System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting physical information of an office and storing it in a database; means for tracking the usage of each area in real time using sensors and sending it to a server; means for generating an optimal office layout by the server based on the information in the database and the real-time usage; means for visualizing the generated layout proposal using 3D modeling software; and means for assisting a user in evaluating and selecting layout options.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, the needs for office environments have changed dramatically with the spread of flexible working styles and remote work. However, traditional office management methods have led to significant space waste and inefficiencies, making efficient operation difficult. Therefore, improving office efficiency, productivity, and optimizing resources have become important issues. [Means for solving the problem]

[0005] The present invention provides a system for improving office efficiency and productivity. The system includes the following means.

[0006] A system including: means for collecting physical information about an office and storing it in a database; means for tracking the usage of each area in real time using sensors and transmitting the data to a server; means for generating an optimal office layout by the server based on the information in the database and the real-time usage data; means for visualizing the generated layout proposals using 3D modeling software; means for assisting users in evaluating and selecting layout options; and means for analyzing past and current usage data and predicting future usage trends. Also included is a system according to claim 2 that transmits real-time data collected by sensors in each area to a server and updates a dashboard, and a system according to claim 3 that uses an AI algorithm to optimize the office layout, taking into account employee roles and departmental relationships.

[0007] "Office physical information" refers to basic configuration information about the office environment, such as the office area, facilities, and available space.

[0008] A "database" is a system for systematically storing and managing collected office physical information and real-time usage data.

[0009] A "sensor" is a device that detects the usage status of each area and conference room within the office and collects that data in real time.

[0010] "Real-time tracking" is the process of collecting sensor-detected data almost instantly and recording it in a database.

[0011] A "server" is a central computing system that collects, processes, and stores data and provides information to client devices as needed.

[0012] An "AI algorithm" is a set of calculation procedures or rules that use artificial intelligence technology to generate the optimal office layout from specific data.

[0013] "3D modeling software" is software that visualizes office layouts in three dimensions, providing users with a more concrete image.

[0014] A "user" is an individual or group that evaluates and selects an office layout, and is the entity that operates the system and makes decisions.

[0015] "Big data analytics" is an analytical technique that collects and analyzes large amounts of past and present usage data to identify trends and patterns.

[0016] "Predictive Model" means a mathematical or statistical model used to forecast future usage trends or resource needs based on collected and analyzed data.

[0017] A "dashboard" is an interface that visually displays collected real-time data and analysis results, allowing administrators to easily check the situation. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention is a system for improving the efficiency and productivity of an office environment, and by combining multiple means, it realizes the optimization of the office layout and the optimal use of resources. The following describes in detail the embodiments of this system.

[0040] System configuration

[0041] 1. Server

[0042] It has the ability to collect physical information about the office (area, equipment, available space, etc.) and store it in a database. It also receives real-time usage data obtained by sensors and runs an AI algorithm that generates the optimal office layout based on that data.

[0043] 2. Terminal

[0044] Data acquired from sensors placed in each area and conference room is sent to a server in real time, and the optimal layout proposals sent from the server can be visualized using 3D modeling software.

[0045] 3. Users

[0046] Each department and employee in the office evaluates and selects a layout based on their role and communication needs. Users can operate the device to view the proposed layout and make adjustments as needed.

[0047] What the program does

[0048] 1. Database creation

[0049] The server collects information about the size, facilities, and available space of the office and stores this information in a database. For example, a 200-square-meter office space might have one projector, three conference rooms, and 50 desks.

[0050] 2. Real-time tracking

[0051] The device collects current usage data from sensors installed in conference rooms and workspaces and sends it to the server. For example, it recognizes that conference room A is currently in use and sends this data to the server.

[0052] 3. Proposal of optimal layout

[0053] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data, such as suggesting placement of the marketing and development departments next to each other.

[0054] 4. Simulation and Visualization

[0055] The device receives the layout proposal sent from the server and visualizes it using 3D modeling software, allowing the user to view the visual data and intuitively understand how the layout will be specifically arranged.

[0056] 5. Decision support

[0057] The user evaluates the multiple layout options provided on the device and selects the most suitable layout, which is then sent back to the server and stored in the database.

[0058] 6. Big data analysis and predictive model building

[0059] The server performs big data analysis based on past and current usage data to predict office usage trends and future resource needs, allowing it to formulate future layout and resource management plans.

[0060] Specific examples

[0061] The server collects physical information about the office and stores, for example, "200 square meters of office space with 50 desks, three conference rooms, and one projector" in a database.

[0062] The terminal receives "meeting in progress" data from a sensor installed in conference room A and sends it to the server.

[0063] Based on real-time usage data and database information, the server uses an AI algorithm to suggest layouts that facilitate collaboration between the marketing and development departments.

[0064] The device displays a 3D model of the proposed layout to the user, who can then use this visual information to evaluate the layout and make any necessary adjustments.

[0065] The user selects the optimal layout and sends the information to the server, which stores the selected layout in a database and creates an execution plan.

[0066] The server analyzes usage data from the past six months and builds a model that predicts future usage trends for conference rooms and desks.

[0067] In this way, the present invention realizes efficient office management and improved productivity.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The server collects physical information about the office (area, equipment, available space, etc.) and stores it in a database. For example, information about 50 desks, 3 conference rooms, and 1 projector in a 200-square-meter office is stored in the database. The data is updated in a timely manner to ensure that it is always up-to-date.

[0071] Step 2:

[0072] The device collects real-time usage information (vacant or in use) from sensors installed in conference rooms and workspaces and sends that data to the server. For example, if the sensor in conference room A detects that the room is in use, the data is immediately sent to the server.

[0073] Step 3:

[0074] The server receives real-time usage data from the devices, records it in a database, and updates the dashboard based on that information, allowing administrators to check current office usage at a glance.

[0075] Step 4:

[0076] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data. Specifically, it considers the roles and communication needs of each department and suggests, for example, placing the marketing and development departments next to each other.

[0077] Step 5:

[0078] The server then sends the generated optimal layout proposal to the terminal, which includes a specific layout diagram and detailed information on the positioning of each department.

[0079] Step 6:

[0080] The device uses 3D modeling software to visualize the layout proposal received from the server, displaying the new office layout in a 3D view that allows users to intuitively understand it.

[0081] Step 7:

[0082] Users can evaluate the layout options provided by their device and select the best option. Users can compare different layouts and choose the arrangement that best suits their specific needs.

[0083] Step 8:

[0084] The user sends the selected layout from their device to the server, which receives this information and stores it in a database.

[0085] Step 9:

[0086] The server will then formulate an execution plan based on the saved new layout and notify the relevant departments, so that the new layout can be implemented quickly.

[0087] Step 10:

[0088] The server performs big data analysis of past and current usage data to build models that predict office usage trends and future resource demands, which can be used to plan future zoning.

[0089] Example 1

[0090] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0091] In modern office environments, efficiently using limited space and maximizing employee productivity are key challenges. However, basic layout changes and resource allocation are often manual and based on experience, with little optimization based on objective data. Furthermore, it is difficult to understand real-time usage and provide appropriate feedback, making it difficult to maintain an optimal office layout.

[0092] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0093] In this invention, the server includes means for collecting physical information about the office and storing it in a database, means for tracking the usage status of each area in real time using sensors and transmitting the data to an information processing device, means for generating an optimal office layout using the information processing device based on the database information and real-time usage data, means for visualizing the generated layout proposal using three-dimensional modeling software, means for supporting users in evaluating and selecting layout options, means for analyzing past and current usage data and predicting future usage trends, means for transmitting real-time data collected by sensors in each area to the information processing device and updating the dashboard, means for optimizing the office layout taking into account employee roles and departmental relationships using an artificial intelligence algorithm, and means for building a predictive model based on the collected data. This enables optimal layout proposals to be made based on the physical information and real-time usage status of the office environment, thereby achieving efficient space utilization and improved productivity.

[0094] "Physical information about the office" is information that indicates specific physical elements such as the area of ​​the office, the number of desks, the number of conference rooms, and the type of equipment.

[0095] A "database" is a system that organizes and stores collected office physical information and real-time usage data, and manages it so that it can be retrieved when needed.

[0096] A "detector" is a sensor that is installed in each area or conference room, detects usage in real time, and transmits the data to an information processing device.

[0097] An "information processing device" is a device, such as a server or computer, that processes collected data and performs analysis and calculations.

[0098] An "optimal office layout" is a proposal for an arrangement that makes efficient use of space within the office and maximizes the improvement of employees' working environment.

[0099] "3D model software" is software for performing 3D modeling and visually displaying office layouts.

[0100] "Users" are people who use the system to evaluate and select layouts, such as office employees and managers.

[0101] A "dashboard" is an interface for visually displaying real-time usage data and analysis results.

[0102] The "artificial intelligence algorithm" is an algorithm that calculates and proposes the optimal layout based on collected data.

[0103] A "predictive model" is a model that predicts future usage trends and required resources based on past and current data.

[0104] The present invention is a system for improving the efficiency and productivity of an office environment, and specific embodiments thereof are described in detail below. This system realizes the optimization of office layout and optimal utilization of resources through cooperation between servers, terminals, and users.

[0105] Hardware and software configuration

[0106] server

[0107] The server plays a central role in managing physical information and real-time usage data collected from each area and conference room in the office. Software such as databases, AI algorithms, and dashboards are installed on the server. The roles of each are as follows:

[0108] Terminal

[0109] The terminals are connected to sensors installed in each area and conference room, and transmit real-time data to a server. They also have 3D modeling software (such as Autodesk or SketchUp) installed, which visualizes the optimal layout proposals sent from the server.

[0110] User

[0111] Users, who are employees or managers in the office, evaluate the layouts proposed by the server via their terminals and select the optimal layout. User feedback is sent to the server and stored in a database.

[0112] Data processing and calculation flow

[0113] Collecting and storing office physical information

[0114] The server collects physical information about the office from administrators and sensors and stores it in a database. A specific example is information management for a 200-square-meter office space with 50 desks, three conference rooms, and one projector.

[0115] Real-time usage data collection and transmission

[0116] The terminal collects usage data in real time from sensors installed in each area and conference room and sends it to the server. For example, if the sensor detects that a meeting is currently taking place in conference room A, it sends this information to the server.

[0117] Generating optimal layouts

[0118] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data. For example, it may recommend placing the marketing and development departments next to each other.

[0119] Layout Visualization

[0120] The device receives the layout proposal sent from the server and visualizes it using 3D modeling software, allowing the user to view the visual data and intuitively understand how the layout will be specifically arranged.

[0121] User ratings and selection

[0122] The user evaluates the multiple layout options provided on the device and selects the most suitable layout, which is then sent back to the server and stored in the database.

[0123] Big data analysis and predictive model building

[0124] The server performs big data analysis based on past and current usage data to predict office usage trends and future resource needs, allowing for the development of future layout and resource management plans.

[0125] Examples of concrete examples and prompts

[0126] Collecting office physical information

[0127] The server collects physical information about a 200-square-meter office space, including 50 desks, three conference rooms, and one projector, and stores it in a database.

[0128] Real-time usage data collection and transmission

[0129] The terminal sends data from a sensor installed in conference room A in real time to the server indicating that a meeting is currently in progress.

[0130] Generating optimal layouts

[0131] The server uses AI algorithms to generate optimal layouts based on real-time usage data and database information, suggesting, for example, that the marketing and development departments be placed next to each other.

[0132] Layout Visualization

[0133] The device displays a 3D model of the proposed layout to the user, who can then use this visual information to evaluate the layout and make any necessary adjustments.

[0134] User ratings and selection

[0135] The user selects the best layout and sends the information to the server, where the selected layout is stored in the database.

[0136] Big Data Analytics

[0137] The server analyzes usage data from the past six months and builds a model that predicts future usage trends for conference rooms and desks.

[0138] Prompt Sentence Examples

[0139] "Please suggest the optimal layout for 50 desks, 4 conference rooms, and 2 projectors in a 300 square meter office space. Conference Room 1 is currently in use."

[0140] In this way, the present invention aims to improve the efficiency and productivity of an office.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1: Collecting and storing office physical information

[0143] The server collects physical information about the office provided by the administrator. This information includes the office area, number of desks, number of conference rooms, and type of equipment. For example, the administrator enters information such as "50 desks, 3 conference rooms, and 1 projector in a 200-square-meter office." The server stores this input data in a database.

[0144] Input: Office area, number of desks, number of conference rooms, facility information

[0145] Output: Office physical information stored in a database

[0146] Step 2: Collect and send real-time usage data

[0147] The terminal collects real-time usage data from detectors installed in each area and conference room. The terminal sends the data detected by the sensor, such as "Conference Room A is currently in use," to the server. This allows the server to constantly update the real-time usage status within the office.

[0148] Input: Usage data detected by the sensor (e.g., usage status of conference room A)

[0149] Output: Real-time usage data sent to the server

[0150] Step 3: Generate the optimal layout

[0151] The server uses AI algorithms to generate optimal office layouts based on the physical information stored in the database and real-time usage data. For example, the server might generate a proposal to place the marketing and development departments next to each other, taking into account the communication patterns and work content of each department.

[0152] Input: Database physical information, real-time usage data

[0153] Output: Optimal layout proposals generated by AI algorithms

[0154] Step 4: Visualize the layout

[0155] The device receives the layout proposal sent from the server and visualizes it using 3D modeling software. For example, the proposed layout can be displayed in three dimensions, allowing users to visually confirm the placement of each desk and conference room. Users can view this visual data and understand how the layout will be specifically planned.

[0156] Input: Optimal layout proposal sent from the server

[0157] Output: Visual data displayed by 3D modeling software

[0158] Step 5: User evaluation and selection

[0159] The user compares and evaluates multiple layout options provided on the device. If the user determines that "Layout A is easy to use," the user sends that selection to the server via the device. The server then stores the selected layout information in a database.

[0160] Input: Multiple layout options

[0161] Output: The selected layout sent to the server

[0162] Step 6: Big data analysis and predictive model building

[0163] The server analyzes past and current usage data to build a model to predict future usage trends and resource needs. For example, the server analyzes data from the past six months and predicts that "conference room usage will increase over the next three months." Based on the results of this analysis, the server creates plans for future resource management and layout adjustments.

[0164] Input: Past and current usage data

[0165] Output: Predictive model and analysis results

[0166] Through the above steps, the system of the present invention realizes efficient office management and improved productivity.

[0167] (Application example 1)

[0168] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0169] In modern factories, systems that can grasp work status in real time and provide optimal layouts are required to achieve efficient work arrangements. However, conventional methods have made it difficult to accurately grasp the utilization status of work areas and dynamically and effectively change the layout. There has also been a lack of technology to predict future utilization trends and efficiently manage resources. This has resulted in issues such as reduced work efficiency and restricted productivity.

[0170] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0171] In this invention, the server includes means for collecting physical information about the office and storing it in a database, means for tracking the utilization status of each area in the factory in real time using sensors and sending the data to the server, means for the server to generate an optimal functional space layout based on the information in the database and the real-time utilization data, means for visualizing the generated layout proposal using 3D modeling software, means for supporting the user in evaluating and selecting layout options, and means for analyzing past and current utilization data and predicting future resource utilization trends. This enables dynamic and efficient optimization of work placement in the factory, improving productivity and making effective use of resources.

[0172] "Office physical information" refers to data relating to the physical structure and layout, such as the area of ​​the workspace, the location of installed machines, and the width of the aisles.

[0173] A "database" is a system that accumulates and manages information and stores it in a form that can be retrieved and used later.

[0174] "Area usage status" is data that indicates the operating status and usage status of robots and machines within the work area.

[0175] A "sensor" is a device that detects changes in the environment or the state of an object and outputs that information as digital data.

[0176] A "server" is a central device that processes and stores data, and is a system that provides necessary information in response to requests from clients.

[0177] "Real-time data" refers to data that is acquired and processed immediately based on the current ongoing status.

[0178] An "optimal layout" is the configuration and arrangement of workspace and equipment to maximize efficiency and productivity.

[0179] "3D modeling software" is software for constructing and displaying objects and scenes in three-dimensional space.

[0180] "Layout options" refer to multiple layout plans, and are options that the user can select and evaluate to determine the optimal layout.

[0181] "Decision support" is the process of providing information and assistance to users in choosing the best option from multiple options.

[0182] "Resource utilization trends" are data that analyzes the usage patterns of equipment and personnel and predicts future demand and supply.

[0183] A "functional space" is a physical space dedicated to carrying out a specific task or activity.

[0184] "Roles and workflow" refers to the specific tasks that robots and workers are responsible for, and the process by which those tasks are carried out in coordination.

[0185] An "AI algorithm" is a program or method for automatically solving a specific problem through machine learning and data analysis.

[0186] This invention is a system that proposes and manages optimal functional space layouts based on real-time data in order to improve work efficiency within factories. This system includes the following components:

[0187] server

[0188] The server has the function of collecting physical information within the factory and storing it in a database. Specifically, it manages information such as the area of ​​the work space, the location of installed machines, and the width of aisles. It also receives real-time usage data obtained by sensors and runs an AI algorithm that generates the optimal layout based on that data. This process uses MySQL for database management and TensorFlow for the AI ​​algorithm.

[0189] Terminal

[0190] The device collects data in real time from sensors placed in each area and sends it to a server. It also has the function of visualizing the optimal layout sent from the server using 3D modeling software (using Unity). Users can intuitively understand this information using smart glasses or a head-mounted display.

[0191] User

[0192] Users, representing each department or worker in the factory, operate terminals to review the proposed layout and make adjustments as necessary. The evaluated layout is then sent back to the server and stored in a database. A model is also built to predict future resource usage trends based on past and current usage data.

[0193] Process example

[0194] Data collection

[0195] The server collects physical information about Area A and stores data such as "work space area of ​​200 square meters, location of installed machines, and aisle width" in a database. The terminal obtains real-time data about the transport robot, such as "operating" or "standby," via sensors and sends it to the server.

[0196] Layout generation and visualization

[0197] The server uses TensorFlow to propose optimal layouts based on the collected data. For example, it might suggest changing the placement of robots to shorten the transport distance. The terminal then creates a 3D model of the proposed layout, allowing the user to view it using smart glasses or a head-mounted display.

[0198] Decision Support and Forecasting

[0199] The user evaluates the proposed layout options and selects the optimal arrangement. This selection information is then sent back to the server and stored in a database. The server then analyzes past usage data and builds a model to predict future resource usage trends.

[0200] Prompt Sentence Examples

[0201] "Train an AI model for factory layout optimization. Use the following input dataset to build a model that predicts the optimal layout based on workspace, robot placement, and aisle width data."

[0202] As described above, the present invention is a system that dynamically and efficiently optimizes work allocation within a factory, thereby improving productivity and making effective use of resources.

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] The server collects physical information within the factory and stores it in a database. Input information includes the area of ​​the work space within the factory, the location of installed machines, and the width of aisles. The server converts this data into digital format and stores it in a MySQL database. The output is the physical information stored in the database.

[0206] Step 2:

[0207] The terminal collects data in real time from sensors placed in each area and sends it to the server. In this step, the sensors obtain the current work status, for example, the status of a transport robot, such as "in operation" or "on standby." The terminal receives real-time data from the sensors as input and sends it to the server. The output is the real-time status data sent to the server.

[0208] Step 3:

[0209] The server receives the real-time data and generates an optimal layout based on the database information and the real-time data. In this step, an AI algorithm calculates the optimal layout using TensorFlow. The input data includes the physical information in the database and real-time state data, and the AI ​​model uses this data to predict the layout. The output is an optimal layout proposal.

[0210] Step 4:

[0211] The terminal visualizes the optimal layout proposal sent from the server using 3D modeling software (Unity). The input includes the optimal layout proposal from the server, and a 3D model is generated using Unity based on this. The output is a 3D model layout that the user can visually confirm.

