system

Abstract

Provide a system. 【Solution means】 A device for receiving information related to a project, A machine learning device for analyzing the information, A device for evaluating the progress of activities based on the analysis, A computing device for optimizing human resources and materials, A device for detecting data from a monitoring device and identifying abnormalities, A device for generating an alert based on the abnormality, A visualization device for visualizing data in real time, A system including a notification device that immediately sends a warning when an abnormality is detected.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In construction projects, the complexity of projects, shortage of personnel, budget overruns, and schedule delays have become major issues. In addition, inadequate safety management at the work site and defects in the design and construction plans are also cited as issues. Due to such issues, the efficiency and safety of the project may be impaired, and the final completion quality may decrease.

Means for Solving the Problems

[0005] This invention provides a system equipped with artificial intelligence that inputs and analyzes project management data to efficiently manage the progress of construction projects. Furthermore, based on the analysis results, it evaluates the progress of the project and optimizes personnel and materials. It can also detect anomalies through real-time monitoring of sensor data and generate notifications quickly. By using building information modeling, it also proposes improvements to the design and construction plans, and is configured to respond quickly to user inquiries using natural language processing. In this way, it is possible to improve the efficiency and safety of projects.

[0006] "Project management data" refers to data that includes the project schedule, budget, progress, and related information.

[0007] "Artificial intelligence means" refers to a program or system that has the ability to analyze data and perform calculations for evaluation and optimization.

[0008] "Analysis" is the process of organizing collected data, understanding it as meaningful information, and evaluating it.

[0009] "Project progress" is an indicator that shows how far along a project is in relation to its plan.

[0010] "Optimization" is the process of making adjustments to maximize the use of limited resources and improve efficiency.

[0011] "Sensor data" refers to environmental or dynamic data collected by sensors installed on-site.

[0012] "Detecting an anomaly" means detecting an event that deviates from normal operation or the environment.

[0013] "Generating a notification" means creating a message to inform relevant parties about detected anomalies or important information.

[0014] "Building Information Modeling" is a technology that constructs information from building design to construction as a digital representation.

[0015] "Natural Language Processing" is a technology for computers to understand, interpret, and generate human language.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Modes for Carrying Out the Invention

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

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

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

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

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

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

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0037] This invention is a system for effectively implementing progress management and safety management in construction projects. This system operates through the coordinated efforts of a server, terminals, and users to manage and analyze various project-related information.

[0038] The server receives project management data entered by users via their terminals and stores this data in a database. The server's role is to analyze the received data using AI and evaluate the project's progress. In this process, it anticipates potential schedule delays and budget overruns and generates optimization plans.

[0039] Furthermore, the server analyzes Building Information Modeling (BIM) data to improve design and construction plans. This allows it to identify design inconsistencies and suggest efficient construction procedures for the entire project.

[0040] Meanwhile, the server acquires data in real time from on-site sensors and drones to monitor site safety. The sensors, for example, measure vibration and temperature in the work area and immediately issue a warning if any abnormalities are detected. This warning is processed by the server and, if necessary, notified to the user via a terminal.

[0041] Users can communicate with the system interactively using a terminal. The terminal displays notifications and optimization suggestions from the server, enabling users to issue on-site work instructions and revise plans based on that information. Users can also ask questions to the system in natural language, and the server provides information immediately in response to those questions.

[0042] For example, if temporary construction work is behind schedule in a construction project, the server proposes a new personnel allocation and material procurement schedule and notifies the user via a terminal. The user then uses this information to decide on additional personnel, thereby improving the project's progress. Additionally, if abnormal vibrations are detected in the work area through on-site safety monitoring, the server immediately issues a notification, allowing the user to take prompt action.

[0043] As described above, this system enables real-time monitoring and efficient management of project progress and safety.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user enters project data.

[0047] Users use their devices to input data such as project schedules, budgets, and progress.

[0048] Step 2:

[0049] The device sends data to the server.

[0050] The terminal transmits the entered data to the server in real time. The data is encrypted and transferred securely.

[0051] Step 3:

[0052] The server receives and stores the data.

[0053] The server stores the data received from the terminal in a database and checks the data's integrity.

[0054] Step 4:

[0055] The server analyzes the project data.

[0056] The server uses AI to analyze data stored in the database and evaluate the project's progress.

[0057] Step 5:

[0058] The server performs prediction and optimization.

[0059] Based on the analysis results, the server runs a predictive algorithm to forecast the possibility of schedule delays and budget overruns. Simultaneously, it calculates the optimal allocation of resources.

[0060] Step 6:

[0061] The server generates the notification.

[0062] Based on information obtained through prediction or analysis, the server generates necessary notifications and alerts and prepares information for the appropriate users.

[0063] Step 7:

[0064] The server monitors the sensor data.

[0065] The server acquires data in real time from on-site sensors and monitors the safety of the environment.

[0066] Step 8:

[0067] The server detects the anomaly and takes action.

[0068] The system analyzes sensor data, immediately issues an alert if an anomaly is detected, and sends a notification to the user.

[0069] Step 9:

[0070] The device displays a notification.

[0071] The terminal displays notifications and suggestions received from the server to the user, prompting immediate action.

[0072] Step 10:

[0073] The user queries the system.

[0074] Users can use their devices to inquire with the system about the project's progress and safety status.

[0075] Step 11:

[0076] The server responds to the query.

[0077] The server uses natural language processing to analyze the user's question, generates an appropriate answer, and sends it back to the user via the terminal.

[0078] Step 12:

[0079] The user executes the optimization suggestion.

[0080] Based on optimization suggestions received from the server, users adjust the project plan and reallocate resources.

[0081] In this way, the system manages project progress and safety in real time, supporting efficient operation.

[0082] (Example 1)

[0083] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0084] Efficient and accurate information gathering and analysis are required for project management and safety management in construction projects. However, conventional systems have limitations in data analysis accuracy and real-time information updates, making it difficult to optimize the entire project. Furthermore, it is difficult to monitor on-site safety in real time, resulting in problems with rapid response to anomalies.

[0085] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0086] In this invention, the server includes means for inputting project management information, analysis means, evaluation means, calculation means, means for monitoring detection device data, means for detecting anomalies, means for generating warnings, notification means, real-time data acquisition means, and communication means. This enables visualization and optimization of project progress, as well as real-time monitoring of on-site safety and rapid response.

[0087] "Project management information" is a general term for data related to a construction project, such as progress, budget, schedule, and work plan.

[0088] "Analysis means" refers to devices or software that perform calculations and analyses based on input project management information, and may particularly utilize artificial intelligence technology.

[0089] "Evaluation methods" refer to functions and processes for determining the progress of a project based on data and information obtained through analysis methods.

[0090] "Computational tools" refer to devices or software that perform calculations necessary to determine the optimal allocation of personnel and materials in order to improve the efficiency of a project.

[0091] "Detection device data" refers to environmental data such as temperature, vibration, and sound collected by sensors and monitoring devices used on-site.

[0092] "Means for detecting anomalies" refer to a system that analyzes detection device data and issues a warning when a condition deviates from predetermined norms or thresholds.

[0093] "Means for generating warnings" refers to a function that creates notifications to alert users or workers based on detected anomalies.

[0094] A "notification method" refers to a communication path used by devices and applications to convey important information to users, such as warnings and optimization suggestions.

[0095] A "real-time data acquisition method" is a system for instantly monitoring the situation on-site and transmitting necessary data to a server in real time.

[0096] "Communication means" refers to network infrastructure and protocols for sending and receiving information between servers, users, and terminals.

[0097] This invention is a system for effectively managing the progress and safety of construction projects, in which a server, terminals, and users work in conjunction. The server receives project management information entered by users through terminals and stores it in a database. The hardware consists of a general-purpose server computer and a cloud storage system. The software utilizes an AI platform for analyzing generative AI models.

[0098] The server uses a generative AI model to analyze the stored data. During this process, the analysis results generate project progress evaluations and optimization proposals. For example, an AI algorithm built using the Python programming language provides suggestions for optimal material placement and schedule adjustments. Users receive and view this information using their devices, which can include mobile devices or desktop computers.

[0099] Furthermore, the server acquires real-time data from on-site sensors and drones to detect anomalies and manage safety. IoT devices are used for this purpose, and the data is transmitted to the server via AWS® IoT Core, etc. If an anomaly is detected, the server sends an alert to the user through the terminal. This process allows the user to respond quickly.

[0100] For example, if the program detects a delay in project progress, it analyzes the cause and proposes a new material ordering schedule. The user then uses this optimization plan to reallocate staff and adjust schedules. Furthermore, when a sensor detects vibrations on-site, the server immediately analyzes the data and sends an alert, allowing the user to take appropriate action on-site.

[0101] Specific examples of prompt messages include, "If temporary construction work is behind schedule in a construction project, please propose a new staffing and material procurement schedule." In this way, collaboration between servers, terminals, and users enables the efficient and secure execution of projects.

[0102] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0103] Step 1:

[0104] Users input project management information using a terminal. This data includes work progress, budget, schedule, and resource allocation. This information is transmitted from the terminal to the server and stored in a database.

[0105] Step 2:

[0106] The server analyzes stored project management information using a generating AI model. This analysis detects schedule delays, resource shortages, and other issues based on the input data. Specifically, the data is normalized and filtered, and the AI ​​model outputs predictions.

[0107] Step 3:

[0108] Based on the analysis results, the server creates a plan to optimize the project's progress. This process uses optimization algorithms to generate proposals for new schedules and resource allocations. The resulting optimization plan is then sent to the terminal as an actionable plan.

[0109] Step 4:

[0110] The server acquires real-time data from the field through sensors and drones. This data includes environmental and safety information from the field, primarily using temperature and vibration measurements as inputs. Based on this information, the server detects anomalies and generates warnings if an anomaly occurs.

[0111] Step 5:

[0112] Warnings and optimization suggestions generated by the server are notified to the terminal. The terminal displays this information to the user, providing them with the information to decide on countermeasures. The user can issue instructions to the field and revise or adjust plans via the terminal.

[0113] Step 6:

[0114] Users can send questions to the server in natural language via their device. The server analyzes these questions using a generative AI model, generates appropriate responses, and sends them back to the device. This allows users to immediately obtain detailed information about the project.

[0115] (Application Example 1)

[0116] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0117] Construction projects require effective, real-time progress and safety management. However, conventional systems have resulted in dispersed monitoring of progress and safety across various departments, and the lack of data integration has hindered rapid response. Therefore, a system is needed that centralizes project management, enables real-time visualization of progress, and allows for immediate response to anomalies.

[0118] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0119] In this invention, the server includes a device for receiving project-related information, a machine learning device for analyzing the information, a device for evaluating the progress of activities based on the analysis, a device for detecting data from monitoring devices and identifying anomalies, and a visualization device for visualizing the data in real time. This makes it possible to grasp the progress of the project in real time and immediately issue warnings when anomalies are detected.

[0120] "Project-related information" refers to a collection of various data related to the progress, resource usage, and safety of a construction project.

[0121] A "machine learning device" is a device equipped with technology to perform tasks such as prediction and classification by analyzing input data and learning patterns.

[0122] A "device for evaluating the progress of activities" is a device that has the function of monitoring the progress of a project and evaluating whether it is proceeding according to schedule.

[0123] A "monitoring device" is a device that records various situations at a project site in real time and uses that data to detect anomalies.

[0124] A "device for identifying anomalies" is a device that analyzes data from monitoring equipment to detect and identify events that are different from the normal state.

[0125] A "data visualization device for real-time visualization" is a device that can instantly and visually display project progress and safety data.

[0126] A "warning notification device" is a device that immediately transmits warning information when an anomaly is detected, prompting appropriate action.

[0127] To implement this invention, the server first receives information related to project management. The server comprehensively aggregates data on project progress, resource usage, and safety, and analyzes it using a machine learning device. As a result, the server functions as a device that evaluates project progress and visualizes its progress.

[0128] Furthermore, the server receives data transmitted in real time from monitoring devices and acts as a device for identifying anomalies. For example, if there are abnormalities in vibration or temperature at a construction site, the server will immediately detect the anomaly and act as an alerting device to draw the attention of those involved.

[0129] Users can receive information from the server through their terminals. These terminals act as visualization devices, displaying project progress and anomaly information in real time. Furthermore, users can query the system in natural language via a natural language processing device using a generative AI model, enabling the server to respond quickly.

[0130] In particular, in large-scale commercial facility construction projects, the system operates by monitoring the progress of each task to ensure smooth operation and guaranteeing safety on site. This real-time visualization of project information allows all stakeholders to make timely decisions and enables efficient management.

[0131] Examples of prompts to input into a generative AI model:

[0132] "You are a project manager at a construction site. You need to efficiently manage site progress and safety by monitoring real-time data and responding immediately to any anomalies. Design an application for this purpose."

[0133] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0134] Step 1:

[0135] The server receives information about the project. Inputs include project progress, resource usage, and safety data. This data is sent to the server as input from sensors and management systems. The server then prepares to store the received data in its database.

[0136] Step 2:

[0137] The server analyzes the received project data using a machine learning device. The input is the project data received in step 1. The server uses a machine learning model to extract patterns and trends from the data. The output is the analysis results regarding the project's progress and predicted risks.

