system

Abstract

We provide the system. [Solution] Means of collecting data from multiple sources, A means of integrating and processing the collected data on the cloud, A means for analyzing data using generative artificial intelligence to predict and detect problems at construction sites, A means for simulating multiple solution scenarios based on analysis results and generating the optimal process plan, A means of notifying workers' terminals of the optimal process plan via the network, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] At construction sites, many problems such as work delays, material shortages, and difficulties in personnel adjustment frequently occur. Such problems not only hinder the progress of the project but may also lead to increased costs and quality degradation. In addition, to solve these problems, a great burden is imposed on site supervisors and workers, and quick and accurate decision-making is required. In conventional systems, each data source is handled fragmentarily, making it difficult to effectively solve problems. Therefore, there is a need for a system that overcomes these problems and improves the efficiency of the entire construction site.

Means for Solving the Problems

[0005] This invention provides means for collecting data from multiple sources and integrating and processing it on the cloud. Furthermore, it enables the prediction and detection of problems at construction sites through analysis using generative artificial intelligence. It also provides means for simulating multiple solution scenarios and generating an optimal project plan. This project plan is notified to workers' terminals via the network, enabling quick and efficient responses to on-site problems. Moreover, by providing means for predicting risks based on past data and suggesting preventative measures in advance, as well as pre-processing means for removing anomalies, it achieves more accurate and comprehensive problem solving.

[0006] "Information sources" refer to multiple different starting points used to collect data, specifically including daily reports, sensors, cameras, and weather data.

[0007] "Cloud" is a general term for computing services provided via the internet, enabling on-demand access to computer resources.

[0008] "Generative artificial intelligence" refers to artificial intelligence technology that analyzes patterns and trends from data, automatically generates insights, and makes suggestions for problem solving.

[0009] "Problem prediction and detection" is the process of identifying potential future problems and ongoing problems through data analysis.

[0010] "Simulation" is a technique that evaluates appropriate problem-solving methods by simulating a given situation in a virtual environment and trying out various scenarios.

[0011] A "project plan" refers to a plan of work procedures and resource allocation optimized under specific conditions, with the aim of efficient project execution.

[0012] "Terminal" refers to a computer device used by a user, specifically including mobile devices such as smartphones and tablets. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Next, 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system for achieving efficient construction site management and is implemented according to the following procedure. The main components are a data acquisition module, a data integration and preprocessing module, an analysis module, a simulation module, and a notification and suggestion module.

[0035] \

[0036] \text{1. Data Collection:}

[0037] \

[0038] The server integrates diverse data sources from the field, such as daily reports, sensor data, camera footage, and weather data, into the cloud. This data collection is performed automatically according to a regular schedule, centralizing data in various formats.

[0039] \

[0040] 2. Data Integration and Preprocessing:

[0041] \

[0042] The server converts the collected raw data into a format suitable for analysis. By standardizing date information and formatting, and by imputing missing data and removing outliers, it achieves accurate data integration.

[0043] \

[0044] 3. Data Analysis:

[0045] \

[0046] The server uses AI to analyze integrated data and predict and detect potential problems at construction sites. For example, it analyzes trends such as delays in material delivery and personnel shortages or surpluses, enabling early development of countermeasures.

[0047] \

[0048] 4. Simulation:

[0049] \

[0050] The server generates multiple solution scenarios for the problems identified through analysis and evaluates them through simulation. The process plan that achieves maximum efficiency in the shortest timeframe is selected.

[0051] \

[0052] \text{5. Notices and Suggestions:}

[0053] \

[0054] The terminal notifies on-site workers and managers of the selected process plan. The notification includes specific work procedures and proposed resource reallocations, and is designed to be provided in a format that can be implemented on-site.

[0055] \

[0056] Specific example:

[0057] \

[0058] For example, suppose an analysis predicts a delay in the supply of material A at a construction site. In this case, the server simulates and optimizes a work schedule that involves engaging in other tasks (e.g., preparation or work in other sections) without waiting for the material to arrive. This schedule is then notified to the workers and site supervisors on their terminals, along with a detailed action plan, making it immediately actionable.

[0059] This allows users to minimize on-site delays and ensure the efficient progress of projects.

[0060] The following describes the processing flow.

[0061] Step 1:

[0062] The server periodically collects data from multiple sources, such as sensors, daily reports, and camera data, and sends it to the cloud. This allows for real-time, centralized access to the latest on-site conditions. Regardless of the data format, all data is integrated and stored in a database.

[0063] Step 2:

[0064] The server converts the collected data into a format that can be analyzed. Specifically, it standardizes data in different formats, fills in missing data, and identifies and removes outliers. This preprocessing ensures the quality of the data.

[0065] Step 3:

[0066] The server utilizes generative artificial intelligence to analyze integrated data and predict potential problems and risks in the future. By comparing it with past project data and performing pattern recognition, it identifies, for example, the possibility of material shortages or work delays.

[0067] Step 4:

[0068] Based on the analysis results, the server simulates and generates multiple solution scenarios to minimize the impact of the problem. It virtually executes the process under each scenario and selects the optimal option from the perspective of cost, time, and resources.

[0069] Step 5:

[0070] The terminal notifies the user of the optimal process plan received from the server. The notification is sent to the worker's or site supervisor's smartphone or tablet and includes specific instructions and procedures. This supports immediate decision-making on-site.

[0071] Step 6:

[0072] Based on notifications from their devices, users make necessary adjustments on-site. They ensure the project progresses efficiently by modifying materials, work sequences, or personnel allocation according to the proposed schedule.

[0073] (Example 1)

[0074] 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."

[0075] In modern work environments, particularly construction sites, it is crucial to efficiently aggregate and utilize data from multiple sources to identify potential problems early and implement appropriate countermeasures. However, conventional methods lack efficient ways to integrate and analyze data in different formats, resulting in wasted time and resources on site.

[0076] 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.

[0077] In this invention, the server includes means for collecting digital information from multiple information sources, means for integrating and processing the collected information on a network, and means for analyzing the information using a generative artificial intelligence model to predict and detect problems at the work site. This enables the use of data from different information sources in a unified manner, and the server provides multiple solution scenarios based on the analysis results, enabling optimal on-site operation.

[0078] "Information sources" refer to the locations and tools from which data is collected, including sensors, cameras, and daily reports.

[0079] "Digital information" refers to data expressed in a format that can be processed by a computer.

[0080] A "network" refers to an interconnected infrastructure that allows multiple computers and devices to communicate with one another.

[0081] "Integration" refers to the process of bringing together data from different formats and origins into a consistent structure.

[0082] "Processing" refers to the process of converting raw data into a format suitable for analysis and optimization.

[0083] A "generative artificial intelligence model" refers to an algorithm that learns from large amounts of data and uses that new information to make predictions and classifications.

[0084] "Analysis" refers to the process of breaking down data into smaller parts and understanding its characteristics and structure.

[0085] "Challenges" refer to obstacles or potential problems in carrying out work.

[0086] "Prediction" refers to the act of estimating future events based on past and present data.

[0087] "Detection" refers to the ability or technique to find specific patterns or anomalies.

[0088] A "solution scenario" refers to a set of steps or methods that present multiple possible solutions to a particular problem.

[0089] A "procedure" refers to the sequence of operations or processes designed to achieve a specific objective.

[0090] "Communication methods" refer to methods and devices for sending and receiving information and data.

[0091] A "device" refers to an instrument that has a specific function and is used by a user to receive and display information.

[0092] This system relies on a server to enable efficient management at the work site. The server has the means to collect digital information from multiple sources, aggregating information such as sensors, cameras, daily reports, and weather data via the network and storing it in the cloud. This process utilizes Python scripts and SFTP to ensure secure and automatic data transfer.

[0093] The collected data is integrated and processed by the server. Specifically, a data frame is constructed using the Pandas library, and date and time formats are standardized. Missing data is imputed and anomalous data is removed using statistical methods. This prepares clean data suitable for analysis.

[0094] Data analysis is performed by a generative artificial intelligence model installed on the server. A neural network using PyTorch or TENSORFLOW® learns from historical data and predicts potential issues in the field (e.g., delays in material supply) at an early stage. The prediction results are then analyzed over time using a recurrent neural network.

[0095] For example, in a construction project, if delays in material delivery are anticipated, the server immediately generates alternative work solution scenarios. The simulation engine uses AnyLogic or Simul8 to evaluate the cost and time of each scenario and select the most efficient procedure.

[0096] Subsequently, the selected procedures are notified to on-site workers and managers via their devices. Using Firebase Cloud Messaging, specific work plans are pushed to the devices in real time. These notifications can be viewed on the workers' mobile devices and tablets.

[0097] As a concrete example, by providing the AI ​​with the prompt, "Please propose the optimal process scenario when there is a delay in material arrival in a construction project," effective countermeasures can be obtained. In this way, users can optimize on-site work, reduce waste, and improve overall efficiency.

[0098] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0099] Step 1:

[0100] The server collects digital information from various sources, such as sensors, cameras, daily reports, and weather data. The input to this collection is the data provided by these sources. The server securely and automatically uploads this data to the cloud using Python scripts and the SFTP protocol. The output is structured data aggregated on the cloud.

[0101] Step 2:

[0102] The server integrates and processes the collected data. Because the input data contains various formats and missing values, the Pandas library is used to convert the data to a standard format and ensure consistency. Additionally, missing data is imputed and anomalous data is removed. The output is a clean dataset suitable for analysis.

[0103] Step 3:

[0104] The server performs data analysis using a generative artificial intelligence model. The input is a clean dataset. The server applies a neural network model using PyTorch or TensorFlow. Through training this model, it predicts future problems (e.g., delays in material arrival) based on time-series data. The output is the prediction of potential problems.

[0105] Step 4:

[0106] The server simulates solution scenarios for predicted challenges. The input consists of multiple solutions based on analysis results. Using simulation engines such as AnyLogic or Simul8, it calculates and evaluates the cost and time required for each scenario. As a result, the optimal work procedure is selected. The output is the optimized process proposal.

[0107] Step 5:

[0108] The terminal notifies workers and managers at the work site of the optimized process plan. The input is the process plan selected by the server. Firebase Cloud Messaging is used to push information to mobile devices and tablets in real time. The output is a notification containing specific work instructions.

[0109] (Application Example 1)

[0110] 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."

[0111] Construction sites face complex work management challenges, with frequent problems such as delays in materials and personnel shortages. Furthermore, site conditions change constantly due to weather and unforeseen events, requiring efficient work adjustments. Traditional systems have struggled to grasp site progress in real time and immediately adjust countermeasures, highlighting the challenge of improving efficiency and adaptability in construction work.

[0112] 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.

[0113] In this invention, the server includes means for collecting data from multiple information sources, means for integrating and processing the collected data on the cloud, and means for analyzing the data using generative artificial intelligence to predict and detect problems at the construction site. This enables workers to check the progress of the site in real time and adjust their work in response to changes in the situation.