[0212] Step 5:

[0213] The user uses smart glasses or a head-mounted display to view and evaluate the proposed layout. The input includes a layout proposal visualized as a 3D model, which the user uses to evaluate and adjust the layout. The output is the evaluated or adjusted layout information.

[0214] Step 6:

[0215] The layout evaluated by the user is sent back to the server and stored in the database. The input is the layout information adjusted by the user, which is sent to the server. The server stores the received data in the database. The output is the updated database information.

[0216] Step 7:

[0217] The server analyzes past and current usage data and builds a model to predict future resource usage trends. The input data includes accumulated usage data, and the AI ​​model is used to predict future supply and demand. The output is a predictive model that shows future resource usage trends.

[0218] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0219] The present invention combines a system that aims to improve the efficiency and productivity of the office environment with an emotion engine that recognizes user emotions, and by integrating physical information about the office, real-time usage status, optimal layout generation using AI, 3D modeling, and emotion data collection and analysis, it provides a more flexible and comfortable office environment for users. The following describes in detail the modes for implementing this system.

[0220] System configuration

[0221] 1. Server

[0222] It has the ability to collect physical information about the office and store it in a database, and also receives real-time usage data obtained by sensors and runs an AI algorithm to generate the optimal office layout based on that data.

[0223] Equipped with an emotion engine, it has the ability to collect and analyze user emotion data, assessing the user's stress level and satisfaction level, and improving layout suggestions based on this.

[0224] 2. Terminal

[0225] Sensors placed in each area and conference room collect usage data in real time and send it to a server. The system also has the function of visualizing optimal layout proposals sent from the server using 3D modeling software.

[0226] It works in conjunction with the emotion engine and has the function of collecting user emotion data in real time and sending it to the server.

[0227] 3. Users

[0228] Each department and employee in the office evaluates and selects a layout based on their role and communication needs. Users can operate the device to view the proposed layout and make adjustments as needed.

[0229] Emotional data collected by the emotion engine is fed back to users, allowing them to check their own stress levels and satisfaction levels.

[0230] What the program does

[0231] 1. Database creation

[0232] The server collects information about the office's size, facilities, and available space, and stores this information in a database. For example, a 200-square-meter office might have 50 desks, three conference rooms, and one projector. The emotion engine also stores user emotion data in a database.

[0233] 2. Real-time tracking

[0234] The device collects real-time usage information (vacant or in use) from sensors installed in conference rooms and workspaces and sends that data to the server. For example, if the sensor in conference room A detects that the room is in use, the data is immediately sent to the server.

[0235] 3. Proposal of optimal layout

[0236] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data. For example, it suggests arranging the marketing and development departments next to each other. It also takes into account data from the emotion engine, prioritizing layouts that result in lower employee stress levels.

[0237] 4. Simulation and Visualization

[0238] The device uses 3D modeling software to visualize the layout proposal received from the server, displaying the new office layout in a 3D view that allows users to intuitively understand it.

[0239] 5. Decision support

[0240] Users evaluate the layout options provided by their device and select the best option. Users can compare different layouts and choose the arrangement that best suits their specific needs. The selection process also takes into account emotional data, allowing users to choose the environment in which they feel most comfortable working.

[0241] 6. Big data analysis and predictive model building

[0242] The server performs big data analysis of past and current usage data to build models that predict office usage trends and future resource demands. The analysis also includes user emotion data collected through an emotion engine to optimize future layout and resource management.

[0243] Specific examples

[0244] The server collects physical information about the office, such as "200 square meters of office space with 50 desks, three conference rooms, and one projector," and stores it in a database. At the same time, it collects user emotional data through an emotion engine and stores it in the database.

[0245] The device receives "in-meeting" data from a sensor installed in conference room A and sends it to the server. In addition, if the emotion engine detects that the user's stress level is high, that data is also sent.

[0246] Based on real-time usage data and database information, the server uses an AI algorithm to suggest layouts that facilitate collaboration between the marketing and development departments. It also takes into account emotional data to suggest layouts that reduce stress levels.

[0247] The device displays a 3D model of the proposed layout to the user, who can then evaluate the layout based on this visual information and select the layout that best suits their stress level.

[0248] The user selects the optimal layout and sends the information to the server, which stores the selected layout in a database and creates an execution plan.

[0249] The server analyzes usage and sentiment data from the past six months to build a model that predicts future trends in conference room and desk usage.

[0250] In this way, the present invention actively utilizes user emotion data to realize efficient office management and improved productivity.

[0251] The processing flow will be explained below.

[0252] Step 1:

[0253] The server collects information about the office's size, facilities, and available space, and stores this information in a database. For example, a 200-square-meter office might have 50 desks, three conference rooms, and one projector. In addition, an emotion engine is used to store users' emotional data (such as stress levels and satisfaction) in the database.

[0254] Step 2:

[0255] The device collects usage status (vacant or occupied) in real time from sensors placed in conference rooms and workspaces and sends the data to the server. For example, if the sensor in conference room A detects that the room is "occupied," the device immediately sends the data to the server. In addition, the device uses an emotion sensor to collect user emotion data (e.g., stress and satisfaction) and sends the data to the server.

[0256] Step 3:

[0257] The server receives real-time usage and emotion data from the devices, records it in a database, and updates the dashboard based on that information, allowing administrators to check the current office usage status and users' emotional state at a glance.

[0258] Step 4:

[0259] The server uses AI algorithms to generate optimal office layouts based on the database's physical information, real-time usage data, and emotional data. For example, when proposing an arrangement where the marketing and development departments are located next to each other, it takes emotional data into account and prioritizes an arrangement that reduces employee stress levels.

[0260] Step 5:

[0261] The server then sends the generated optimal layout proposal to the terminal, which includes a specific layout diagram and detailed information on the positioning of each department.

[0262] Step 6:

[0263] The device uses 3D modeling software to visualize the layout proposal received from the server, displaying the new office layout in a 3D view that allows users to intuitively understand it.

[0264] Step 7:

[0265] Users can evaluate the layout options provided on their device and select the best option. Users can compare different layouts and choose the arrangement that best suits their specific needs and their own emotional data. Emotional data is also taken into consideration when making a selection, allowing users to choose the environment in which they feel most comfortable working.

[0266] Step 8:

[0267] The user sends the selected layout from their device to the server, which receives this information and stores it in a database.

[0268] Step 9:

[0269] The server will then formulate an execution plan based on the saved new layout and notify the relevant departments, so that the new layout can be implemented quickly.

[0270] Step 10:

[0271] The server performs big data analysis of past and current usage data and sentiment data to build models that predict office usage trends and future resource demands. The analyzed data and predictive models are used to help with future zoning planning.

[0272] Example 2

[0273] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0274] While conventional office layout management systems were capable of optimizing layouts based on physical information and usage data, they lacked the ability to propose layouts that took into account users' emotions and stress levels. This could result in layouts that were efficient but uncomfortable for employees, negatively impacting productivity and employee satisfaction. Furthermore, while real-time usage data was collected and analyzed, it was not possible to use it to build predictive models that considered emotional data or for future resource management, limiting the ability to optimize long-term office operations.

[0275] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting physical information about the office and storing it in a database, a means for tracking the usage status of each area in real time using sensors and transmitting the data to the server, a means for generating an optimal office layout based on the database information and real-time usage data, a means for visualizing the generated layout proposal using 3D modeling software, a means for supporting the user in evaluating and selecting layout options, a means for analyzing past and current usage data and predicting future usage trends, and a means for collecting and analyzing user emotion data and improving the office layout based on the data. This makes it possible to propose an optimal office layout that takes into account not only real-time usage status but also user emotion data. Long-term office operation optimization can also be achieved by predicting future resource demands and usage trends.

[0276] "Physical information about the office" refers to information about the physical structure and facilities of the office, such as the size of the office, facilities, available space, desk layout, and number of conference rooms.

[0277] A "database" is a collection of information that stores collected office physical information, usage data, and user emotional data, and can be searched and updated as needed.

[0278] A "sensor" is a device that is installed in a specific area of ​​an office, such as a conference room or workspace, and detects usage status (such as whether the room is vacant or in use) and environmental information in real time.

[0279] The "server" is a central processing unit that receives data sent from sensors and generates an optimal office layout based on the information stored in the database.

[0280] An "AI algorithm" is a collection of calculation methods and logical processes that analyze database information and real-time usage data to generate optimal office layouts.

[0281] "3D modeling software" is software that visualizes the generated office layout proposal in three dimensions and displays it in a way that users can intuitively understand.

[0282] "User emotion data" is information about the user's psychological state, such as the user's stress level and satisfaction level.

[0283] "Real-time usage data" is information collected from sensors in each area about the current office usage status (for example, whether a conference room is in use or vacant).

[0284] A "means for assisting in the evaluation and selection of layout options" is a support system that allows users to compare multiple proposed layouts and select the option that best suits them.

[0285] "Past and current usage data" refers to information on past office usage and current area usage, and is data used to predict future usage trends.

[0286] "Usage trend forecasting" is the process of analyzing collected data to estimate future office usage and resource demands.

[0287] The present invention is a system for optimizing office efficiency and improving user comfort, which combines and implements several main functions and means, specific embodiments of which are described in detail below.

[0288] System configuration

[0289] 1. Server

[0290] The server collects physical information about the office (size, facilities, available space, etc.) and stores this information in a database. Specifically, it manages information such as "200 square meters of office space with 50 desks, three conference rooms, and one projector" through sensors and manual input. At the same time, it uses an emotion engine to collect user emotional data (such as stress levels and satisfaction) and stores this information in the database.

[0291] 2. Terminal

[0292] The device collects real-time usage data from sensors placed in each area and conference room of the office and sends it to the server. For example, if the sensor in conference room A detects that the room is in use, it immediately sends that information to the server. The device also collects user emotion data in real time and sends it to the server.

[0293] 3. Users

[0294] Users can evaluate the layout options provided by the device and select the best option. Users can compare different layouts and choose the arrangement that best suits them. Emotional data is also taken into consideration when making a selection, allowing them to choose an environment that allows them to work comfortably.

[0295] Data Processing and AI Algorithms

[0296] The server uses an AI algorithm (e.g., a generative AI model) to generate the optimal office layout based on database information and real-time usage data. Specifically, it proposes an arrangement that facilitates collaboration between the marketing and development departments. It also takes into account emotional data, prioritizing layouts that result in low employee stress levels.

[0297] Visualization and Decision Support

[0298] The terminal visualizes the optimal layout proposal received from the server using 3D modeling software. For example, the proposed layout can be displayed in 3D view, allowing the user to intuitively understand it. New desk arrangements, meeting room locations, etc. are visually displayed.

[0299] Big data analysis and predictive model building

[0300] The server analyzes past and current usage data to build a model that predicts future office usage trends and resource demands. For example, it analyzes trends such as frequently used conference rooms and popular desk locations and makes predictions such as "Conference Room A will be frequently used in the morning." This allows for future resource management and layout optimization.

[0301] Specific examples

[0302] The server collects physical information about the office and stores it in a database, such as "200 square meters of office space with 50 desks, three conference rooms, and one projector." At the same time, it collects emotional data such as users' stress levels and satisfaction levels through an emotion engine and stores this data in the database.

[0303] The device receives real-time data about the meeting from a sensor installed in conference room A and sends it to the server. If real-time emotional data (e.g., high stress level) is detected from the user's device, the device also sends that data to the server.

[0304] Based on real-time usage data and database information, the server uses an AI algorithm to propose layouts that facilitate collaboration between the marketing and development departments. It also takes into account emotional data to provide layouts that reduce stress levels.

[0305] The device visualizes the proposed layout using 3D modeling software and displays a visual 3D view, allowing users to evaluate the layout and select the arrangement that best suits their stress level.

[0306] The user selects the optimal layout and sends the information to the server, which stores the selected layout in a database and creates an execution plan.

[0307] The server analyzes usage and emotion data from the past six months to predict future trends in conference room and desk usage, and also makes suggestions for future layout changes based on the analysis results.

[0308] This system makes it possible to actively utilize users' emotional data, leading to efficient office operations and improved productivity.

[0309] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0310] Step 1:

[0311] The server collects physical information about the office and stores it in a database. Specifically, using sensors and a manual input system, information such as "a 200-square-meter office with 50 desks, three conference rooms, and one projector" is entered and saved in the database. The input data is information about the office's size and facilities, and the output is the physical information stored in the database.

[0312] Step 2:

[0313] The terminal collects real-time usage data from sensors placed in each area and conference room and sends it to the server. For example, if the sensor in conference room A detects that the room is in use, it sends that information to the server as input data. The input is real-time data from the sensor, and the output is updated usage information on the server.

[0314] Step 3:

[0315] The server uses an AI algorithm to generate the optimal office layout based on the collected real-time usage data and physical information stored in the database. Specifically, it proposes an arrangement where the marketing department and development department are located next to each other. The input is the physical information in the database and real-time usage data, and the output is a proposal for the optimal office layout.

[0316] Step 4:

[0317] The terminal visualizes the optimal layout proposal received from the server using 3D modeling software. The generated 3D view displays the new desk arrangement and meeting room locations. The input is the layout data from the server, and the output is the 3D modeling view displayed to the user.

[0318] Step 5:

[0319] The user evaluates the proposed layout options via the terminal and selects the optimal one. The user checks the displayed 3D view and makes adjustments such as "moving the marketing department's desk closer to the window." The input is the layout proposal made by the 3D modeling software, and the output is the final selected office layout information.

[0320] Step 6:

[0321] The server saves the final layout determined by the user in a database. It receives the user's selections and stores specific layout change information, such as "place the marketing department desks by the window." The input is the final layout selection information from the user, and the output is the layout information saved in the database.

[0322] Step 7:

[0323] The server analyzes past and present usage data and emotion data to build a model that predicts future usage trends. For example, it predicts that "Conference Room A will be frequently used in the morning." The input is past and present usage data and emotion data, and the output is the prediction model and its results.

[0324] (Application example 2)

[0325] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0326] Conventional office environment optimization systems could generate layouts based on physical layout and usage data, but they were unable to consider user emotional data. This made it difficult to maximize users' psychological comfort and productivity. In particular, the lack of technology to analyze users' emotions in real time on-site and adjust the environment based on that information made it impossible to reduce user stress and provide a more comfortable working environment.

[0327] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting physical information about the office and storing it in a database, means for tracking the usage status of each area in real time using sensors and transmitting the data to the server, means for the server to generate an optimal office layout based on the information in the database and the real-time usage status data, means for visualizing the generated layout proposal using 3D modeling software, means for supporting the user in evaluating and selecting layout options, means for analyzing past and current usage data and predicting future usage trends, means for analyzing user emotions in real time, and means for dynamically adjusting the office environment (physical information, working environment) based on the user's emotional data. This makes it possible to optimize the office environment taking user emotional data into consideration.

[0328] A "server" is a central processing unit that collects, stores, analyzes, and communicates data to other devices.

[0329] "Office physical information" refers to data on physical characteristics such as equipment, furniture, and space allocation within the office.

[0330] A "database" is a system for systematically storing and managing collected information and data.

[0331] A "sensor" is a device that detects and measures data about the physical environment or situation in real time.

[0332] "Real-time usage data" is data collected by sensors that indicates the current usage status of each area.

[0333] An "optimal office layout" is the best way to arrange equipment, furniture, and space to provide an efficient working environment.

[0334] "3D modeling software" means software that creates three-dimensional shapes to visually represent a proposed layout.

[0335] "User evaluation and selection of layout options" refers to the process in which a user evaluates and selects the most suitable layout from multiple layouts presented to them.

[0336] "Past and current usage data" refers to data relating to the usage history and current usage status of the office.

[0337] "Predicting future usage trends" means analyzing collected data to estimate future office usage patterns.

[0338] "Analyzing user emotions in real time" means using sensors and analytical software to instantly determine the user's emotional state.

[0339] "Dynamic adjustment of the office environment based on user emotional data" means making timely changes to office elements such as lighting, layout, and acoustics based on the results of user emotional analysis.

[0340] This invention relates to a system that optimizes the environment in a physical store in real time. Specifically, the system collects emotional data from users (customers in this case) and dynamically adjusts the environment (lighting, background music, layout, etc.) based on that data to improve customer comfort and store operational efficiency.

[0341] System configuration

[0342] 1. Server

[0343] Data collection and storage: The server receives customer facial image data captured from devices such as smart glasses and analyzes it to generate emotion data, which is then stored in a database.

[0344] Analysis and proposal: Based on the emotion data received in real time, the server uses an AI algorithm to calculate optimal store environment adjustments (lighting, background music, layout, etc.).

[0345] Sending commands: The calculated environmental adjustment commands are sent to the appropriate devices and executed.

[0346] 2. Device

[0347] Data capture: The device, such as smart glasses, captures an image of the customer's face and sends this data to the server, which uses a specific SDK for emotion recognition (e.g., EmotionRecognition SDK).

[0348] Adjusting the environment: Receives commands from the server and adjusts lighting, sound systems, moving parts of the layout, etc.

[0349] 3. Users (Staff)

[0350] Device operation: Wearing smart glasses, the robot patrols the store and captures the customer's state.

[0351] Evaluate and adjust: Review environment adjustments based on customer sentiment data and make manual tweaks as needed.

[0352] Example

[0353] 1. In-store demonstration:

[0354] When a customer enters a store, staff wearing smart glasses capture the customer's facial expression.

[0355] The server analyzes the acquired facial expression data in real time and determines the customer's emotional state, such as "relaxed" or "satisfied."

[0356] Based on commands from the server, the store's background music is changed to music that helps customers relax, and the lighting is also changed to warm colors that help customers relax.

[0357] Example prompt: "Analyze whether this customer is relaxed or stressed and provide appropriate background music and lighting settings."

[0358] 2. How to operate the system:

[0359] Staff can check customer sentiment data in real time through the UI of the smart glasses.

[0360] If necessary, manually adjust your preferences and they will be saved in the database so that they are reflected in future suggestions.

[0361] This invention makes it possible to adjust the environment of a physical store in real time according to the emotional state of customers, which not only improves customer satisfaction but also contributes to the efficiency of store operations.

[0362] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0363] Step 1:

[0364] The terminal captures the customer's facial image data using the smart glasses. The input is the customer's facial image data captured by the smart glasses' camera, and the output is the raw image data sent to the EmotionRecognition SDK.

[0365] Step 2:

[0366] The device uses the EmotionRecognition SDK to analyze the customer's facial image data in real time and generate emotion data. The input is the raw image data obtained in step 1, and the output is data indicating the customer's emotional state (relaxed, stressed, etc.).

[0367] Step 3:

[0368] The terminal transmits the generated emotion data to the server. The input is the emotion data generated by the terminal, and the output is the emotion data transmitted to the server.

[0369] Step 4:

[0370] The server analyzes the received emotion data and generates an optimal command for adjusting the environment. The input is the emotion data obtained in step 3, and the output is the command data for adjusting the environment.

[0371] Step 5:

[0372] The server sends an instruction for adjusting the environment to the terminal. The input is the instruction data generated in step 4, and the output is the instruction data sent to the terminal.

[0373] Step 6:

[0374] The terminal dynamically adjusts environmental elements such as lighting, background music, and layout in the store based on command data received from the server. The input is the command data received from the server, and the output is the actual result of the environmental adjustment.

[0375] Step 7:

[0376] The user (staff member) checks the status of the environmental adjustment through the UI of the smart glasses and manually fine-tunes it if necessary. The input is the environmental adjustment result displayed on the UI of the smart glasses, and the output is the result of the manual adjustment by the staff member.