[0138] Step 3:

[0139] The server sends the progress evaluation results to the terminal via a visualization device. The input data is the analysis results from step 2. The server uses visualization software (e.g., Pandas, Matplotlib) to visually represent the progress in graphs and charts. The output is a real-time dashboard of the project.

[0140] Step 4:

[0141] The server analyzes real-time data from monitoring devices and identifies anomalies. Inputs include information such as vibration, temperature, and humidity from various sensors. The server uses an anomaly detection algorithm to identify data points that deviate from the normal range. Outputs are warning messages indicating that an anomaly has been detected.

[0142] Step 5:

[0143] The server activates an alerting device to immediately issue a warning when an anomaly is detected. The input is the warning information from step 4. The server utilizes a notification service to send a warning via push notification or email to the relevant user's device. The output is a real-time alert informing the user of the anomaly.

[0144] Step 6:

[0145] The user reviews information received from the server via their terminal and responds as needed. Input consists of visualization data and warning information provided by the server. Based on this information, the user makes project decisions and implements resource reallocation and plan revisions. Output consists of project improvement suggestions and instructions.

[0146] Step 7:

[0147] Users can query the server in natural language through a generative AI model. The input is the user's question. The server uses a natural language processing engine to analyze the question and provide appropriate information. The output is the answer to the user's question.

[0148] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0149] This invention provides a system that enables more personalized communication and decision-making in construction project progress management by taking user emotions into consideration. This system is a complex system involving servers, terminals, and users, and in addition to managing and analyzing project data, it can monitor and reflect the emotional state of users.

[0150] The server simultaneously acquires project management data and user emotion data entered by the user through the terminal. The server's AI analyzes this data and generates optimal resource allocation and work instructions that take into account the user's emotional state, along with an evaluation of the project's progress.

[0151] Furthermore, the server is equipped with an emotion engine that analyzes the user's emotional state in real time through user input and feedback. For example, if the system determines that the user is experiencing stress, it can generate a notification message as a mitigation measure and offer suggestions to reduce the burden on the project.

[0152] The device presents the user with not only notifications and optimization suggestions received from the server, but also emotion-based responses and suggestions. This allows the user to choose appropriate actions that match their current emotional state, facilitating smooth project progress.

[0153] When a user queries the system using natural language, the server uses natural language processing and an emotion engine to generate a response appropriate to the user's emotional state. This makes user interactions more human-like and enables quick and appropriate responses to project challenges and risks.

[0154] For example, if a user is feeling pressured during construction, the server's emotion engine can sense this and send messages through the terminal to encourage relaxation or suggest revising work assignments. By adjusting project responses based on user emotions in this way, on-site safety and efficiency can be further enhanced.

[0155] Thus, with this invention, construction project management goes beyond mere data analysis; it also takes into account user emotions, making it possible to improve the overall project success rate.

[0156] The following describes the processing flow.

[0157] Step 1:

[0158] Users enter project data and sentiment data.

[0159] Users use their devices to input data such as project schedules and budget status, and also record their emotional state through questionnaires or voice input.

[0160] Step 2:

[0161] The device sends data to the server.

[0162] The terminal encrypts the entered project data and emotion data and sends them to the server.

[0163] Step 3:

[0164] The server receives the data and saves it to the database.

[0165] The server receives project data and emotion data sent from the terminal and stores them in a database.

[0166] Step 4:

[0167] The server analyzes the project data.

[0168] The server uses AI to analyze project data and evaluate its progress and resource needs.

[0169] Step 5:

[0170] The server analyzes the emotional data.

[0171] The server uses an emotion engine to analyze the user's emotional data and understand their emotional state.

[0172] Step 6:

[0173] The server calculates project optimization.

[0174] The server calculates the optimal resource allocation and work instructions while considering the project's progress and the users' emotional state.

[0175] Step 7:

[0176] The server generates a notification and sends it to the device.

[0177] The server generates and sends notifications to the terminal based on the project's progress and messages tailored to the user's emotional state.

[0178] Step 8:

[0179] The device displays a notification.

[0180] The terminal displays information received from the server on the screen and prompts the user. This includes ongoing issues and suggestions for improvement.

[0181] Step 9:

[0182] The user queries the system.

[0183] Users send feedback to the system via their devices, including questions and emotional responses about the project.

[0184] Step 10:

[0185] The server responds using natural language processing and sentiment analysis.

[0186] The server analyzes user inquiries using natural language processing and an emotion engine, generates responses appropriate to the user's emotional state, and sends them back to the terminal.

[0187] Through this process, the system integrates project management and user emotions, providing an optimized management structure.

[0188] (Example 2)

[0189] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0190] In project management, while progress tracking and resource optimization are important, there is a lack of consideration for users' emotional states. This can lead to decreased project efficiency and team satisfaction. Therefore, there is a need for technology that integrates project management data and emotional data to provide appropriate responses and suggestions based on users' emotions.

[0191] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0192] In this invention, the server includes means for inputting project management data and emotional data, artificial intelligence means for analyzing the project management data and emotional data, and means for evaluating the project progress and emotional state based on the analysis. This makes it possible to generate resource allocation and work instructions that take into account the emotional state as well as the evaluation of the project progress.

[0193] "Project management data" refers to data that includes information about the project's progress, resource allocation, schedule, and so on.

[0194] "Emotional data" refers to data that includes information reflecting the user's emotional state, such as stress, satisfaction, and motivation.

[0195] "Artificial intelligence tools" refers to machine learning algorithms and natural language processing techniques used to analyze project management data and sentiment data.

[0196] "Computational means" refers to processes and programs used to perform calculations necessary for optimizing personnel and resources.

[0197] "Natural language processing means" refers to technologies used to respond to inquiries from users in natural language and generate appropriate responses.

[0198] "Resource allocation" refers to the appropriate placement and use plan of the various resources and personnel required for a project.

[0199] "Emotional state" refers to the user's emotional characteristics, such as stress, anxiety, and satisfaction.

[0200] "Proposal generation" refers to the process of devising solutions and improvement measures to provide to users based on evaluation results.

[0201] This invention provides a system for optimizing project management by integrating and analyzing progress data and user sentiment data in the field of project management. This system consists of a server, terminals, and users, and enables the generation of optimal suggestions based on project management data and sentiment.

[0202] The server receives information from terminals to input project management data and sentiment data. This data includes information about project progress, resource allocation, and the user's emotional state. The server uses machine learning libraries such as TENSORFLOW® and PyTorch, as well as natural language processing techniques, to analyze this data. This makes it possible to evaluate project progress while simultaneously generating resource allocations and work instructions that take the user's emotional state into account.

[0203] As a concrete example, when the server receives the prompt "Is the project progressing smoothly?", it analyzes project progress data and user sentiment data. If the progress is good, it generates a response such as "The project is progressing according to plan." On the other hand, if the user is feeling anxious, it provides a sentiment-sensitive response such as "Progress is fine, but we will consider allocating additional resources."

[0204] The terminal plays the role of displaying notifications and suggestions from the server to the user, who can then choose actions based on the information and suggestions presented. User feedback forms a loop throughout the system, enabling continuous improvement. In this way, the embodiment of the present invention can increase the efficiency of project management while simultaneously meeting the emotional needs of the user.

[0205] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0206] Step 1:

[0207] The user inputs project management data and emotional data through a terminal. This input takes the form of progress reports and questionnaires indicating emotional states. The terminal sends this input information to the server as digital data. The output is the data sent to the server.

[0208] Step 2:

[0209] The server analyzes project management data and sentiment data received from the terminal. This analysis utilizes machine learning algorithms using TensorFlow and PyTorch, as well as natural language processing techniques. The input consists of various data received from the user, and the output is the analysis results. Specifically, progress is evaluated and the user's emotional state is determined.

[0210] Step 3:

[0211] The server evaluates the overall project progress and the user's emotional state based on the analysis results. The input here is the analysis results from step 2. The server processes the data to optimize resource allocation and generate work instructions that respond to emotions, creating optimization proposals and suggestions as output.

[0212] Step 4:

[0213] The server sends the generated optimization suggestions and proposals to the terminal. The terminal presents this information to the user. In this step, the notifications and suggestions sent from the server become input, and the terminal visualizes them and presents them to the user. The output is the information displayed to the user.

[0214] Step 5:

[0215] Users review suggestions presented by the server via their terminal and provide feedback as needed. This feedback is returned directly to the server and used in the next analysis. Input consists of the user's selections and comments, while output is new data to be used in the next analysis.

[0216] Step 6:

[0217] The server uses natural language processing techniques to generate responses to user inquiries. This process also takes into account the user's emotional state. The input is the user's inquiry, and the output is the response message. This allows the system to provide a more human-like interaction with the user.

[0218] (Application Example 2)

[0219] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0220] In modern construction project management, it is essential not only to understand technical progress but also to communicate with workers and managers while considering their emotional well-being. Traditional systems fail to address diverse stressors, impacting overall project efficiency and worker safety. To address this problem, there is an urgent need to develop a system that can appropriately manage the emotional state of participants in real time, along with project progress, enabling effective resource allocation and work instructions.

[0221] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0222] In this invention, the server includes means for inputting project management information, artificial intelligence means for analyzing the project management information, and means for evaluating the project progress based on the analysis. This enables accurate analysis of project management information. It also includes calculation means for optimizing resources and materials, means for monitoring sensor data and detecting anomalies, and means for generating notifications based on the anomalies. Furthermore, it includes means for monitoring the user's emotional state and analyzing emotional data, and means for presenting the user with emotionally appropriate messages based on the emotional data. This enables efficient project management through a comprehensive system that integrates project progress management and human-centered management.

[0223] "Project management information" refers to detailed data regarding the progress, schedule, resources, and task assignments of a construction project.

[0224] "Artificial intelligence tools" refer to methods that utilize algorithms and technologies to analyze data, learn from it, and support decision-making.

[0225] "Computational means" refers to functions or processes used for processing and calculating data.

[0226] "Sensor data" refers to information about the surrounding environment and equipment collected by sensors.

[0227] "Means for generating notifications" refers to a process or function for sending notifications to a user when specific conditions or abnormal states are detected.

[0228] "Emotional state" refers to the type and degree of emotion a user is feeling at that particular time.

[0229] "Emotional data" refers to information about a user's emotions obtained by analyzing their facial expressions, tone of voice, writing style, and other factors.

[0230] "Means of presenting a message" refers to methods or functions used to convey information to users visually or audibly.

[0231] The server first receives project management information as input. This includes schedules, resources, and work progress information related to the construction project. The server is equipped with artificial intelligence capabilities to analyze this information and evaluate the project's progress. For example, it can determine whether the planned work is progressing according to schedule.

[0232] Furthermore, the system utilizes computational methods to optimize resources and materials. This optimization allows for efficient project progress without wasting necessary resources. In addition, it monitors sensor data and generates notifications to inform users if an anomaly is detected. This enables a rapid response, ensuring the safety and efficiency of the project.

[0233] Furthermore, the system provides a means of sentiment analysis to monitor the user's emotional state. When a user accesses the system using a terminal, their facial expressions and tone of voice are analyzed using the camera and microphone, and their emotions are evaluated in real time. The sentiment data obtained here is used to generate appropriate responses and messages for the user. For example, if the system determines that the user is feeling stressed, the server will send a message encouraging relaxation and suggest ways to alleviate the burden.

[0234] As a concrete example, when workers at a construction site check the progress of a project through smart glasses, the program can analyze project management information and display appropriate instructions based on emotional data. The prompt text input to the generating AI model might be something like, "If the user is feeling very tired, what kind of break suggestion would be best?"

[0235] In this way, efficient and secure project management is achieved through collaboration between servers, terminals, and users.

[0236] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0237] Step 1:

[0238] The server receives project management information from the terminal. This information includes data on schedules, resource allocation, and work progress. This data is stored in a database and prepared as input for analysis by artificial intelligence. The output at this stage is project management information ready for analysis.

[0239] Step 2:

[0240] The server analyzes the received project management information using artificial intelligence. This analysis process evaluates schedule progress and resource usage. If necessary, optimizations to resources and work processes are proposed. The resulting output is an evaluation report on the project's progress.

[0241] Step 3:

[0242] The server optimizes resources and materials as needed, based on project progress evaluation reports. It uses computational tools to create an efficient resource allocation plan, avoiding excessive resource use and duplicated tasks. As a result, an optimized resource allocation instruction is output.

[0243] Step 4:

[0244] The server continuously monitors sensor data and generates notifications to promptly address any anomalies detected. For example, if abnormal temperature changes or vibrations are detected, an alert is immediately sent to the administrator. The input for this step is sensor data, and the output is an anomaly notification.

[0245] Step 5:

[0246] When a user checks the project status via their device, the server monitors the user's emotional state using camera and microphone inputs. It collects and analyzes user emotional data in real time by analyzing facial expressions and voice tone. The output is user emotional evaluation data.

[0247] Step 6:

[0248] The server generates appropriate messages and suggestions based on the user's emotional assessment data. For example, if the user is feeling stressed, it will offer suggestions to help them relax or provide options to reduce their workload. The output of this step is a message to the user tailored to their emotional state.