[0114] "Information sources" refer to the diverse sources used to obtain data, which allows the system to collect comprehensive information.

[0115] "Means of data collection" refers to the process and methods for extracting and aggregating necessary data from multiple sources.

[0116] "Cloud" refers to distributed computing resources accessible via the internet, providing an environment for data storage and processing.

[0117] "Means of integration and processing" refers to a series of actions that centralize collected data in multiple formats and prepare it for analysis and interpretation.

[0118] "Generative artificial intelligence" refers to the ability of computers to analyze large amounts of data and use models to make predictions and inferences.

[0119] "Means of data analysis" refers to methods for investigating and evaluating current and future trends using collected and integrated data.

[0120] "Means for predicting and detecting problems" refers to methods for identifying potential issues and risks in advance based on patterns and anomalies found through data analysis.

[0121] "Methods for simulating solution scenarios" refer to procedures for verifying various improvement measures and countermeasures in a virtual environment for an identified problem.

[0122] "Methods for generating process plans" refer to methods for designing the most effective work procedures and schedules based on the results of simulations.

[0123] "Means of notification via a network" refers to methods for transmitting generated information or instructions to a worker's device using communication technology.

[0124] An "information terminal" refers to an electronic device that can be carried by a worker and is capable of receiving and displaying notified information.

[0125] This invention is a system that supports the efficient management of construction sites and aims to automate a series of processes from data collection and analysis to simulation and notification. The following describes embodiments for carrying out this invention.

[0126] The server first collects data from multiple sources. Specifically, it aggregates daily construction site reports, sensor data, camera footage, weather data, and other data into the cloud. This can utilize cloud platforms such as Microsoft Azure® and Google Cloud Platform. The server integrates the collected data and converts it into a format suitable for analysis. During this process, it standardizes the data format and removes missing and outlier values.

[0127] Next, the server analyzes the integrated data using generative artificial intelligence. This analysis is performed by a generative AI model to predict potential problems and identify risks at the construction site. Based on the data provided by the AI, the server simulates multiple solution scenarios.

[0128] The generated optimal work plan is notified to the worker's information terminal via the network. Smartphones are used here, allowing workers to check the progress on site in real time and adjust their work as needed. For example, if outdoor work is interrupted by rain, they can immediately receive a notification suggesting a shift to indoor work.

[0129] An example of a prompt message is given to the AI ​​model: "Based on the current weather and material supply situation, please suggest the next work step." The entire system works together to dramatically improve the efficiency and adaptability of construction sites.

[0130] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0131] Step 1:

[0132] The server collects diverse data from construction sites, including daily reports, sensor data, camera footage, and weather data. It utilizes a cloud platform to acquire information from various data sources via APIs and other means. The input here is raw data from each information source, while the output is a unified dataset on the cloud.

[0133] Step 2:

[0134] The server converts the collected raw data into a format suitable for analysis. The data standardization process includes unifying date formats and removing outliers. The input is the integrated data obtained in step 1, and the output is clean, consistent, and analysis-ready data.

[0135] Step 3:

[0136] The server analyzes clean data using a generative AI model. Here, it predicts potential problems regarding material arrival forecasts and personnel allocation optimization. The input is pre-processed data, and the output is potential problem and risk information as a result of the analysis.

[0137] Step 4:

[0138] The server generates multiple solution scenarios based on the analysis results and simulates each scenario. The simulations involve optimizing process efficiency and resource allocation. The input is the analysis results, and the output is the optimal process plan.

[0139] Step 5:

[0140] The server notifies the terminal via the network of the optimal process plan. The terminal user can then review this plan as a concrete work procedure and adjust the work as needed. The input is the optimal process plan, and the output is specific action guidelines for the field.

[0141] Step 6:

[0142] The user checks the notification received on their device and adjusts their on-site work based on the suggested tasks. For example, if outdoor work is interrupted by rain, the user can immediately perform the alternative tasks suggested by the system. The input is the notification content displayed on the device, and the output is the new work flow to be performed by the user.

[0143] 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.

[0144] This invention integrates an emotion engine that recognizes user emotions into an efficient management system for construction sites, thereby achieving a user-friendly interface and decision support. The system includes a data acquisition module, a data integration and preprocessing module, an analysis module, a simulation module, a notification and suggestion module, and an emotion engine module.

[0145] \

[0146] \text{1. Data Collection and Integration:}

[0147] \

[0148] The server aggregates diverse data from the field and converts it into a unified format. This data includes sensor information, daily reports, camera footage, and weather data.

[0149] \

[0150] 2. Data Analysis and Simulation:

[0151] \

[0152] The server analyzes the collected data using artificial intelligence. This allows it to predict future problems and risks and generate appropriate solution scenarios. A simulation module evaluates these scenarios and selects the most efficient process plan.

[0153] \

[0154] 3. User recognition using an emotion engine:

[0155] \

[0156] The device uses an emotion engine to recognize the user's emotions in real time from the camera and voice input. This emotion data is reflected in the proposed process plans and interfaces, enabling responses that take into account the user's stress and anxiety.

[0157] \

[0158] 4. Notifications and Suggestions:

[0159] \

[0160] The device adjusts and notifies the user of the optimal process plan based on their emotional state. For example, if the user is feeling stressed, it will respond by providing concise and easy-to-understand language and additional support information.

[0161] \

[0162] Specific example:

[0163] \

[0164] At a construction site, if the weather suddenly changes and could disrupt work, the server analyzes this information and generates an appropriate work plan. The terminal recognizes the site supervisor's emotions, and if the supervisor is feeling anxious, for example, it provides a work plan with emphasized explanations, along with easily accessible support information, thereby reducing unnecessary stress.

[0165] This system enables process management that takes user emotions into consideration, improving efficiency and comfort at construction sites.

[0166] The following describes the processing flow.

[0167] Step 1:

[0168] The server periodically collects data from various sources, such as sensors, cameras, daily reports, and weather data, and sends it to the cloud. This allows for centralized management of the latest information from the field.

[0169] Step 2:

[0170] The server standardizes the collected data and preprocesses it into a format suitable for analysis. By filling in missing data and eliminating outliers, the accuracy of the analysis is improved.

[0171] Step 3:

[0172] The server uses generative artificial intelligence to analyze integrated data and predict potential problems and risks. Based on the analysis results, it detects the possibility of material shortages or work delays predicted in the near future.

[0173] Step 4:

[0174] The server generates multiple solution scenarios based on the analysis results and evaluates them through simulation. It then generates the selected optimal process plan and verifies its feasibility.

[0175] Step 5:

[0176] The device uses an emotion engine to recognize the user's emotions through the camera and microphone, and determines the user's stress level and emotional state.

[0177] Step 6:

[0178] The device adjusts the process plan according to the user's emotions, and notifies them with improved presentation and additional information. For example, it adds supportive information to reassure users who are feeling anxious.

[0179] Step 7:

[0180] Users check notifications from their devices and modify actual processes and personnel allocations based on the proposed adjustments. This ensures efficient and stress-free project execution.

[0181] (Example 2)

[0182] 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".

[0183] At construction sites and other work environments, there is a need to efficiently collect data from various sources to enable early problem detection and the development of optimal project plans. However, the complexity and diverse formats of the data hinder real-time analysis and decision-making. Furthermore, while it is desirable to take into account the emotions of on-site workers to enable more flexible and appropriate responses, conventional systems do not adequately achieve this.

[0184] 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.

[0185] In this invention, the server includes means for collecting data from multiple sources, means for integrating and transforming the collected data on a data processing infrastructure, and means for analyzing the data using a trained model to predict problems in the work site. This enables the development of flexible process plans that take into account the user's emotional state and provides rapid decision-making support.

[0186] "Information sources" refer to a variety of devices and systems that transmit data, including sensors, reports, video equipment, and weather information systems.

[0187] "Data processing infrastructure" refers to computing resources and platforms for storing, integrating, transforming, and analyzing collected data, and typically consists of computer networks and cloud environments.

[0188] A "pre-trained model" refers to an algorithm that has been trained in advance using machine learning or artificial intelligence techniques, and is used for data analysis and prediction.

[0189] A "work site" refers to a place where physical work such as construction or manufacturing takes place, and where there is a need to improve operational efficiency and safety.

[0190] "Means of predicting problems" refers to techniques and methods used to identify potential risks and obstacles in advance through data analysis.

[0191] A "process plan" refers to a work plan or procedure manual that has been formulated with consideration for work efficiency and safety.

[0192] "Developing flexible process plans" refers to the process of creating optimal work procedures in real time based on data analysis, tailored to the specific situation.

[0193] "Decision support" refers to technologies and methods that provide information and schedules to enable optimal and effective choices in tasks and operations.

[0194] This invention is a system that enables efficient work management at construction sites and other locations. It primarily involves a server and terminals working together to provide the user with the most suitable work plan. The details are described below.

[0195] The server collects data from the field through IoT devices and data collection platforms. Specifically, it aggregates sensor information, daily reports, camera footage, weather data, etc., integrates this data on a cloud infrastructure, and converts it to JSON format as needed. This standardizes the data so that it can be used for data analysis and forecasting.

[0196] The server then analyzes the collected data using machine learning frameworks such as TensorFlow. Here, historical and real-time data are combined to predict future risks and problems. This analysis allows for the early detection of issues such as the risk of work interruptions under specific weather conditions or material shortages.

[0197] The user's device receives data sent from the server and utilizes facial recognition technology and voice analysis tools (e.g., Microsoft's Emotion API) to analyze the user's emotions in real time. It senses stress and anxiety from the user's facial expressions and voice, and incorporates corresponding feedback into the process plan.

[0198] This system offers a concrete example of flexible process management in response to weather fluctuations. For instance, if the weather suddenly deteriorates during the day, the server analyzes weather data and suggests temporary work suspensions or shift changes. The terminal determines whether the site supervisor is feeling anxious and notifies them of specific support measures as needed.

[0199] An example of a prompt message is, "Based on the latest work site data, analyze the site supervisor's emotions and generate suggestions to reduce stress," which allows the system to provide suggestions tailored to the user.

[0200] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0201] Step 1:

[0202] The server collects sensor data, daily reports, camera footage, and weather data from construction sites. This information is input via IoT devices and data collection platforms. The server converts the data, provided in different formats, into a unified format (e.g., JSON). This standardizes the data, making it ready for later analysis.

[0203] Step 2:

[0204] The server analyzes unified data using a machine learning framework (e.g., TensorFlow). The input data is combined with historical and real-time data and processed by a generative AI model. This analysis process predicts future risks and problems, such as the risk of work interruptions due to weather changes. The analysis results are output as a risk assessment and proposed countermeasures.

[0205] Step 3:

[0206] The server generates multiple process plans based on the analysis results and selects the optimal plan through simulation. The input data analysis results are evaluated by the simulation module. This results in the output of efficient work schedules and resource allocation plans.