[0377] Step 8:

[0378] The server saves the results of the manual adjustment in a database and updates the data to reflect the results in subsequent environmental adjustments. The input is the environmental data updated by the staff, and the output is the updated data saved in the database.

[0379] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0380] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0381] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0382] [Second embodiment]

[0383] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0384] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0385] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0386] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0387] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0388] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0389] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0390] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0391] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0392] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0393] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0394] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0395] The present invention is a system for improving the efficiency and productivity of an office environment, and by combining multiple means, it realizes the optimization of the office layout and the optimal use of resources. The following describes in detail the embodiments of this system.

[0396] System configuration

[0397] 1. Server

[0398] It has the ability to collect physical information about the office (area, equipment, available space, etc.) and store it in a database. It also receives real-time usage data obtained by sensors and runs an AI algorithm that generates the optimal office layout based on that data.

[0399] 2. Terminal

[0400] Data acquired from sensors placed in each area and conference room is sent to a server in real time, and the optimal layout proposals sent from the server can be visualized using 3D modeling software.

[0401] 3. Users

[0402] Each department and employee in the office evaluates and selects a layout based on their role and communication needs. Users can operate the device to view the proposed layout and make adjustments as needed.

[0403] What the program does

[0404] 1. Database creation

[0405] The server collects information about the size, facilities, and available space of the office and stores this information in a database. For example, a 200-square-meter office space might have one projector, three conference rooms, and 50 desks.

[0406] 2. Real-time tracking

[0407] The device collects current usage data from sensors installed in conference rooms and workspaces and sends it to the server. For example, it recognizes that conference room A is currently in use and sends this data to the server.

[0408] 3. Proposal of optimal layout

[0409] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data, such as suggesting placement of the marketing and development departments next to each other.

[0410] 4. Simulation and Visualization

[0411] The device receives the layout proposal sent from the server and visualizes it using 3D modeling software, allowing the user to view the visual data and intuitively understand how the layout will be specifically arranged.

[0412] 5. Decision support

[0413] The user evaluates the multiple layout options provided on the device and selects the most suitable layout, which is then sent back to the server and stored in the database.

[0414] 6. Big data analysis and predictive model building

[0415] The server performs big data analysis based on past and current usage data to predict office usage trends and future resource needs, allowing it to formulate future layout and resource management plans.

[0416] Specific examples

[0417] The server collects physical information about the office and stores, for example, "200 square meters of office space with 50 desks, three conference rooms, and one projector" in a database.

[0418] The terminal receives "meeting in progress" data from a sensor installed in conference room A and sends it to the server.

[0419] Based on real-time usage data and database information, the server uses an AI algorithm to suggest layouts that facilitate collaboration between the marketing and development departments.

[0420] The device displays a 3D model of the proposed layout to the user, who can then use this visual information to evaluate the layout and make any necessary adjustments.

[0421] The user selects the optimal layout and sends the information to the server, which stores the selected layout in a database and creates an execution plan.

[0422] The server analyzes usage data from the past six months and builds a model that predicts future usage trends for conference rooms and desks.

[0423] In this way, the present invention realizes efficient office management and improved productivity.

[0424] The processing flow will be explained below.

[0425] Step 1:

[0426] The server collects physical information about the office (area, equipment, available space, etc.) and stores it in a database. For example, information about 50 desks, 3 conference rooms, and 1 projector in a 200-square-meter office is stored in the database. The data is updated in a timely manner to ensure that it is always up-to-date.

[0427] Step 2:

[0428] The device collects real-time usage information (vacant or in use) from sensors installed in conference rooms and workspaces and sends that data to the server. For example, if the sensor in conference room A detects that the room is in use, the data is immediately sent to the server.

[0429] Step 3:

[0430] The server receives real-time usage data from the devices, records it in a database, and updates the dashboard based on that information, allowing administrators to check current office usage at a glance.

[0431] Step 4:

[0432] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data. Specifically, it considers the roles and communication needs of each department and suggests, for example, placing the marketing and development departments next to each other.

[0433] Step 5:

[0434] The server then sends the generated optimal layout proposal to the terminal, which includes a specific layout diagram and detailed information on the positioning of each department.

[0435] Step 6:

[0436] The device uses 3D modeling software to visualize the layout proposal received from the server, displaying the new office layout in a 3D view that allows users to intuitively understand it.

[0437] Step 7:

[0438] Users can evaluate the layout options provided by their device and select the best option. Users can compare different layouts and choose the arrangement that best suits their specific needs.

[0439] Step 8:

[0440] The user sends the selected layout from their device to the server, which receives this information and stores it in a database.

[0441] Step 9:

[0442] The server will then formulate an execution plan based on the saved new layout and notify the relevant departments, so that the new layout can be implemented quickly.

[0443] Step 10:

[0444] The server performs big data analysis of past and current usage data to build models that predict office usage trends and future resource demands, which can be used to plan future zoning.

[0445] Example 1

[0446] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0447] In modern office environments, efficiently using limited space and maximizing employee productivity are key challenges. However, basic layout changes and resource allocation are often manual and based on experience, with little optimization based on objective data. Furthermore, it is difficult to understand real-time usage and provide appropriate feedback, making it difficult to maintain an optimal office layout.

[0448] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0449] In this invention, the server includes means for collecting physical information about the office and storing it in a database, means for tracking the usage status of each area in real time using sensors and transmitting the data to an information processing device, means for generating an optimal office layout using the information processing device based on the database information and real-time usage data, means for visualizing the generated layout proposal using three-dimensional modeling software, means for supporting users in evaluating and selecting layout options, means for analyzing past and current usage data and predicting future usage trends, means for transmitting real-time data collected by sensors in each area to the information processing device and updating the dashboard, means for optimizing the office layout taking into account employee roles and departmental relationships using an artificial intelligence algorithm, and means for building a predictive model based on the collected data. This enables optimal layout proposals to be made based on the physical information and real-time usage status of the office environment, thereby achieving efficient space utilization and improved productivity.

[0450] "Physical information about the office" is information that indicates specific physical elements such as the area of ​​the office, the number of desks, the number of conference rooms, and the type of equipment.

[0451] A "database" is a system that organizes and stores collected office physical information and real-time usage data, and manages it so that it can be retrieved when needed.

[0452] A "detector" is a sensor that is installed in each area or conference room, detects usage in real time, and transmits the data to an information processing device.

[0453] An "information processing device" is a device, such as a server or computer, that processes collected data and performs analysis and calculations.

[0454] An "optimal office layout" is a proposal for an arrangement that makes efficient use of space within the office and maximizes the improvement of employees' working environment.

[0455] "3D model software" is software for performing 3D modeling and visually displaying office layouts.

[0456] "Users" are people who use the system to evaluate and select layouts, such as office employees and managers.

[0457] A "dashboard" is an interface for visually displaying real-time usage data and analysis results.

[0458] The "artificial intelligence algorithm" is an algorithm that calculates and proposes the optimal layout based on collected data.

[0459] A "predictive model" is a model that predicts future usage trends and required resources based on past and current data.

[0460] The present invention is a system for improving the efficiency and productivity of an office environment, and specific embodiments thereof are described in detail below. This system realizes the optimization of office layout and optimal utilization of resources through cooperation between servers, terminals, and users.

[0461] Hardware and software configuration

[0462] server

[0463] The server plays a central role in managing physical information and real-time usage data collected from each area and conference room in the office. Software such as databases, AI algorithms, and dashboards are installed on the server. The roles of each are as follows:

[0464] Terminal

[0465] The terminals are connected to sensors installed in each area and conference room, and transmit real-time data to a server. They also have 3D modeling software (such as Autodesk or SketchUp) installed, which visualizes the optimal layout proposals sent from the server.

[0466] User

[0467] Users, who are employees or managers in the office, evaluate the layouts proposed by the server via their terminals and select the optimal layout. User feedback is sent to the server and stored in a database.

[0468] Data processing and calculation flow

[0469] Collecting and storing office physical information

[0470] The server collects physical information about the office from administrators and sensors and stores it in a database. A specific example is information management for a 200-square-meter office space with 50 desks, three conference rooms, and one projector.

[0471] Real-time usage data collection and transmission

[0472] The terminal collects usage data in real time from sensors installed in each area and conference room and sends it to the server. For example, if the sensor detects that a meeting is currently taking place in conference room A, it sends this information to the server.

[0473] Generating optimal layouts

[0474] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data. For example, it may recommend placing the marketing and development departments next to each other.

[0475] Layout Visualization

[0476] The device receives the layout proposal sent from the server and visualizes it using 3D modeling software, allowing the user to view the visual data and intuitively understand how the layout will be specifically arranged.

[0477] User ratings and selection

[0478] The user evaluates the multiple layout options provided on the device and selects the most suitable layout, which is then sent back to the server and stored in the database.

[0479] Big data analysis and predictive model building

[0480] The server performs big data analysis based on past and current usage data to predict office usage trends and future resource needs, allowing for the development of future layout and resource management plans.

[0481] Examples of concrete examples and prompts

[0482] Collecting office physical information

[0483] The server collects physical information about a 200-square-meter office space, including 50 desks, three conference rooms, and one projector, and stores it in a database.

[0484] Real-time usage data collection and transmission

[0485] The terminal sends data from a sensor installed in conference room A in real time to the server indicating that a meeting is currently in progress.

[0486] Generating optimal layouts

[0487] The server uses AI algorithms to generate optimal layouts based on real-time usage data and database information, suggesting, for example, that the marketing and development departments be placed next to each other.

[0488] Layout Visualization

[0489] The device displays a 3D model of the proposed layout to the user, who can then use this visual information to evaluate the layout and make any necessary adjustments.

[0490] User ratings and selection

[0491] The user selects the best layout and sends the information to the server, where the selected layout is stored in the database.

[0492] Big Data Analytics

[0493] The server analyzes usage data from the past six months and builds a model that predicts future usage trends for conference rooms and desks.

[0494] Prompt Sentence Examples

[0495] "Please suggest the optimal layout for 50 desks, 4 conference rooms, and 2 projectors in a 300 square meter office space. Conference Room 1 is currently in use."

[0496] In this way, the present invention aims to improve the efficiency and productivity of an office.

[0497] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0498] Step 1: Collecting and storing office physical information

[0499] The server collects physical information about the office provided by the administrator. This information includes the office area, number of desks, number of conference rooms, and type of equipment. For example, the administrator enters information such as "50 desks, 3 conference rooms, and 1 projector in a 200-square-meter office." The server stores this input data in a database.

[0500] Input: Office area, number of desks, number of conference rooms, facility information

[0501] Output: Office physical information stored in a database

[0502] Step 2: Collect and send real-time usage data

[0503] The terminal collects real-time usage data from detectors installed in each area and conference room. The terminal sends the data detected by the sensor, such as "Conference Room A is currently in use," to the server. This allows the server to constantly update the real-time usage status within the office.

[0504] Input: Usage data detected by the sensor (e.g., usage status of conference room A)

[0505] Output: Real-time usage data sent to the server

[0506] Step 3: Generate the optimal layout

[0507] The server uses AI algorithms to generate optimal office layouts based on the physical information stored in the database and real-time usage data. For example, the server might generate a proposal to place the marketing and development departments next to each other, taking into account the communication patterns and work content of each department.

[0508] Input: Database physical information, real-time usage data

[0509] Output: Optimal layout proposals generated by AI algorithms

[0510] Step 4: Visualize the layout

[0511] The device receives the layout proposal sent from the server and visualizes it using 3D modeling software. For example, the proposed layout can be displayed in three dimensions, allowing users to visually confirm the placement of each desk and conference room. Users can view this visual data and understand how the layout will be specifically planned.

[0512] Input: Optimal layout proposal sent from the server

[0513] Output: Visual data displayed by 3D modeling software

[0514] Step 5: User evaluation and selection

[0515] The user compares and evaluates multiple layout options provided on the device. If the user determines that "Layout A is easy to use," the user sends that selection to the server via the device. The server then stores the selected layout information in a database.

[0516] Input: Multiple layout options

[0517] Output: The selected layout sent to the server

[0518] Step 6: Big data analysis and predictive model building

[0519] The server analyzes past and current usage data to build a model to predict future usage trends and resource needs. For example, the server analyzes data from the past six months and predicts that "conference room usage will increase over the next three months." Based on the results of this analysis, the server creates plans for future resource management and layout adjustments.

[0520] Input: Past and current usage data

[0521] Output: Predictive model and analysis results

[0522] Through the above steps, the system of the present invention realizes efficient office management and improved productivity.

[0523] (Application example 1)

[0524] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0525] In modern factories, systems that can grasp work status in real time and provide optimal layouts are required to achieve efficient work arrangements. However, conventional methods have made it difficult to accurately grasp the utilization status of work areas and dynamically and effectively change the layout. There has also been a lack of technology to predict future utilization trends and efficiently manage resources. This has resulted in issues such as reduced work efficiency and restricted productivity.

[0526] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0527] In this invention, the server includes means for collecting physical information about the office and storing it in a database, means for tracking the utilization status of each area in the factory in real time using sensors and sending the data to the server, means for the server to generate an optimal functional space layout based on the information in the database and the real-time utilization data, means for visualizing the generated layout proposal using 3D modeling software, means for supporting the user in evaluating and selecting layout options, and means for analyzing past and current utilization data and predicting future resource utilization trends. This enables dynamic and efficient optimization of work placement in the factory, improving productivity and making effective use of resources.

[0528] "Office physical information" refers to data relating to the physical structure and layout, such as the area of ​​the workspace, the location of installed machines, and the width of the aisles.

[0529] A "database" is a system that accumulates and manages information and stores it in a form that can be retrieved and used later.

[0530] "Area usage status" is data that indicates the operating status and usage status of robots and machines within the work area.

[0531] A "sensor" is a device that detects changes in the environment or the state of an object and outputs that information as digital data.

[0532] A "server" is a central device that processes and stores data, and is a system that provides necessary information in response to requests from clients.

[0533] "Real-time data" refers to data that is acquired and processed immediately based on the current ongoing status.

[0534] An "optimal layout" is the configuration and arrangement of workspace and equipment to maximize efficiency and productivity.

[0535] "3D modeling software" is software for constructing and displaying objects and scenes in three-dimensional space.

[0536] "Layout options" refer to multiple layout plans, and are options that the user can select and evaluate to determine the optimal layout.

[0537] "Decision support" is the process of providing information and assistance to users in choosing the best option from multiple options.

[0538] "Resource utilization trends" are data that analyzes the usage patterns of equipment and personnel and predicts future demand and supply.

[0539] A "functional space" is a physical space dedicated to carrying out a specific task or activity.

[0540] "Roles and workflow" refers to the specific tasks that robots and workers are responsible for, and the process by which those tasks are carried out in coordination.

[0541] An "AI algorithm" is a program or method for automatically solving a specific problem through machine learning and data analysis.

[0542] This invention is a system that proposes and manages optimal functional space layouts based on real-time data in order to improve work efficiency within factories. This system includes the following components:

[0543] server

[0544] The server has the function of collecting physical information within the factory and storing it in a database. Specifically, it manages information such as the area of ​​the work space, the location of installed machines, and the width of aisles. It also receives real-time usage data obtained by sensors and runs an AI algorithm that generates the optimal layout based on that data. This process uses MySQL for database management and TensorFlow for the AI ​​algorithm.

[0545] Terminal

[0546] The device collects data in real time from sensors placed in each area and sends it to a server. It also has the function of visualizing the optimal layout sent from the server using 3D modeling software (using Unity). Users can intuitively understand this information using smart glasses or a head-mounted display.

[0547] User

[0548] Users, representing each department or worker in the factory, operate terminals to review the proposed layout and make adjustments as necessary. The evaluated layout is then sent back to the server and stored in a database. A model is also built to predict future resource usage trends based on past and current usage data.

[0549] Process example

[0550] Data collection

[0551] The server collects physical information about Area A and stores data such as "work space area of ​​200 square meters, location of installed machines, and aisle width" in a database. The terminal obtains real-time data about the transport robot, such as "operating" or "standby," via sensors and sends it to the server.

[0552] Layout generation and visualization

[0553] The server uses TensorFlow to propose optimal layouts based on the collected data. For example, it might suggest changing the placement of robots to shorten the transport distance. The terminal then creates a 3D model of the proposed layout, allowing the user to view it using smart glasses or a head-mounted display.

[0554] Decision Support and Forecasting

[0555] The user evaluates the proposed layout options and selects the optimal arrangement. This selection information is then sent back to the server and stored in a database. The server then analyzes past usage data and builds a model to predict future resource usage trends.

[0556] Prompt Sentence Examples

[0557] "Train an AI model for factory layout optimization. Use the following input dataset to build a model that predicts the optimal layout based on workspace, robot placement, and aisle width data."

[0558] As described above, the present invention is a system that dynamically and efficiently optimizes work allocation within a factory, thereby improving productivity and making effective use of resources.

[0559] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0560] Step 1:

[0561] The server collects physical information within the factory and stores it in a database. Input information includes the area of ​​the work space within the factory, the location of installed machines, and the width of aisles. The server converts this data into digital format and stores it in a MySQL database. The output is the physical information stored in the database.

[0562] Step 2:

[0563] The terminal collects data in real time from sensors placed in each area and sends it to the server. In this step, the sensors obtain the current work status, for example, the status of a transport robot, such as "in operation" or "on standby." The terminal receives real-time data from the sensors as input and sends it to the server. The output is the real-time status data sent to the server.

[0564] Step 3:

[0565] The server receives the real-time data and generates an optimal layout based on the database information and the real-time data. In this step, an AI algorithm calculates the optimal layout using TensorFlow. The input data includes the physical information in the database and real-time state data, and the AI ​​model uses this data to predict the layout. The output is an optimal layout proposal.

[0566] Step 4:

[0567] The terminal visualizes the optimal layout proposal sent from the server using 3D modeling software (Unity). The input includes the optimal layout proposal from the server, and a 3D model is generated using Unity based on this. The output is a 3D model layout that the user can visually confirm.

[0568] Step 5:

[0569] The user uses smart glasses or a head-mounted display to view and evaluate the proposed layout. The input includes a layout proposal visualized as a 3D model, which the user uses to evaluate and adjust the layout. The output is the evaluated or adjusted layout information.

[0570] Step 6:

[0571] The layout evaluated by the user is sent back to the server and stored in the database. The input is the layout information adjusted by the user, which is sent to the server. The server stores the received data in the database. The output is the updated database information.

[0572] Step 7:

[0573] The server analyzes past and current usage data and builds a model to predict future resource usage trends. The input data includes accumulated usage data, and the AI ​​model is used to predict future supply and demand. The output is a predictive model that shows future resource usage trends.

[0574] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0575] The present invention combines a system that aims to improve the efficiency and productivity of the office environment with an emotion engine that recognizes user emotions, and by integrating physical information about the office, real-time usage status, optimal layout generation using AI, 3D modeling, and emotion data collection and analysis, it provides a more flexible and comfortable office environment for users. The following describes in detail the modes for implementing this system.

[0576] System configuration

[0577] 1. Server

[0578] It has the ability to collect physical information about the office and store it in a database, and also receives real-time usage data obtained by sensors and runs an AI algorithm to generate the optimal office layout based on that data.

[0579] Equipped with an emotion engine, it has the ability to collect and analyze user emotion data, assessing the user's stress level and satisfaction level, and improving layout suggestions based on this.

[0580] 2. Terminal

[0581] Sensors placed in each area and conference room collect usage data in real time and send it to a server. The system also has the function of visualizing optimal layout proposals sent from the server using 3D modeling software.

[0582] It works in conjunction with the emotion engine and has the function of collecting user emotion data in real time and sending it to the server.

[0583] 3. Users

[0584] Each department and employee in the office evaluates and selects a layout based on their role and communication needs. Users can operate the device to view the proposed layout and make adjustments as needed.