[0249] Step 7:

[0250] The user receives generated messages and selects the appropriate action. This facilitates project progress and reduces the user's burden. The output is the results of the actions taken in the field based on the user's selection.

[0251] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0252] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0253] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0254] [Second Embodiment]

[0255] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0256] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0257] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0258] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0259] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0260] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0261] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0262] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0263] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0264] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0265] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0266] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0267] This invention is a system for effectively implementing progress management and safety management in construction projects. This system operates through the coordinated efforts of a server, terminals, and users to manage and analyze various project-related information.

[0268] The server receives project management data entered by users via their terminals and stores this data in a database. The server's role is to analyze the received data using AI and evaluate the project's progress. In this process, it anticipates potential schedule delays and budget overruns and generates optimization plans.

[0269] Furthermore, the server analyzes Building Information Modeling (BIM) data to improve design and construction plans. This allows it to identify design inconsistencies and suggest efficient construction procedures for the entire project.

[0270] Meanwhile, the server acquires data in real time from on-site sensors and drones to monitor site safety. The sensors, for example, measure vibration and temperature in the work area and immediately issue a warning if any abnormalities are detected. This warning is processed by the server and, if necessary, notified to the user via a terminal.

[0271] Users can communicate with the system interactively using a terminal. The terminal displays notifications and optimization suggestions from the server, enabling users to issue on-site work instructions and revise plans based on that information. Users can also ask questions to the system in natural language, and the server provides information immediately in response to those questions.

[0272] For example, if temporary construction work is behind schedule in a construction project, the server proposes a new personnel allocation and material procurement schedule and notifies the user via a terminal. The user then uses this information to decide on additional personnel, thereby improving the project's progress. Additionally, if abnormal vibrations are detected in the work area through on-site safety monitoring, the server immediately issues a notification, allowing the user to take prompt action.

[0273] As described above, this system enables real-time monitoring and efficient management of project progress and safety.

[0274] The following describes the processing flow.

[0275] Step 1:

[0276] The user enters project data.

[0277] The user uses the terminal to input data such as the project schedule, budget, and progress status.

[0278] Step 2:

[0279] The terminal sends the data to the server.

[0280] The terminal sends the input data to the server in real time. The data is encrypted and transferred securely.

[0281] Step 3:

[0282] The server receives and stores the data.

[0283] The server stores the data received from the terminal in the database and checks the data integrity.

[0284] Step 4:

[0285] The server analyzes the project data.

[0286] The server uses AI means to analyze the data stored in the database and evaluate the progress of the project.

[0287] Step 5:

[0288] The server performs prediction and optimization.

[0289] Based on the analysis results, the server executes a prediction algorithm to predict the possibility of schedule delays and budget overruns. At the same time, it calculates the optimal allocation of resources.

[0290] Step 6:

[0291] The server generates notifications.

[0292] Based on the information obtained from the prediction or analysis, the server generates the necessary notifications and alerts and prepares information for the appropriate users.

[0293] Step 7:

[0294] The server monitors sensor data

[0295] The server acquires data in real time from on-site sensors and monitors the environmental safety.

[0296] Step 8:

[0297] The server detects and responds to anomalies

[0298] Analyze the sensor data, and if an anomaly is detected, immediately issue an alert and send a notification to the user.

[0299] Step 9:

[0300] The terminal displays the notification

[0301] The terminal displays the notifications and suggestions received from the server to the user and prompts for immediate response.

[0302] Step 10:

[0303] The user queries the system

[0304] The user queries the system through the terminal about questions regarding the progress and safety status of the project.

[0305] Step 11:

[0306] The server responds to the query

[0307] The server analyzes the user's question using natural language processing, generates an appropriate answer, and returns it to the user through the terminal.

[0308] [[ID=​​​​

[0310] Based on optimization suggestions received from the server, users adjust the project plan and reallocate resources.

[0311] In this way, the system manages project progress and safety in real time, supporting efficient operation.

[0312] (Example 1)

[0313] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0314] Efficient and accurate information gathering and analysis are required for project management and safety management in construction projects. However, conventional systems have limitations in data analysis accuracy and real-time information updates, making it difficult to optimize the entire project. Furthermore, it is difficult to monitor on-site safety in real time, resulting in problems with rapid response to anomalies.

[0315] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0316] In this invention, the server includes means for inputting project management information, analysis means, evaluation means, calculation means, means for monitoring detection device data, means for detecting anomalies, means for generating warnings, notification means, real-time data acquisition means, and communication means. This enables visualization and optimization of project progress, as well as real-time monitoring of on-site safety and rapid response.

[0317] "Project management information" is a general term for data related to a construction project, such as progress, budget, schedule, and work plan.

[0318] "Analysis means" refers to devices or software that perform calculations and analyses based on input project management information, and may particularly utilize artificial intelligence technology.

[0319] "Evaluation methods" refer to functions and processes for determining the progress of a project based on data and information obtained through analysis methods.

[0320] "Computational tools" refer to devices or software that perform calculations necessary to determine the optimal allocation of personnel and materials in order to improve the efficiency of a project.

[0321] "Detection device data" refers to environmental data such as temperature, vibration, and sound collected by sensors and monitoring devices used on-site.

[0322] "Means for detecting anomalies" refer to a system that analyzes detection device data and issues a warning when a condition deviates from predetermined norms or thresholds.

[0323] "Means for generating warnings" refers to a function that creates notifications to alert users or workers based on detected anomalies.

[0324] A "notification method" refers to a communication path used by devices and applications to convey important information to users, such as warnings and optimization suggestions.

[0325] A "real-time data acquisition method" is a system for instantly monitoring the situation on-site and transmitting necessary data to a server in real time.

[0326] "Communication means" refers to network infrastructure and protocols for sending and receiving information between servers, users, and terminals.

[0327] This invention is a system for effectively managing the progress and safety of construction projects, in which a server, terminals, and users work in conjunction. The server receives project management information entered by users through terminals and stores it in a database. The hardware consists of a general-purpose server computer and a cloud storage system. The software utilizes an AI platform for analyzing generative AI models.

[0328] The server uses a generative AI model to analyze the stored data. During this process, the analysis results generate project progress evaluations and optimization proposals. For example, an AI algorithm built using the Python programming language provides suggestions for optimal material placement and schedule adjustments. Users receive and view this information using their devices, which can include mobile devices or desktop computers.

[0329] Furthermore, the server acquires real-time data from on-site sensors and drones to detect anomalies and manage safety. IoT devices are used for this purpose, and the data is transmitted to the server via AWS IoT Core, etc. If an anomaly is detected, the server sends an alert to the user through the terminal. This process allows the user to respond quickly.

[0330] For example, if the program detects a delay in project progress, it analyzes the cause and proposes a new material ordering schedule. The user then uses this optimization plan to reallocate staff and adjust schedules. Furthermore, when a sensor detects vibrations on-site, the server immediately analyzes the data and sends an alert, allowing the user to take appropriate action on-site.

[0331] Specific examples of prompt messages include, "If temporary construction work is behind schedule in a construction project, please propose a new staffing and material procurement schedule." In this way, collaboration between servers, terminals, and users enables the efficient and secure execution of projects.

[0332] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0333] Step 1:

[0334] Users input project management information using a terminal. This data includes work progress, budget, schedule, and resource allocation. This information is transmitted from the terminal to the server and stored in a database.

[0335] Step 2:

[0336] The server analyzes stored project management information using a generating AI model. This analysis detects schedule delays, resource shortages, and other issues based on the input data. Specifically, the data is normalized and filtered, and the AI ​​model outputs predictions.

[0337] Step 3:

[0338] Based on the analysis results, the server creates a plan to optimize the project's progress. This process uses optimization algorithms to generate proposals for new schedules and resource allocations. The resulting optimization plan is then sent to the terminal as an actionable plan.

[0339] Step 4:

[0340] The server acquires real-time data from the field through sensors and drones. This data includes environmental and safety information from the field, primarily using temperature and vibration measurements as inputs. Based on this information, the server detects anomalies and generates warnings if an anomaly occurs.

[0341] Step 5:

[0342] Warnings and optimization suggestions generated by the server are notified to the terminal. The terminal displays this information to the user, providing them with the information to decide on countermeasures. The user can issue instructions to the field and revise or adjust plans via the terminal.

[0343] Step 6:

[0344] Users can send questions to the server in natural language via their device. The server analyzes these questions using a generative AI model, generates appropriate responses, and sends them back to the device. This allows users to immediately obtain detailed information about the project.

[0345] (Application Example 1)

[0346] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0347] Construction projects require effective, real-time progress and safety management. However, conventional systems have resulted in dispersed monitoring of progress and safety across various departments, and the lack of data integration has hindered rapid response. Therefore, a system is needed that centralizes project management, enables real-time visualization of progress, and allows for immediate response to anomalies.

[0348] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0349] In this invention, the server includes a device for receiving project-related information, a machine learning device for analyzing the information, a device for evaluating the progress of activities based on the analysis, a device for detecting data from monitoring devices and identifying anomalies, and a visualization device for visualizing the data in real time. This makes it possible to grasp the progress of the project in real time and immediately issue warnings when anomalies are detected.

[0350] "Project-related information" refers to a collection of various data related to the progress, resource usage, and safety of a construction project.

[0351] A "machine learning device" is a device equipped with technology to perform tasks such as prediction and classification by analyzing input data and learning patterns.

[0352] A "device for evaluating the progress of activities" is a device that has the function of monitoring the progress of a project and evaluating whether it is proceeding according to schedule.

[0353] A "monitoring device" is a device that records various situations at a project site in real time and uses that data to detect anomalies.

[0354] A "device for identifying anomalies" is a device that analyzes data from monitoring equipment to detect and identify events that are different from the normal state.

[0355] A "data visualization device for real-time visualization" is a device that can instantly and visually display project progress and safety data.

[0356] A "warning notification device" is a device that immediately transmits warning information when an anomaly is detected, prompting appropriate action.

[0357] To implement this invention, the server first receives information related to project management. The server comprehensively aggregates data on project progress, resource usage, and safety, and analyzes it using a machine learning device. As a result, the server functions as a device that evaluates project progress and visualizes its progress.

[0358] Furthermore, the server receives data transmitted in real time from monitoring devices and acts as a device for identifying anomalies. For example, if there are abnormalities in vibration or temperature at a construction site, the server will immediately detect the anomaly and act as an alerting device to draw the attention of those involved.

[0359] Users can receive information from the server through their terminals. These terminals act as visualization devices, displaying project progress and anomaly information in real time. Furthermore, users can query the system in natural language via a natural language processing device using a generative AI model, enabling the server to respond quickly.

[0360] In particular, in large-scale commercial facility construction projects, the system operates by monitoring the progress of each task to ensure smooth operation and guaranteeing safety on site. This real-time visualization of project information allows all stakeholders to make timely decisions and enables efficient management.

[0361] Examples of prompts to input into a generative AI model:

[0362] "You are a project manager at a construction site. You need to efficiently manage site progress and safety by monitoring real-time data and responding immediately to any anomalies. Design an application for this purpose."

[0363] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0364] Step 1:

[0365] The server receives information about the project. Inputs include project progress, resource usage, and safety data. This data is sent to the server as input from sensors and management systems. The server then prepares to store the received data in its database.

[0366] Step 2:

[0367] The server analyzes the received project data using a machine learning device. The input is the project data received in step 1. The server uses a machine learning model to extract patterns and trends from the data. The output is the analysis results regarding the project's progress and predicted risks.

[0368] Step 3:

[0369] The server sends the progress evaluation results to the terminal via a visualization device. The input data is the analysis results from step 2. The server uses visualization software (e.g., Pandas, Matplotlib) to visually represent the progress in graphs and charts. The output is a real-time dashboard of the project.

[0370] Step 4:

[0371] The server analyzes real-time data from monitoring devices and identifies anomalies. Inputs include information such as vibration, temperature, and humidity from various sensors. The server uses an anomaly detection algorithm to identify data points that deviate from the normal range. Outputs are warning messages indicating that an anomaly has been detected.

[0372] Step 5:

[0373] The server activates an alerting device to immediately issue a warning when an anomaly is detected. The input is the warning information from step 4. The server utilizes a notification service to send a warning via push notification or email to the relevant user's device. The output is a real-time alert informing the user of the anomaly.

[0374] Step 6:

[0375] The user reviews information received from the server via their terminal and responds as needed. Input consists of visualization data and warning information provided by the server. Based on this information, the user makes project decisions and implements resource reallocation and plan revisions. Output consists of project improvement suggestions and instructions.

[0376] Step 7:

[0377] Users can query the server in natural language through a generative AI model. The input is the user's question. The server uses a natural language processing engine to analyze the question and provide appropriate information. The output is the answer to the user's question.

[0378] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0379] This invention provides a system that enables more personalized communication and decision-making in construction project progress management by taking user emotions into consideration. This system is a complex system involving servers, terminals, and users, and in addition to managing and analyzing project data, it can monitor and reflect the emotional state of users.