[0207] Step 4:

[0208] The terminal notifies the user of the optimal process plan received from the server. During this process, real-time emotional data obtained from the user's camera and microphone is input. The terminal uses facial recognition technology and voice analysis tools to evaluate the user's emotional state. If the user is experiencing high stress, the terminal outputs and displays a process plan that emphasizes specific support and advice.

[0209] Step 5:

[0210] Based on the provided process plan and supporting information, users make on-site decisions. User feedback and new data are returned to Step 1 and incorporated into the data collection process. This allows the system to continuously update and optimize information, enabling it to continue providing suggestions that meet user needs.

[0211] (Application Example 2)

[0212] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0213] Construction site management processes are complex and involve a variety of intertwined factors, making traditional statistical data insufficient. On-site leadership and the mental state of workers significantly impact management efficiency. Furthermore, rapid responses to unexpected situations and sudden environmental changes are required. Therefore, a management system capable of flexible process proposals that take workers' emotions into consideration is necessary.

[0214] 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.

[0215] In this invention, the server includes means for collecting data from multiple information sources, means for integrating and processing the collected data on the cloud, means for analyzing the data using generative artificial intelligence to predict and detect problems at construction sites, means for simulating multiple solution scenarios based on the analysis results and generating an optimal process plan, means for recognizing the user's emotions and adjusting the process plan based on their emotional state, and means for notifying the worker's mobile terminal of the adjusted optimal process plan via the network. This enables flexible and effective process management that takes into account the correlation between understanding the situation at the site and emotions.

[0216] A "source of information" refers to any basis or origin for providing data or information, including devices such as sensors, cameras, and audio devices.

[0217] "Cloud" refers to a distributed data storage and computing resource located on the internet, providing an environment for data storage and processing.

[0218] "Generative artificial intelligence" refers to a system that utilizes machine learning algorithms to extract patterns from data and has the ability to make predictions and judgments.

[0219] "Analysis" is the process of thoroughly examining data and extracting information and knowledge.

[0220] "Simulation" is a technique that simulates real-world systems and situations to predict the outcomes of different scenarios.

[0221] "User emotions" refer to the psychological sensations and states experienced by the user, and are inferred from voice and facial expressions.

[0222] A "project plan" is a plan or scenario that outlines the steps and schedule of a task or process.

[0223] A "network" is a communication infrastructure used to send and receive data between different devices.

[0224] A "mobile device" is a portable device capable of performing information processing, and includes devices such as smartphones and tablets.

[0225] The system for realizing this invention mainly consists of a server and terminals. The server is equipped with hardware and software environments for collecting data from various sources and integrating and processing it on the cloud. Specifically, it uses cloud platforms such as Amazon Web Services (AWS®) and Microsoft Azure, and performs data analysis using programming languages ​​such as Python and R for data processing.

[0226] The server uses generative artificial intelligence models to analyze data in real time. This analysis detects problems at construction sites and simulates solutions. Machine learning libraries such as TensorFlow and PyTorch are particularly utilized.

[0227] The device utilizes input devices such as cameras and microphones to recognize the user's emotions. By using Microsoft's Azure Face API and Google Cloud's Natural Language API, it infers emotions from the user's facial expressions and voice, and sends this information to the server. This information is then used to adjust the process plan.

[0228] The server sends the adjusted project plan to the terminal, and the mobile device notifies the user. This notification is optimized according to the user's emotional state.

[0229] As a concrete example, consider a situation at a construction site where a sudden change in weather affects the work schedule. The server analyzes weather data and generates an alternative schedule. If the user expresses concern, the terminal provides reassurance by sending a notification containing a detailed explanation.

[0230] An example of a prompt message is, "Based on the sentiment analysis of the current situation, please generate alternative work plans to reduce stress."

[0231] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0232] Step 1:

[0233] The server collects various information from construction sites through sensors, cameras, microphones, and other means. Inputs include daily site reports, video data, audio data, and weather data. This data is uploaded to the server and undergoes data integration processing to convert it into a unified format. The output is an integrated dataset.

[0234] Step 2:

[0235] The server performs analysis using a generative AI model. It receives an integrated dataset as input and analyzes the data using machine learning algorithms. Through this analysis, it predicts problems and risks in the work. As output, it generates analysis results and a risk assessment report.

[0236] Step 3:

[0237] The server performs scenario simulations based on the analysis results. Using the analysis results as input, it generates multiple possible solution scenarios. The simulation selects the most efficient process plan. The selected process plan is generated as output.

[0238] Step 4:

[0239] The device recognizes the user's emotions in real time using the camera and microphone. It acquires the user's video and audio data as input. This data is analyzed using an emotion recognition engine to evaluate the user's emotional state. As output, it generates recognized emotion data.

[0240] Step 5:

[0241] The server adjusts the process plan considering the user's emotional data. It uses the selected process plan and recognized emotional data as input. Based on prompts, it optimizes the process plan to reduce user stress. The adjusted process plan is generated as output.

[0242] Step 6:

[0243] The terminal notifies the user of the optimized process plan. It receives the adjusted process plan as input. It presents it clearly to the user via the notification system, adding supplementary explanations to alleviate concerns. As output, it sends a notification to the user containing details of the adjusted plan.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] [Second Embodiment]

[0248] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0249] 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.

[0250] 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).

[0251] 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.

[0252] 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.

[0253] 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).

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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".

[0260] This invention is a system for achieving efficient construction site management and is implemented according to the following procedure. The main components are a data acquisition module, a data integration and preprocessing module, an analysis module, a simulation module, and a notification and suggestion module.

[0261] \

[0262] \text{1. Data Collection:}

[0263] \

[0264] The server integrates diverse data sources from the field, such as daily reports, sensor data, camera footage, and weather data, into the cloud. This data collection is performed automatically according to a regular schedule, centralizing data in various formats.

[0265] \

[0266] 2. Data Integration and Preprocessing:

[0267] \

[0268] The server converts the collected raw data into a format suitable for analysis. By standardizing date information and formatting, and by imputing missing data and removing outliers, it achieves accurate data integration.

[0269] \

[0270] 3. Data Analysis:

[0271] \

[0272] The server uses AI to analyze integrated data and predict and detect potential problems at construction sites. For example, it analyzes trends such as delays in material delivery and personnel shortages or surpluses, enabling early development of countermeasures.

[0273] \

[0274] 4. Simulation:

[0275] \

[0276] The server generates multiple solution scenarios for the problems identified through analysis and evaluates them through simulation. The process plan that achieves maximum efficiency in the shortest timeframe is selected.

[0277] \

[0278] \text{5. Notices and Suggestions:}

[0279] \[

[0280] The terminal notifies the selected process plan to the on-site workers and managers. The notification content includes specific work procedures and resource reallocation plans, and is designed to be provided in a form that can be implemented on-site.

[0281] \[

[0282] Specific example:

[0283] \[

[0284] For example, in a certain construction site, it is predicted by analysis that the supply of material A will be delayed. In this case, the server simulates a process plan that allows workers to engage in other tasks (such as preparatory work or work in other sections) without waiting for the arrival of the material, and attempts to optimize it. The terminal notifies this process plan to the workers and on-site supervisors along with a specific action plan, making it immediately executable.

[0285] This enables users to minimize on-site delays and ensure the efficient progress of the project.

[0286] The following describes the processing flow.

[0287] Step 1:

[0288] The server periodically collects data from multiple information sources such as sensors, daily reports, and camera data to the cloud. As a result, the latest situation on-site is unified in real-time. Even if the data formats are diverse, all are integrated and stored in a database.

[0289] Step 2:

[0290] The server converts the collected data into a format that can be analyzed. Specifically, it standardizes data in different formats, fills in missing data, and identifies and removes outliers. This preprocessing ensures the quality of the data.

[0291] Step 3:

[0292] The server utilizes generative artificial intelligence to analyze integrated data and predict potential problems and risks in the future. By comparing it with past project data and performing pattern recognition, it identifies, for example, the possibility of material shortages or work delays.

[0293] Step 4:

[0294] Based on the analysis results, the server simulates and generates multiple solution scenarios to minimize the impact of the problem. It virtually executes the process under each scenario and selects the optimal option from the perspective of cost, time, and resources.

[0295] Step 5:

[0296] The terminal notifies the user of the optimal process plan received from the server. The notification is sent to the worker's or site supervisor's smartphone or tablet and includes specific instructions and procedures. This supports immediate decision-making on-site.

[0297] Step 6:

[0298] Based on notifications from their devices, users make necessary adjustments on-site. They ensure the project progresses efficiently by modifying materials, work sequences, or personnel allocation according to the proposed schedule.

[0299] (Example 1)

[0300] 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."

[0301] In modern work sites, especially construction sites, it is very important to efficiently aggregate and utilize data obtained from multiple information sources, discover potential problems at the site at an early stage, and take appropriate measures. However, in conventional methods, an efficient method for integrating and analyzing data in different formats has not been established, resulting in waste of time and resources at the site.

[0302] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Example 1 is realized by the following respective means.

[0303] In this invention, the server includes means for collecting digital information from a plurality of information sources, means for integrating and processing the collected information on a network, and means for analyzing the information using a generated artificial intelligence model to predict and detect problems at the work site. Thereby, data from different information sources can be used in a unified form, and the server provides a plurality of solution scenarios based on the analysis results, enabling optimal on-site operation.

[0304] "Information source" refers to the location or tool where data is collected, including sensors, cameras, daily reports, etc.

[0305] "Digital information" refers to data expressed in a form that can be processed by a computer.

[0306] "Network" refers to an interconnected infrastructure for multiple computers and devices to communicate with each other.

[0307] "Integration" refers to the process of combining data in different formats and origins into a consistent structure.

[0308] "Processing" refers to the process of converting raw data into a form suitable for analysis and optimization.

[0309] A "generative artificial intelligence model" refers to an algorithm that learns from large amounts of data and uses that new information to make predictions and classifications.

[0310] "Analysis" refers to the process of breaking down data into smaller parts and understanding its characteristics and structure.

[0311] "Challenges" refer to obstacles or potential problems in carrying out work.

[0312] "Prediction" refers to the act of estimating future events based on past and present data.

[0313] "Detection" refers to the ability or technique to find specific patterns or anomalies.

[0314] A "solution scenario" refers to a set of steps or methods that present multiple possible solutions to a particular problem.

[0315] A "procedure" refers to the sequence of operations or processes designed to achieve a specific objective.

[0316] "Communication methods" refer to methods and devices for sending and receiving information and data.

[0317] A "device" refers to an instrument that has a specific function and is used by a user to receive and display information.

[0318] This system relies on a server to enable efficient management at the work site. The server has the means to collect digital information from multiple sources, aggregating information such as sensors, cameras, daily reports, and weather data via the network and storing it in the cloud. This process utilizes Python scripts and SFTP to ensure secure and automatic data transfer.

[0319] The collected data is integrated and processed by the server. Specifically, a data frame is constructed using the Pandas library, and date and time formats are standardized. Missing data is imputed and anomalous data is removed using statistical methods. This prepares clean data suitable for analysis.