[0585] Emotional data collected by the emotion engine is fed back to users, allowing them to check their own stress levels and satisfaction levels.

[0586] What the program does

[0587] 1. Database creation

[0588] The server collects information about the office's size, facilities, and available space, and stores this information in a database. For example, a 200-square-meter office might have 50 desks, three conference rooms, and one projector. The emotion engine also stores user emotion data in a database.

[0589] 2. Real-time tracking

[0590] The device collects real-time usage information (vacant or in use) from sensors installed in conference rooms and workspaces and sends that data to the server. For example, if the sensor in conference room A detects that the room is in use, the data is immediately sent to the server.

[0591] 3. Proposal of optimal layout

[0592] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data. For example, it suggests arranging the marketing and development departments next to each other. It also takes into account data from the emotion engine, prioritizing layouts that result in lower employee stress levels.

[0593] 4. Simulation and Visualization

[0594] The device uses 3D modeling software to visualize the layout proposal received from the server, displaying the new office layout in a 3D view that allows users to intuitively understand it.

[0595] 5. Decision support

[0596] Users evaluate the layout options provided by their device and select the best option. Users can compare different layouts and choose the arrangement that best suits their specific needs. The selection process also takes into account emotional data, allowing users to choose the environment in which they feel most comfortable working.

[0597] 6. Big data analysis and predictive model building

[0598] The server performs big data analysis of past and current usage data to build models that predict office usage trends and future resource demands. The analysis also includes user emotion data collected through an emotion engine to optimize future layout and resource management.

[0599] Specific examples

[0600] The server collects physical information about the office, such as "200 square meters of office space with 50 desks, three conference rooms, and one projector," and stores it in a database. At the same time, it collects user emotional data through an emotion engine and stores it in the database.

[0601] The device receives "in-meeting" data from a sensor installed in conference room A and sends it to the server. In addition, if the emotion engine detects that the user's stress level is high, that data is also sent.

[0602] Based on real-time usage data and database information, the server uses an AI algorithm to suggest layouts that facilitate collaboration between the marketing and development departments. It also takes into account emotional data to suggest layouts that reduce stress levels.

[0603] The device displays a 3D model of the proposed layout to the user, who can then evaluate the layout based on this visual information and select the layout that best suits their stress level.

[0604] The user selects the optimal layout and sends the information to the server, which stores the selected layout in a database and creates an execution plan.

[0605] The server analyzes usage and sentiment data from the past six months to build a model that predicts future trends in conference room and desk usage.

[0606] In this way, the present invention actively utilizes user emotion data to realize efficient office management and improved productivity.

[0607] The processing flow will be explained below.

[0608] Step 1:

[0609] The server collects information about the office's size, facilities, and available space, and stores this information in a database. For example, a 200-square-meter office might have 50 desks, three conference rooms, and one projector. In addition, an emotion engine is used to store users' emotional data (such as stress levels and satisfaction) in the database.

[0610] Step 2:

[0611] The device collects usage status (vacant or occupied) in real time from sensors placed in conference rooms and workspaces and sends the data to the server. For example, if the sensor in conference room A detects that the room is "occupied," the device immediately sends the data to the server. In addition, the device uses an emotion sensor to collect user emotion data (e.g., stress and satisfaction) and sends the data to the server.

[0612] Step 3:

[0613] The server receives real-time usage and emotion data from the devices, records it in a database, and updates the dashboard based on that information, allowing administrators to check the current office usage status and users' emotional state at a glance.

[0614] Step 4:

[0615] The server uses AI algorithms to generate optimal office layouts based on the database's physical information, real-time usage data, and emotional data. For example, when proposing an arrangement where the marketing and development departments are located next to each other, it takes emotional data into account and prioritizes an arrangement that reduces employee stress levels.

[0616] Step 5:

[0617] The server then sends the generated optimal layout proposal to the terminal, which includes a specific layout diagram and detailed information on the positioning of each department.

[0618] Step 6:

[0619] The device uses 3D modeling software to visualize the layout proposal received from the server, displaying the new office layout in a 3D view that allows users to intuitively understand it.

[0620] Step 7:

[0621] Users can evaluate the layout options provided on their device and select the best option. Users can compare different layouts and choose the arrangement that best suits their specific needs and their own emotional data. Emotional data is also taken into consideration when making a selection, allowing users to choose the environment in which they feel most comfortable working.

[0622] Step 8:

[0623] The user sends the selected layout from their device to the server, which receives this information and stores it in a database.

[0624] Step 9:

[0625] The server will then formulate an execution plan based on the saved new layout and notify the relevant departments, so that the new layout can be implemented quickly.

[0626] Step 10:

[0627] The server performs big data analysis of past and current usage data and sentiment data to build models that predict office usage trends and future resource demands. The analyzed data and predictive models are used to help with future zoning planning.

[0628] Example 2

[0629] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0630] While conventional office layout management systems were capable of optimizing layouts based on physical information and usage data, they lacked the ability to propose layouts that took into account users' emotions and stress levels. This could result in layouts that were efficient but uncomfortable for employees, negatively impacting productivity and employee satisfaction. Furthermore, while real-time usage data was collected and analyzed, it was not possible to use it to build predictive models that considered emotional data or for future resource management, limiting the ability to optimize long-term office operations.

[0631] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting physical information about the office and storing it in a database, a means for tracking the usage status of each area in real time using sensors and transmitting the data to the server, a means for generating an optimal office layout based on the database information and real-time usage data, a means for visualizing the generated layout proposal using 3D modeling software, a means for supporting the user in evaluating and selecting layout options, a means for analyzing past and current usage data and predicting future usage trends, and a means for collecting and analyzing user emotion data and improving the office layout based on the data. This makes it possible to propose an optimal office layout that takes into account not only real-time usage status but also user emotion data. Long-term office operation optimization can also be achieved by predicting future resource demands and usage trends.

[0632] "Physical information about the office" refers to information about the physical structure and facilities of the office, such as the size of the office, facilities, available space, desk layout, and number of conference rooms.

[0633] A "database" is a collection of information that stores collected office physical information, usage data, and user emotional data, and can be searched and updated as needed.

[0634] A "sensor" is a device that is installed in a specific area of ​​an office, such as a conference room or workspace, and detects usage status (such as whether the room is vacant or in use) and environmental information in real time.

[0635] The "server" is a central processing unit that receives data sent from sensors and generates an optimal office layout based on the information stored in the database.

[0636] An "AI algorithm" is a collection of calculation methods and logical processes that analyze database information and real-time usage data to generate optimal office layouts.

[0637] "3D modeling software" is software that visualizes the generated office layout proposal in three dimensions and displays it in a way that users can intuitively understand.

[0638] "User emotion data" is information about the user's psychological state, such as the user's stress level and satisfaction level.

[0639] "Real-time usage data" is information collected from sensors in each area about the current office usage status (for example, whether a conference room is in use or vacant).

[0640] A "means for assisting in the evaluation and selection of layout options" is a support system that allows users to compare multiple proposed layouts and select the option that best suits them.

[0641] "Past and current usage data" refers to information on past office usage and current area usage, and is data used to predict future usage trends.

[0642] "Usage trend forecasting" is the process of analyzing collected data to estimate future office usage and resource demands.

[0643] The present invention is a system for optimizing office efficiency and improving user comfort, which combines and implements several main functions and means, specific embodiments of which are described in detail below.

[0644] System configuration

[0645] 1. Server

[0646] The server collects physical information about the office (size, facilities, available space, etc.) and stores this information in a database. Specifically, it manages information such as "200 square meters of office space with 50 desks, three conference rooms, and one projector" through sensors and manual input. At the same time, it uses an emotion engine to collect user emotional data (such as stress levels and satisfaction) and stores this information in the database.

[0647] 2. Terminal

[0648] The device collects real-time usage data from sensors placed in each area and conference room of the office and sends it to the server. For example, if the sensor in conference room A detects that the room is in use, it immediately sends that information to the server. The device also collects user emotion data in real time and sends it to the server.

[0649] 3. Users

[0650] Users can evaluate the layout options provided by the device and select the best option. Users can compare different layouts and choose the arrangement that best suits them. Emotional data is also taken into consideration when making a selection, allowing them to choose an environment that allows them to work comfortably.

[0651] Data Processing and AI Algorithms

[0652] The server uses an AI algorithm (e.g., a generative AI model) to generate the optimal office layout based on database information and real-time usage data. Specifically, it proposes an arrangement that facilitates collaboration between the marketing and development departments. It also takes into account emotional data, prioritizing layouts that result in low employee stress levels.

[0653] Visualization and Decision Support

[0654] The terminal visualizes the optimal layout proposal received from the server using 3D modeling software. For example, the proposed layout can be displayed in 3D view, allowing the user to intuitively understand it. New desk arrangements, meeting room locations, etc. are visually displayed.

[0655] Big data analysis and predictive model building

[0656] The server analyzes past and current usage data to build a model that predicts future office usage trends and resource demands. For example, it analyzes trends such as frequently used conference rooms and popular desk locations and makes predictions such as "Conference Room A will be frequently used in the morning." This allows for future resource management and layout optimization.

[0657] Specific examples

[0658] The server collects physical information about the office and stores it in a database, such as "200 square meters of office space with 50 desks, three conference rooms, and one projector." At the same time, it collects emotional data such as users' stress levels and satisfaction levels through an emotion engine and stores this data in the database.

[0659] The device receives real-time data about the meeting from a sensor installed in conference room A and sends it to the server. If real-time emotional data (e.g., high stress level) is detected from the user's device, the device also sends that data to the server.

[0660] Based on real-time usage data and database information, the server uses an AI algorithm to propose layouts that facilitate collaboration between the marketing and development departments. It also takes into account emotional data to provide layouts that reduce stress levels.

[0661] The device visualizes the proposed layout using 3D modeling software and displays a visual 3D view, allowing users to evaluate the layout and select the arrangement that best suits their stress level.

[0662] The user selects the optimal layout and sends the information to the server, which stores the selected layout in a database and creates an execution plan.

[0663] The server analyzes usage and emotion data from the past six months to predict future trends in conference room and desk usage, and also makes suggestions for future layout changes based on the analysis results.

[0664] This system makes it possible to actively utilize users' emotional data, leading to efficient office operations and improved productivity.

[0665] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0666] Step 1:

[0667] The server collects physical information about the office and stores it in a database. Specifically, using sensors and a manual input system, information such as "a 200-square-meter office with 50 desks, three conference rooms, and one projector" is entered and saved in the database. The input data is information about the office's size and facilities, and the output is the physical information stored in the database.

[0668] Step 2:

[0669] The terminal collects real-time usage data from sensors placed in each area and conference room and sends it to the server. For example, if the sensor in conference room A detects that the room is in use, it sends that information to the server as input data. The input is real-time data from the sensor, and the output is updated usage information on the server.

[0670] Step 3:

[0671] The server uses an AI algorithm to generate the optimal office layout based on the collected real-time usage data and physical information stored in the database. Specifically, it proposes an arrangement where the marketing department and development department are located next to each other. The input is the physical information in the database and real-time usage data, and the output is a proposal for the optimal office layout.

[0672] Step 4:

[0673] The terminal visualizes the optimal layout proposal received from the server using 3D modeling software. The generated 3D view displays the new desk arrangement and meeting room locations. The input is the layout data from the server, and the output is the 3D modeling view displayed to the user.

[0674] Step 5:

[0675] The user evaluates the proposed layout options via the terminal and selects the optimal one. The user checks the displayed 3D view and makes adjustments such as "moving the marketing department's desk closer to the window." The input is the layout proposal made by the 3D modeling software, and the output is the final selected office layout information.

[0676] Step 6:

[0677] The server saves the final layout determined by the user in a database. It receives the user's selections and stores specific layout change information, such as "place the marketing department desks by the window." The input is the final layout selection information from the user, and the output is the layout information saved in the database.

[0678] Step 7:

[0679] The server analyzes past and present usage data and emotion data to build a model that predicts future usage trends. For example, it predicts that "Conference Room A will be frequently used in the morning." The input is past and present usage data and emotion data, and the output is the prediction model and its results.

[0680] (Application example 2)

[0681] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0682] Conventional office environment optimization systems could generate layouts based on physical layout and usage data, but they were unable to consider user emotional data. This made it difficult to maximize users' psychological comfort and productivity. In particular, the lack of technology to analyze users' emotions in real time on-site and adjust the environment based on that information made it impossible to reduce user stress and provide a more comfortable working environment.

[0683] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting physical information about the office and storing it in a database, means for tracking the usage status of each area in real time using sensors and transmitting the data to the server, means for the server to generate an optimal office layout based on the information in the database and the real-time usage status data, means for visualizing the generated layout proposal using 3D modeling software, means for supporting the user in evaluating and selecting layout options, means for analyzing past and current usage data and predicting future usage trends, means for analyzing user emotions in real time, and means for dynamically adjusting the office environment (physical information, working environment) based on the user's emotional data. This makes it possible to optimize the office environment taking user emotional data into consideration.

[0684] A "server" is a central processing unit that collects, stores, analyzes, and communicates data to other devices.

[0685] "Office physical information" refers to data on physical characteristics such as equipment, furniture, and space allocation within the office.

[0686] A "database" is a system for systematically storing and managing collected information and data.

[0687] A "sensor" is a device that detects and measures data about the physical environment or situation in real time.

[0688] "Real-time usage data" is data collected by sensors that indicates the current usage status of each area.

[0689] An "optimal office layout" is the best way to arrange equipment, furniture, and space to provide an efficient working environment.

[0690] "3D modeling software" means software that creates three-dimensional shapes to visually represent a proposed layout.

[0691] "User evaluation and selection of layout options" refers to the process in which a user evaluates and selects the most suitable layout from multiple layouts presented to them.

[0692] "Past and current usage data" refers to data relating to the usage history and current usage status of the office.

[0693] "Predicting future usage trends" means analyzing collected data to estimate future office usage patterns.

[0694] "Analyzing user emotions in real time" means using sensors and analytical software to instantly determine the user's emotional state.

[0695] "Dynamic adjustment of the office environment based on user emotional data" means making timely changes to office elements such as lighting, layout, and acoustics based on the results of user emotional analysis.

[0696] This invention relates to a system that optimizes the environment in a physical store in real time. Specifically, the system collects emotional data from users (customers in this case) and dynamically adjusts the environment (lighting, background music, layout, etc.) based on that data to improve customer comfort and store operational efficiency.

[0697] System configuration

[0698] 1. Server

[0699] Data collection and storage: The server receives customer facial image data captured from devices such as smart glasses and analyzes it to generate emotion data, which is then stored in a database.

[0700] Analysis and proposal: Based on the emotion data received in real time, the server uses an AI algorithm to calculate optimal store environment adjustments (lighting, background music, layout, etc.).

[0701] Sending commands: The calculated environmental adjustment commands are sent to the appropriate devices and executed.

[0702] 2. Device

[0703] Data capture: The device, such as smart glasses, captures an image of the customer's face and sends this data to the server, which uses a specific SDK for emotion recognition (e.g., EmotionRecognition SDK).

[0704] Adjusting the environment: Receives commands from the server and adjusts lighting, sound systems, moving parts of the layout, etc.

[0705] 3. Users (Staff)

[0706] Device operation: Wearing smart glasses, the robot patrols the store and captures the customer's state.

[0707] Evaluate and adjust: Review environment adjustments based on customer sentiment data and make manual tweaks as needed.

[0708] Example

[0709] 1. In-store demonstration:

[0710] When a customer enters a store, staff wearing smart glasses capture the customer's facial expression.

[0711] The server analyzes the acquired facial expression data in real time and determines the customer's emotional state, such as "relaxed" or "satisfied."

[0712] Based on commands from the server, the store's background music is changed to music that helps customers relax, and the lighting is also changed to warm colors that help customers relax.

[0713] Example prompt: "Analyze whether this customer is relaxed or stressed and provide appropriate background music and lighting settings."

[0714] 2. How to operate the system:

[0715] Staff can check customer sentiment data in real time through the UI of the smart glasses.

[0716] If necessary, manually adjust your preferences and they will be saved in the database so that they are reflected in future suggestions.

[0717] This invention makes it possible to adjust the environment of a physical store in real time according to the emotional state of customers, which not only improves customer satisfaction but also contributes to the efficiency of store operations.

[0718] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0719] Step 1:

[0720] The terminal captures the customer's facial image data using the smart glasses. The input is the customer's facial image data captured by the smart glasses' camera, and the output is the raw image data sent to the EmotionRecognition SDK.

[0721] Step 2:

[0722] The device uses the EmotionRecognition SDK to analyze the customer's facial image data in real time and generate emotion data. The input is the raw image data obtained in step 1, and the output is data indicating the customer's emotional state (relaxed, stressed, etc.).

[0723] Step 3:

[0724] The terminal transmits the generated emotion data to the server. The input is the emotion data generated by the terminal, and the output is the emotion data transmitted to the server.

[0725] Step 4:

[0726] The server analyzes the received emotion data and generates an optimal command for adjusting the environment. The input is the emotion data obtained in step 3, and the output is the command data for adjusting the environment.

[0727] Step 5:

[0728] The server sends an instruction for adjusting the environment to the terminal. The input is the instruction data generated in step 4, and the output is the instruction data sent to the terminal.

[0729] Step 6:

[0730] The terminal dynamically adjusts environmental elements such as lighting, background music, and layout in the store based on command data received from the server. The input is the command data received from the server, and the output is the actual result of the environmental adjustment.

[0731] Step 7:

[0732] The user (staff member) checks the status of the environmental adjustment through the UI of the smart glasses and manually fine-tunes it if necessary. The input is the environmental adjustment result displayed on the UI of the smart glasses, and the output is the result of the manual adjustment by the staff member.

[0733] Step 8:

[0734] The server saves the results of the manual adjustment in a database and updates the data to reflect the results in subsequent environmental adjustments. The input is the environmental data updated by the staff, and the output is the updated data saved in the database.

[0735] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0736] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0737] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0738] [Third embodiment]

[0739] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0740] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0741] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0742] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0743] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0744] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0745] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0746] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0747] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0748] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0749] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0750] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0751] The present invention is a system for improving the efficiency and productivity of an office environment, and by combining multiple means, it realizes the optimization of the office layout and the optimal use of resources. The following describes in detail the embodiments of this system.

[0752] System configuration

[0753] 1. Server

[0754] It has the ability to collect physical information about the office (area, equipment, available space, etc.) and store it in a database. It also receives real-time usage data obtained by sensors and runs an AI algorithm that generates the optimal office layout based on that data.

[0755] 2. Terminal

[0756] Data acquired from sensors placed in each area and conference room is sent to a server in real time, and the optimal layout proposals sent from the server can be visualized using 3D modeling software.

[0757] 3. Users

[0758] Each department and employee in the office evaluates and selects a layout based on their role and communication needs. Users can operate the device to view the proposed layout and make adjustments as needed.

[0759] What the program does

[0760] 1. Database creation

[0761] The server collects information about the size, facilities, and available space of the office and stores this information in a database. For example, a 200-square-meter office space might have one projector, three conference rooms, and 50 desks.

[0762] 2. Real-time tracking

[0763] The device collects current usage data from sensors installed in conference rooms and workspaces and sends it to the server. For example, it recognizes that conference room A is currently in use and sends this data to the server.

[0764] 3. Proposal of optimal layout

[0765] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data, such as suggesting placement of the marketing and development departments next to each other.

[0766] 4. Simulation and Visualization

[0767] The device receives the layout proposal sent from the server and visualizes it using 3D modeling software, allowing the user to view the visual data and intuitively understand how the layout will be specifically arranged.