[0380] The server simultaneously acquires project management data and user emotion data entered by the user through the terminal. The server's AI analyzes this data and generates optimal resource allocation and work instructions that take into account the user's emotional state, along with an evaluation of the project's progress.

[0381] Furthermore, the server is equipped with an emotion engine that analyzes the user's emotional state in real time through user input and feedback. For example, if the system determines that the user is experiencing stress, it can generate a notification message as a mitigation measure and offer suggestions to reduce the burden on the project.

[0382] The device presents the user with not only notifications and optimization suggestions received from the server, but also emotion-based responses and suggestions. This allows the user to choose appropriate actions that match their current emotional state, facilitating smooth project progress.

[0383] When a user queries the system using natural language, the server uses natural language processing and an emotion engine to generate a response appropriate to the user's emotional state. This makes user interactions more human-like and enables quick and appropriate responses to project challenges and risks.

[0384] For example, if a user is feeling pressured during construction, the server's emotion engine can sense this and send messages through the terminal to encourage relaxation or suggest revising work assignments. By adjusting project responses based on user emotions in this way, on-site safety and efficiency can be further enhanced.

[0385] Thus, with this invention, construction project management goes beyond mere data analysis; it also takes into account user emotions, making it possible to improve the overall project success rate.

[0386] The following describes the processing flow.

[0387] Step 1:

[0388] Users enter project data and sentiment data.

[0389] Users use their devices to input data such as project schedules and budget status, and also record their emotional state through questionnaires or voice input.

[0390] Step 2:

[0391] The device sends data to the server.

[0392] The terminal encrypts the entered project data and emotion data and sends them to the server.

[0393] Step 3:

[0394] The server receives the data and saves it to the database.

[0395] The server receives project data and emotion data sent from the terminal and stores them in a database.

[0396] Step 4:

[0397] The server analyzes the project data.

[0398] The server uses AI to analyze project data and evaluate its progress and resource needs.

[0399] Step 5:

[0400] The server analyzes the emotional data.

[0401] The server uses an emotion engine to analyze the user's emotional data and understand their emotional state.

[0402] Step 6:

[0403] The server calculates project optimization.

[0404] The server calculates the optimal resource allocation and work instructions while considering the project's progress and the users' emotional state.

[0405] Step 7:

[0406] The server generates a notification and sends it to the device.

[0407] The server generates and sends notifications to the terminal based on the project's progress and messages tailored to the user's emotional state.

[0408] Step 8:

[0409] The device displays a notification.

[0410] The terminal displays information received from the server on the screen and prompts the user. This includes ongoing issues and suggestions for improvement.

[0411] Step 9:

[0412] The user queries the system.

[0413] Users send feedback to the system via their devices, including questions and emotional responses about the project.

[0414] Step 10:

[0415] The server responds using natural language processing and sentiment analysis.

[0416] The server analyzes user inquiries using natural language processing and an emotion engine, generates responses appropriate to the user's emotional state, and sends them back to the terminal.

[0417] Through this process, the system integrates project management and user emotions, providing an optimized management structure.

[0418] (Example 2)

[0419] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0420] In project management, while progress tracking and resource optimization are important, there is a lack of consideration for users' emotional states. This can lead to decreased project efficiency and team satisfaction. Therefore, there is a need for technology that integrates project management data and emotional data to provide appropriate responses and suggestions based on users' emotions.

[0421] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0422] In this invention, the server includes means for inputting project management data and emotional data, artificial intelligence means for analyzing the project management data and emotional data, and means for evaluating the project progress and emotional state based on the analysis. This makes it possible to generate resource allocation and work instructions that take into account the emotional state as well as the evaluation of the project progress.

[0423] "Project management data" refers to data that includes information about the project's progress, resource allocation, schedule, and so on.

[0424] "Emotional data" refers to data that includes information reflecting the user's emotional state, such as stress, satisfaction, and motivation.

[0425] "Artificial intelligence tools" refers to machine learning algorithms and natural language processing techniques used to analyze project management data and sentiment data.

[0426] "Computational means" refers to processes and programs used to perform calculations necessary for optimizing personnel and resources.

[0427] "Natural language processing means" refers to technologies used to respond to inquiries from users in natural language and generate appropriate responses.

[0428] "Resource allocation" refers to the appropriate placement and use plan of the various resources and personnel required for a project.

[0429] "Emotional state" refers to the user's emotional characteristics, such as stress, anxiety, and satisfaction.

[0430] "Proposal generation" refers to the process of devising solutions and improvement measures to provide to users based on evaluation results.

[0431] This invention provides a system for optimizing project management by integrating and analyzing progress data and user sentiment data in the field of project management. This system consists of a server, terminals, and users, and enables the generation of optimal suggestions based on project management data and sentiment.

[0432] The server receives information from terminals to input project management data and sentiment data. This data includes information about project progress, resource allocation, and the user's emotional state. The server uses machine learning libraries such as TensorFlow and PyTorch, as well as natural language processing techniques, to analyze this data. This makes it possible to evaluate project progress while simultaneously generating resource allocations and work instructions that take the user's emotional state into account.

[0433] As a concrete example, when the server receives the prompt "Is the project progressing smoothly?", it analyzes project progress data and user sentiment data. If the progress is good, it generates a response such as "The project is progressing according to plan." On the other hand, if the user is feeling anxious, it provides a sentiment-sensitive response such as "Progress is fine, but we will consider allocating additional resources."

[0434] The terminal plays the role of displaying notifications and suggestions from the server to the user, who can then choose actions based on the information and suggestions presented. User feedback forms a loop throughout the system, enabling continuous improvement. In this way, the embodiment of the present invention can increase the efficiency of project management while simultaneously meeting the emotional needs of the user.

[0435] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0436] Step 1:

[0437] The user inputs project management data and emotional data through a terminal. This input takes the form of progress reports and questionnaires indicating emotional states. The terminal sends this input information to the server as digital data. The output is the data sent to the server.

[0438] Step 2:

[0439] The server analyzes project management data and sentiment data received from the terminal. This analysis utilizes machine learning algorithms using TensorFlow and PyTorch, as well as natural language processing techniques. The input consists of various data received from the user, and the output is the analysis results. Specifically, progress is evaluated and the user's emotional state is determined.

[0440] Step 3:

[0441] The server evaluates the overall project progress and the user's emotional state based on the analysis results. The input here is the analysis results from step 2. The server processes the data to optimize resource allocation and generate work instructions that respond to emotions, creating optimization proposals and suggestions as output.

[0442] Step 4:

[0443] The server sends the generated optimization suggestions and proposals to the terminal. The terminal presents this information to the user. In this step, the notifications and suggestions sent from the server become input, and the terminal visualizes them and presents them to the user. The output is the information displayed to the user.

[0444] Step 5:

[0445] Users review suggestions presented by the server via their terminal and provide feedback as needed. This feedback is returned directly to the server and used in the next analysis. Input consists of the user's selections and comments, while output is new data to be used in the next analysis.

[0446] Step 6:

[0447] The server uses natural language processing techniques to generate responses to user inquiries. This process also takes into account the user's emotional state. The input is the user's inquiry, and the output is the response message. This allows the system to provide a more human-like interaction with the user.

[0448] (Application Example 2)

[0449] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0450] In modern construction project management, it is essential not only to understand technical progress but also to communicate with workers and managers while considering their emotional well-being. Traditional systems fail to address diverse stressors, impacting overall project efficiency and worker safety. To address this problem, there is an urgent need to develop a system that can appropriately manage the emotional state of participants in real time, along with project progress, enabling effective resource allocation and work instructions.

[0451] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0452] In this invention, the server includes means for inputting project management information, artificial intelligence means for analyzing the project management information, and means for evaluating the project progress based on the analysis. This enables accurate analysis of project management information. It also includes calculation means for optimizing resources and materials, means for monitoring sensor data and detecting anomalies, and means for generating notifications based on the anomalies. Furthermore, it includes means for monitoring the user's emotional state and analyzing emotional data, and means for presenting the user with emotionally appropriate messages based on the emotional data. This enables efficient project management through a comprehensive system that integrates project progress management and human-centered management.

[0453] "Project management information" refers to detailed data regarding the progress, schedule, resources, and task assignments of a construction project.

[0454] "Artificial intelligence tools" refer to methods that utilize algorithms and technologies to analyze data, learn from it, and support decision-making.

[0455] "Computational means" refers to functions or processes used for processing and calculating data.

[0456] "Sensor data" refers to information about the surrounding environment and equipment collected by sensors.

[0457] "Means for generating notifications" refers to a process or function for sending notifications to a user when specific conditions or abnormal states are detected.

[0458] "Emotional state" refers to the type and degree of emotion a user is feeling at that particular time.

[0459] "Emotional data" refers to information about a user's emotions obtained by analyzing their facial expressions, tone of voice, writing style, and other factors.

[0460] "Means of presenting a message" refers to methods or functions used to convey information to users visually or audibly.

[0461] The server first receives project management information as input. This includes schedules, resources, and work progress information related to the construction project. The server is equipped with artificial intelligence capabilities to analyze this information and evaluate the project's progress. For example, it can determine whether the planned work is progressing according to schedule.

[0462] Furthermore, the system utilizes computational methods to optimize resources and materials. This optimization allows for efficient project progress without wasting necessary resources. In addition, it monitors sensor data and generates notifications to inform users if an anomaly is detected. This enables a rapid response, ensuring the safety and efficiency of the project.

[0463] Furthermore, the system provides a means of sentiment analysis to monitor the user's emotional state. When a user accesses the system using a terminal, their facial expressions and tone of voice are analyzed using the camera and microphone, and their emotions are evaluated in real time. The sentiment data obtained here is used to generate appropriate responses and messages for the user. For example, if the system determines that the user is feeling stressed, the server will send a message encouraging relaxation and suggest ways to alleviate the burden.

[0464] As a concrete example, when workers at a construction site check the progress of a project through smart glasses, the program can analyze project management information and display appropriate instructions based on emotional data. The prompt text input to the generating AI model might be something like, "If the user is feeling very tired, what kind of break suggestion would be best?"

[0465] In this way, efficient and secure project management is achieved through collaboration between servers, terminals, and users.

[0466] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0467] Step 1:

[0468] The server receives project management information from the terminal. This information includes data on schedules, resource allocation, and work progress. This data is stored in a database and prepared as input for analysis by artificial intelligence. The output at this stage is project management information ready for analysis.

[0469] Step 2:

[0470] The server analyzes the received project management information using artificial intelligence. This analysis process evaluates schedule progress and resource usage. If necessary, optimizations to resources and work processes are proposed. The resulting output is an evaluation report on the project's progress.

[0471] Step 3:

[0472] The server optimizes resources and materials as needed, based on project progress evaluation reports. It uses computational tools to create an efficient resource allocation plan, avoiding excessive resource use and duplicated tasks. As a result, an optimized resource allocation instruction is output.

[0473] Step 4:

[0474] The server continuously monitors sensor data and generates notifications to promptly address any anomalies detected. For example, if abnormal temperature changes or vibrations are detected, an alert is immediately sent to the administrator. The input for this step is sensor data, and the output is an anomaly notification.

[0475] Step 5:

[0476] When a user checks the project status via their device, the server monitors the user's emotional state using camera and microphone inputs. It collects and analyzes user emotional data in real time by analyzing facial expressions and voice tone. The output is user emotional evaluation data.

[0477] Step 6:

[0478] The server generates appropriate messages and suggestions based on the user's emotional assessment data. For example, if the user is feeling stressed, it will offer suggestions to help them relax or provide options to reduce their workload. The output of this step is a message to the user tailored to their emotional state.

[0479] Step 7:

[0480] The user receives generated messages and selects the appropriate action. This facilitates project progress and reduces the user's burden. The output is the results of the actions taken in the field based on the user's selection.

[0481] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0482] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0483] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0484] [Third Embodiment]

[0485] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0486] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0487] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0488] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0489] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0490] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0491] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0492] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0493] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0494] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0495] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0496] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0497] This invention is a system for effectively implementing progress management and safety management in construction projects. This system operates through the coordinated efforts of a server, terminals, and users to manage and analyze various project-related information.

[0498] The server receives project management data entered by users via their terminals and stores this data in a database. The server's role is to analyze the received data using AI and evaluate the project's progress. In this process, it anticipates potential schedule delays and budget overruns and generates optimization plans.

[0499] Furthermore, the server analyzes Building Information Modeling (BIM) data to improve design and construction plans. This allows it to identify design inconsistencies and suggest efficient construction procedures for the entire project.

[0500] Meanwhile, the server acquires data in real time from on-site sensors and drones to monitor site safety. The sensors, for example, measure vibration and temperature in the work area and immediately issue a warning if any abnormalities are detected. This warning is processed by the server and, if necessary, notified to the user via a terminal.

[0501] Users can communicate with the system interactively using a terminal. The terminal displays notifications and optimization suggestions from the server, enabling users to issue on-site work instructions and revise plans based on that information. Users can also ask questions to the system in natural language, and the server provides information immediately in response to those questions.

[0502] For example, if temporary construction work is behind schedule in a construction project, the server proposes a new personnel allocation and material procurement schedule and notifies the user via a terminal. The user then uses this information to decide on additional personnel, thereby improving the project's progress. Additionally, if abnormal vibrations are detected in the work area through on-site safety monitoring, the server immediately issues a notification, allowing the user to take prompt action.