[0320] Data analysis is performed by a generative artificial intelligence model installed on the server. A neural network using PyTorch or TensorFlow learns from historical data and predicts potential problems in the field (e.g., delays in material supply) at an early stage. The prediction results are then analyzed over time using a recurrent neural network.

[0321] For example, in a construction project, if delays in material delivery are anticipated, the server immediately generates alternative work solution scenarios. The simulation engine uses AnyLogic or Simul8 to evaluate the cost and time of each scenario and select the most efficient procedure.

[0322] Subsequently, the selected procedures are notified to on-site workers and managers via their devices. Using Firebase Cloud Messaging, specific work plans are pushed to the devices in real time. These notifications can be viewed on the workers' mobile devices and tablets.

[0323] As a concrete example, by providing the AI ​​with the prompt, "Please propose the optimal process scenario when there is a delay in material arrival in a construction project," effective countermeasures can be obtained. In this way, users can optimize on-site work, reduce waste, and improve overall efficiency.

[0324] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0325] Step 1:

[0326] The server collects digital information from various sources, such as sensors, cameras, daily reports, and weather data. The input to this collection is the data provided by these sources. The server securely and automatically uploads this data to the cloud using Python scripts and the SFTP protocol. The output is structured data aggregated on the cloud.

[0327] Step 2:

[0328] The server integrates and processes the collected data. Because the input data contains various formats and missing values, the Pandas library is used to convert the data to a standard format and ensure consistency. Additionally, missing data is imputed and anomalous data is removed. The output is a clean dataset suitable for analysis.

[0329] Step 3:

[0330] The server performs data analysis using a generative artificial intelligence model. The input is a clean dataset. The server applies a neural network model using PyTorch or TensorFlow. Through training this model, it predicts future problems (e.g., delays in material arrival) based on time-series data. The output is the prediction of potential problems.

[0331] Step 4:

[0332] The server simulates solution scenarios for predicted challenges. The input consists of multiple solutions based on analysis results. Using simulation engines such as AnyLogic or Simul8, it calculates and evaluates the cost and time required for each scenario. As a result, the optimal work procedure is selected. The output is the optimized process proposal.

[0333] Step 5:

[0334] The terminal notifies workers and managers at the work site of the optimized process plan. The input is the process plan selected by the server. Firebase Cloud Messaging is used to push information to mobile devices and tablets in real time. The output is a notification containing specific work instructions.

[0335] (Application Example 1)

[0336] 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."

[0337] Construction sites face complex work management challenges, with frequent problems such as delays in materials and personnel shortages. Furthermore, site conditions change constantly due to weather and unforeseen events, requiring efficient work adjustments. Traditional systems have struggled to grasp site progress in real time and immediately adjust countermeasures, highlighting the challenge of improving efficiency and adaptability in construction work.

[0338] 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.

[0339] In this invention, the server includes means for collecting data from multiple information sources, means for integrating and processing the collected data on the cloud, and means for analyzing the data using generative artificial intelligence to predict and detect problems at the construction site. This enables workers to check the progress of the site in real time and adjust their work in response to changes in the situation.

[0340] "Information sources" refer to the diverse sources used to obtain data, which allows the system to collect comprehensive information.

[0341] "Means of data collection" refers to the process and methods for extracting and aggregating necessary data from multiple sources.

[0342] "Cloud" refers to distributed computing resources accessible via the internet, providing an environment for data storage and processing.

[0343] "Means of integration and processing" refers to a series of actions that centralize collected data in multiple formats and prepare it for analysis and interpretation.

[0344] "Generative artificial intelligence" refers to the ability of computers to analyze large amounts of data and use models to make predictions and inferences.

[0345] "Means of data analysis" refers to methods for investigating and evaluating current and future trends using collected and integrated data.

[0346] "Means for predicting and detecting problems" refers to methods for identifying potential issues and risks in advance based on patterns and anomalies found through data analysis.

[0347] "Methods for simulating solution scenarios" refer to procedures for verifying various improvement measures and countermeasures in a virtual environment for an identified problem.

[0348] "Methods for generating process plans" refer to methods for designing the most effective work procedures and schedules based on the results of simulations.

[0349] "Means of notification via a network" refers to methods for transmitting generated information or instructions to a worker's device using communication technology.

[0350] An "information terminal" refers to an electronic device that can be carried by a worker and is capable of receiving and displaying notified information.

[0351] This invention is a system that supports the efficient management of construction sites and aims to automate a series of processes from data collection and analysis to simulation and notification. The following describes embodiments for carrying out this invention.

[0352] The server first collects data from multiple sources. Specifically, it aggregates daily reports from construction sites, sensor data, camera footage, weather data, and other data into the cloud. This can utilize cloud platforms such as Microsoft Azure or Google Cloud Platform. The server then integrates the collected data and converts it into a format suitable for analysis. During this process, it standardizes the data format and removes missing and outlier values.

[0353] Next, the server analyzes the integrated data using generative artificial intelligence. This analysis is performed by a generative AI model to predict potential problems and identify risks at the construction site. Based on the data provided by the AI, the server simulates multiple solution scenarios.

[0354] The generated optimal work plan is notified to the worker's information terminal via the network. Smartphones are used here, allowing workers to check the progress on site in real time and adjust their work as needed. For example, if outdoor work is interrupted by rain, they can immediately receive a notification suggesting a shift to indoor work.

[0355] An example of a prompt message is given to the AI ​​model: "Based on the current weather and material supply situation, please suggest the next work step." The entire system works together to dramatically improve the efficiency and adaptability of construction sites.

[0356] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0357] Step 1:

[0358] The server collects diverse data from construction sites, including daily reports, sensor data, camera footage, and weather data. It utilizes a cloud platform to acquire information from various data sources via APIs and other means. The input here is raw data from each information source, while the output is a unified dataset on the cloud.

[0359] Step 2:

[0360] The server converts the collected raw data into a format suitable for analysis. The data standardization process includes unifying date formats and removing outliers. The input is the integrated data obtained in step 1, and the output is clean, consistent, and analysis-ready data.

[0361] Step 3:

[0362] The server analyzes clean data using a generative AI model. Here, it predicts potential problems regarding material arrival forecasts and personnel allocation optimization. The input is pre-processed data, and the output is potential problem and risk information as a result of the analysis.

[0363] Step 4:

[0364] The server generates multiple solution scenarios based on the analysis results and simulates each scenario. The simulations involve optimizing process efficiency and resource allocation. The input is the analysis results, and the output is the optimal process plan.

[0365] Step 5:

[0366] The server notifies the terminal via the network of the optimal process plan. The terminal user can then review this plan as a concrete work procedure and adjust the work as needed. The input is the optimal process plan, and the output is specific action guidelines for the field.

[0367] Step 6:

[0368] The user checks the notification received on their device and adjusts their on-site work based on the suggested tasks. For example, if outdoor work is interrupted by rain, the user can immediately perform the alternative tasks suggested by the system. The input is the notification content displayed on the device, and the output is the new work flow to be performed by the user.

[0369] 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.

[0370] This invention integrates an emotion engine that recognizes user emotions into an efficient management system for construction sites, thereby achieving a user-friendly interface and decision support. The system includes a data acquisition module, a data integration and preprocessing module, an analysis module, a simulation module, a notification and suggestion module, and an emotion engine module.

[0371] \

[0372] \text{1. Data Collection and Integration:}

[0373] \

[0374] The server aggregates diverse data from the field and converts it into a unified format. This data includes sensor information, daily reports, camera footage, and weather data.

[0375] \

[0376] 2. Data Analysis and Simulation:

[0377] \

[0378] The server analyzes the collected data using artificial intelligence. This allows it to predict future problems and risks and generate appropriate solution scenarios. A simulation module evaluates these scenarios and selects the most efficient process plan.

[0379] \

[0380] 3. User recognition using an emotion engine:

[0381] \

[0382] The device uses an emotion engine to recognize the user's emotions in real time from the camera and voice input. This emotion data is reflected in the proposed process plans and interfaces, enabling responses that take into account the user's stress and anxiety.

[0383] \

[0384] 4. Notifications and Suggestions:

[0385] \

[0386] The device adjusts and notifies the user of the optimal process plan based on their emotional state. For example, if the user is feeling stressed, it will respond by providing concise and easy-to-understand language and additional support information.

[0387] \

[0388] Specific example:

[0389] \

[0390] At a construction site, if the weather suddenly changes and could disrupt work, the server analyzes this information and generates an appropriate work plan. The terminal recognizes the site supervisor's emotions, and if the supervisor is feeling anxious, for example, it provides a work plan with emphasized explanations, along with easily accessible support information, thereby reducing unnecessary stress.

[0391] This system enables process management that takes user emotions into consideration, improving efficiency and comfort at construction sites.

[0392] The following describes the processing flow.

[0393] Step 1:

[0394] The server periodically collects data from various sources, such as sensors, cameras, daily reports, and weather data, and sends it to the cloud. This allows for centralized management of the latest information from the field.

[0395] Step 2:

[0396] The server standardizes the collected data and preprocesses it into a format suitable for analysis. By filling in missing data and eliminating outliers, the accuracy of the analysis is improved.

[0397] Step 3:

[0398] The server uses generative artificial intelligence to analyze integrated data and predict potential problems and risks. Based on the analysis results, it detects the possibility of material shortages or work delays predicted in the near future.

[0399] Step 4:

[0400] The server generates multiple solution scenarios based on the analysis results and evaluates them through simulation. It then generates the selected optimal process plan and verifies its feasibility.

[0401] Step 5:

[0402] The device uses an emotion engine to recognize the user's emotions through the camera and microphone, and determines the user's stress level and emotional state.

[0403] Step 6:

[0404] The device adjusts the process plan according to the user's emotions, and notifies them with improved presentation and additional information. For example, it adds supportive information to reassure users who are feeling anxious.

[0405] Step 7:

[0406] Users check notifications from their devices and modify actual processes and personnel allocations based on the proposed adjustments. This ensures efficient and stress-free project execution.

[0407] (Example 2)

[0408] 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".

[0409] At construction sites and other work environments, there is a need to efficiently collect data from various sources to enable early problem detection and the development of optimal project plans. However, the complexity and diverse formats of the data hinder real-time analysis and decision-making. Furthermore, while it is desirable to take into account the emotions of on-site workers to enable more flexible and appropriate responses, conventional systems do not adequately achieve this.

[0410] 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.

[0411] In this invention, the server includes means for collecting data from multiple sources, means for integrating and transforming the collected data on a data processing infrastructure, and means for analyzing the data using a trained model to predict problems in the work site. This enables the development of flexible process plans that take into account the user's emotional state and provides rapid decision-making support.

[0412] "Information sources" refer to a variety of devices and systems that transmit data, including sensors, reports, video equipment, and weather information systems.

[0413] "Data processing infrastructure" refers to computing resources and platforms for storing, integrating, transforming, and analyzing collected data, and typically consists of computer networks and cloud environments.