[0768] 5. Decision support

[0769] The user evaluates the multiple layout options provided on the device and selects the most suitable layout, which is then sent back to the server and stored in the database.

[0770] 6. Big data analysis and predictive model building

[0771] The server performs big data analysis based on past and current usage data to predict office usage trends and future resource needs, allowing it to formulate future layout and resource management plans.

[0772] Specific examples

[0773] The server collects physical information about the office and stores, for example, "200 square meters of office space with 50 desks, three conference rooms, and one projector" in a database.

[0774] The terminal receives "meeting in progress" data from a sensor installed in conference room A and sends it to the server.

[0775] Based on real-time usage data and database information, the server uses an AI algorithm to suggest layouts that facilitate collaboration between the marketing and development departments.

[0776] The device displays a 3D model of the proposed layout to the user, who can then use this visual information to evaluate the layout and make any necessary adjustments.

[0777] The user selects the optimal layout and sends the information to the server, which stores the selected layout in a database and creates an execution plan.

[0778] The server analyzes usage data from the past six months and builds a model that predicts future usage trends for conference rooms and desks.

[0779] In this way, the present invention realizes efficient office management and improved productivity.

[0780] The processing flow will be explained below.

[0781] Step 1:

[0782] The server collects physical information about the office (area, equipment, available space, etc.) and stores it in a database. For example, information about 50 desks, 3 conference rooms, and 1 projector in a 200-square-meter office is stored in the database. The data is updated in a timely manner to ensure that it is always up-to-date.

[0783] Step 2:

[0784] The device collects real-time usage information (vacant or in use) from sensors installed in conference rooms and workspaces and sends that data to the server. For example, if the sensor in conference room A detects that the room is in use, the data is immediately sent to the server.

[0785] Step 3:

[0786] The server receives real-time usage data from the devices, records it in a database, and updates the dashboard based on that information, allowing administrators to check current office usage at a glance.

[0787] Step 4:

[0788] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data. Specifically, it considers the roles and communication needs of each department and suggests, for example, placing the marketing and development departments next to each other.

[0789] Step 5:

[0790] The server then sends the generated optimal layout proposal to the terminal, which includes a specific layout diagram and detailed information on the positioning of each department.

[0791] Step 6:

[0792] The device uses 3D modeling software to visualize the layout proposal received from the server, displaying the new office layout in a 3D view that allows users to intuitively understand it.

[0793] Step 7:

[0794] Users can evaluate the layout options provided by their device and select the best option. Users can compare different layouts and choose the arrangement that best suits their specific needs.

[0795] Step 8:

[0796] The user sends the selected layout from their device to the server, which receives this information and stores it in a database.

[0797] Step 9:

[0798] The server will then formulate an execution plan based on the saved new layout and notify the relevant departments, so that the new layout can be implemented quickly.

[0799] Step 10:

[0800] The server performs big data analysis of past and current usage data to build models that predict office usage trends and future resource demands, which can be used to plan future zoning.

[0801] Example 1

[0802] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0803] In modern office environments, efficiently using limited space and maximizing employee productivity are key challenges. However, basic layout changes and resource allocation are often manual and based on experience, with little optimization based on objective data. Furthermore, it is difficult to understand real-time usage and provide appropriate feedback, making it difficult to maintain an optimal office layout.

[0804] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0805] In this invention, the server includes means for collecting physical information about the office and storing it in a database, means for tracking the usage status of each area in real time using sensors and transmitting the data to an information processing device, means for generating an optimal office layout using the information processing device based on the database information and real-time usage data, means for visualizing the generated layout proposal using three-dimensional modeling software, means for supporting users in evaluating and selecting layout options, means for analyzing past and current usage data and predicting future usage trends, means for transmitting real-time data collected by sensors in each area to the information processing device and updating the dashboard, means for optimizing the office layout taking into account employee roles and departmental relationships using an artificial intelligence algorithm, and means for building a predictive model based on the collected data. This enables optimal layout proposals to be made based on the physical information and real-time usage status of the office environment, thereby achieving efficient space utilization and improved productivity.

[0806] "Physical information about the office" is information that indicates specific physical elements such as the area of ​​the office, the number of desks, the number of conference rooms, and the type of equipment.

[0807] A "database" is a system that organizes and stores collected office physical information and real-time usage data, and manages it so that it can be retrieved when needed.

[0808] A "detector" is a sensor that is installed in each area or conference room, detects usage in real time, and transmits the data to an information processing device.

[0809] An "information processing device" is a device, such as a server or computer, that processes collected data and performs analysis and calculations.

[0810] An "optimal office layout" is a proposal for an arrangement that makes efficient use of space within the office and maximizes the improvement of employees' working environment.

[0811] "3D model software" is software for performing 3D modeling and visually displaying office layouts.

[0812] "Users" are people who use the system to evaluate and select layouts, such as office employees and managers.

[0813] A "dashboard" is an interface for visually displaying real-time usage data and analysis results.

[0814] The "artificial intelligence algorithm" is an algorithm that calculates and proposes the optimal layout based on collected data.

[0815] A "predictive model" is a model that predicts future usage trends and required resources based on past and current data.

[0816] The present invention is a system for improving the efficiency and productivity of an office environment, and specific embodiments thereof are described in detail below. This system realizes the optimization of office layout and optimal utilization of resources through cooperation between servers, terminals, and users.

[0817] Hardware and software configuration

[0818] server

[0819] The server plays a central role in managing physical information and real-time usage data collected from each area and conference room in the office. Software such as databases, AI algorithms, and dashboards are installed on the server. The roles of each are as follows:

[0820] Terminal

[0821] The terminals are connected to sensors installed in each area and conference room, and transmit real-time data to a server. They also have 3D modeling software (such as Autodesk or SketchUp) installed, which visualizes the optimal layout proposals sent from the server.

[0822] User

[0823] Users, who are employees or managers in the office, evaluate the layouts proposed by the server via their terminals and select the optimal layout. User feedback is sent to the server and stored in a database.

[0824] Data processing and calculation flow

[0825] Collecting and storing office physical information

[0826] The server collects physical information about the office from administrators and sensors and stores it in a database. A specific example is information management for a 200-square-meter office space with 50 desks, three conference rooms, and one projector.

[0827] Real-time usage data collection and transmission

[0828] The terminal collects usage data in real time from sensors installed in each area and conference room and sends it to the server. For example, if the sensor detects that a meeting is currently taking place in conference room A, it sends this information to the server.

[0829] Generating optimal layouts

[0830] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data. For example, it may recommend placing the marketing and development departments next to each other.

[0831] Layout Visualization

[0832] The device receives the layout proposal sent from the server and visualizes it using 3D modeling software, allowing the user to view the visual data and intuitively understand how the layout will be specifically arranged.

[0833] User ratings and selection

[0834] The user evaluates the multiple layout options provided on the device and selects the most suitable layout, which is then sent back to the server and stored in the database.

[0835] Big data analysis and predictive model building

[0836] The server performs big data analysis based on past and current usage data to predict office usage trends and future resource needs, allowing for the development of future layout and resource management plans.

[0837] Examples of concrete examples and prompts

[0838] Collecting office physical information

[0839] The server collects physical information about a 200-square-meter office space, including 50 desks, three conference rooms, and one projector, and stores it in a database.

[0840] Real-time usage data collection and transmission

[0841] The terminal sends data from a sensor installed in conference room A in real time to the server indicating that a meeting is currently in progress.

[0842] Generating optimal layouts

[0843] The server uses AI algorithms to generate optimal layouts based on real-time usage data and database information, suggesting, for example, that the marketing and development departments be placed next to each other.

[0844] Layout Visualization

[0845] The device displays a 3D model of the proposed layout to the user, who can then use this visual information to evaluate the layout and make any necessary adjustments.

[0846] User ratings and selection

[0847] The user selects the best layout and sends the information to the server, where the selected layout is stored in the database.

[0848] Big Data Analytics

[0849] The server analyzes usage data from the past six months and builds a model that predicts future usage trends for conference rooms and desks.

[0850] Prompt Sentence Examples

[0851] "Please suggest the optimal layout for 50 desks, 4 conference rooms, and 2 projectors in a 300 square meter office space. Conference Room 1 is currently in use."

[0852] In this way, the present invention aims to improve the efficiency and productivity of an office.

[0853] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0854] Step 1: Collecting and storing office physical information

[0855] The server collects physical information about the office provided by the administrator. This information includes the office area, number of desks, number of conference rooms, and type of equipment. For example, the administrator enters information such as "50 desks, 3 conference rooms, and 1 projector in a 200-square-meter office." The server stores this input data in a database.

[0856] Input: Office area, number of desks, number of conference rooms, facility information

[0857] Output: Office physical information stored in a database

[0858] Step 2: Collect and send real-time usage data

[0859] The terminal collects real-time usage data from detectors installed in each area and conference room. The terminal sends the data detected by the sensor, such as "Conference Room A is currently in use," to the server. This allows the server to constantly update the real-time usage status within the office.

[0860] Input: Usage data detected by the sensor (e.g., usage status of conference room A)

[0861] Output: Real-time usage data sent to the server

[0862] Step 3: Generate the optimal layout

[0863] The server uses AI algorithms to generate optimal office layouts based on the physical information stored in the database and real-time usage data. For example, the server might generate a proposal to place the marketing and development departments next to each other, taking into account the communication patterns and work content of each department.

[0864] Input: Database physical information, real-time usage data

[0865] Output: Optimal layout proposals generated by AI algorithms

[0866] Step 4: Visualize the layout

[0867] The device receives the layout proposal sent from the server and visualizes it using 3D modeling software. For example, the proposed layout can be displayed in three dimensions, allowing users to visually confirm the placement of each desk and conference room. Users can view this visual data and understand how the layout will be specifically planned.

[0868] Input: Optimal layout proposal sent from the server

[0869] Output: Visual data displayed by 3D modeling software

[0870] Step 5: User evaluation and selection

[0871] The user compares and evaluates multiple layout options provided on the device. If the user determines that "Layout A is easy to use," the user sends that selection to the server via the device. The server then stores the selected layout information in a database.

[0872] Input: Multiple layout options

[0873] Output: The selected layout sent to the server

[0874] Step 6: Big data analysis and predictive model building

[0875] The server analyzes past and current usage data to build a model to predict future usage trends and resource needs. For example, the server analyzes data from the past six months and predicts that "conference room usage will increase over the next three months." Based on the results of this analysis, the server creates plans for future resource management and layout adjustments.

[0876] Input: Past and current usage data

[0877] Output: Predictive model and analysis results

[0878] Through the above steps, the system of the present invention realizes efficient office management and improved productivity.

[0879] (Application example 1)

[0880] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0881] In modern factories, systems that can grasp work status in real time and provide optimal layouts are required to achieve efficient work arrangements. However, conventional methods have made it difficult to accurately grasp the utilization status of work areas and dynamically and effectively change the layout. There has also been a lack of technology to predict future utilization trends and efficiently manage resources. This has resulted in issues such as reduced work efficiency and restricted productivity.

[0882] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0883] In this invention, the server includes means for collecting physical information about the office and storing it in a database, means for tracking the utilization status of each area in the factory in real time using sensors and sending the data to the server, means for the server to generate an optimal functional space layout based on the information in the database and the real-time utilization data, means for visualizing the generated layout proposal using 3D modeling software, means for supporting the user in evaluating and selecting layout options, and means for analyzing past and current utilization data and predicting future resource utilization trends. This enables dynamic and efficient optimization of work placement in the factory, improving productivity and making effective use of resources.

[0884] "Office physical information" refers to data relating to the physical structure and layout, such as the area of ​​the workspace, the location of installed machines, and the width of the aisles.

[0885] A "database" is a system that accumulates and manages information and stores it in a form that can be retrieved and used later.

[0886] "Area usage status" is data that indicates the operating status and usage status of robots and machines within the work area.

[0887] A "sensor" is a device that detects changes in the environment or the state of an object and outputs that information as digital data.

[0888] A "server" is a central device that processes and stores data, and is a system that provides necessary information in response to requests from clients.

[0889] "Real-time data" refers to data that is acquired and processed immediately based on the current ongoing status.

[0890] An "optimal layout" is the configuration and arrangement of workspace and equipment to maximize efficiency and productivity.

[0891] "3D modeling software" is software for constructing and displaying objects and scenes in three-dimensional space.

[0892] "Layout options" refer to multiple layout plans, and are options that the user can select and evaluate to determine the optimal layout.

[0893] "Decision support" is the process of providing information and assistance to users in choosing the best option from multiple options.

[0894] "Resource utilization trends" are data that analyzes the usage patterns of equipment and personnel and predicts future demand and supply.

[0895] A "functional space" is a physical space dedicated to carrying out a specific task or activity.

[0896] "Roles and workflow" refers to the specific tasks that robots and workers are responsible for, and the process by which those tasks are carried out in coordination.

[0897] An "AI algorithm" is a program or method for automatically solving a specific problem through machine learning and data analysis.

[0898] This invention is a system that proposes and manages optimal functional space layouts based on real-time data in order to improve work efficiency within factories. This system includes the following components:

[0899] server

[0900] The server has the function of collecting physical information within the factory and storing it in a database. Specifically, it manages information such as the area of ​​the work space, the location of installed machines, and the width of aisles. It also receives real-time usage data obtained by sensors and runs an AI algorithm that generates the optimal layout based on that data. This process uses MySQL for database management and TensorFlow for the AI ​​algorithm.

[0901] Terminal

[0902] The device collects data in real time from sensors placed in each area and sends it to a server. It also has the function of visualizing the optimal layout sent from the server using 3D modeling software (using Unity). Users can intuitively understand this information using smart glasses or a head-mounted display.

[0903] User

[0904] Users, representing each department or worker in the factory, operate terminals to review the proposed layout and make adjustments as necessary. The evaluated layout is then sent back to the server and stored in a database. A model is also built to predict future resource usage trends based on past and current usage data.

[0905] Process example

[0906] Data collection

[0907] The server collects physical information about Area A and stores data such as "work space area of ​​200 square meters, location of installed machines, and aisle width" in a database. The terminal obtains real-time data about the transport robot, such as "operating" or "standby," via sensors and sends it to the server.

[0908] Layout generation and visualization

[0909] The server uses TensorFlow to propose optimal layouts based on the collected data. For example, it might suggest changing the placement of robots to shorten the transport distance. The terminal then creates a 3D model of the proposed layout, allowing the user to view it using smart glasses or a head-mounted display.

[0910] Decision Support and Forecasting

[0911] The user evaluates the proposed layout options and selects the optimal arrangement. This selection information is then sent back to the server and stored in a database. The server then analyzes past usage data and builds a model to predict future resource usage trends.

[0912] Prompt Sentence Examples

[0913] "Train an AI model for factory layout optimization. Use the following input dataset to build a model that predicts the optimal layout based on workspace, robot placement, and aisle width data."

[0914] As described above, the present invention is a system that dynamically and efficiently optimizes work allocation within a factory, thereby improving productivity and making effective use of resources.

[0915] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0916] Step 1:

[0917] The server collects physical information within the factory and stores it in a database. Input information includes the area of ​​the work space within the factory, the location of installed machines, and the width of aisles. The server converts this data into digital format and stores it in a MySQL database. The output is the physical information stored in the database.

[0918] Step 2:

[0919] The terminal collects data in real time from sensors placed in each area and sends it to the server. In this step, the sensors obtain the current work status, for example, the status of a transport robot, such as "in operation" or "on standby." The terminal receives real-time data from the sensors as input and sends it to the server. The output is the real-time status data sent to the server.

[0920] Step 3:

[0921] The server receives the real-time data and generates an optimal layout based on the database information and the real-time data. In this step, an AI algorithm calculates the optimal layout using TensorFlow. The input data includes the physical information in the database and real-time state data, and the AI ​​model uses this data to predict the layout. The output is an optimal layout proposal.

[0922] Step 4:

[0923] The terminal visualizes the optimal layout proposal sent from the server using 3D modeling software (Unity). The input includes the optimal layout proposal from the server, and a 3D model is generated using Unity based on this. The output is a 3D model layout that the user can visually confirm.

[0924] Step 5:

[0925] The user uses smart glasses or a head-mounted display to view and evaluate the proposed layout. The input includes a layout proposal visualized as a 3D model, which the user uses to evaluate and adjust the layout. The output is the evaluated or adjusted layout information.

[0926] Step 6:

[0927] The layout evaluated by the user is sent back to the server and stored in the database. The input is the layout information adjusted by the user, which is sent to the server. The server stores the received data in the database. The output is the updated database information.

[0928] Step 7:

[0929] The server analyzes past and current usage data and builds a model to predict future resource usage trends. The input data includes accumulated usage data, and the AI ​​model is used to predict future supply and demand. The output is a predictive model that shows future resource usage trends.

[0930] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0931] The present invention combines a system that aims to improve the efficiency and productivity of the office environment with an emotion engine that recognizes user emotions, and by integrating physical information about the office, real-time usage status, optimal layout generation using AI, 3D modeling, and emotion data collection and analysis, it provides a more flexible and comfortable office environment for users. The following describes in detail the modes for implementing this system.

[0932] System configuration

[0933] 1. Server

[0934] It has the ability to collect physical information about the office and store it in a database, and also receives real-time usage data obtained by sensors and runs an AI algorithm to generate the optimal office layout based on that data.

[0935] Equipped with an emotion engine, it has the ability to collect and analyze user emotion data, assessing the user's stress level and satisfaction level, and improving layout suggestions based on this.

[0936] 2. Terminal

[0937] Sensors placed in each area and conference room collect usage data in real time and send it to a server. The system also has the function of visualizing optimal layout proposals sent from the server using 3D modeling software.

[0938] It works in conjunction with the emotion engine and has the function of collecting user emotion data in real time and sending it to the server.

[0939] 3. Users

[0940] Each department and employee in the office evaluates and selects a layout based on their role and communication needs. Users can operate the device to view the proposed layout and make adjustments as needed.

[0941] Emotional data collected by the emotion engine is fed back to users, allowing them to check their own stress levels and satisfaction levels.

[0942] What the program does

[0943] 1. Database creation

[0944] The server collects information about the office's size, facilities, and available space, and stores this information in a database. For example, a 200-square-meter office might have 50 desks, three conference rooms, and one projector. The emotion engine also stores user emotion data in a database.

[0945] 2. Real-time tracking

[0946] The device collects real-time usage information (vacant or in use) from sensors installed in conference rooms and workspaces and sends that data to the server. For example, if the sensor in conference room A detects that the room is in use, the data is immediately sent to the server.

[0947] 3. Proposal of optimal layout

[0948] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data. For example, it suggests arranging the marketing and development departments next to each other. It also takes into account data from the emotion engine, prioritizing layouts that result in lower employee stress levels.

[0949] 4. Simulation and Visualization

[0950] The device uses 3D modeling software to visualize the layout proposal received from the server, displaying the new office layout in a 3D view that allows users to intuitively understand it.

[0951] 5. Decision support

[0952] Users evaluate the layout options provided by their device and select the best option. Users can compare different layouts and choose the arrangement that best suits their specific needs. The selection process also takes into account emotional data, allowing users to choose the environment in which they feel most comfortable working.

[0953] 6. Big data analysis and predictive model building

[0954] The server performs big data analysis of past and current usage data to build models that predict office usage trends and future resource demands. The analysis also includes user emotion data collected through an emotion engine to optimize future layout and resource management.

[0955] Specific examples

[0956] The server collects physical information about the office, such as "200 square meters of office space with 50 desks, three conference rooms, and one projector," and stores it in a database. At the same time, it collects user emotional data through an emotion engine and stores it in the database.

[0957] The device receives "in-meeting" data from a sensor installed in conference room A and sends it to the server. In addition, if the emotion engine detects that the user's stress level is high, that data is also sent.