[0503] As described above, this system enables real-time monitoring and efficient management of project progress and safety.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] The user enters project data.

[0507] Users use their devices to input data such as project schedules, budgets, and progress.

[0508] Step 2:

[0509] The device sends data to the server.

[0510] The terminal transmits the entered data to the server in real time. The data is encrypted and transferred securely.

[0511] Step 3:

[0512] The server receives and stores the data.

[0513] The server stores the data received from the terminal in a database and checks the data's integrity.

[0514] Step 4:

[0515] The server analyzes the project data.

[0516] The server uses AI to analyze data stored in the database and evaluate the project's progress.

[0517] Step 5:

[0518] The server performs prediction and optimization.

[0519] Based on the analysis results, the server runs a predictive algorithm to forecast the possibility of schedule delays and budget overruns. Simultaneously, it calculates the optimal allocation of resources.

[0520] Step 6:

[0521] The server generates the notification.

[0522] Based on information obtained through prediction or analysis, the server generates necessary notifications and alerts and prepares information for the appropriate users.

[0523] Step 7:

[0524] The server monitors the sensor data.

[0525] The server acquires data in real time from on-site sensors and monitors the safety of the environment.

[0526] Step 8:

[0527] The server detects the anomaly and takes action.

[0528] The system analyzes sensor data, immediately issues an alert if an anomaly is detected, and sends a notification to the user.

[0529] Step 9:

[0530] The device displays a notification.

[0531] The terminal displays notifications and suggestions received from the server to the user, prompting immediate action.

[0532] Step 10:

[0533] The user queries the system.

[0534] Users can use their devices to inquire with the system about the project's progress and safety status.

[0535] Step 11:

[0536] The server responds to the query.

[0537] The server uses natural language processing to analyze the user's question, generates an appropriate answer, and sends it back to the user via the terminal.

[0538] Step 12:

[0539] The user executes the optimization suggestion.

[0540] Based on optimization suggestions received from the server, users adjust the project plan and reallocate resources.

[0541] In this way, the system manages project progress and safety in real time, supporting efficient operation.

[0542] (Example 1)

[0543] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0544] Efficient and accurate information gathering and analysis are required for project management and safety management in construction projects. However, conventional systems have limitations in data analysis accuracy and real-time information updates, making it difficult to optimize the entire project. Furthermore, it is difficult to monitor on-site safety in real time, resulting in problems with rapid response to anomalies.

[0545] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0546] In this invention, the server includes means for inputting project management information, analysis means, evaluation means, calculation means, means for monitoring detection device data, means for detecting anomalies, means for generating warnings, notification means, real-time data acquisition means, and communication means. This enables visualization and optimization of project progress, as well as real-time monitoring of on-site safety and rapid response.

[0547] "Project management information" is a general term for data related to a construction project, such as progress, budget, schedule, and work plan.

[0548] "Analysis means" refers to devices or software that perform calculations and analyses based on input project management information, and may particularly utilize artificial intelligence technology.

[0549] "Evaluation methods" refer to functions and processes for determining the progress of a project based on data and information obtained through analysis methods.

[0550] "Computational tools" refer to devices or software that perform calculations necessary to determine the optimal allocation of personnel and materials in order to improve the efficiency of a project.

[0551] "Detection device data" refers to environmental data such as temperature, vibration, and sound collected by sensors and monitoring devices used on-site.

[0552] "Means for detecting anomalies" refer to a system that analyzes detection device data and issues a warning when a condition deviates from predetermined norms or thresholds.

[0553] "Means for generating warnings" refers to a function that creates notifications to alert users or workers based on detected anomalies.

[0554] A "notification method" refers to a communication path used by devices and applications to convey important information to users, such as warnings and optimization suggestions.

[0555] A "real-time data acquisition method" is a system for instantly monitoring the situation on-site and transmitting necessary data to a server in real time.

[0556] "Communication means" refers to network infrastructure and protocols for sending and receiving information between servers, users, and terminals.

[0557] This invention is a system for effectively managing the progress and safety of construction projects, in which a server, terminals, and users work in conjunction. The server receives project management information entered by users through terminals and stores it in a database. The hardware consists of a general-purpose server computer and a cloud storage system. The software utilizes an AI platform for analyzing generative AI models.

[0558] The server uses a generative AI model to analyze the stored data. During this process, the analysis results generate project progress evaluations and optimization proposals. For example, an AI algorithm built using the Python programming language provides suggestions for optimal material placement and schedule adjustments. Users receive and view this information using their devices, which can include mobile devices or desktop computers.

[0559] Furthermore, the server acquires real-time data from on-site sensors and drones to detect anomalies and manage safety. IoT devices are used for this purpose, and the data is transmitted to the server via AWS IoT Core, etc. If an anomaly is detected, the server sends an alert to the user through the terminal. This process allows the user to respond quickly.

[0560] For example, if the program detects a delay in project progress, it analyzes the cause and proposes a new material ordering schedule. The user then uses this optimization plan to reallocate staff and adjust schedules. Furthermore, when a sensor detects vibrations on-site, the server immediately analyzes the data and sends an alert, allowing the user to take appropriate action on-site.

[0561] Specific examples of prompt messages include, "If temporary construction work is behind schedule in a construction project, please propose a new staffing and material procurement schedule." In this way, collaboration between servers, terminals, and users enables the efficient and secure execution of projects.

[0562] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0563] Step 1:

[0564] Users input project management information using a terminal. This data includes work progress, budget, schedule, and resource allocation. This information is transmitted from the terminal to the server and stored in a database.

[0565] Step 2:

[0566] The server analyzes stored project management information using a generating AI model. This analysis detects schedule delays, resource shortages, and other issues based on the input data. Specifically, the data is normalized and filtered, and the AI ​​model outputs predictions.

[0567] Step 3:

[0568] Based on the analysis results, the server creates a plan to optimize the project's progress. This process uses optimization algorithms to generate proposals for new schedules and resource allocations. The resulting optimization plan is then sent to the terminal as an actionable plan.

[0569] Step 4:

[0570] The server acquires real-time data from the field through sensors and drones. This data includes environmental and safety information from the field, primarily using temperature and vibration measurements as inputs. Based on this information, the server detects anomalies and generates warnings if an anomaly occurs.

[0571] Step 5:

[0572] Warnings and optimization suggestions generated by the server are notified to the terminal. The terminal displays this information to the user, providing them with the information to decide on countermeasures. The user can issue instructions to the field and revise or adjust plans via the terminal.

[0573] Step 6:

[0574] Users can send questions to the server in natural language via their device. The server analyzes these questions using a generative AI model, generates appropriate responses, and sends them back to the device. This allows users to immediately obtain detailed information about the project.

[0575] (Application Example 1)

[0576] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0577] Construction projects require effective, real-time progress and safety management. However, conventional systems have resulted in dispersed monitoring of progress and safety across various departments, and the lack of data integration has hindered rapid response. Therefore, a system is needed that centralizes project management, enables real-time visualization of progress, and allows for immediate response to anomalies.

[0578] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0579] In this invention, the server includes a device for receiving project-related information, a machine learning device for analyzing the information, a device for evaluating the progress of activities based on the analysis, a device for detecting data from monitoring devices and identifying anomalies, and a visualization device for visualizing the data in real time. This makes it possible to grasp the progress of the project in real time and immediately issue warnings when anomalies are detected.

[0580] "Project-related information" refers to a collection of various data related to the progress, resource usage, and safety of a construction project.

[0581] A "machine learning device" is a device equipped with technology to perform tasks such as prediction and classification by analyzing input data and learning patterns.

[0582] A "device for evaluating the progress of activities" is a device that has the function of monitoring the progress of a project and evaluating whether it is proceeding according to schedule.

[0583] A "monitoring device" is a device that records various situations at a project site in real time and uses that data to detect anomalies.

[0584] A "device for identifying anomalies" is a device that analyzes data from monitoring equipment to detect and identify events that are different from the normal state.

[0585] A "data visualization device for real-time visualization" is a device that can instantly and visually display project progress and safety data.

[0586] A "warning notification device" is a device that immediately transmits warning information when an anomaly is detected, prompting appropriate action.

[0587] To implement this invention, the server first receives information related to project management. The server comprehensively aggregates data on project progress, resource usage, and safety, and analyzes it using a machine learning device. As a result, the server functions as a device that evaluates project progress and visualizes its progress.

[0588] Furthermore, the server receives data transmitted in real time from monitoring devices and acts as a device for identifying anomalies. For example, if there are abnormalities in vibration or temperature at a construction site, the server will immediately detect the anomaly and act as an alerting device to draw the attention of those involved.

[0589] Users can receive information from the server through their terminals. These terminals act as visualization devices, displaying project progress and anomaly information in real time. Furthermore, users can query the system in natural language via a natural language processing device using a generative AI model, enabling the server to respond quickly.

[0590] In particular, in large-scale commercial facility construction projects, the system operates by monitoring the progress of each task to ensure smooth operation and guaranteeing safety on site. This real-time visualization of project information allows all stakeholders to make timely decisions and enables efficient management.

[0591] Examples of prompts to input into a generative AI model:

[0592] "You are a project manager at a construction site. You need to efficiently manage site progress and safety by monitoring real-time data and responding immediately to any anomalies. Design an application for this purpose."

[0593] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0594] Step 1:

[0595] The server receives information about the project. Inputs include project progress, resource usage, and safety data. This data is sent to the server as input from sensors and management systems. The server then prepares to store the received data in its database.

[0596] Step 2:

[0597] The server analyzes the received project data using a machine learning device. The input is the project data received in step 1. The server uses a machine learning model to extract patterns and trends from the data. The output is the analysis results regarding the project's progress and predicted risks.

[0598] Step 3:

[0599] The server sends the progress evaluation results to the terminal via a visualization device. The input data is the analysis results from step 2. The server uses visualization software (e.g., Pandas, Matplotlib) to visually represent the progress in graphs and charts. The output is a real-time dashboard of the project.

[0600] Step 4:

[0601] The server analyzes real-time data from monitoring devices and identifies anomalies. Inputs include information such as vibration, temperature, and humidity from various sensors. The server uses an anomaly detection algorithm to identify data points that deviate from the normal range. Outputs are warning messages indicating that an anomaly has been detected.

[0602] Step 5:

[0603] The server activates an alerting device to immediately issue a warning when an anomaly is detected. The input is the warning information from step 4. The server utilizes a notification service to send a warning via push notification or email to the relevant user's device. The output is a real-time alert informing the user of the anomaly.

[0604] Step 6:

[0605] The user reviews information received from the server via their terminal and responds as needed. Input consists of visualization data and warning information provided by the server. Based on this information, the user makes project decisions and implements resource reallocation and plan revisions. Output consists of project improvement suggestions and instructions.

[0606] Step 7:

[0607] Users can query the server in natural language through a generative AI model. The input is the user's question. The server uses a natural language processing engine to analyze the question and provide appropriate information. The output is the answer to the user's question.

[0608] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0609] This invention provides a system that enables more personalized communication and decision-making in construction project progress management by taking user emotions into consideration. This system is a complex system involving servers, terminals, and users, and in addition to managing and analyzing project data, it can monitor and reflect the emotional state of users.

[0610] The server simultaneously acquires project management data and user emotion data entered by the user through the terminal. The server's AI analyzes this data and generates optimal resource allocation and work instructions that take into account the user's emotional state, along with an evaluation of the project's progress.

[0611] Furthermore, the server is equipped with an emotion engine that analyzes the user's emotional state in real time through user input and feedback. For example, if the system determines that the user is experiencing stress, it can generate a notification message as a mitigation measure and offer suggestions to reduce the burden on the project.

[0612] The device presents the user with not only notifications and optimization suggestions received from the server, but also emotion-based responses and suggestions. This allows the user to choose appropriate actions that match their current emotional state, facilitating smooth project progress.

[0613] When a user queries the system using natural language, the server uses natural language processing and an emotion engine to generate a response appropriate to the user's emotional state. This makes user interactions more human-like and enables quick and appropriate responses to project challenges and risks.

[0614] For example, if a user is feeling pressured during construction, the server's emotion engine can sense this and send messages through the terminal to encourage relaxation or suggest revising work assignments. By adjusting project responses based on user emotions in this way, on-site safety and efficiency can be further enhanced.

[0615] Thus, with this invention, construction project management goes beyond mere data analysis; it also takes into account user emotions, making it possible to improve the overall project success rate.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] Users enter project data and sentiment data.

[0619] Users use their devices to input data such as project schedules and budget status, and also record their emotional state through questionnaires or voice input.

[0620] Step 2:

[0621] The device sends data to the server.

[0622] The terminal encrypts the entered project data and emotion data and sends them to the server.

[0623] Step 3:

[0624] The server receives the data and saves it to the database.

[0625] The server receives project data and emotion data sent from the terminal and stores them in a database.

[0626] Step 4:

[0627] The server analyzes the project data.

[0628] The server uses AI to analyze project data and evaluate its progress and resource needs.

[0629] Step 5:

[0630] The server analyzes the emotional data.