[0414] A "pre-trained model" refers to an algorithm that has been trained in advance using machine learning or artificial intelligence techniques, and is used for data analysis and prediction.

[0415] A "work site" refers to a place where physical work such as construction or manufacturing takes place, and where there is a need to improve operational efficiency and safety.

[0416] "Means of predicting problems" refers to techniques and methods used to identify potential risks and obstacles in advance through data analysis.

[0417] A "process plan" refers to a work plan or procedure manual that has been formulated with consideration for work efficiency and safety.

[0418] "Developing flexible process plans" refers to the process of creating optimal work procedures in real time based on data analysis, tailored to the specific situation.

[0419] "Decision support" refers to technologies and methods that provide information and schedules to enable optimal and effective choices in tasks and operations.

[0420] This invention is a system that enables efficient work management at construction sites and other locations. It primarily involves a server and terminals working together to provide the user with the most suitable work plan. The details are described below.

[0421] The server collects data from the field through IoT devices and data collection platforms. Specifically, it aggregates sensor information, daily reports, camera footage, weather data, etc., integrates this data on a cloud infrastructure, and converts it to JSON format as needed. This standardizes the data so that it can be used for data analysis and forecasting.

[0422] The server then analyzes the collected data using machine learning frameworks such as TensorFlow. Here, historical and real-time data are combined to predict future risks and problems. This analysis allows for the early detection of issues such as the risk of work interruptions under specific weather conditions or material shortages.

[0423] The user's device receives data sent from the server and utilizes facial recognition technology and voice analysis tools (e.g., Microsoft's Emotion API) to analyze the user's emotions in real time. It senses stress and anxiety from the user's facial expressions and voice, and incorporates corresponding feedback into the process plan.

[0424] This system offers a concrete example of flexible process management in response to weather fluctuations. For instance, if the weather suddenly deteriorates during the day, the server analyzes weather data and suggests temporary work suspensions or shift changes. The terminal determines whether the site supervisor is feeling anxious and notifies them of specific support measures as needed.

[0425] An example of a prompt message is, "Based on the latest work site data, analyze the site supervisor's emotions and generate suggestions to reduce stress," which allows the system to provide suggestions tailored to the user.

[0426] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0427] Step 1:

[0428] The server collects sensor data, daily reports, camera footage, and weather data from construction sites. This information is input via IoT devices and data collection platforms. The server converts the data, provided in different formats, into a unified format (e.g., JSON). This standardizes the data, making it ready for later analysis.

[0429] Step 2:

[0430] The server analyzes unified data using a machine learning framework (e.g., TensorFlow). The input data is combined with historical and real-time data and processed by a generative AI model. This analysis process predicts future risks and problems, such as the risk of work interruptions due to weather changes. The analysis results are output as a risk assessment and proposed countermeasures.

[0431] Step 3:

[0432] The server generates multiple process plans based on the analysis results and selects the optimal plan through simulation. The input data analysis results are evaluated by the simulation module. This results in the output of efficient work schedules and resource allocation plans.

[0433] Step 4:

[0434] The terminal notifies the user of the optimal process plan received from the server. During this process, real-time emotional data obtained from the user's camera and microphone is input. The terminal uses facial recognition technology and voice analysis tools to evaluate the user's emotional state. If the user is experiencing high stress, the terminal outputs and displays a process plan that emphasizes specific support and advice.

[0435] Step 5:

[0436] Based on the provided process plan and supporting information, users make on-site decisions. User feedback and new data are returned to Step 1 and incorporated into the data collection process. This allows the system to continuously update and optimize information, enabling it to continue providing suggestions that meet user needs.

[0437] (Application Example 2)

[0438] 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."

[0439] Construction site management processes are complex and involve a variety of intertwined factors, making traditional statistical data insufficient. On-site leadership and the mental state of workers significantly impact management efficiency. Furthermore, rapid responses to unexpected situations and sudden environmental changes are required. Therefore, a management system capable of flexible process proposals that take workers' emotions into consideration is necessary.

[0440] 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.

[0441] In this invention, the server includes means for collecting data from multiple information sources, means for integrating and processing the collected data on the cloud, means for analyzing the data using generative artificial intelligence to predict and detect problems at construction sites, means for simulating multiple solution scenarios based on the analysis results and generating an optimal process plan, means for recognizing the user's emotions and adjusting the process plan based on their emotional state, and means for notifying the worker's mobile terminal of the adjusted optimal process plan via the network. This enables flexible and effective process management that takes into account the correlation between understanding the situation at the site and emotions.

[0442] A "source of information" refers to any basis or origin for providing data or information, including devices such as sensors, cameras, and audio devices.

[0443] "Cloud" refers to a distributed data storage and computing resource located on the internet, providing an environment for data storage and processing.

[0444] "Generative artificial intelligence" refers to a system that utilizes machine learning algorithms to extract patterns from data and has the ability to make predictions and judgments.

[0445] "Analysis" is the process of thoroughly examining data and extracting information and knowledge.

[0446] "Simulation" is a technique that simulates real-world systems and situations to predict the outcomes of different scenarios.

[0447] "User emotions" refer to the psychological sensations and states experienced by the user, and are inferred from voice and facial expressions.

[0448] A "project plan" is a plan or scenario that outlines the steps and schedule of a task or process.

[0449] A "network" is a communication infrastructure used to send and receive data between different devices.

[0450] A "mobile device" is a portable device capable of performing information processing, and includes devices such as smartphones and tablets.

[0451] The system for realizing this invention mainly consists of a server and terminals. The server is equipped with hardware and software environments for collecting data from various sources and integrating and processing it on the cloud. Specifically, it uses cloud platforms such as Amazon Web Services (AWS) and Microsoft Azure, and performs data analysis using programming languages ​​such as Python and R for data processing.

[0452] The server uses generative artificial intelligence models to analyze data in real time. This analysis detects problems at construction sites and simulates solutions. Machine learning libraries such as TensorFlow and PyTorch are particularly utilized.

[0453] The device utilizes input devices such as cameras and microphones to recognize the user's emotions. By using Microsoft's Azure Face API and Google Cloud's Natural Language API, it infers emotions from the user's facial expressions and voice, and sends this information to the server. This information is then used to adjust the process plan.

[0454] The server sends the adjusted project plan to the terminal, and the mobile device notifies the user. This notification is optimized according to the user's emotional state.

[0455] As a concrete example, consider a situation at a construction site where a sudden change in weather affects the work schedule. The server analyzes weather data and generates an alternative schedule. If the user expresses concern, the terminal provides reassurance by sending a notification containing a detailed explanation.

[0456] An example of a prompt message is, "Based on the sentiment analysis of the current situation, please generate alternative work plans to reduce stress."

[0457] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0458] Step 1:

[0459] The server collects various information from construction sites through sensors, cameras, microphones, and other means. Inputs include daily site reports, video data, audio data, and weather data. This data is uploaded to the server and undergoes data integration processing to convert it into a unified format. The output is an integrated dataset.

[0460] Step 2:

[0461] The server performs analysis using a generative AI model. It receives an integrated dataset as input and analyzes the data using machine learning algorithms. Through this analysis, it predicts problems and risks in the work. As output, it generates analysis results and a risk assessment report.

[0462] Step 3:

[0463] The server performs scenario simulations based on the analysis results. Using the analysis results as input, it generates multiple possible solution scenarios. The simulation selects the most efficient process plan. The selected process plan is generated as output.

[0464] Step 4:

[0465] The device recognizes the user's emotions in real time using the camera and microphone. It acquires the user's video and audio data as input. This data is analyzed using an emotion recognition engine to evaluate the user's emotional state. As output, it generates recognized emotion data.

[0466] Step 5:

[0467] The server adjusts the process plan considering the user's emotional data. It uses the selected process plan and recognized emotional data as input. Based on prompts, it optimizes the process plan to reduce user stress. The adjusted process plan is generated as output.

[0468] Step 6:

[0469] The terminal notifies the user of the optimized process plan. It receives the adjusted process plan as input. It presents it clearly to the user via the notification system, adding supplementary explanations to alleviate concerns. As output, it sends a notification to the user containing details of the adjusted plan.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] [Third Embodiment]

[0474] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0475] 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.

[0476] 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).

[0477] 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.

[0478] 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.

[0479] 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).

[0480] 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.

[0481] 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.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] 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".

[0486] This invention is a system for achieving efficient construction site management and is implemented according to the following procedure. The main components are a data acquisition module, a data integration and preprocessing module, an analysis module, a simulation module, and a notification and suggestion module.

[0487] \

[0488] \text{1. Data Collection:}

[0489] \

[0490] The server integrates diverse data sources from the field, such as daily reports, sensor data, camera footage, and weather data, into the cloud. This data collection is performed automatically according to a regular schedule, centralizing data in various formats.

[0491] \

[0492] 2. Data Integration and Preprocessing:

[0493] \

[0494] The server converts the collected raw data into a format suitable for analysis. By standardizing date information and formatting, and by imputing missing data and removing outliers, it achieves accurate data integration.

[0495] \

[0496] 3. Data Analysis:

[0497] \

[0498] The server uses AI to analyze integrated data and predict and detect potential problems at construction sites. For example, it analyzes trends such as delays in material delivery and personnel shortages or surpluses, enabling early development of countermeasures.

[0499] \

[0500] 4. Simulation:

[0501] \

[0502] The server generates multiple solution scenarios for the problems identified through analysis and evaluates them through simulation. The process plan that achieves maximum efficiency in the shortest timeframe is selected.

[0503] \

[0504] \text{5. Notices and Suggestions:}

[0505] \

[0506] The terminal notifies on-site workers and managers of the selected process plan. The notification includes specific work procedures and proposed resource reallocations, and is designed to be provided in a format that can be implemented on-site.

[0507] \

[0508] Specific example:

[0509] \

[0510] For example, suppose an analysis predicts a delay in the supply of material A at a construction site. In this case, the server simulates and optimizes a work schedule that involves engaging in other tasks (e.g., preparation or work in other sections) without waiting for the material to arrive. This schedule is then notified to the workers and site supervisors on their terminals, along with a detailed action plan, making it immediately actionable.

[0511] This allows users to minimize on-site delays and ensure the efficient progress of projects.

[0512] The following describes the processing flow.

[0513] Step 1:

[0514] The server periodically collects data from multiple sources, such as sensors, daily reports, and camera data, and sends it to the cloud. This allows for real-time, centralized access to the latest on-site conditions. Regardless of the data format, all data is integrated and stored in a database.

[0515] Step 2:

[0516] The server converts the collected data into a format that can be analyzed. Specifically, it standardizes data in different formats, fills in missing data, and identifies and removes outliers. This preprocessing ensures the quality of the data.

[0517] Step 3:

[0518] The server utilizes generative artificial intelligence to analyze integrated data and predict potential problems and risks in the future. By comparing it with past project data and performing pattern recognition, it identifies, for example, the possibility of material shortages or work delays.