[0958] Based on real-time usage data and database information, the server uses an AI algorithm to suggest layouts that facilitate collaboration between the marketing and development departments. It also takes into account emotional data to suggest layouts that reduce stress levels.

[0959] The device displays a 3D model of the proposed layout to the user, who can then evaluate the layout based on this visual information and select the layout that best suits their stress level.

[0960] The user selects the optimal layout and sends the information to the server, which stores the selected layout in a database and creates an execution plan.

[0961] The server analyzes usage and sentiment data from the past six months to build a model that predicts future trends in conference room and desk usage.

[0962] In this way, the present invention actively utilizes user emotion data to realize efficient office management and improved productivity.

[0963] The processing flow will be explained below.

[0964] Step 1:

[0965] The server collects information about the office's size, facilities, and available space, and stores this information in a database. For example, a 200-square-meter office might have 50 desks, three conference rooms, and one projector. In addition, an emotion engine is used to store users' emotional data (such as stress levels and satisfaction) in the database.

[0966] Step 2:

[0967] The device collects usage status (vacant or occupied) in real time from sensors placed in conference rooms and workspaces and sends the data to the server. For example, if the sensor in conference room A detects that the room is "occupied," the device immediately sends the data to the server. In addition, the device uses an emotion sensor to collect user emotion data (e.g., stress and satisfaction) and sends the data to the server.

[0968] Step 3:

[0969] The server receives real-time usage and emotion data from the devices, records it in a database, and updates the dashboard based on that information, allowing administrators to check the current office usage status and users' emotional state at a glance.

[0970] Step 4:

[0971] The server uses AI algorithms to generate optimal office layouts based on the database's physical information, real-time usage data, and emotional data. For example, when proposing an arrangement where the marketing and development departments are located next to each other, it takes emotional data into account and prioritizes an arrangement that reduces employee stress levels.

[0972] Step 5:

[0973] The server then sends the generated optimal layout proposal to the terminal, which includes a specific layout diagram and detailed information on the positioning of each department.

[0974] Step 6:

[0975] The device uses 3D modeling software to visualize the layout proposal received from the server, displaying the new office layout in a 3D view that allows users to intuitively understand it.

[0976] Step 7:

[0977] Users can evaluate the layout options provided on their device and select the best option. Users can compare different layouts and choose the arrangement that best suits their specific needs and their own emotional data. Emotional data is also taken into consideration when making a selection, allowing users to choose the environment in which they feel most comfortable working.

[0978] Step 8:

[0979] The user sends the selected layout from their device to the server, which receives this information and stores it in a database.

[0980] Step 9:

[0981] The server will then formulate an execution plan based on the saved new layout and notify the relevant departments, so that the new layout can be implemented quickly.

[0982] Step 10:

[0983] The server performs big data analysis of past and current usage data and sentiment data to build models that predict office usage trends and future resource demands. The analyzed data and predictive models are used to help with future zoning planning.

[0984] Example 2

[0985] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0986] While conventional office layout management systems were capable of optimizing layouts based on physical information and usage data, they lacked the ability to propose layouts that took into account users' emotions and stress levels. This could result in layouts that were efficient but uncomfortable for employees, negatively impacting productivity and employee satisfaction. Furthermore, while real-time usage data was collected and analyzed, it was not possible to use it to build predictive models that considered emotional data or for future resource management, limiting the ability to optimize long-term office operations.

[0987] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting physical information about the office and storing it in a database, a means for tracking the usage status of each area in real time using sensors and transmitting the data to the server, a means for generating an optimal office layout based on the database information and real-time usage data, a means for visualizing the generated layout proposal using 3D modeling software, a means for supporting the user in evaluating and selecting layout options, a means for analyzing past and current usage data and predicting future usage trends, and a means for collecting and analyzing user emotion data and improving the office layout based on the data. This makes it possible to propose an optimal office layout that takes into account not only real-time usage status but also user emotion data. Long-term office operation optimization can also be achieved by predicting future resource demands and usage trends.

[0988] "Physical information about the office" refers to information about the physical structure and facilities of the office, such as the size of the office, facilities, available space, desk layout, and number of conference rooms.

[0989] A "database" is a collection of information that stores collected office physical information, usage data, and user emotional data, and can be searched and updated as needed.

[0990] A "sensor" is a device that is installed in a specific area of ​​an office, such as a conference room or workspace, and detects usage status (such as whether the room is vacant or in use) and environmental information in real time.

[0991] The "server" is a central processing unit that receives data sent from sensors and generates an optimal office layout based on the information stored in the database.

[0992] An "AI algorithm" is a collection of calculation methods and logical processes that analyze database information and real-time usage data to generate optimal office layouts.

[0993] "3D modeling software" is software that visualizes the generated office layout proposal in three dimensions and displays it in a way that users can intuitively understand.

[0994] "User emotion data" is information about the user's psychological state, such as the user's stress level and satisfaction level.

[0995] "Real-time usage data" is information collected from sensors in each area about the current office usage status (for example, whether a conference room is in use or vacant).

[0996] A "means for assisting in the evaluation and selection of layout options" is a support system that allows users to compare multiple proposed layouts and select the option that best suits them.

[0997] "Past and current usage data" refers to information on past office usage and current area usage, and is data used to predict future usage trends.

[0998] "Usage trend forecasting" is the process of analyzing collected data to estimate future office usage and resource demands.

[0999] The present invention is a system for optimizing office efficiency and improving user comfort, which combines and implements several main functions and means, specific embodiments of which are described in detail below.

[1000] System configuration

[1001] 1. Server

[1002] The server collects physical information about the office (size, facilities, available space, etc.) and stores this information in a database. Specifically, it manages information such as "200 square meters of office space with 50 desks, three conference rooms, and one projector" through sensors and manual input. At the same time, it uses an emotion engine to collect user emotional data (such as stress levels and satisfaction) and stores this information in the database.

[1003] 2. Terminal

[1004] The device collects real-time usage data from sensors placed in each area and conference room of the office and sends it to the server. For example, if the sensor in conference room A detects that the room is in use, it immediately sends that information to the server. The device also collects user emotion data in real time and sends it to the server.

[1005] 3. Users

[1006] Users can evaluate the layout options provided by the device and select the best option. Users can compare different layouts and choose the arrangement that best suits them. Emotional data is also taken into consideration when making a selection, allowing them to choose an environment that allows them to work comfortably.

[1007] Data Processing and AI Algorithms

[1008] The server uses an AI algorithm (e.g., a generative AI model) to generate the optimal office layout based on database information and real-time usage data. Specifically, it proposes an arrangement that facilitates collaboration between the marketing and development departments. It also takes into account emotional data, prioritizing layouts that result in low employee stress levels.

[1009] Visualization and Decision Support

[1010] The terminal visualizes the optimal layout proposal received from the server using 3D modeling software. For example, the proposed layout can be displayed in 3D view, allowing the user to intuitively understand it. New desk arrangements, meeting room locations, etc. are visually displayed.

[1011] Big data analysis and predictive model building

[1012] The server analyzes past and current usage data to build a model that predicts future office usage trends and resource demands. For example, it analyzes trends such as frequently used conference rooms and popular desk locations and makes predictions such as "Conference Room A will be frequently used in the morning." This allows for future resource management and layout optimization.

[1013] Specific examples

[1014] The server collects physical information about the office and stores it in a database, such as "200 square meters of office space with 50 desks, three conference rooms, and one projector." At the same time, it collects emotional data such as users' stress levels and satisfaction levels through an emotion engine and stores this data in the database.

[1015] The device receives real-time data about the meeting from a sensor installed in conference room A and sends it to the server. If real-time emotional data (e.g., high stress level) is detected from the user's device, the device also sends that data to the server.

[1016] Based on real-time usage data and database information, the server uses an AI algorithm to propose layouts that facilitate collaboration between the marketing and development departments. It also takes into account emotional data to provide layouts that reduce stress levels.

[1017] The device visualizes the proposed layout using 3D modeling software and displays a visual 3D view, allowing users to evaluate the layout and select the arrangement that best suits their stress level.

[1018] The user selects the optimal layout and sends the information to the server, which stores the selected layout in a database and creates an execution plan.

[1019] The server analyzes usage and emotion data from the past six months to predict future trends in conference room and desk usage, and also makes suggestions for future layout changes based on the analysis results.

[1020] This system makes it possible to actively utilize users' emotional data, leading to efficient office operations and improved productivity.

[1021] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1022] Step 1:

[1023] The server collects physical information about the office and stores it in a database. Specifically, using sensors and a manual input system, information such as "a 200-square-meter office with 50 desks, three conference rooms, and one projector" is entered and saved in the database. The input data is information about the office's size and facilities, and the output is the physical information stored in the database.

[1024] Step 2:

[1025] The terminal collects real-time usage data from sensors placed in each area and conference room and sends it to the server. For example, if the sensor in conference room A detects that the room is in use, it sends that information to the server as input data. The input is real-time data from the sensor, and the output is updated usage information on the server.

[1026] Step 3:

[1027] The server uses an AI algorithm to generate the optimal office layout based on the collected real-time usage data and physical information stored in the database. Specifically, it proposes an arrangement where the marketing department and development department are located next to each other. The input is the physical information in the database and real-time usage data, and the output is a proposal for the optimal office layout.

[1028] Step 4:

[1029] The terminal visualizes the optimal layout proposal received from the server using 3D modeling software. The generated 3D view displays the new desk arrangement and meeting room locations. The input is the layout data from the server, and the output is the 3D modeling view displayed to the user.

[1030] Step 5:

[1031] The user evaluates the proposed layout options via the terminal and selects the optimal one. The user checks the displayed 3D view and makes adjustments such as "moving the marketing department's desk closer to the window." The input is the layout proposal made by the 3D modeling software, and the output is the final selected office layout information.

[1032] Step 6:

[1033] The server saves the final layout determined by the user in a database. It receives the user's selections and stores specific layout change information, such as "place the marketing department desks by the window." The input is the final layout selection information from the user, and the output is the layout information saved in the database.

[1034] Step 7:

[1035] The server analyzes past and present usage data and emotion data to build a model that predicts future usage trends. For example, it predicts that "Conference Room A will be frequently used in the morning." The input is past and present usage data and emotion data, and the output is the prediction model and its results.

[1036] (Application example 2)

[1037] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1038] Conventional office environment optimization systems could generate layouts based on physical layout and usage data, but they were unable to consider user emotional data. This made it difficult to maximize users' psychological comfort and productivity. In particular, the lack of technology to analyze users' emotions in real time on-site and adjust the environment based on that information made it impossible to reduce user stress and provide a more comfortable working environment.

[1039] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting physical information about the office and storing it in a database, means for tracking the usage status of each area in real time using sensors and transmitting the data to the server, means for the server to generate an optimal office layout based on the information in the database and the real-time usage status data, means for visualizing the generated layout proposal using 3D modeling software, means for supporting the user in evaluating and selecting layout options, means for analyzing past and current usage data and predicting future usage trends, means for analyzing user emotions in real time, and means for dynamically adjusting the office environment (physical information, working environment) based on the user's emotional data. This makes it possible to optimize the office environment taking user emotional data into consideration.

[1040] A "server" is a central processing unit that collects, stores, analyzes, and communicates data to other devices.

[1041] "Office physical information" refers to data on physical characteristics such as equipment, furniture, and space allocation within the office.

[1042] A "database" is a system for systematically storing and managing collected information and data.

[1043] A "sensor" is a device that detects and measures data about the physical environment or situation in real time.

[1044] "Real-time usage data" is data collected by sensors that indicates the current usage status of each area.

[1045] An "optimal office layout" is the best way to arrange equipment, furniture, and space to provide an efficient working environment.

[1046] "3D modeling software" means software that creates three-dimensional shapes to visually represent a proposed layout.

[1047] "User evaluation and selection of layout options" refers to the process in which a user evaluates and selects the most suitable layout from multiple layouts presented to them.

[1048] "Past and current usage data" refers to data relating to the usage history and current usage status of the office.

[1049] "Predicting future usage trends" means analyzing collected data to estimate future office usage patterns.

[1050] "Analyzing user emotions in real time" means using sensors and analytical software to instantly determine the user's emotional state.

[1051] "Dynamic adjustment of the office environment based on user emotional data" means making timely changes to office elements such as lighting, layout, and acoustics based on the results of user emotional analysis.

[1052] This invention relates to a system that optimizes the environment in a physical store in real time. Specifically, the system collects emotional data from users (customers in this case) and dynamically adjusts the environment (lighting, background music, layout, etc.) based on that data to improve customer comfort and store operational efficiency.

[1053] System configuration

[1054] 1. Server

[1055] Data collection and storage: The server receives customer facial image data captured from devices such as smart glasses and analyzes it to generate emotion data, which is then stored in a database.

[1056] Analysis and proposal: Based on the emotion data received in real time, the server uses an AI algorithm to calculate optimal store environment adjustments (lighting, background music, layout, etc.).

[1057] Sending commands: The calculated environmental adjustment commands are sent to the appropriate devices and executed.

[1058] 2. Device

[1059] Data capture: The device, such as smart glasses, captures an image of the customer's face and sends this data to the server, which uses a specific SDK for emotion recognition (e.g., EmotionRecognition SDK).

[1060] Adjusting the environment: Receives commands from the server and adjusts lighting, sound systems, moving parts of the layout, etc.

[1061] 3. Users (Staff)

[1062] Device operation: Wearing smart glasses, the robot patrols the store and captures the customer's state.

[1063] Evaluate and adjust: Review environment adjustments based on customer sentiment data and make manual tweaks as needed.

[1064] Example

[1065] 1. In-store demonstration:

[1066] When a customer enters a store, staff wearing smart glasses capture the customer's facial expression.

[1067] The server analyzes the acquired facial expression data in real time and determines the customer's emotional state, such as "relaxed" or "satisfied."

[1068] Based on commands from the server, the store's background music is changed to music that helps customers relax, and the lighting is also changed to warm colors that help customers relax.

[1069] Example prompt: "Analyze whether this customer is relaxed or stressed and provide appropriate background music and lighting settings."

[1070] 2. How to operate the system:

[1071] Staff can check customer sentiment data in real time through the UI of the smart glasses.

[1072] If necessary, manually adjust your preferences and they will be saved in the database so that they are reflected in future suggestions.

[1073] This invention makes it possible to adjust the environment of a physical store in real time according to the emotional state of customers, which not only improves customer satisfaction but also contributes to the efficiency of store operations.

[1074] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1075] Step 1:

[1076] The terminal captures the customer's facial image data using the smart glasses. The input is the customer's facial image data captured by the smart glasses' camera, and the output is the raw image data sent to the EmotionRecognition SDK.

[1077] Step 2:

[1078] The device uses the EmotionRecognition SDK to analyze the customer's facial image data in real time and generate emotion data. The input is the raw image data obtained in step 1, and the output is data indicating the customer's emotional state (relaxed, stressed, etc.).

[1079] Step 3:

[1080] The terminal transmits the generated emotion data to the server. The input is the emotion data generated by the terminal, and the output is the emotion data transmitted to the server.

[1081] Step 4:

[1082] The server analyzes the received emotion data and generates an optimal command for adjusting the environment. The input is the emotion data obtained in step 3, and the output is the command data for adjusting the environment.

[1083] Step 5:

[1084] The server sends an instruction for adjusting the environment to the terminal. The input is the instruction data generated in step 4, and the output is the instruction data sent to the terminal.

[1085] Step 6:

[1086] The terminal dynamically adjusts environmental elements such as lighting, background music, and layout in the store based on command data received from the server. The input is the command data received from the server, and the output is the actual result of the environmental adjustment.

[1087] Step 7:

[1088] The user (staff member) checks the status of the environmental adjustment through the UI of the smart glasses and manually fine-tunes it if necessary. The input is the environmental adjustment result displayed on the UI of the smart glasses, and the output is the result of the manual adjustment by the staff member.

[1089] Step 8:

[1090] The server saves the results of the manual adjustment in a database and updates the data to reflect the results in subsequent environmental adjustments. The input is the environmental data updated by the staff, and the output is the updated data saved in the database.

[1091] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1093] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1094] [Fourth embodiment]

[1095] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1096] 7, a 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.

[1097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1098] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1099] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1102] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1103] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1104] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1106] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1107] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1108] The present invention is a system for improving the efficiency and productivity of an office environment, and by combining multiple means, it realizes the optimization of the office layout and the optimal use of resources. The following describes in detail the embodiments of this system.

[1109] System configuration

[1110] 1. Server

[1111] It has the ability to collect physical information about the office (area, equipment, available space, etc.) and store it in a database. It also receives real-time usage data obtained by sensors and runs an AI algorithm that generates the optimal office layout based on that data.

[1112] 2. Terminal

[1113] Data acquired from sensors placed in each area and conference room is sent to a server in real time, and the optimal layout proposals sent from the server can be visualized using 3D modeling software.

[1114] 3. Users

[1115] Each department and employee in the office evaluates and selects a layout based on their role and communication needs. Users can operate the device to view the proposed layout and make adjustments as needed.

[1116] What the program does

[1117] 1. Database creation

[1118] The server collects information about the size, facilities, and available space of the office and stores this information in a database. For example, a 200-square-meter office space might have one projector, three conference rooms, and 50 desks.

[1119] 2. Real-time tracking

[1120] The device collects current usage data from sensors installed in conference rooms and workspaces and sends it to the server. For example, it recognizes that conference room A is currently in use and sends this data to the server.

[1121] 3. Proposal of optimal layout

[1122] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data, such as suggesting placement of the marketing and development departments next to each other.

[1123] 4. Simulation and Visualization

[1124] The device receives the layout proposal sent from the server and visualizes it using 3D modeling software, allowing the user to view the visual data and intuitively understand how the layout will be specifically arranged.

[1125] 5. Decision support

[1126] The user evaluates the multiple layout options provided on the device and selects the most suitable layout, which is then sent back to the server and stored in the database.

[1127] 6. Big data analysis and predictive model building

[1128] The server performs big data analysis based on past and current usage data to predict office usage trends and future resource needs, allowing it to formulate future layout and resource management plans.

[1129] Specific examples

[1130] The server collects physical information about the office and stores, for example, "200 square meters of office space with 50 desks, three conference rooms, and one projector" in a database.

[1131] The terminal receives "meeting in progress" data from a sensor installed in conference room A and sends it to the server.

[1132] Based on real-time usage data and database information, the server uses an AI algorithm to suggest layouts that facilitate collaboration between the marketing and development departments.

[1133] The device displays a 3D model of the proposed layout to the user, who can then use this visual information to evaluate the layout and make any necessary adjustments.

[1134] The user selects the optimal layout and sends the information to the server, which stores the selected layout in a database and creates an execution plan.

[1135] The server analyzes usage data from the past six months and builds a model that predicts future usage trends for conference rooms and desks.

[1136] In this way, the present invention realizes efficient office management and improved productivity.

[1137] The processing flow will be explained below.

[1138] Step 1:

[1139] The server collects physical information about the office (area, equipment, available space, etc.) and stores it in a database. For example, information about 50 desks, 3 conference rooms, and 1 projector in a 200-square-meter office is stored in the database. The data is updated in a timely manner to ensure that it is always up-to-date.

[1140] Step 2:

[1141] The device collects real-time usage information (vacant or in use) from sensors installed in conference rooms and workspaces and sends that data to the server. For example, if the sensor in conference room A detects that the room is in use, the data is immediately sent to the server.

[1142] Step 3:

[1143] The server receives real-time usage data from the devices, records it in a database, and updates the dashboard based on that information, allowing administrators to check current office usage at a glance.

[1144] Step 4:

[1145] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data. Specifically, it considers the roles and communication needs of each department and suggests, for example, placing the marketing and development departments next to each other.

