above.

[0806] (Claim 1)

[0807] A means for acquiring past meeting space usage information and future reservation information, and for pre-processing this data,

[0808] A means for executing a machine learning algorithm to predict the demand for meeting spaces using the aforementioned preprocessed data,

[0809] Means for generating a plan for dynamically arranging movable partitions and modular fixtures within a meeting space according to the predicted demand,

[0810] Means for transmitting instructions to a device for reconfiguring the meeting space based on the generated plan,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, further comprising means for visualizing the generated plan and presenting it to a user terminal.

[0814] (Claim 3)

[0815] The system according to claim 1, further comprising means for collecting actual usage information after the reconfiguration of the meeting space and feeding it back into the generation of plans for subsequent meetings.

[0816] "Example 1"

[0817] (Claim 1)

[0818] A means for obtaining past collective space usage information and future reservation information, and for preprocessing these values,

[0819] A means for executing a machine learning method for predicting demand in a set space using the aforementioned preprocessed numerical values,

[0820] Means for generating a plan to dynamically arrange movable compartments and prefabricated fixtures within a collective space according to the predicted demand,

[0821] Means for transmitting instructions to a device for reconstructing a collective space based on the generated plan,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, further comprising means for visualizing the generated plan and presenting it to a user terminal.

[0825] (Claim 3)

[0826] The system according to claim 1, further comprising means for collecting actual usage information after the reconstruction of the set space and feeding it back into the generation of plans for subsequent times.

[0827] "Application Example 1"

[0828] (Claim 1)

[0829] A means for acquiring past space usage information and future demand information, and for preprocessing this data,

[0830] A means for performing machine learning to predict spatial demand using the aforementioned preprocessed data,

[0831] Means for generating a plan for dynamically arranging movable partitions and modular components according to the predicted demand,

[0832] A means for transmitting control instructions for reconfiguring a flexible workspace based on the generated plan,

[0833] A means of providing visual support by presenting planning information to a work-related visual device,

[0834] A means of using an autonomous mobile device to arrange items according to a plan,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, further comprising means for visualizing effective traffic flow and arrangement of items based on the generated plan.

[0838] (Claim 3)

[0839] The system according to claim 1, further comprising means for collecting actual usage information after the reconstruction of the space and feeding it back into optimization for subsequent uses.

[0840] "Example 2 of combining an emotion engine"

[0841] (Claim 1)

[0842] A means for acquiring past meeting space usage information and future reservation information, and for pre-processing this information,

[0843] A means for executing a machine learning algorithm to predict the demand for meeting space using the pre-processed information,

[0844] Means for generating a plan for dynamically arranging movable partitions and modular equipment within a meeting space according to the predicted demand,

[0845] A means for recognizing the user's emotional state by analyzing information from a camera and audio input device,

[0846] means for generating control commands to adjust environmental conditions based on the user's emotional state,

[0847] Means for transmitting commands to a device for reconfiguring the meeting space based on the generated plan,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, further comprising means for visualizing the generated plan and presenting it to a user terminal.

[0851] (Claim 3)

[0852] The system according to claim 1, further comprising means for collecting actual usage information after the reconfiguration of the meeting space and feeding it back into the generation of plans for subsequent meetings.

[0853] "Application example 2 when combining with an emotional engine"

[0854] (Claim 1)

[0855] A means for acquiring past space usage information and future reservation information, and for preprocessing this data,

[0856] A means for executing a machine learning algorithm for predicting spatial demand using the aforementioned preprocessed data,

[0857] Means for generating a plan for dynamically arranging movable partitions and multipurpose fixtures within a space according to the predicted demand,

[0858] Means for transmitting instructions to a device for reconstructing space based on the generated plan,

[0859] A means of analyzing a person's emotional state in real time using emotion recognition technology,

[0860] Means for adjusting the spatial environment based on the aforementioned emotional state,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, further comprising means for visualizing the generated plan and presenting it to a user terminal.

[0864] (Claim 3)

[0865] The system according to claim 1, further comprising means for collecting actual usage information after the reconstruction of the space and feeding it back into the generation of plans for the next time and beyond. [Explanation of Symbols]

[0866] 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 means for acquiring past meeting space usage information and future reservation information, and for pre-processing this data, A means for executing a machine learning algorithm to predict the demand for meeting spaces using the aforementioned preprocessed data, Means for generating a plan for dynamically arranging movable partitions and modular fixtures within a meeting space according to the predicted demand, Means for transmitting instructions to a device for reconfiguring the meeting space based on the generated plan, A system that includes this.

2. The system according to claim 1, further comprising means for visualizing the generated plan and presenting it to a user terminal.

3. The system according to claim 1, further comprising means for collecting actual usage information after the reconfiguration of the meeting space and feeding it back into the generation of plans for subsequent meetings.

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