[0519] Step 4:

[0520] Based on the analysis results, the server simulates and generates multiple solution scenarios to minimize the impact of the problem. It virtually executes the process under each scenario and selects the optimal option from the perspective of cost, time, and resources.

[0521] Step 5:

[0522] The terminal notifies the user of the optimal process plan received from the server. The notification is sent to the worker's or site supervisor's smartphone or tablet and includes specific instructions and procedures. This supports immediate decision-making on-site.

[0523] Step 6:

[0524] Based on notifications from their devices, users make necessary adjustments on-site. They ensure the project progresses efficiently by modifying materials, work sequences, or personnel allocation according to the proposed schedule.

[0525] (Example 1)

[0526] 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."

[0527] In modern work environments, particularly construction sites, it is crucial to efficiently aggregate and utilize data from multiple sources to identify potential problems early and implement appropriate countermeasures. However, conventional methods lack efficient ways to integrate and analyze data in different formats, resulting in wasted time and resources on site.

[0528] 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.

[0529] In this invention, the server includes means for collecting digital information from multiple information sources, means for integrating and processing the collected information on a network, and means for analyzing the information using a generative artificial intelligence model to predict and detect problems at the work site. This enables the use of data from different information sources in a unified manner, and the server provides multiple solution scenarios based on the analysis results, enabling optimal on-site operation.

[0530] "Information sources" refer to the locations and tools from which data is collected, including sensors, cameras, and daily reports.

[0531] "Digital information" refers to data expressed in a format that can be processed by a computer.

[0532] A "network" refers to an interconnected infrastructure that allows multiple computers and devices to communicate with one another.

[0533] "Integration" refers to the process of bringing together data from different formats and origins into a consistent structure.

[0534] "Processing" refers to the process of converting raw data into a format suitable for analysis and optimization.

[0535] A "generative artificial intelligence model" refers to an algorithm that learns from large amounts of data and uses that new information to make predictions and classifications.

[0536] "Analysis" refers to the process of breaking down data into smaller parts and understanding its characteristics and structure.

[0537] "Challenges" refer to obstacles or potential problems in carrying out work.

[0538] "Prediction" refers to the act of estimating future events based on past and present data.

[0539] "Detection" refers to the ability or technique to find specific patterns or anomalies.

[0540] A "solution scenario" refers to a set of steps or methods that present multiple possible solutions to a particular problem.

[0541] A "procedure" refers to the sequence of operations or processes designed to achieve a specific objective.

[0542] "Communication methods" refer to methods and devices for sending and receiving information and data.

[0543] A "device" refers to an instrument that has a specific function and is used by a user to receive and display information.

[0544] This system relies on a server to enable efficient management at the work site. The server has the means to collect digital information from multiple sources, aggregating information such as sensors, cameras, daily reports, and weather data via the network and storing it in the cloud. This process utilizes Python scripts and SFTP to ensure secure and automatic data transfer.

[0545] The collected data is integrated and processed by the server. Specifically, a data frame is constructed using the Pandas library, and date and time formats are standardized. Missing data is imputed and anomalous data is removed using statistical methods. This prepares clean data suitable for analysis.

[0546] Data analysis is performed by a generative artificial intelligence model installed on the server. A neural network using PyTorch or TensorFlow learns from historical data and predicts potential problems in the field (e.g., delays in material supply) at an early stage. The prediction results are then analyzed over time using a recurrent neural network.

[0547] For example, in a construction project, if delays in material delivery are anticipated, the server immediately generates alternative work solution scenarios. The simulation engine uses AnyLogic or Simul8 to evaluate the cost and time of each scenario and select the most efficient procedure.

[0548] Subsequently, the selected procedures are notified to on-site workers and managers via their devices. Using Firebase Cloud Messaging, specific work plans are pushed to the devices in real time. These notifications can be viewed on the workers' mobile devices and tablets.

[0549] As a concrete example, by providing the AI ​​with the prompt, "Please propose the optimal process scenario when there is a delay in material arrival in a construction project," effective countermeasures can be obtained. In this way, users can optimize on-site work, reduce waste, and improve overall efficiency.

[0550] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0551] Step 1:

[0552] The server collects digital information from various sources, such as sensors, cameras, daily reports, and weather data. The input to this collection is the data provided by these sources. The server securely and automatically uploads this data to the cloud using Python scripts and the SFTP protocol. The output is structured data aggregated on the cloud.

[0553] Step 2:

[0554] The server integrates and processes the collected data. Because the input data contains various formats and missing values, the Pandas library is used to convert the data to a standard format and ensure consistency. Additionally, missing data is imputed and anomalous data is removed. The output is a clean dataset suitable for analysis.

[0555] Step 3:

[0556] The server performs data analysis using a generative artificial intelligence model. The input is a clean dataset. The server applies a neural network model using PyTorch or TensorFlow. Through training this model, it predicts future problems (e.g., delays in material arrival) based on time-series data. The output is the prediction of potential problems.

[0557] Step 4:

[0558] The server simulates solution scenarios for predicted challenges. The input consists of multiple solutions based on analysis results. Using simulation engines such as AnyLogic or Simul8, it calculates and evaluates the cost and time required for each scenario. As a result, the optimal work procedure is selected. The output is the optimized process proposal.

[0559] Step 5:

[0560] The terminal notifies workers and managers at the work site of the optimized process plan. The input is the process plan selected by the server. Firebase Cloud Messaging is used to push information to mobile devices and tablets in real time. The output is a notification containing specific work instructions.

[0561] (Application Example 1)

[0562] 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."

[0563] Construction sites face complex work management challenges, with frequent problems such as delays in materials and personnel shortages. Furthermore, site conditions change constantly due to weather and unforeseen events, requiring efficient work adjustments. Traditional systems have struggled to grasp site progress in real time and immediately adjust countermeasures, highlighting the challenge of improving efficiency and adaptability in construction work.

[0564] 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.

[0565] In this invention, the server includes means for collecting data from multiple information sources, means for integrating and processing the collected data on the cloud, and means for analyzing the data using generative artificial intelligence to predict and detect problems at the construction site. This enables workers to check the progress of the site in real time and adjust their work in response to changes in the situation.

[0566] "Information sources" refer to the diverse sources used to obtain data, which allows the system to collect comprehensive information.

[0567] "Means of data collection" refers to the process and methods for extracting and aggregating necessary data from multiple sources.

[0568] "Cloud" refers to distributed computing resources accessible via the internet, providing an environment for data storage and processing.

[0569] "Means of integration and processing" refers to a series of actions that centralize collected data in multiple formats and prepare it for analysis and interpretation.

[0570] "Generative artificial intelligence" refers to the ability of computers to analyze large amounts of data and use models to make predictions and inferences.

[0571] "Means of data analysis" refers to methods for investigating and evaluating current and future trends using collected and integrated data.

[0572] "Means for predicting and detecting problems" refers to methods for identifying potential issues and risks in advance based on patterns and anomalies found through data analysis.

[0573] "Methods for simulating solution scenarios" refer to procedures for verifying various improvement measures and countermeasures in a virtual environment for an identified problem.

[0574] "Methods for generating process plans" refer to methods for designing the most effective work procedures and schedules based on the results of simulations.

[0575] "Means of notification via a network" refers to methods for transmitting generated information or instructions to a worker's device using communication technology.

[0576] An "information terminal" refers to an electronic device that can be carried by a worker and is capable of receiving and displaying notified information.

[0577] This invention is a system that supports the efficient management of construction sites and aims to automate a series of processes from data collection and analysis to simulation and notification. The following describes embodiments for carrying out this invention.

[0578] The server first collects data from multiple sources. Specifically, it aggregates daily reports from construction sites, sensor data, camera footage, weather data, and other data into the cloud. This can utilize cloud platforms such as Microsoft Azure or Google Cloud Platform. The server then integrates the collected data and converts it into a format suitable for analysis. During this process, it standardizes the data format and removes missing and outlier values.

[0579] Next, the server analyzes the integrated data using generative artificial intelligence. This analysis is performed by a generative AI model to predict potential problems and identify risks at the construction site. Based on the data provided by the AI, the server simulates multiple solution scenarios.

[0580] The generated optimal work plan is notified to the worker's information terminal via the network. Smartphones are used here, allowing workers to check the progress on site in real time and adjust their work as needed. For example, if outdoor work is interrupted by rain, they can immediately receive a notification suggesting a shift to indoor work.

[0581] An example of a prompt message is given to the AI ​​model: "Based on the current weather and material supply situation, please suggest the next work step." The entire system works together to dramatically improve the efficiency and adaptability of construction sites.

[0582] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0583] Step 1:

[0584] The server collects diverse data from construction sites, including daily reports, sensor data, camera footage, and weather data. It utilizes a cloud platform to acquire information from various data sources via APIs and other means. The input here is raw data from each information source, while the output is a unified dataset on the cloud.

[0585] Step 2:

[0586] The server converts the collected raw data into a format suitable for analysis. The data standardization process includes unifying date formats and removing outliers. The input is the integrated data obtained in step 1, and the output is clean, consistent, and analysis-ready data.

[0587] Step 3:

[0588] The server analyzes clean data using a generative AI model. Here, it predicts potential problems regarding material arrival forecasts and personnel allocation optimization. The input is pre-processed data, and the output is potential problem and risk information as a result of the analysis.

[0589] Step 4:

[0590] The server generates multiple solution scenarios based on the analysis results and simulates each scenario. The simulations involve optimizing process efficiency and resource allocation. The input is the analysis results, and the output is the optimal process plan.

[0591] Step 5:

[0592] The server notifies the terminal via the network of the optimal process plan. The terminal user can then review this plan as a concrete work procedure and adjust the work as needed. The input is the optimal process plan, and the output is specific action guidelines for the field.

[0593] Step 6:

[0594] The user checks the notification received on their device and adjusts their on-site work based on the suggested tasks. For example, if outdoor work is interrupted by rain, the user can immediately perform the alternative tasks suggested by the system. The input is the notification content displayed on the device, and the output is the new work flow to be performed by the user.

[0595] 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.

[0596] This invention integrates an emotion engine that recognizes user emotions into an efficient management system for construction sites, thereby achieving a user-friendly interface and decision support. The system includes a data acquisition module, a data integration and preprocessing module, an analysis module, a simulation module, a notification and suggestion module, and an emotion engine module.

[0597] \

[0598] \text{1. Data Collection and Integration:}

[0599] \

[0600] The server aggregates diverse data from the field and converts it into a unified format. This data includes sensor information, daily reports, camera footage, and weather data.

[0601] \

[0602] 2. Data Analysis and Simulation:

[0603] \

[0604] The server analyzes the collected data using artificial intelligence. This allows it to predict future problems and risks and generate appropriate solution scenarios. A simulation module evaluates these scenarios and selects the most efficient process plan.

[0605] \

[0606] 3. User recognition using an emotion engine:

[0607] \

[0608] The device uses an emotion engine to recognize the user's emotions in real time from the camera and voice input. This emotion data is reflected in the proposed process plans and interfaces, enabling responses that take into account the user's stress and anxiety.