[1146] Step 5:

[1147] The server then sends the generated optimal layout proposal to the terminal, which includes a specific layout diagram and detailed information on the positioning of each department.

[1148] Step 6:

[1149] The device uses 3D modeling software to visualize the layout proposal received from the server, displaying the new office layout in a 3D view that allows users to intuitively understand it.

[1150] Step 7:

[1151] Users can evaluate the layout options provided by their device and select the best option. Users can compare different layouts and choose the arrangement that best suits their specific needs.

[1152] Step 8:

[1153] The user sends the selected layout from their device to the server, which receives this information and stores it in a database.

[1154] Step 9:

[1155] The server will then formulate an execution plan based on the saved new layout and notify the relevant departments, so that the new layout can be implemented quickly.

[1156] Step 10:

[1157] The server performs big data analysis of past and current usage data to build models that predict office usage trends and future resource demands, which can be used to plan future zoning.

[1158] Example 1

[1159] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1160] In modern office environments, efficiently using limited space and maximizing employee productivity are key challenges. However, basic layout changes and resource allocation are often manual and based on experience, with little optimization based on objective data. Furthermore, it is difficult to understand real-time usage and provide appropriate feedback, making it difficult to maintain an optimal office layout.

[1161] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1162] In this invention, the server includes means for collecting physical information about the office and storing it in a database, means for tracking the usage status of each area in real time using sensors and transmitting the data to an information processing device, means for generating an optimal office layout using the information processing device based on the database information and real-time usage data, means for visualizing the generated layout proposal using three-dimensional modeling software, means for supporting users in evaluating and selecting layout options, means for analyzing past and current usage data and predicting future usage trends, means for transmitting real-time data collected by sensors in each area to the information processing device and updating the dashboard, means for optimizing the office layout taking into account employee roles and departmental relationships using an artificial intelligence algorithm, and means for building a predictive model based on the collected data. This enables optimal layout proposals to be made based on the physical information and real-time usage status of the office environment, thereby achieving efficient space utilization and improved productivity.

[1163] "Physical information about the office" is information that indicates specific physical elements such as the area of ​​the office, the number of desks, the number of conference rooms, and the type of equipment.

[1164] A "database" is a system that organizes and stores collected office physical information and real-time usage data, and manages it so that it can be retrieved when needed.

[1165] A "detector" is a sensor that is installed in each area or conference room, detects usage in real time, and transmits the data to an information processing device.

[1166] An "information processing device" is a device, such as a server or computer, that processes collected data and performs analysis and calculations.

[1167] An "optimal office layout" is a proposal for an arrangement that makes efficient use of space within the office and maximizes the improvement of employees' working environment.

[1168] "3D model software" is software for performing 3D modeling and visually displaying office layouts.

[1169] "Users" are people who use the system to evaluate and select layouts, such as office employees and managers.

[1170] A "dashboard" is an interface for visually displaying real-time usage data and analysis results.

[1171] The "artificial intelligence algorithm" is an algorithm that calculates and proposes the optimal layout based on collected data.

[1172] A "predictive model" is a model that predicts future usage trends and required resources based on past and current data.

[1173] The present invention is a system for improving the efficiency and productivity of an office environment, and specific embodiments thereof are described in detail below. This system realizes the optimization of office layout and optimal utilization of resources through cooperation between servers, terminals, and users.

[1174] Hardware and software configuration

[1175] server

[1176] The server plays a central role in managing physical information and real-time usage data collected from each area and conference room in the office. Software such as databases, AI algorithms, and dashboards are installed on the server. The roles of each are as follows:

[1177] Terminal

[1178] The terminals are connected to sensors installed in each area and conference room, and transmit real-time data to a server. They also have 3D modeling software (such as Autodesk or SketchUp) installed, which visualizes the optimal layout proposals sent from the server.

[1179] User

[1180] Users, who are employees or managers in the office, evaluate the layouts proposed by the server via their terminals and select the optimal layout. User feedback is sent to the server and stored in a database.

[1181] Data processing and calculation flow

[1182] Collecting and storing office physical information

[1183] The server collects physical information about the office from administrators and sensors and stores it in a database. A specific example is information management for a 200-square-meter office space with 50 desks, three conference rooms, and one projector.

[1184] Real-time usage data collection and transmission

[1185] The terminal collects usage data in real time from sensors installed in each area and conference room and sends it to the server. For example, if the sensor detects that a meeting is currently taking place in conference room A, it sends this information to the server.

[1186] Generating optimal layouts

[1187] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data. For example, it may recommend placing the marketing and development departments next to each other.

[1188] Layout Visualization

[1189] The device receives the layout proposal sent from the server and visualizes it using 3D modeling software, allowing the user to view the visual data and intuitively understand how the layout will be specifically arranged.

[1190] User ratings and selection

[1191] The user evaluates the multiple layout options provided on the device and selects the most suitable layout, which is then sent back to the server and stored in the database.

[1192] Big data analysis and predictive model building

[1193] The server performs big data analysis based on past and current usage data to predict office usage trends and future resource needs, allowing for the development of future layout and resource management plans.

[1194] Examples of concrete examples and prompts

[1195] Collecting office physical information

[1196] The server collects physical information about a 200-square-meter office space, including 50 desks, three conference rooms, and one projector, and stores it in a database.

[1197] Real-time usage data collection and transmission

[1198] The terminal sends data from a sensor installed in conference room A in real time to the server indicating that a meeting is currently in progress.

[1199] Generating optimal layouts

[1200] The server uses AI algorithms to generate optimal layouts based on real-time usage data and database information, suggesting, for example, that the marketing and development departments be placed next to each other.

[1201] Layout Visualization

[1202] The device displays a 3D model of the proposed layout to the user, who can then use this visual information to evaluate the layout and make any necessary adjustments.

[1203] User ratings and selection

[1204] The user selects the best layout and sends the information to the server, where the selected layout is stored in the database.

[1205] Big Data Analytics

[1206] The server analyzes usage data from the past six months and builds a model that predicts future usage trends for conference rooms and desks.

[1207] Prompt Sentence Examples

[1208] "Please suggest the optimal layout for 50 desks, 4 conference rooms, and 2 projectors in a 300 square meter office space. Conference Room 1 is currently in use."

[1209] In this way, the present invention aims to improve the efficiency and productivity of an office.

[1210] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1211] Step 1: Collecting and storing office physical information

[1212] The server collects physical information about the office provided by the administrator. This information includes the office area, number of desks, number of conference rooms, and type of equipment. For example, the administrator enters information such as "50 desks, 3 conference rooms, and 1 projector in a 200-square-meter office." The server stores this input data in a database.

[1213] Input: Office area, number of desks, number of conference rooms, facility information

[1214] Output: Office physical information stored in a database

[1215] Step 2: Collect and send real-time usage data

[1216] The terminal collects real-time usage data from detectors installed in each area and conference room. The terminal sends the data detected by the sensor, such as "Conference Room A is currently in use," to the server. This allows the server to constantly update the real-time usage status within the office.

[1217] Input: Usage data detected by the sensor (e.g., usage status of conference room A)

[1218] Output: Real-time usage data sent to the server

[1219] Step 3: Generate the optimal layout

[1220] The server uses AI algorithms to generate optimal office layouts based on the physical information stored in the database and real-time usage data. For example, the server might generate a proposal to place the marketing and development departments next to each other, taking into account the communication patterns and work content of each department.

[1221] Input: Database physical information, real-time usage data

[1222] Output: Optimal layout proposals generated by AI algorithms

[1223] Step 4: Visualize the layout

[1224] The device receives the layout proposal sent from the server and visualizes it using 3D modeling software. For example, the proposed layout can be displayed in three dimensions, allowing users to visually confirm the placement of each desk and conference room. Users can view this visual data and understand how the layout will be specifically planned.

[1225] Input: Optimal layout proposal sent from the server

[1226] Output: Visual data displayed by 3D modeling software

[1227] Step 5: User evaluation and selection

[1228] The user compares and evaluates multiple layout options provided on the device. If the user determines that "Layout A is easy to use," the user sends that selection to the server via the device. The server then stores the selected layout information in a database.

[1229] Input: Multiple layout options

[1230] Output: The selected layout sent to the server

[1231] Step 6: Big data analysis and predictive model building

[1232] The server analyzes past and current usage data to build a model to predict future usage trends and resource needs. For example, the server analyzes data from the past six months and predicts that "conference room usage will increase over the next three months." Based on the results of this analysis, the server creates plans for future resource management and layout adjustments.

[1233] Input: Past and current usage data

[1234] Output: Predictive model and analysis results

[1235] Through the above steps, the system of the present invention realizes efficient office management and improved productivity.

[1236] (Application example 1)

[1237] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1238] In modern factories, systems that can grasp work status in real time and provide optimal layouts are required to achieve efficient work arrangements. However, conventional methods have made it difficult to accurately grasp the utilization status of work areas and dynamically and effectively change the layout. There has also been a lack of technology to predict future utilization trends and efficiently manage resources. This has resulted in issues such as reduced work efficiency and restricted productivity.

[1239] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1240] In this invention, the server includes means for collecting physical information about the office and storing it in a database, means for tracking the utilization status of each area in the factory in real time using sensors and sending the data to the server, means for the server to generate an optimal functional space layout based on the information in the database and the real-time utilization data, means for visualizing the generated layout proposal using 3D modeling software, means for supporting the user in evaluating and selecting layout options, and means for analyzing past and current utilization data and predicting future resource utilization trends. This enables dynamic and efficient optimization of work placement in the factory, improving productivity and making effective use of resources.

[1241] "Office physical information" refers to data relating to the physical structure and layout, such as the area of ​​the workspace, the location of installed machines, and the width of the aisles.

[1242] A "database" is a system that accumulates and manages information and stores it in a form that can be retrieved and used later.

[1243] "Area usage status" is data that indicates the operating status and usage status of robots and machines within the work area.

[1244] A "sensor" is a device that detects changes in the environment or the state of an object and outputs that information as digital data.

[1245] A "server" is a central device that processes and stores data, and is a system that provides necessary information in response to requests from clients.

[1246] "Real-time data" refers to data that is acquired and processed immediately based on the current ongoing status.

[1247] An "optimal layout" is the configuration and arrangement of workspace and equipment to maximize efficiency and productivity.

[1248] "3D modeling software" is software for constructing and displaying objects and scenes in three-dimensional space.

[1249] "Layout options" refer to multiple layout plans, and are options that the user can select and evaluate to determine the optimal layout.

[1250] "Decision support" is the process of providing information and assistance to users in choosing the best option from multiple options.

[1251] "Resource utilization trends" are data that analyzes the usage patterns of equipment and personnel and predicts future demand and supply.

[1252] A "functional space" is a physical space dedicated to carrying out a specific task or activity.

[1253] "Roles and workflow" refers to the specific tasks that robots and workers are responsible for, and the process by which those tasks are carried out in coordination.

[1254] An "AI algorithm" is a program or method for automatically solving a specific problem through machine learning and data analysis.

[1255] This invention is a system that proposes and manages optimal functional space layouts based on real-time data in order to improve work efficiency within factories. This system includes the following components:

[1256] server

[1257] The server has the function of collecting physical information within the factory and storing it in a database. Specifically, it manages information such as the area of ​​the work space, the location of installed machines, and the width of aisles. It also receives real-time usage data obtained by sensors and runs an AI algorithm that generates the optimal layout based on that data. This process uses MySQL for database management and TensorFlow for the AI ​​algorithm.

[1258] Terminal

[1259] The device collects data in real time from sensors placed in each area and sends it to a server. It also has the function of visualizing the optimal layout sent from the server using 3D modeling software (using Unity). Users can intuitively understand this information using smart glasses or a head-mounted display.

[1260] User

[1261] Users, representing each department or worker in the factory, operate terminals to review the proposed layout and make adjustments as necessary. The evaluated layout is then sent back to the server and stored in a database. A model is also built to predict future resource usage trends based on past and current usage data.

[1262] Process example

[1263] Data collection

[1264] The server collects physical information about Area A and stores data such as "work space area of ​​200 square meters, location of installed machines, and aisle width" in a database. The terminal obtains real-time data about the transport robot, such as "operating" or "standby," via sensors and sends it to the server.

[1265] Layout generation and visualization

[1266] The server uses TensorFlow to propose optimal layouts based on the collected data. For example, it might suggest changing the placement of robots to shorten the transport distance. The terminal then creates a 3D model of the proposed layout, allowing the user to view it using smart glasses or a head-mounted display.

[1267] Decision Support and Forecasting

[1268] The user evaluates the proposed layout options and selects the optimal arrangement. This selection information is then sent back to the server and stored in a database. The server then analyzes past usage data and builds a model to predict future resource usage trends.

[1269] Prompt Sentence Examples

[1270] "Train an AI model for factory layout optimization. Use the following input dataset to build a model that predicts the optimal layout based on workspace, robot placement, and aisle width data."

[1271] As described above, the present invention is a system that dynamically and efficiently optimizes work allocation within a factory, thereby improving productivity and making effective use of resources.

[1272] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1273] Step 1:

[1274] The server collects physical information within the factory and stores it in a database. Input information includes the area of ​​the work space within the factory, the location of installed machines, and the width of aisles. The server converts this data into digital format and stores it in a MySQL database. The output is the physical information stored in the database.

[1275] Step 2:

[1276] The terminal collects data in real time from sensors placed in each area and sends it to the server. In this step, the sensors obtain the current work status, for example, the status of a transport robot, such as "in operation" or "on standby." The terminal receives real-time data from the sensors as input and sends it to the server. The output is the real-time status data sent to the server.

[1277] Step 3:

[1278] The server receives the real-time data and generates an optimal layout based on the database information and the real-time data. In this step, an AI algorithm calculates the optimal layout using TensorFlow. The input data includes the physical information in the database and real-time state data, and the AI ​​model uses this data to predict the layout. The output is an optimal layout proposal.

[1279] Step 4:

[1280] The terminal visualizes the optimal layout proposal sent from the server using 3D modeling software (Unity). The input includes the optimal layout proposal from the server, and a 3D model is generated using Unity based on this. The output is a 3D model layout that the user can visually confirm.

[1281] Step 5:

[1282] The user uses smart glasses or a head-mounted display to view and evaluate the proposed layout. The input includes a layout proposal visualized as a 3D model, which the user uses to evaluate and adjust the layout. The output is the evaluated or adjusted layout information.

[1283] Step 6:

[1284] The layout evaluated by the user is sent back to the server and stored in the database. The input is the layout information adjusted by the user, which is sent to the server. The server stores the received data in the database. The output is the updated database information.

[1285] Step 7:

[1286] The server analyzes past and current usage data and builds a model to predict future resource usage trends. The input data includes accumulated usage data, and the AI ​​model is used to predict future supply and demand. The output is a predictive model that shows future resource usage trends.

[1287] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1288] The present invention combines a system that aims to improve the efficiency and productivity of the office environment with an emotion engine that recognizes user emotions, and by integrating physical information about the office, real-time usage status, optimal layout generation using AI, 3D modeling, and emotion data collection and analysis, it provides a more flexible and comfortable office environment for users. The following describes in detail the modes for implementing this system.

[1289] System configuration

[1290] 1. Server

[1291] It has the ability to collect physical information about the office and store it in a database, and also receives real-time usage data obtained by sensors and runs an AI algorithm to generate the optimal office layout based on that data.

[1292] Equipped with an emotion engine, it has the ability to collect and analyze user emotion data, assessing the user's stress level and satisfaction level, and improving layout suggestions based on this.

[1293] 2. Terminal

[1294] Sensors placed in each area and conference room collect usage data in real time and send it to a server. The system also has the function of visualizing optimal layout proposals sent from the server using 3D modeling software.

[1295] It works in conjunction with the emotion engine and has the function of collecting user emotion data in real time and sending it to the server.

[1296] 3. Users

[1297] Each department and employee in the office evaluates and selects a layout based on their role and communication needs. Users can operate the device to view the proposed layout and make adjustments as needed.

[1298] Emotional data collected by the emotion engine is fed back to users, allowing them to check their own stress levels and satisfaction levels.

[1299] What the program does

[1300] 1. Database creation

[1301] The server collects information about the office's size, facilities, and available space, and stores this information in a database. For example, a 200-square-meter office might have 50 desks, three conference rooms, and one projector. The emotion engine also stores user emotion data in a database.

[1302] 2. Real-time tracking

[1303] The device collects real-time usage information (vacant or in use) from sensors installed in conference rooms and workspaces and sends that data to the server. For example, if the sensor in conference room A detects that the room is in use, the data is immediately sent to the server.

[1304] 3. Proposal of optimal layout

[1305] The server uses AI algorithms to generate optimal office layouts based on database information and real-time usage data. For example, it suggests arranging the marketing and development departments next to each other. It also takes into account data from the emotion engine, prioritizing layouts that result in lower employee stress levels.

[1306] 4. Simulation and Visualization

[1307] The device uses 3D modeling software to visualize the layout proposal received from the server, displaying the new office layout in a 3D view that allows users to intuitively understand it.

[1308] 5. Decision support

[1309] Users evaluate the layout options provided by their device and select the best option. Users can compare different layouts and choose the arrangement that best suits their specific needs. The selection process also takes into account emotional data, allowing users to choose the environment in which they feel most comfortable working.

[1310] 6. Big data analysis and predictive model building

[1311] The server performs big data analysis of past and current usage data to build models that predict office usage trends and future resource demands. The analysis also includes user emotion data collected through an emotion engine to optimize future layout and resource management.

[1312] Specific examples

[1313] The server collects physical information about the office, such as "200 square meters of office space with 50 desks, three conference rooms, and one projector," and stores it in a database. At the same time, it collects user emotional data through an emotion engine and stores it in the database.

[1314] The device receives "in-meeting" data from a sensor installed in conference room A and sends it to the server. In addition, if the emotion engine detects that the user's stress level is high, that data is also sent.

[1315] Based on real-time usage data and database information, the server uses an AI algorithm to suggest layouts that facilitate collaboration between the marketing and development departments. It also takes into account emotional data to suggest layouts that reduce stress levels.

[1316] The device displays a 3D model of the proposed layout to the user, who can then evaluate the layout based on this visual information and select the layout that best suits their stress level.

[1317] The user selects the optimal layout and sends the information to the server, which stores the selected layout in a database and creates an execution plan.

[1318] The server analyzes usage and sentiment data from the past six months to build a model that predicts future trends in conference room and desk usage.

[1319] In this way, the present invention actively utilizes user emotion data to realize efficient office management and improved productivity.

[1320] The processing flow will be explained below.

[1321] Step 1:

[1322] The server collects information about the office's size, facilities, and available space, and stores this information in a database. For example, a 200-square-meter office might have 50 desks, three conference rooms, and one projector. In addition, an emotion engine is used to store users' emotional data (such as stress levels and satisfaction) in the database.

[1323] Step 2:

[1324] The device collects usage status (vacant or occupied) in real time from sensors placed in conference rooms and workspaces and sends the data to the server. For example, if the sensor in conference room A detects that the room is "occupied," the device immediately sends the data to the server. In addition, the device uses an emotion sensor to collect user emotion data (e.g., stress and satisfaction) and sends the data to the server.

[1325] Step 3:

[1326] The server receives real-time usage and emotion data from the devices, records it in a database, and updates the dashboard based on that information, allowing administrators to check the current office usage status and users' emotional state at a glance.

[1327] Step 4:

[1328] The server uses AI algorithms to generate optimal office layouts based on the database's physical information, real-time usage data, and emotional data. For example, when proposing an arrangement where the marketing and development departments are located next to each other, it takes emotional data into account and prioritizes an arrangement that reduces employee stress levels.