[0631] The server uses an emotion engine to analyze the user's emotional data and understand their emotional state.

[0632] Step 6:

[0633] The server calculates project optimization.

[0634] The server calculates the optimal resource allocation and work instructions while considering the project's progress and the users' emotional state.

[0635] Step 7:

[0636] The server generates a notification and sends it to the device.

[0637] The server generates and sends notifications to the terminal based on the project's progress and messages tailored to the user's emotional state.

[0638] Step 8:

[0639] The device displays a notification.

[0640] The terminal displays information received from the server on the screen and prompts the user. This includes ongoing issues and suggestions for improvement.

[0641] Step 9:

[0642] The user queries the system.

[0643] Users send feedback to the system via their devices, including questions and emotional responses about the project.

[0644] Step 10:

[0645] The server responds using natural language processing and sentiment analysis.

[0646] The server analyzes user inquiries using natural language processing and an emotion engine, generates responses appropriate to the user's emotional state, and sends them back to the terminal.

[0647] Through this process, the system integrates project management and user emotions, providing an optimized management structure.

[0648] (Example 2)

[0649] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0650] In project management, while progress tracking and resource optimization are important, there is a lack of consideration for users' emotional states. This can lead to decreased project efficiency and team satisfaction. Therefore, there is a need for technology that integrates project management data and emotional data to provide appropriate responses and suggestions based on users' emotions.

[0651] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0652] In this invention, the server includes means for inputting project management data and emotional data, artificial intelligence means for analyzing the project management data and emotional data, and means for evaluating the project progress and emotional state based on the analysis. This makes it possible to generate resource allocation and work instructions that take into account the emotional state as well as the evaluation of the project progress.

[0653] "Project management data" refers to data that includes information about the project's progress, resource allocation, schedule, and so on.

[0654] "Emotional data" refers to data that includes information reflecting the user's emotional state, such as stress, satisfaction, and motivation.

[0655] "Artificial intelligence tools" refers to machine learning algorithms and natural language processing techniques used to analyze project management data and sentiment data.

[0656] "Computational means" refers to processes and programs used to perform calculations necessary for optimizing personnel and resources.

[0657] "Natural language processing means" refers to technologies used to respond to inquiries from users in natural language and generate appropriate responses.

[0658] "Resource allocation" refers to the appropriate placement and use plan of the various resources and personnel required for a project.

[0659] "Emotional state" refers to the user's emotional characteristics, such as stress, anxiety, and satisfaction.

[0660] "Proposal generation" refers to the process of devising solutions and improvement measures to provide to users based on evaluation results.

[0661] This invention provides a system for optimizing project management by integrating and analyzing progress data and user sentiment data in the field of project management. This system consists of a server, terminals, and users, and enables the generation of optimal suggestions based on project management data and sentiment.

[0662] The server receives information from terminals to input project management data and sentiment data. This data includes information about project progress, resource allocation, and the user's emotional state. The server uses machine learning libraries such as TensorFlow and PyTorch, as well as natural language processing techniques, to analyze this data. This makes it possible to evaluate project progress while simultaneously generating resource allocations and work instructions that take the user's emotional state into account.

[0663] As a concrete example, when the server receives the prompt "Is the project progressing smoothly?", it analyzes project progress data and user sentiment data. If the progress is good, it generates a response such as "The project is progressing according to plan." On the other hand, if the user is feeling anxious, it provides a sentiment-sensitive response such as "Progress is fine, but we will consider allocating additional resources."

[0664] The terminal plays the role of displaying notifications and suggestions from the server to the user, who can then choose actions based on the information and suggestions presented. User feedback forms a loop throughout the system, enabling continuous improvement. In this way, the embodiment of the present invention can increase the efficiency of project management while simultaneously meeting the emotional needs of the user.

[0665] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0666] Step 1:

[0667] The user inputs project management data and emotional data through a terminal. This input takes the form of progress reports and questionnaires indicating emotional states. The terminal sends this input information to the server as digital data. The output is the data sent to the server.

[0668] Step 2:

[0669] The server analyzes project management data and sentiment data received from the terminal. This analysis utilizes machine learning algorithms using TensorFlow and PyTorch, as well as natural language processing techniques. The input consists of various data received from the user, and the output is the analysis results. Specifically, progress is evaluated and the user's emotional state is determined.

[0670] Step 3:

[0671] The server evaluates the overall project progress and the user's emotional state based on the analysis results. The input here is the analysis results from step 2. The server processes the data to optimize resource allocation and generate work instructions that respond to emotions, creating optimization proposals and suggestions as output.

[0672] Step 4:

[0673] The server sends the generated optimization suggestions and proposals to the terminal. The terminal presents this information to the user. In this step, the notifications and suggestions sent from the server become input, and the terminal visualizes them and presents them to the user. The output is the information displayed to the user.

[0674] Step 5:

[0675] Users review suggestions presented by the server via their terminal and provide feedback as needed. This feedback is returned directly to the server and used in the next analysis. Input consists of the user's selections and comments, while output is new data to be used in the next analysis.

[0676] Step 6:

[0677] The server uses natural language processing techniques to generate responses to user inquiries. This process also takes into account the user's emotional state. The input is the user's inquiry, and the output is the response message. This allows the system to provide a more human-like interaction with the user.

[0678] (Application Example 2)

[0679] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0680] In modern construction project management, it is essential not only to understand technical progress but also to communicate with workers and managers while considering their emotional well-being. Traditional systems fail to address diverse stressors, impacting overall project efficiency and worker safety. To address this problem, there is an urgent need to develop a system that can appropriately manage the emotional state of participants in real time, along with project progress, enabling effective resource allocation and work instructions.

[0681] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0682] In this invention, the server includes means for inputting project management information, artificial intelligence means for analyzing the project management information, and means for evaluating the project progress based on the analysis. This enables accurate analysis of project management information. It also includes calculation means for optimizing resources and materials, means for monitoring sensor data and detecting anomalies, and means for generating notifications based on the anomalies. Furthermore, it includes means for monitoring the user's emotional state and analyzing emotional data, and means for presenting the user with emotionally appropriate messages based on the emotional data. This enables efficient project management through a comprehensive system that integrates project progress management and human-centered management.

[0683] "Project management information" refers to detailed data regarding the progress, schedule, resources, and task assignments of a construction project.

[0684] "Artificial intelligence tools" refer to methods that utilize algorithms and technologies to analyze data, learn from it, and support decision-making.

[0685] "Computational means" refers to functions or processes used for processing and calculating data.

[0686] "Sensor data" refers to information about the surrounding environment and equipment collected by sensors.

[0687] "Means for generating notifications" refers to a process or function for sending notifications to a user when specific conditions or abnormal states are detected.

[0688] "Emotional state" refers to the type and degree of emotion a user is feeling at that particular time.

[0689] "Emotional data" refers to information about a user's emotions obtained by analyzing their facial expressions, tone of voice, writing style, and other factors.

[0690] "Means of presenting a message" refers to methods or functions used to convey information to users visually or audibly.

[0691] The server first receives project management information as input. This includes schedules, resources, and work progress information related to the construction project. The server is equipped with artificial intelligence capabilities to analyze this information and evaluate the project's progress. For example, it can determine whether the planned work is progressing according to schedule.

[0692] Furthermore, the system utilizes computational methods to optimize resources and materials. This optimization allows for efficient project progress without wasting necessary resources. In addition, it monitors sensor data and generates notifications to inform users if an anomaly is detected. This enables a rapid response, ensuring the safety and efficiency of the project.

[0693] Furthermore, the system provides a means of sentiment analysis to monitor the user's emotional state. When a user accesses the system using a terminal, their facial expressions and tone of voice are analyzed using the camera and microphone, and their emotions are evaluated in real time. The sentiment data obtained here is used to generate appropriate responses and messages for the user. For example, if the system determines that the user is feeling stressed, the server will send a message encouraging relaxation and suggest ways to alleviate the burden.

[0694] As a concrete example, when workers at a construction site check the progress of a project through smart glasses, the program can analyze project management information and display appropriate instructions based on emotional data. The prompt text input to the generating AI model might be something like, "If the user is feeling very tired, what kind of break suggestion would be best?"

[0695] In this way, efficient and secure project management is achieved through collaboration between servers, terminals, and users.

[0696] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0697] Step 1:

[0698] The server receives project management information from the terminal. This information includes data on schedules, resource allocation, and work progress. This data is stored in a database and prepared as input for analysis by artificial intelligence. The output at this stage is project management information ready for analysis.

[0699] Step 2:

[0700] The server analyzes the received project management information using artificial intelligence. This analysis process evaluates schedule progress and resource usage. If necessary, optimizations to resources and work processes are proposed. The resulting output is an evaluation report on the project's progress.

[0701] Step 3:

[0702] The server optimizes resources and materials as needed, based on project progress evaluation reports. It uses computational tools to create an efficient resource allocation plan, avoiding excessive resource use and duplicated tasks. As a result, an optimized resource allocation instruction is output.

[0703] Step 4:

[0704] The server continuously monitors sensor data and generates notifications to promptly address any anomalies detected. For example, if abnormal temperature changes or vibrations are detected, an alert is immediately sent to the administrator. The input for this step is sensor data, and the output is an anomaly notification.

[0705] Step 5:

[0706] When a user checks the project status via their device, the server monitors the user's emotional state using camera and microphone inputs. It collects and analyzes user emotional data in real time by analyzing facial expressions and voice tone. The output is user emotional evaluation data.

[0707] Step 6:

[0708] The server generates appropriate messages and suggestions based on the user's emotional assessment data. For example, if the user is feeling stressed, it will offer suggestions to help them relax or provide options to reduce their workload. The output of this step is a message to the user tailored to their emotional state.

[0709] Step 7:

[0710] The user receives generated messages and selects the appropriate action. This facilitates project progress and reduces the user's burden. The output is the results of the actions taken in the field based on the user's selection.

[0711] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0712] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0713] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0714] [Fourth Embodiment]

[0715] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0716] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0717] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0718] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0719] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0720] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0721] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0722] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0723] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0724] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0725] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0726] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0727] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0728] This invention is a system for effectively implementing progress management and safety management in construction projects. This system operates through the coordinated efforts of a server, terminals, and users to manage and analyze various project-related information.

[0729] The server receives project management data entered by users via their terminals and stores this data in a database. The server's role is to analyze the received data using AI and evaluate the project's progress. In this process, it anticipates potential schedule delays and budget overruns and generates optimization plans.

[0730] Furthermore, the server analyzes Building Information Modeling (BIM) data to improve design and construction plans. This allows it to identify design inconsistencies and suggest efficient construction procedures for the entire project.

[0731] Meanwhile, the server acquires data in real time from on-site sensors and drones to monitor site safety. The sensors, for example, measure vibration and temperature in the work area and immediately issue a warning if any abnormalities are detected. This warning is processed by the server and, if necessary, notified to the user via a terminal.

[0732] Users can communicate with the system interactively using a terminal. The terminal displays notifications and optimization suggestions from the server, enabling users to issue on-site work instructions and revise plans based on that information. Users can also ask questions to the system in natural language, and the server provides information immediately in response to those questions.

[0733] For example, if temporary construction work is behind schedule in a construction project, the server proposes a new personnel allocation and material procurement schedule and notifies the user via a terminal. The user then uses this information to decide on additional personnel, thereby improving the project's progress. Additionally, if abnormal vibrations are detected in the work area through on-site safety monitoring, the server immediately issues a notification, allowing the user to take prompt action.

[0734] As described above, this system enables real-time monitoring and efficient management of project progress and safety.

[0735] The following describes the processing flow.

[0736] Step 1:

[0737] The user enters project data.

[0738] Users use their devices to input data such as project schedules, budgets, and progress.

[0739] Step 2:

[0740] The device sends data to the server.

[0741] The terminal transmits the entered data to the server in real time. The data is encrypted and transferred securely.

[0742] Step 3:

[0743] The server receives and stores the data.

[0744] The server stores the data received from the terminal in a database and checks the data's integrity.

[0745] Step 4:

[0746] The server analyzes the project data.

[0747] The server uses AI to analyze data stored in the database and evaluate the project's progress.

[0748] Step 5:

[0749] The server performs prediction and optimization.

[0750] Based on the analysis results, the server runs a predictive algorithm to forecast the possibility of schedule delays and budget overruns. Simultaneously, it calculates the optimal allocation of resources.

[0751] Step 6:

[0752] The server generates the notification.

[0753] Based on information obtained through prediction or analysis, the server generates necessary notifications and alerts and prepares information for the appropriate users.

[0754] Step 7:

[0755] The server monitors the sensor data.

[0756] The server acquires data in real time from on-site sensors and monitors the safety of the environment.

[0757] Step 8:

[0758] The server detects the anomaly and takes action.

[0759] The system analyzes sensor data, immediately issues an alert if an anomaly is detected, and sends a notification to the user.

[0760] Step 9:

[0761] The device displays a notification.

[0762] The terminal displays notifications and suggestions received from the server to the user, prompting immediate action.

[0763] Step 10:

[0764] The user queries the system.

[0765] Users can use their devices to inquire with the system about the project's progress and safety status.