[0609] \

[0610] 4. Notifications and Suggestions:

[0611] \

[0612] The device adjusts and notifies the user of the optimal process plan based on their emotional state. For example, if the user is feeling stressed, it will respond by providing concise and easy-to-understand language and additional support information.

[0613] \

[0614] Specific example:

[0615] \

[0616] At a construction site, if the weather suddenly changes and could disrupt work, the server analyzes this information and generates an appropriate work plan. The terminal recognizes the site supervisor's emotions, and if the supervisor is feeling anxious, for example, it provides a work plan with emphasized explanations, along with easily accessible support information, thereby reducing unnecessary stress.

[0617] This system enables process management that takes user emotions into consideration, improving efficiency and comfort at construction sites.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] The server periodically collects data from various sources, such as sensors, cameras, daily reports, and weather data, and sends it to the cloud. This allows for centralized management of the latest information from the field.

[0621] Step 2:

[0622] The server standardizes the collected data and preprocesses it into a format suitable for analysis. By filling in missing data and eliminating outliers, the accuracy of the analysis is improved.

[0623] Step 3:

[0624] The server uses generative artificial intelligence to analyze integrated data and predict potential problems and risks. Based on the analysis results, it detects the possibility of material shortages or work delays predicted in the near future.

[0625] Step 4:

[0626] The server generates multiple solution scenarios based on the analysis results and evaluates them through simulation. It then generates the selected optimal process plan and verifies its feasibility.

[0627] Step 5:

[0628] The device uses an emotion engine to recognize the user's emotions through the camera and microphone, and determines the user's stress level and emotional state.

[0629] Step 6:

[0630] The device adjusts the process plan according to the user's emotions, and notifies them with improved presentation and additional information. For example, it adds supportive information to reassure users who are feeling anxious.

[0631] Step 7:

[0632] Users check notifications from their devices and modify actual processes and personnel allocations based on the proposed adjustments. This ensures efficient and stress-free project execution.

[0633] (Example 2)

[0634] 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."

[0635] At construction sites and other work environments, there is a need to efficiently collect data from various sources to enable early problem detection and the development of optimal project plans. However, the complexity and diverse formats of the data hinder real-time analysis and decision-making. Furthermore, while it is desirable to take into account the emotions of on-site workers to enable more flexible and appropriate responses, conventional systems do not adequately achieve this.

[0636] 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.

[0637] In this invention, the server includes means for collecting data from multiple sources, means for integrating and transforming the collected data on a data processing infrastructure, and means for analyzing the data using a trained model to predict problems in the work site. This enables the development of flexible process plans that take into account the user's emotional state and provides rapid decision-making support.

[0638] "Information sources" refer to a variety of devices and systems that transmit data, including sensors, reports, video equipment, and weather information systems.

[0639] "Data processing infrastructure" refers to computing resources and platforms for storing, integrating, transforming, and analyzing collected data, and typically consists of computer networks and cloud environments.

[0640] A "pre-trained model" refers to an algorithm that has been trained in advance using machine learning or artificial intelligence techniques, and is used for data analysis and prediction.

[0641] A "work site" refers to a place where physical work such as construction or manufacturing takes place, and where there is a need to improve operational efficiency and safety.

[0642] "Means of predicting problems" refers to techniques and methods used to identify potential risks and obstacles in advance through data analysis.

[0643] A "process plan" refers to a work plan or procedure manual that has been formulated with consideration for work efficiency and safety.

[0644] "Developing flexible process plans" refers to the process of creating optimal work procedures in real time based on data analysis, tailored to the specific situation.

[0645] "Decision support" refers to technologies and methods that provide information and schedules to enable optimal and effective choices in tasks and operations.

[0646] This invention is a system that enables efficient work management at construction sites and other locations. It primarily involves a server and terminals working together to provide the user with the most suitable work plan. The details are described below.

[0647] The server collects data from the field through IoT devices and data collection platforms. Specifically, it aggregates sensor information, daily reports, camera footage, weather data, etc., integrates this data on a cloud infrastructure, and converts it to JSON format as needed. This standardizes the data so that it can be used for data analysis and forecasting.

[0648] The server then analyzes the collected data using machine learning frameworks such as TensorFlow. Here, historical and real-time data are combined to predict future risks and problems. This analysis allows for the early detection of issues such as the risk of work interruptions under specific weather conditions or material shortages.

[0649] The user's device receives data sent from the server and utilizes facial recognition technology and voice analysis tools (e.g., Microsoft's Emotion API) to analyze the user's emotions in real time. It senses stress and anxiety from the user's facial expressions and voice, and incorporates corresponding feedback into the process plan.

[0650] This system offers a concrete example of flexible process management in response to weather fluctuations. For instance, if the weather suddenly deteriorates during the day, the server analyzes weather data and suggests temporary work suspensions or shift changes. The terminal determines whether the site supervisor is feeling anxious and notifies them of specific support measures as needed.

[0651] An example of a prompt message is, "Based on the latest work site data, analyze the site supervisor's emotions and generate suggestions to reduce stress," which allows the system to provide suggestions tailored to the user.

[0652] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0653] Step 1:

[0654] The server collects sensor data, daily reports, camera footage, and weather data from construction sites. This information is input via IoT devices and data collection platforms. The server converts the data, provided in different formats, into a unified format (e.g., JSON). This standardizes the data, making it ready for later analysis.

[0655] Step 2:

[0656] The server analyzes unified data using a machine learning framework (e.g., TensorFlow). The input data is combined with historical and real-time data and processed by a generative AI model. This analysis process predicts future risks and problems, such as the risk of work interruptions due to weather changes. The analysis results are output as a risk assessment and proposed countermeasures.

[0657] Step 3:

[0658] The server generates multiple process plans based on the analysis results and selects the optimal plan through simulation. The input data analysis results are evaluated by the simulation module. This results in the output of efficient work schedules and resource allocation plans.

[0659] Step 4:

[0660] The terminal notifies the user of the optimal process plan received from the server. During this process, real-time emotional data obtained from the user's camera and microphone is input. The terminal uses facial recognition technology and voice analysis tools to evaluate the user's emotional state. If the user is experiencing high stress, the terminal outputs and displays a process plan that emphasizes specific support and advice.

[0661] Step 5:

[0662] Based on the provided process plan and supporting information, users make on-site decisions. User feedback and new data are returned to Step 1 and incorporated into the data collection process. This allows the system to continuously update and optimize information, enabling it to continue providing suggestions that meet user needs.

[0663] (Application Example 2)

[0664] 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."

[0665] Construction site management processes are complex and involve a variety of intertwined factors, making traditional statistical data insufficient. On-site leadership and the mental state of workers significantly impact management efficiency. Furthermore, rapid responses to unexpected situations and sudden environmental changes are required. Therefore, a management system capable of flexible process proposals that take workers' emotions into consideration is necessary.

[0666] 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.

[0667] In this invention, the server includes means for collecting data from multiple information sources, means for integrating and processing the collected data on the cloud, means for analyzing the data using generative artificial intelligence to predict and detect problems at construction sites, means for simulating multiple solution scenarios based on the analysis results and generating an optimal process plan, means for recognizing the user's emotions and adjusting the process plan based on their emotional state, and means for notifying the worker's mobile terminal of the adjusted optimal process plan via the network. This enables flexible and effective process management that takes into account the correlation between understanding the situation at the site and emotions.

[0668] A "source of information" refers to any basis or origin for providing data or information, including devices such as sensors, cameras, and audio devices.

[0669] "Cloud" refers to a distributed data storage and computing resource located on the internet, providing an environment for data storage and processing.

[0670] "Generative artificial intelligence" refers to a system that utilizes machine learning algorithms to extract patterns from data and has the ability to make predictions and judgments.

[0671] "Analysis" is the process of thoroughly examining data and extracting information and knowledge.

[0672] "Simulation" is a technique that simulates real-world systems and situations to predict the outcomes of different scenarios.

[0673] "User emotions" refer to the psychological sensations and states experienced by the user, and are inferred from voice and facial expressions.

[0674] A "project plan" is a plan or scenario that outlines the steps and schedule of a task or process.

[0675] A "network" is a communication infrastructure used to send and receive data between different devices.

[0676] A "mobile device" is a portable device capable of performing information processing, and includes devices such as smartphones and tablets.

[0677] The system for realizing this invention mainly consists of a server and terminals. The server is equipped with hardware and software environments for collecting data from various sources and integrating and processing it on the cloud. Specifically, it uses cloud platforms such as Amazon Web Services (AWS) and Microsoft Azure, and performs data analysis using programming languages ​​such as Python and R for data processing.

[0678] The server uses generative artificial intelligence models to analyze data in real time. This analysis detects problems at construction sites and simulates solutions. Machine learning libraries such as TensorFlow and PyTorch are particularly utilized.

[0679] The device utilizes input devices such as cameras and microphones to recognize the user's emotions. By using Microsoft's Azure Face API and Google Cloud's Natural Language API, it infers emotions from the user's facial expressions and voice, and sends this information to the server. This information is then used to adjust the process plan.

[0680] The server sends the adjusted project plan to the terminal, and the mobile device notifies the user. This notification is optimized according to the user's emotional state.

[0681] As a concrete example, consider a situation at a construction site where a sudden change in weather affects the work schedule. The server analyzes weather data and generates an alternative schedule. If the user expresses concern, the terminal provides reassurance by sending a notification containing a detailed explanation.

[0682] An example of a prompt message is, "Based on the sentiment analysis of the current situation, please generate alternative work plans to reduce stress."

[0683] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0684] Step 1:

[0685] The server collects various information from construction sites through sensors, cameras, microphones, and other means. Inputs include daily site reports, video data, audio data, and weather data. This data is uploaded to the server and undergoes data integration processing to convert it into a unified format. The output is an integrated dataset.

[0686] Step 2:

[0687] The server performs analysis using a generative AI model. It receives an integrated dataset as input and analyzes the data using machine learning algorithms. Through this analysis, it predicts problems and risks in the work. As output, it generates analysis results and a risk assessment report.

[0688] Step 3:

[0689] The server performs scenario simulations based on the analysis results. Using the analysis results as input, it generates multiple possible solution scenarios. The simulation selects the most efficient process plan. The selected process plan is generated as output.

[0690] Step 4:

[0691] The device recognizes the user's emotions in real time using the camera and microphone. It acquires the user's video and audio data as input. This data is analyzed using an emotion recognition engine to evaluate the user's emotional state. As output, it generates recognized emotion data.

[0692] Step 5:

[0693] The server adjusts the process plan considering the user's emotional data. It uses the selected process plan and recognized emotional data as input. Based on prompts, it optimizes the process plan to reduce user stress. The adjusted process plan is generated as output.