[1329] Step 5:

[1330] The server then sends the generated optimal layout proposal to the terminal, which includes a specific layout diagram and detailed information on the positioning of each department.

[1331] Step 6:

[1332] The device uses 3D modeling software to visualize the layout proposal received from the server, displaying the new office layout in a 3D view that allows users to intuitively understand it.

[1333] Step 7:

[1334] Users can evaluate the layout options provided on their device and select the best option. Users can compare different layouts and choose the arrangement that best suits their specific needs and their own emotional data. Emotional data is also taken into consideration when making a selection, allowing users to choose the environment in which they feel most comfortable working.

[1335] Step 8:

[1336] The user sends the selected layout from their device to the server, which receives this information and stores it in a database.

[1337] Step 9:

[1338] The server will then formulate an execution plan based on the saved new layout and notify the relevant departments, so that the new layout can be implemented quickly.

[1339] Step 10:

[1340] The server performs big data analysis of past and current usage data and sentiment data to build models that predict office usage trends and future resource demands. The analyzed data and predictive models are used to help with future zoning planning.

[1341] Example 2

[1342] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1343] While conventional office layout management systems were capable of optimizing layouts based on physical information and usage data, they lacked the ability to propose layouts that took into account users' emotions and stress levels. This could result in layouts that were efficient but uncomfortable for employees, negatively impacting productivity and employee satisfaction. Furthermore, while real-time usage data was collected and analyzed, it was not possible to use it to build predictive models that considered emotional data or for future resource management, limiting the ability to optimize long-term office operations.

[1344] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting physical information about the office and storing it in a database, a means for tracking the usage status of each area in real time using sensors and transmitting the data to the server, a means for generating an optimal office layout based on the database information and real-time usage data, a means for visualizing the generated layout proposal using 3D modeling software, a means for supporting the user in evaluating and selecting layout options, a means for analyzing past and current usage data and predicting future usage trends, and a means for collecting and analyzing user emotion data and improving the office layout based on the data. This makes it possible to propose an optimal office layout that takes into account not only real-time usage status but also user emotion data. Long-term office operation optimization can also be achieved by predicting future resource demands and usage trends.

[1345] "Physical information about the office" refers to information about the physical structure and facilities of the office, such as the size of the office, facilities, available space, desk layout, and number of conference rooms.

[1346] A "database" is a collection of information that stores collected office physical information, usage data, and user emotional data, and can be searched and updated as needed.

[1347] A "sensor" is a device that is installed in a specific area of ​​an office, such as a conference room or workspace, and detects usage status (such as whether the room is vacant or in use) and environmental information in real time.

[1348] The "server" is a central processing unit that receives data sent from sensors and generates an optimal office layout based on the information stored in the database.

[1349] An "AI algorithm" is a collection of calculation methods and logical processes that analyze database information and real-time usage data to generate optimal office layouts.

[1350] "3D modeling software" is software that visualizes the generated office layout proposal in three dimensions and displays it in a way that users can intuitively understand.

[1351] "User emotion data" is information about the user's psychological state, such as the user's stress level and satisfaction level.

[1352] "Real-time usage data" is information collected from sensors in each area about the current office usage status (for example, whether a conference room is in use or vacant).

[1353] A "means for assisting in the evaluation and selection of layout options" is a support system that allows users to compare multiple proposed layouts and select the option that best suits them.

[1354] "Past and current usage data" refers to information on past office usage and current area usage, and is data used to predict future usage trends.

[1355] "Usage trend forecasting" is the process of analyzing collected data to estimate future office usage and resource demands.

[1356] The present invention is a system for optimizing office efficiency and improving user comfort, which combines and implements several main functions and means, specific embodiments of which are described in detail below.

[1357] System configuration

[1358] 1. Server

[1359] The server collects physical information about the office (size, facilities, available space, etc.) and stores this information in a database. Specifically, it manages information such as "200 square meters of office space with 50 desks, three conference rooms, and one projector" through sensors and manual input. At the same time, it uses an emotion engine to collect user emotional data (such as stress levels and satisfaction) and stores this information in the database.

[1360] 2. Terminal

[1361] The device collects real-time usage data from sensors placed in each area and conference room of the office and sends it to the server. For example, if the sensor in conference room A detects that the room is in use, it immediately sends that information to the server. The device also collects user emotion data in real time and sends it to the server.

[1362] 3. Users

[1363] Users can evaluate the layout options provided by the device and select the best option. Users can compare different layouts and choose the arrangement that best suits them. Emotional data is also taken into consideration when making a selection, allowing them to choose an environment that allows them to work comfortably.

[1364] Data Processing and AI Algorithms

[1365] The server uses an AI algorithm (e.g., a generative AI model) to generate the optimal office layout based on database information and real-time usage data. Specifically, it proposes an arrangement that facilitates collaboration between the marketing and development departments. It also takes into account emotional data, prioritizing layouts that result in low employee stress levels.

[1366] Visualization and Decision Support

[1367] The terminal visualizes the optimal layout proposal received from the server using 3D modeling software. For example, the proposed layout can be displayed in 3D view, allowing the user to intuitively understand it. New desk arrangements, meeting room locations, etc. are visually displayed.

[1368] Big data analysis and predictive model building

[1369] The server analyzes past and current usage data to build a model that predicts future office usage trends and resource demands. For example, it analyzes trends such as frequently used conference rooms and popular desk locations and makes predictions such as "Conference Room A will be frequently used in the morning." This allows for future resource management and layout optimization.

[1370] Specific examples

[1371] The server collects physical information about the office and stores it in a database, such as "200 square meters of office space with 50 desks, three conference rooms, and one projector." At the same time, it collects emotional data such as users' stress levels and satisfaction levels through an emotion engine and stores this data in the database.

[1372] The device receives real-time data about the meeting from a sensor installed in conference room A and sends it to the server. If real-time emotional data (e.g., high stress level) is detected from the user's device, the device also sends that data to the server.

[1373] Based on real-time usage data and database information, the server uses an AI algorithm to propose layouts that facilitate collaboration between the marketing and development departments. It also takes into account emotional data to provide layouts that reduce stress levels.

[1374] The device visualizes the proposed layout using 3D modeling software and displays a visual 3D view, allowing users to evaluate the layout and select the arrangement that best suits their stress level.

[1375] The user selects the optimal layout and sends the information to the server, which stores the selected layout in a database and creates an execution plan.

[1376] The server analyzes usage and emotion data from the past six months to predict future trends in conference room and desk usage, and also makes suggestions for future layout changes based on the analysis results.

[1377] This system makes it possible to actively utilize users' emotional data, leading to efficient office operations and improved productivity.

[1378] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1379] Step 1:

[1380] The server collects physical information about the office and stores it in a database. Specifically, using sensors and a manual input system, information such as "a 200-square-meter office with 50 desks, three conference rooms, and one projector" is entered and saved in the database. The input data is information about the office's size and facilities, and the output is the physical information stored in the database.

[1381] Step 2:

[1382] The terminal collects real-time usage data from sensors placed in each area and conference room and sends it to the server. For example, if the sensor in conference room A detects that the room is in use, it sends that information to the server as input data. The input is real-time data from the sensor, and the output is updated usage information on the server.

[1383] Step 3:

[1384] The server uses an AI algorithm to generate the optimal office layout based on the collected real-time usage data and physical information stored in the database. Specifically, it proposes an arrangement where the marketing department and development department are located next to each other. The input is the physical information in the database and real-time usage data, and the output is a proposal for the optimal office layout.

[1385] Step 4:

[1386] The terminal visualizes the optimal layout proposal received from the server using 3D modeling software. The generated 3D view displays the new desk arrangement and meeting room locations. The input is the layout data from the server, and the output is the 3D modeling view displayed to the user.

[1387] Step 5:

[1388] The user evaluates the proposed layout options via the terminal and selects the optimal one. The user checks the displayed 3D view and makes adjustments such as "moving the marketing department's desk closer to the window." The input is the layout proposal made by the 3D modeling software, and the output is the final selected office layout information.

[1389] Step 6:

[1390] The server saves the final layout determined by the user in a database. It receives the user's selections and stores specific layout change information, such as "place the marketing department desks by the window." The input is the final layout selection information from the user, and the output is the layout information saved in the database.

[1391] Step 7:

[1392] The server analyzes past and present usage data and emotion data to build a model that predicts future usage trends. For example, it predicts that "Conference Room A will be frequently used in the morning." The input is past and present usage data and emotion data, and the output is the prediction model and its results.

[1393] (Application example 2)

[1394] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1395] Conventional office environment optimization systems could generate layouts based on physical layout and usage data, but they were unable to consider user emotional data. This made it difficult to maximize users' psychological comfort and productivity. In particular, the lack of technology to analyze users' emotions in real time on-site and adjust the environment based on that information made it impossible to reduce user stress and provide a more comfortable working environment.

[1396] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting physical information about the office and storing it in a database, means for tracking the usage status of each area in real time using sensors and transmitting the data to the server, means for the server to generate an optimal office layout based on the information in the database and the real-time usage status data, means for visualizing the generated layout proposal using 3D modeling software, means for supporting the user in evaluating and selecting layout options, means for analyzing past and current usage data and predicting future usage trends, means for analyzing user emotions in real time, and means for dynamically adjusting the office environment (physical information, working environment) based on the user's emotional data. This makes it possible to optimize the office environment taking user emotional data into consideration.

[1397] A "server" is a central processing unit that collects, stores, analyzes, and communicates data to other devices.

[1398] "Office physical information" refers to data on physical characteristics such as equipment, furniture, and space allocation within the office.

[1399] A "database" is a system for systematically storing and managing collected information and data.

[1400] A "sensor" is a device that detects and measures data about the physical environment or situation in real time.

[1401] "Real-time usage data" is data collected by sensors that indicates the current usage status of each area.

[1402] An "optimal office layout" is the best way to arrange equipment, furniture, and space to provide an efficient working environment.

[1403] "3D modeling software" means software that creates three-dimensional shapes to visually represent a proposed layout.

[1404] "User evaluation and selection of layout options" refers to the process in which a user evaluates and selects the most suitable layout from multiple layouts presented to them.

[1405] "Past and current usage data" refers to data relating to the usage history and current usage status of the office.

[1406] "Predicting future usage trends" means analyzing collected data to estimate future office usage patterns.

[1407] "Analyzing user emotions in real time" means using sensors and analytical software to instantly determine the user's emotional state.

[1408] "Dynamic adjustment of the office environment based on user emotional data" means making timely changes to office elements such as lighting, layout, and acoustics based on the results of user emotional analysis.

[1409] This invention relates to a system that optimizes the environment in a physical store in real time. Specifically, the system collects emotional data from users (customers in this case) and dynamically adjusts the environment (lighting, background music, layout, etc.) based on that data to improve customer comfort and store operational efficiency.

[1410] System configuration

[1411] 1. Server

[1412] Data collection and storage: The server receives customer facial image data captured from devices such as smart glasses and analyzes it to generate emotion data, which is then stored in a database.

[1413] Analysis and proposal: Based on the emotion data received in real time, the server uses an AI algorithm to calculate optimal store environment adjustments (lighting, background music, layout, etc.).

[1414] Sending commands: The calculated environmental adjustment commands are sent to the appropriate devices and executed.

[1415] 2. Device

[1416] Data capture: The device, such as smart glasses, captures an image of the customer's face and sends this data to the server, which uses a specific SDK for emotion recognition (e.g., EmotionRecognition SDK).

[1417] Adjusting the environment: Receives commands from the server and adjusts lighting, sound systems, moving parts of the layout, etc.

[1418] 3. Users (Staff)

[1419] Device operation: Wearing smart glasses, the robot patrols the store and captures the customer's state.

[1420] Evaluate and adjust: Review environment adjustments based on customer sentiment data and make manual tweaks as needed.

[1421] Example

[1422] 1. In-store demonstration:

[1423] When a customer enters a store, staff wearing smart glasses capture the customer's facial expression.

[1424] The server analyzes the acquired facial expression data in real time and determines the customer's emotional state, such as "relaxed" or "satisfied."

[1425] Based on commands from the server, the store's background music is changed to music that helps customers relax, and the lighting is also changed to warm colors that help customers relax.

[1426] Example prompt: "Analyze whether this customer is relaxed or stressed and provide appropriate background music and lighting settings."

[1427] 2. How to operate the system:

[1428] Staff can check customer sentiment data in real time through the UI of the smart glasses.

[1429] If necessary, manually adjust your preferences and they will be saved in the database so that they are reflected in future suggestions.

[1430] This invention makes it possible to adjust the environment of a physical store in real time according to the emotional state of customers, which not only improves customer satisfaction but also contributes to the efficiency of store operations.

[1431] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1432] Step 1:

[1433] The terminal captures the customer's facial image data using the smart glasses. The input is the customer's facial image data captured by the smart glasses' camera, and the output is the raw image data sent to the EmotionRecognition SDK.

[1434] Step 2:

[1435] The device uses the EmotionRecognition SDK to analyze the customer's facial image data in real time and generate emotion data. The input is the raw image data obtained in step 1, and the output is data indicating the customer's emotional state (relaxed, stressed, etc.).

[1436] Step 3:

[1437] The terminal transmits the generated emotion data to the server. The input is the emotion data generated by the terminal, and the output is the emotion data transmitted to the server.

[1438] Step 4:

[1439] The server analyzes the received emotion data and generates an optimal command for adjusting the environment. The input is the emotion data obtained in step 3, and the output is the command data for adjusting the environment.

[1440] Step 5:

[1441] The server sends an instruction for adjusting the environment to the terminal. The input is the instruction data generated in step 4, and the output is the instruction data sent to the terminal.

[1442] Step 6:

[1443] The terminal dynamically adjusts environmental elements such as lighting, background music, and layout in the store based on command data received from the server. The input is the command data received from the server, and the output is the actual result of the environmental adjustment.

[1444] Step 7:

[1445] The user (staff member) checks the status of the environmental adjustment through the UI of the smart glasses and manually fine-tunes it if necessary. The input is the environmental adjustment result displayed on the UI of the smart glasses, and the output is the result of the manual adjustment by the staff member.

[1446] Step 8:

[1447] The server saves the results of the manual adjustment in a database and updates the data to reflect the results in subsequent environmental adjustments. The input is the environmental data updated by the staff, and the output is the updated data saved in the database.

[1448] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1449] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1450] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1451] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1452] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1453] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1454] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1455] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1456] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1457] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1458] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1459] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1460] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1461] 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.

[1462] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1463] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1464] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1465] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1466] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1467] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1468] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1469] The following is further disclosed regarding the above embodiment.

[1470] (Claim 1)

[1471] a means for collecting and storing physical office information in a database;

[1472] A means of tracking the usage of each area in real time using sensors and sending that data to a server;

[1473] A means for generating an optimal office layout based on database information and real-time usage data by a server;

[1474] A means for visualizing the generated layout proposal using 3D modeling software;

[1475] means for assisting a user in evaluating and selecting layout options;

[1476] a means of analyzing past and current usage data and predicting future usage trends;

[1477] A system including:

[1478] (Claim 2)

[1479] 10. The system of claim 1, wherein real-time data collected by sensors in each area is sent to a server to update a dashboard.

[1480] (Claim 3)

[1481] The system of claim 1, which uses an AI algorithm to optimize office layout taking into account employee roles and departmental relationships.

[1482] "Example 1"

[1483] (Claim 1)

[1484] a means for collecting and storing physical office information in a database;

[1485] a means for tracking the usage status of each area in real time using a detector and transmitting the data to an information processing device;

[1486] means for generating an optimal office layout based on the database information and real-time usage data by an information processing device;

[1487] A means for visualizing the generated layout proposal using 3D modeling software;

[1488] a means for assisting a user in evaluating and selecting layout options;

[1489] a means of analyzing past and current usage data and predicting future usage trends;

[1490] a means for transmitting real-time data collected by the detectors in each area to an information processing device and updating a dashboard;

[1491] A means of optimizing office layouts using artificial intelligence algorithms, taking into account employee roles and departmental relationships;

[1492] a means for building predictive models based on the collected data;

[1493] A system including:

[1494] (Claim 2)

[1495] The system of claim 1, wherein real-time data collected by the detectors in each area is transmitted to an information processing device to update a dashboard.

[1496] (Claim 3)

[1497] 10. The system of claim 1, which uses an artificial intelligence algorithm to optimize office layout taking into account employee roles and departmental relationships.

[1498] "Application Example 1"

[1499] Claim Amendment:

[1500] (Claim 1)

[1501] a means for collecting and storing physical office information in a database;

[1502] A method to track the usage status of each area in the factory in real time using sensors and send that data to a server.

[1503] a means for generating an optimal layout of functional spaces based on the information in the database and real-time usage data by a server;

[1504] A means for visualizing the generated layout proposal using 3D modeling software;

[1505] means for assisting a user in evaluating and selecting layout options;

[1506] a means of analyzing past and current utilization data and predicting future resource utilization trends;

[1507] A system including:

[1508] (Claim 2)

[1509] 10. The system of claim 1, wherein real-time data collected by sensors in each area is sent to a server to update a dashboard.

[1510] (Claim 3)

[1511] The system of claim 1, which uses an AI algorithm to optimize the layout of functional spaces taking into account the relationships between robot roles and work flows.

[1512] "Example 2: Combining Emotion Engines"

[1513] (Claim 1)

[1514] a means for collecting and storing physical office information in a database;

[1515] A means of tracking the usage of each area in real time using sensors and sending that data to a server;

[1516] A means for generating an optimal office layout based on database information and real-time usage data by a server;

[1517] A means for visualizing the generated layout proposal using 3D modeling software;

[1518] means for assisting a user in evaluating and selecting layout options;

[1519] a means of analyzing past and current usage data and predicting future usage trends;

[1520] A method for collecting and analyzing user emotional data and improving office layout based on that data.

[1521] A system including:

[1522] (Claim 2)

[1523] 10. The system of claim 1, wherein real-time data collected by sensors in each area is sent to a server to update a dashboard.

[1524] (Claim 3)

[1525] The system of claim 1 uses AI algorithms to optimize office layout taking into account employee roles and departmental relationships, and also takes into account emotional data to reduce stress levels.

[1526] "Application example 2 when combining emotion engines"

[1527] (Claim 1)

[1528] a means for collecting and storing physical office information in a database;

[1529] A means of tracking the usage of each area in real time using sensors and sending that data to a server;

[1530] A means for generating an optimal office layout based on database information and real-time usage data by a server;

[1531] A means for visualizing the generated layout proposal using 3D modeling software;

[1532] means for assisting a user in evaluating and selecting layout options;

[1533] a means of analyzing past and current usage data and predicting future usage trends;

[1534] A means of analyzing user emotions in real time;

[1535] A means for dynamically adjusting the office environment (physical information, working environment) based on the user's emotional data;

[1536] A system including:

[1537] (Claim 2)

[1538] 10. The system of claim 1, wherein real-time data collected by sensors in each area is sent to a server to update a dashboard.

[1539] (Claim 3)

[1540] The system of claim 1, which uses an AI algorithm to optimize office layout taking into account employee roles and departmental relationships. [Explanation of symbols]

[1541] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting and storing physical office information in a database; A means of tracking the usage of each area in real time using sensors and sending that data to a server; A means for generating an optimal office layout based on database information and real-time usage data by a server; A means for visualizing the generated layout proposal using 3D modeling software; means for assisting a user in evaluating and selecting layout options; a means of analyzing past and current usage data and predicting future usage trends; A system including:

2. The system of claim 1 , wherein real-time data collected by sensors in each area is transmitted to a server to update a dashboard.

3. The system of claim 1, which uses an AI algorithm to optimize office layout taking into account employee roles and departmental relationships.

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