[0766] Step 11:

[0767] The server responds to the query.

[0768] The server uses natural language processing to analyze the user's question, generates an appropriate answer, and sends it back to the user via the terminal.

[0769] Step 12:

[0770] The user executes the optimization suggestion.

[0771] Based on optimization suggestions received from the server, users adjust the project plan and reallocate resources.

[0772] In this way, the system manages project progress and safety in real time, supporting efficient operation.

[0773] (Example 1)

[0774] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0775] Efficient and accurate information gathering and analysis are required for project management and safety management in construction projects. However, conventional systems have limitations in data analysis accuracy and real-time information updates, making it difficult to optimize the entire project. Furthermore, it is difficult to monitor on-site safety in real time, resulting in problems with rapid response to anomalies.

[0776] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0777] In this invention, the server includes means for inputting project management information, analysis means, evaluation means, calculation means, means for monitoring detection device data, means for detecting anomalies, means for generating warnings, notification means, real-time data acquisition means, and communication means. This enables visualization and optimization of project progress, as well as real-time monitoring of on-site safety and rapid response.

[0778] "Project management information" is a general term for data related to a construction project, such as progress, budget, schedule, and work plan.

[0779] "Analysis means" refers to devices or software that perform calculations and analyses based on input project management information, and may particularly utilize artificial intelligence technology.

[0780] "Evaluation methods" refer to functions and processes for determining the progress of a project based on data and information obtained through analysis methods.

[0781] "Computational tools" refer to devices or software that perform calculations necessary to determine the optimal allocation of personnel and materials in order to improve the efficiency of a project.

[0782] "Detection device data" refers to environmental data such as temperature, vibration, and sound collected by sensors and monitoring devices used on-site.

[0783] "Means for detecting anomalies" refer to a system that analyzes detection device data and issues a warning when a condition deviates from predetermined norms or thresholds.

[0784] "Means for generating warnings" refers to a function that creates notifications to alert users or workers based on detected anomalies.

[0785] A "notification method" refers to a communication path used by devices and applications to convey important information to users, such as warnings and optimization suggestions.

[0786] A "real-time data acquisition method" is a system for instantly monitoring the situation on-site and transmitting necessary data to a server in real time.

[0787] "Communication means" refers to network infrastructure and protocols for sending and receiving information between servers, users, and terminals.

[0788] This invention is a system for effectively managing the progress and safety of construction projects, in which a server, terminals, and users work in conjunction. The server receives project management information entered by users through terminals and stores it in a database. The hardware consists of a general-purpose server computer and a cloud storage system. The software utilizes an AI platform for analyzing generative AI models.

[0789] The server uses a generative AI model to analyze the stored data. During this process, the analysis results generate project progress evaluations and optimization proposals. For example, an AI algorithm built using the Python programming language provides suggestions for optimal material placement and schedule adjustments. Users receive and view this information using their devices, which can include mobile devices or desktop computers.

[0790] Furthermore, the server acquires real-time data from on-site sensors and drones to detect anomalies and manage safety. IoT devices are used for this purpose, and the data is transmitted to the server via AWS IoT Core, etc. If an anomaly is detected, the server sends an alert to the user through the terminal. This process allows the user to respond quickly.

[0791] For example, if the program detects a delay in project progress, it analyzes the cause and proposes a new material ordering schedule. The user then uses this optimization plan to reallocate staff and adjust schedules. Furthermore, when a sensor detects vibrations on-site, the server immediately analyzes the data and sends an alert, allowing the user to take appropriate action on-site.

[0792] Specific examples of prompt messages include, "If temporary construction work is behind schedule in a construction project, please propose a new staffing and material procurement schedule." In this way, collaboration between servers, terminals, and users enables the efficient and secure execution of projects.

[0793] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0794] Step 1:

[0795] Users input project management information using a terminal. This data includes work progress, budget, schedule, and resource allocation. This information is transmitted from the terminal to the server and stored in a database.

[0796] Step 2:

[0797] The server analyzes stored project management information using a generating AI model. This analysis detects schedule delays, resource shortages, and other issues based on the input data. Specifically, the data is normalized and filtered, and the AI ​​model outputs predictions.

[0798] Step 3:

[0799] Based on the analysis results, the server creates a plan to optimize the project's progress. This process uses optimization algorithms to generate proposals for new schedules and resource allocations. The resulting optimization plan is then sent to the terminal as an actionable plan.

[0800] Step 4:

[0801] The server acquires real-time data from the field through sensors and drones. This data includes environmental and safety information from the field, primarily using temperature and vibration measurements as inputs. Based on this information, the server detects anomalies and generates warnings if an anomaly occurs.

[0802] Step 5:

[0803] Warnings and optimization suggestions generated by the server are notified to the terminal. The terminal displays this information to the user, providing them with the information to decide on countermeasures. The user can issue instructions to the field and revise or adjust plans via the terminal.

[0804] Step 6:

[0805] Users can send questions to the server in natural language via their device. The server analyzes these questions using a generative AI model, generates appropriate responses, and sends them back to the device. This allows users to immediately obtain detailed information about the project.

[0806] (Application Example 1)

[0807] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0808] Construction projects require effective, real-time progress and safety management. However, conventional systems have resulted in dispersed monitoring of progress and safety across various departments, and the lack of data integration has hindered rapid response. Therefore, a system is needed that centralizes project management, enables real-time visualization of progress, and allows for immediate response to anomalies.

[0809] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0810] In this invention, the server includes a device for receiving project-related information, a machine learning device for analyzing the information, a device for evaluating the progress of activities based on the analysis, a device for detecting data from monitoring devices and identifying anomalies, and a visualization device for visualizing the data in real time. This makes it possible to grasp the progress of the project in real time and immediately issue warnings when anomalies are detected.

[0811] "Project-related information" refers to a collection of various data related to the progress, resource usage, and safety of a construction project.

[0812] A "machine learning device" is a device equipped with technology to perform tasks such as prediction and classification by analyzing input data and learning patterns.

[0813] A "device for evaluating the progress of activities" is a device that has the function of monitoring the progress of a project and evaluating whether it is proceeding according to schedule.

[0814] A "monitoring device" is a device that records various situations at a project site in real time and uses that data to detect anomalies.

[0815] A "device for identifying anomalies" is a device that analyzes data from monitoring equipment to detect and identify events that are different from the normal state.

[0816] A "data visualization device for real-time visualization" is a device that can instantly and visually display project progress and safety data.

[0817] A "warning notification device" is a device that immediately transmits warning information when an anomaly is detected, prompting appropriate action.

[0818] To implement this invention, the server first receives information related to project management. The server comprehensively aggregates data on project progress, resource usage, and safety, and analyzes it using a machine learning device. As a result, the server functions as a device that evaluates project progress and visualizes its progress.

[0819] Furthermore, the server receives data transmitted in real time from monitoring devices and acts as a device for identifying anomalies. For example, if there are abnormalities in vibration or temperature at a construction site, the server will immediately detect the anomaly and act as an alerting device to draw the attention of those involved.

[0820] Users can receive information from the server through their terminals. These terminals act as visualization devices, displaying project progress and anomaly information in real time. Furthermore, users can query the system in natural language via a natural language processing device using a generative AI model, enabling the server to respond quickly.

[0821] In particular, in large-scale commercial facility construction projects, the system operates by monitoring the progress of each task to ensure smooth operation and guaranteeing safety on site. This real-time visualization of project information allows all stakeholders to make timely decisions and enables efficient management.

[0822] Examples of prompts to input into a generative AI model:

[0823] "You are a project manager at a construction site. You need to efficiently manage site progress and safety by monitoring real-time data and responding immediately to any anomalies. Design an application for this purpose."

[0824] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0825] Step 1:

[0826] The server receives information about the project. Inputs include project progress, resource usage, and safety data. This data is sent to the server as input from sensors and management systems. The server then prepares to store the received data in its database.

[0827] Step 2:

[0828] The server analyzes the received project data using a machine learning device. The input is the project data received in step 1. The server uses a machine learning model to extract patterns and trends from the data. The output is the analysis results regarding the project's progress and predicted risks.

[0829] Step 3:

[0830] The server sends the progress evaluation results to the terminal via a visualization device. The input data is the analysis results from step 2. The server uses visualization software (e.g., Pandas, Matplotlib) to visually represent the progress in graphs and charts. The output is a real-time dashboard of the project.

[0831] Step 4:

[0832] The server analyzes real-time data from monitoring devices and identifies anomalies. Inputs include information such as vibration, temperature, and humidity from various sensors. The server uses an anomaly detection algorithm to identify data points that deviate from the normal range. Outputs are warning messages indicating that an anomaly has been detected.

[0833] Step 5:

[0834] The server activates an alerting device to immediately issue a warning when an anomaly is detected. The input is the warning information from step 4. The server utilizes a notification service to send a warning via push notification or email to the relevant user's device. The output is a real-time alert informing the user of the anomaly.

[0835] Step 6:

[0836] The user reviews information received from the server via their terminal and responds as needed. Input consists of visualization data and warning information provided by the server. Based on this information, the user makes project decisions and implements resource reallocation and plan revisions. Output consists of project improvement suggestions and instructions.

[0837] Step 7:

[0838] Users can query the server in natural language through a generative AI model. The input is the user's question. The server uses a natural language processing engine to analyze the question and provide appropriate information. The output is the answer to the user's question.

[0839] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0840] This invention provides a system that enables more personalized communication and decision-making in construction project progress management by taking user emotions into consideration. This system is a complex system involving servers, terminals, and users, and in addition to managing and analyzing project data, it can monitor and reflect the emotional state of users.

[0841] The server simultaneously acquires project management data and user emotion data entered by the user through the terminal. The server's AI analyzes this data and generates optimal resource allocation and work instructions that take into account the user's emotional state, along with an evaluation of the project's progress.

[0842] Furthermore, the server is equipped with an emotion engine that analyzes the user's emotional state in real time through user input and feedback. For example, if the system determines that the user is experiencing stress, it can generate a notification message as a mitigation measure and offer suggestions to reduce the burden on the project.

[0843] The device presents the user with not only notifications and optimization suggestions received from the server, but also emotion-based responses and suggestions. This allows the user to choose appropriate actions that match their current emotional state, facilitating smooth project progress.

[0844] When a user queries the system using natural language, the server uses natural language processing and an emotion engine to generate a response appropriate to the user's emotional state. This makes user interactions more human-like and enables quick and appropriate responses to project challenges and risks.

[0845] For example, if a user is feeling pressured during construction, the server's emotion engine can sense this and send messages through the terminal to encourage relaxation or suggest revising work assignments. By adjusting project responses based on user emotions in this way, on-site safety and efficiency can be further enhanced.

[0846] Thus, with this invention, construction project management goes beyond mere data analysis; it also takes into account user emotions, making it possible to improve the overall project success rate.

[0847] The following describes the processing flow.

[0848] Step 1:

[0849] Users enter project data and sentiment data.

[0850] Users use their devices to input data such as project schedules and budget status, and also record their emotional state through questionnaires or voice input.

[0851] Step 2:

[0852] The device sends data to the server.

[0853] The terminal encrypts the entered project data and emotion data and sends them to the server.

[0854] Step 3:

[0855] The server receives the data and saves it to the database.

[0856] The server receives project data and emotion data sent from the terminal and stores them in a database.

[0857] Step 4:

[0858] The server analyzes the project data.

[0859] The server uses AI to analyze project data and evaluate its progress and resource needs.

[0860] Step 5:

[0861] The server analyzes the emotional data.

[0862] The server uses an emotion engine to analyze the user's emotional data and understand their emotional state.

[0863] Step 6:

[0864] The server calculates project optimization.

[0865] The server calculates the optimal resource allocation and work instructions while considering the project's progress and the users' emotional state.

[0866] Step 7:

[0867] The server generates a notification and sends it to the device.

[0868] The server generates and sends notifications to the terminal based on the project's progress and messages tailored to the user's emotional state.

[0869] Step 8:

[0870] The device displays a notification.

[0871] The terminal displays information received from the server on the screen and prompts the user. This includes ongoing issues and suggestions for improvement.

[0872] Step 9:

[0873] The user queries the system.

[0874] Users send feedback to the system via their devices, including questions and emotional responses about the project.

[0875] Step 10:

[0876] The server responds using natural language processing and sentiment analysis.

[0877] The server analyzes user inquiries using natural language processing and an emotion engine, generates responses appropriate to the user's emotional state, and sends them back to the terminal.

[0878] Through this process, the system integrates project management and user emotions, providing an optimized management structure.

[0879] (Example 2)

[0880] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0881] In project management, while progress tracking and resource optimization are important, there is a lack of consideration for users' emotional states. This can lead to decreased project efficiency and team satisfaction. Therefore, there is a need for technology that integrates project management data and emotional data to provide appropriate responses and suggestions based on users' emotions.

[0882] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0883] In this invention, the server includes means for inputting project management data and emotional data, artificial intelligence means for analyzing the project management data and emotional data, and means for evaluating the project progress and emotional state based on the analysis. This makes it possible to generate resource allocation and work instructions that take into account the emotional state as well as the evaluation of the project progress.

[0884] "Project management data" refers to data that includes information about the project's progress, resource allocation, schedule, and so on.

[0885] "Emotional data" refers to data that includes information reflecting the user's emotional state, such as stress, satisfaction, and motivation.

[0886] "Artificial intelligence tools" refers to machine learning algorithms and natural language processing techniques used to analyze project management data and sentiment data.

[0887] "Computational means" refers to processes and programs used to perform calculations necessary for optimizing personnel and resources.

[0888] "Natural language processing means" refers to technologies used to respond to inquiries from users in natural language and generate appropriate responses.

[0889] "Resource allocation" refers to the appropriate placement and use plan of the various resources and personnel required for a project.

[0890] "Emotional state" refers to the user's emotional characteristics, such as stress, anxiety, and satisfaction.

[0891] "Proposal generation" refers to the process of devising solutions and improvement measures to provide to users based on evaluation results.

[0892] This invention provides a system for optimizing project management by integrating and analyzing progress data and user sentiment data in the field of project management. This system consists of a server, terminals, and users, and enables the generation of optimal suggestions based on project management data and sentiment.

[0893] The server receives information from terminals to input project management data and sentiment data. This data includes information about project progress, resource allocation, and the user's emotional state. The server uses machine learning libraries such as TensorFlow and PyTorch, as well as natural language processing techniques, to analyze this data. This makes it possible to evaluate project progress while simultaneously generating resource allocations and work instructions that take the user's emotional state into account.

[0894] As a concrete example, when the server receives the prompt "Is the project progressing smoothly?", it analyzes project progress data and user sentiment data. If the progress is good, it generates a response such as "The project is progressing according to plan." On the other hand, if the user is feeling anxious, it provides a sentiment-sensitive response such as "Progress is fine, but we will consider allocating additional resources."

[0895] The terminal plays the role of displaying notifications and suggestions from the server to the user, who can then choose actions based on the information and suggestions presented. User feedback forms a loop throughout the system, enabling continuous improvement. In this way, the embodiment of the present invention can increase the efficiency of project management while simultaneously meeting the emotional needs of the user.

[0896] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0897] Step 1:

[0898] The user inputs project management data and emotional data through a terminal. This input takes the form of progress reports and questionnaires indicating emotional states. The terminal sends this input information to the server as digital data. The output is the data sent to the server.

[0899] Step 2:

[0900] The server analyzes project management data and sentiment data received from the terminal. This analysis utilizes machine learning algorithms using TensorFlow and PyTorch, as well as natural language processing techniques. The input consists of various data received from the user, and the output is the analysis results. Specifically, progress is evaluated and the user's emotional state is determined.

[0901] Step 3:

[0902] The server evaluates the overall project progress and the user's emotional state based on the analysis results. The input here is the analysis results from step 2. The server processes the data to optimize resource allocation and generate work instructions that respond to emotions, creating optimization proposals and suggestions as output.

[0903] Step 4:

[0904] The server sends the generated optimization suggestions and proposals to the terminal. The terminal presents this information to the user. In this step, the notifications and suggestions sent from the server become input, and the terminal visualizes them and presents them to the user. The output is the information displayed to the user.

[0905] Step 5:

[0906] Users review suggestions presented by the server via their terminal and provide feedback as needed. This feedback is returned directly to the server and used in the next analysis. Input consists of the user's selections and comments, while output is new data to be used in the next analysis.

[0907] Step 6:

[0908] The server uses natural language processing techniques to generate responses to user inquiries. This process also takes into account the user's emotional state. The input is the user's inquiry, and the output is the response message. This allows the system to provide a more human-like interaction with the user.

[0909] (Application Example 2)

[0910] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0911] In modern construction project management, it is essential not only to understand technical progress but also to communicate with workers and managers while considering their emotional well-being. Traditional systems fail to address diverse stressors, impacting overall project efficiency and worker safety. To address this problem, there is an urgent need to develop a system that can appropriately manage the emotional state of participants in real time, along with project progress, enabling effective resource allocation and work instructions.

[0912] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0913] In this invention, the server includes means for inputting project management information, artificial intelligence means for analyzing the project management information, and means for evaluating the project progress based on the analysis. This enables accurate analysis of project management information. It also includes calculation means for optimizing resources and materials, means for monitoring sensor data and detecting anomalies, and means for generating notifications based on the anomalies. Furthermore, it includes means for monitoring the user's emotional state and analyzing emotional data, and means for presenting the user with emotionally appropriate messages based on the emotional data. This enables efficient project management through a comprehensive system that integrates project progress management and human-centered management.

[0914] "Project management information" refers to detailed data regarding the progress, schedule, resources, and task assignments of a construction project.

[0915] "Artificial intelligence tools" refer to methods that utilize algorithms and technologies to analyze data, learn from it, and support decision-making.

[0916] "Computational means" refers to functions or processes used for processing and calculating data.

[0917] "Sensor data" refers to information about the surrounding environment and equipment collected by sensors.

[0918] "Means for generating notifications" refers to a process or function for sending notifications to a user when specific conditions or abnormal states are detected.

[0919] "Emotional state" refers to the type and degree of emotion a user is feeling at that particular time.

[0920] "Emotional data" refers to information about a user's emotions obtained by analyzing their facial expressions, tone of voice, writing style, and other factors.

[0921] "Means of presenting a message" refers to methods or functions used to convey information to users visually or audibly.

[0922] The server first receives project management information as input. This includes schedules, resources, and work progress information related to the construction project. The server is equipped with artificial intelligence capabilities to analyze this information and evaluate the project's progress. For example, it can determine whether the planned work is progressing according to schedule.

[0923] Furthermore, the system utilizes computational methods to optimize resources and materials. This optimization allows for efficient project progress without wasting necessary resources. In addition, it monitors sensor data and generates notifications to inform users if an anomaly is detected. This enables a rapid response, ensuring the safety and efficiency of the project.

[0924] Furthermore, the system provides a means of sentiment analysis to monitor the user's emotional state. When a user accesses the system using a terminal, their facial expressions and tone of voice are analyzed using the camera and microphone, and their emotions are evaluated in real time. The sentiment data obtained here is used to generate appropriate responses and messages for the user. For example, if the system determines that the user is feeling stressed, the server will send a message encouraging relaxation and suggest ways to alleviate the burden.

[0925] As a concrete example, when workers at a construction site check the progress of a project through smart glasses, the program can analyze project management information and display appropriate instructions based on emotional data. The prompt text input to the generating AI model might be something like, "If the user is feeling very tired, what kind of break suggestion would be best?"

[0926] In this way, efficient and secure project management is achieved through collaboration between servers, terminals, and users.

[0927] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0928] Step 1:

[0929] The server receives project management information from the terminal. This information includes data on schedules, resource allocation, and work progress. This data is stored in a database and prepared as input for analysis by artificial intelligence. The output at this stage is project management information ready for analysis.

[0930] Step 2:

[0931] The server analyzes the received project management information using artificial intelligence. This analysis process evaluates schedule progress and resource usage. If necessary, optimizations to resources and work processes are proposed. The resulting output is an evaluation report on the project's progress.

[0932] Step 3:

[0933] The server optimizes resources and materials as needed, based on project progress evaluation reports. It uses computational tools to create an efficient resource allocation plan, avoiding excessive resource use and duplicated tasks. As a result, an optimized resource allocation instruction is output.

[0934] Step 4:

[0935] The server continuously monitors sensor data and generates notifications to promptly address any anomalies detected. For example, if abnormal temperature changes or vibrations are detected, an alert is immediately sent to the administrator. The input for this step is sensor data, and the output is an anomaly notification.

[0936] Step 5:

[0937] When a user checks the project status via their device, the server monitors the user's emotional state using camera and microphone inputs. It collects and analyzes user emotional data in real time by analyzing facial expressions and voice tone. The output is user emotional evaluation data.

[0938] Step 6:

[0939] The server generates appropriate messages and suggestions based on the user's emotional assessment data. For example, if the user is feeling stressed, it will offer suggestions to help them relax or provide options to reduce their workload. The output of this step is a message to the user tailored to their emotional state.

[0940] Step 7:

[0941] The user receives generated messages and selects the appropriate action. This facilitates project progress and reduces the user's burden. The output is the results of the actions taken in the field based on the user's selection.

[0942] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0943] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0944] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0945] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0946] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0947] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0948] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0949] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0950] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0951] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0952] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0953] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0954] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0955] 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.

[0956] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0957] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0958] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0959] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0960] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0961] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0962] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0963] The following is further disclosed regarding the embodiments described above.

[0964] (Claim 1)

[0965] A means of entering project management data,

[0966] Artificial intelligence means for analyzing the aforementioned project management data,

[0967] A means for evaluating the project progress based on the aforementioned analysis,

[0968] A calculation means for optimizing personnel and materials,

[0969] A means of monitoring sensor data and detecting anomalies,

[0970] A system including means for generating a notification based on the aforementioned anomaly.

[0971] (Claim 2)

[0972] The system according to claim 1, comprising means for analyzing building information modeling data in order to improve the design and construction plans of a construction site.

[0973] (Claim 3)

[0974] The system according to claim 1, further comprising natural language processing means for providing natural language responses to user inquiries.

[0975] "Example 1"

[0976] (Claim 1)

[0977] A means of entering project management information,

[0978] Artificial intelligence means for analyzing the aforementioned project management information,

[0979] A means for evaluating the project progress based on the aforementioned analysis,

[0980] A computational means for optimizing resources and materials,

[0981] A means for monitoring detection device data and detecting anomalies,

[0982] Means for generating a warning based on the aforementioned anomaly,

[0983] Means for notifying the output device of the aforementioned warnings and optimization results,

[0984] A means of acquiring data from the field in real time,

[0985] A system that includes communication means enabling mutual information exchange.

[0986] (Claim 2)

[0987] The system according to claim 1, comprising means for analyzing three-dimensional model data in order to improve the design and construction planning of a construction area.

[0988] (Claim 3)

[0989] The system according to claim 1, comprising natural language processing means for providing natural language responses to user inquiries.

[0990] "Application Example 1"

[0991] (Claim 1)

[0992] A device for receiving project-related information,

[0993] A machine learning device for analyzing the aforementioned information,

[0994] A device for evaluating the progress of activity based on the aforementioned analysis,

[0995] A computing device for optimizing human resources and materials,

[0996] A device that detects data from monitoring equipment and identifies abnormalities,

[0997] A device that generates a notification based on the aforementioned abnormality,

[0998] A visualization device for visualizing data in real time,

[0999] A system that includes an alarm device that immediately issues a warning when an anomaly is detected.

[1000] (Claim 2)

[1001] The system according to claim 1, comprising a device for analyzing building information modeling information in order to improve design and work procedures at a construction site.

[1002] (Claim 3)

[1003] The system according to claim 1, comprising a natural language processing device for providing responses in natural language to user questions.

[1004] "Example 2 of combining an emotion engine"

[1005] (Claim 1)

[1006] A means of inputting project management data and sentiment data,

[1007] Artificial intelligence means for analyzing the aforementioned project management data and emotional data,

[1008] A means for evaluating the project progress and emotional state based on the aforementioned analysis,

[1009] A computing means for optimizing personnel and resources,

[1010] A means for monitoring emotional states and generating situation-appropriate responses,

[1011] A system that includes means for generating optimal suggestions based on the user's emotions.

[1012] (Claim 2)

[1013] The system according to claim 1, comprising means for providing guidance to improve the design and construction planning of a construction site based on sentiment data.

[1014] (Claim 3)

[1015] The system according to claim 1, comprising natural language processing means for providing natural language responses to user inquiries and for generating appropriate responses according to the user's emotional state.

[1016] "Application example 2 when combining with an emotional engine"

[1017] (Claim 1)

[1018] A means of entering project management information,

[1019] Artificial intelligence means for analyzing the aforementioned project management information,

[1020] A means for evaluating the project progress based on the aforementioned analysis,

[1021] A computational means for optimizing resources and materials,

[1022] A means of monitoring sensor data and detecting anomalies,

[1023] Means for generating a notification based on the aforementioned anomaly,

[1024] A means of monitoring the user's emotional state and analyzing emotional data,

[1025] A means for presenting a message to the user that corresponds to their emotions, based on the aforementioned emotion data,

[1026] A system that includes this.

[1027] (Claim 2)

[1028] The system according to claim 1, comprising means for analyzing structural information modeling data in order to improve the design and construction planning of a construction site.

[1029] (Claim 3)

[1030] The system according to claim 1, further comprising language processing means for providing natural language responses to user inquiries. [Explanation of Symbols]

[1031] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device for receiving project-related information, A machine learning device for analyzing the aforementioned information, A device for evaluating the progress of activity based on the aforementioned analysis, A computing device for optimizing human resources and materials, A device that detects data from monitoring equipment and identifies abnormalities, A device that generates a notification based on the aforementioned abnormality, A visualization device for visualizing data in real time, A system that includes an alarm device that immediately issues a warning when an anomaly is detected.

2. The system according to claim 1, comprising a device for analyzing building information modeling information in order to improve design and work procedures at a construction site.

3. The system according to claim 1, comprising a natural language processing device for providing responses in natural language to user questions.

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