[0694] Step 6:

[0695] The terminal notifies the user of the optimized process plan. It receives the adjusted process plan as input. It presents it clearly to the user via the notification system, adding supplementary explanations to alleviate concerns. As output, it sends a notification to the user containing details of the adjusted plan.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] [Fourth Embodiment]

[0700] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0701] 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.

[0702] 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).

[0703] 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.

[0704] 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.

[0705] 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).

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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".

[0713] This invention is a system for achieving efficient construction site management and is implemented according to the following procedure. The main components are a data acquisition module, a data integration and preprocessing module, an analysis module, a simulation module, and a notification and suggestion module.

[0714] \

[0715] \text{1. Data Collection:}

[0716] \

[0717] The server integrates diverse data sources from the field, such as daily reports, sensor data, camera footage, and weather data, into the cloud. This data collection is performed automatically according to a regular schedule, centralizing data in various formats.

[0718] \

[0719] 2. Data Integration and Preprocessing:

[0720] \

[0721] The server converts the collected raw data into a format suitable for analysis. By standardizing date information and formatting, and by imputing missing data and removing outliers, it achieves accurate data integration.

[0722] \

[0723] 3. Data Analysis:

[0724] \

[0725] The server uses AI to analyze integrated data and predict and detect potential problems at construction sites. For example, it analyzes trends such as delays in material delivery and personnel shortages or surpluses, enabling early development of countermeasures.

[0726] \

[0727] 4. Simulation:

[0728] \

[0729] The server generates multiple solution scenarios for the problems identified through analysis and evaluates them through simulation. The process plan that achieves maximum efficiency in the shortest timeframe is selected.

[0730] \

[0731] \text{5. Notices and Suggestions:}

[0732] \

[0733] The terminal notifies on-site workers and managers of the selected process plan. The notification includes specific work procedures and proposed resource reallocations, and is designed to be provided in a format that can be implemented on-site.

[0734] \

[0735] Specific example:

[0736] \

[0737] For example, suppose an analysis predicts a delay in the supply of material A at a construction site. In this case, the server simulates and optimizes a work schedule that involves engaging in other tasks (e.g., preparation or work in other sections) without waiting for the material to arrive. This schedule is then notified to the workers and site supervisors on their terminals, along with a detailed action plan, making it immediately actionable.

[0738] This allows users to minimize on-site delays and ensure the efficient progress of projects.

[0739] The following describes the processing flow.

[0740] Step 1:

[0741] The server periodically collects data from multiple sources, such as sensors, daily reports, and camera data, and sends it to the cloud. This allows for real-time, centralized access to the latest on-site conditions. Regardless of the data format, all data is integrated and stored in a database.

[0742] Step 2:

[0743] The server converts the collected data into a format that can be analyzed. Specifically, it standardizes data in different formats, fills in missing data, and identifies and removes outliers. This preprocessing ensures the quality of the data.

[0744] Step 3:

[0745] The server utilizes generative artificial intelligence to analyze integrated data and predict potential problems and risks in the future. By comparing it with past project data and performing pattern recognition, it identifies, for example, the possibility of material shortages or work delays.

[0746] Step 4:

[0747] Based on the analysis results, the server simulates and generates multiple solution scenarios to minimize the impact of the problem. It virtually executes the process under each scenario and selects the optimal option from the perspective of cost, time, and resources.

[0748] Step 5:

[0749] The terminal notifies the user of the optimal process plan received from the server. The notification is sent to the worker's or site supervisor's smartphone or tablet and includes specific instructions and procedures. This supports immediate decision-making on-site.

[0750] Step 6:

[0751] Based on notifications from their devices, users make necessary adjustments on-site. They ensure the project progresses efficiently by modifying materials, work sequences, or personnel allocation according to the proposed schedule.

[0752] (Example 1)

[0753] 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".

[0754] In modern work environments, particularly construction sites, it is crucial to efficiently aggregate and utilize data from multiple sources to identify potential problems early and implement appropriate countermeasures. However, conventional methods lack efficient ways to integrate and analyze data in different formats, resulting in wasted time and resources on site.

[0755] 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.

[0756] In this invention, the server includes means for collecting digital information from multiple information sources, means for integrating and processing the collected information on a network, and means for analyzing the information using a generative artificial intelligence model to predict and detect problems at the work site. This enables the use of data from different information sources in a unified manner, and the server provides multiple solution scenarios based on the analysis results, enabling optimal on-site operation.

[0757] "Information sources" refer to the locations and tools from which data is collected, including sensors, cameras, and daily reports.

[0758] "Digital information" refers to data expressed in a format that can be processed by a computer.

[0759] A "network" refers to an interconnected infrastructure that allows multiple computers and devices to communicate with one another.

[0760] "Integration" refers to the process of bringing together data from different formats and origins into a consistent structure.

[0761] "Processing" refers to the process of converting raw data into a format suitable for analysis and optimization.

[0762] A "generative artificial intelligence model" refers to an algorithm that learns from large amounts of data and uses that new information to make predictions and classifications.

[0763] "Analysis" refers to the process of breaking down data into smaller parts and understanding its characteristics and structure.

[0764] "Challenges" refer to obstacles or potential problems in carrying out work.

[0765] "Prediction" refers to the act of estimating future events based on past and present data.

[0766] "Detection" refers to the ability or technique to find specific patterns or anomalies.

[0767] A "solution scenario" refers to a set of steps or methods that present multiple possible solutions to a particular problem.

[0768] A "procedure" refers to the sequence of operations or processes designed to achieve a specific objective.

[0769] "Communication methods" refer to methods and devices for sending and receiving information and data.

[0770] A "device" refers to an instrument that has a specific function and is used by a user to receive and display information.

[0771] This system relies on a server to enable efficient management at the work site. The server has the means to collect digital information from multiple sources, aggregating information such as sensors, cameras, daily reports, and weather data via the network and storing it in the cloud. This process utilizes Python scripts and SFTP to ensure secure and automatic data transfer.

[0772] The collected data is integrated and processed by the server. Specifically, a data frame is constructed using the Pandas library, and date and time formats are standardized. Missing data is imputed and anomalous data is removed using statistical methods. This prepares clean data suitable for analysis.

[0773] Data analysis is performed by a generative artificial intelligence model installed on the server. A neural network using PyTorch or TensorFlow learns from historical data and predicts potential problems in the field (e.g., delays in material supply) at an early stage. The prediction results are then analyzed over time using a recurrent neural network.

[0774] For example, in a construction project, if delays in material delivery are anticipated, the server immediately generates alternative work solution scenarios. The simulation engine uses AnyLogic or Simul8 to evaluate the cost and time of each scenario and select the most efficient procedure.

[0775] Subsequently, the selected procedures are notified to on-site workers and managers via their devices. Using Firebase Cloud Messaging, specific work plans are pushed to the devices in real time. These notifications can be viewed on the workers' mobile devices and tablets.

[0776] As a concrete example, by providing the AI ​​with the prompt, "Please propose the optimal process scenario when there is a delay in material arrival in a construction project," effective countermeasures can be obtained. In this way, users can optimize on-site work, reduce waste, and improve overall efficiency.

[0777] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0778] Step 1:

[0779] The server collects digital information from various sources, such as sensors, cameras, daily reports, and weather data. The input to this collection is the data provided by these sources. The server securely and automatically uploads this data to the cloud using Python scripts and the SFTP protocol. The output is structured data aggregated on the cloud.

[0780] Step 2:

[0781] The server integrates and processes the collected data. Because the input data contains various formats and missing values, the Pandas library is used to convert the data to a standard format and ensure consistency. Additionally, missing data is imputed and anomalous data is removed. The output is a clean dataset suitable for analysis.

[0782] Step 3:

[0783] The server performs data analysis using a generative artificial intelligence model. The input is a clean dataset. The server applies a neural network model using PyTorch or TensorFlow. Through training this model, it predicts future problems (e.g., delays in material arrival) based on time-series data. The output is the prediction of potential problems.

[0784] Step 4:

[0785] The server simulates solution scenarios for predicted challenges. The input consists of multiple solutions based on analysis results. Using simulation engines such as AnyLogic or Simul8, it calculates and evaluates the cost and time required for each scenario. As a result, the optimal work procedure is selected. The output is the optimized process proposal.

[0786] Step 5:

[0787] The terminal notifies workers and managers at the work site of the optimized process plan. The input is the process plan selected by the server. Firebase Cloud Messaging is used to push information to mobile devices and tablets in real time. The output is a notification containing specific work instructions.

[0788] (Application Example 1)

[0789] 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".

[0790] Construction sites face complex work management challenges, with frequent problems such as delays in materials and personnel shortages. Furthermore, site conditions change constantly due to weather and unforeseen events, requiring efficient work adjustments. Traditional systems have struggled to grasp site progress in real time and immediately adjust countermeasures, highlighting the challenge of improving efficiency and adaptability in construction work.

[0791] 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.

[0792] In this invention, the server includes means for collecting data from multiple information sources, means for integrating and processing the collected data on the cloud, and means for analyzing the data using generative artificial intelligence to predict and detect problems at the construction site. This enables workers to check the progress of the site in real time and adjust their work in response to changes in the situation.

[0793] "Information sources" refer to the diverse sources used to obtain data, which allows the system to collect comprehensive information.

[0794] "Means of data collection" refers to the process and methods for extracting and aggregating necessary data from multiple sources.

[0795] "Cloud" refers to distributed computing resources accessible via the internet, providing an environment for data storage and processing.

[0796] "Means of integration and processing" refers to a series of actions that centralize collected data in multiple formats and prepare it for analysis and interpretation.

[0797] "Generative artificial intelligence" refers to the ability of computers to analyze large amounts of data and use models to make predictions and inferences.

[0798] "Means of data analysis" refers to methods for investigating and evaluating current and future trends using collected and integrated data.

[0799] "Means for predicting and detecting problems" refers to methods for identifying potential issues and risks in advance based on patterns and anomalies found through data analysis.

[0800] "Methods for simulating solution scenarios" refer to procedures for verifying various improvement measures and countermeasures in a virtual environment for an identified problem.

[0801] "Methods for generating process plans" refer to methods for designing the most effective work procedures and schedules based on the results of simulations.

[0802] "Means of notification via a network" refers to methods for transmitting generated information or instructions to a worker's device using communication technology.

[0803] An "information terminal" refers to an electronic device that can be carried by a worker and is capable of receiving and displaying notified information.

[0804] This invention is a system that supports the efficient management of construction sites and aims to automate a series of processes from data collection and analysis to simulation and notification. The following describes embodiments for carrying out this invention.

[0805] The server first collects data from multiple sources. Specifically, it aggregates daily reports from construction sites, sensor data, camera footage, weather data, and other data into the cloud. This can utilize cloud platforms such as Microsoft Azure or Google Cloud Platform. The server then integrates the collected data and converts it into a format suitable for analysis. During this process, it standardizes the data format and removes missing and outlier values.

[0806] Next, the server analyzes the integrated data using generative artificial intelligence. This analysis is performed by a generative AI model to predict potential problems and identify risks at the construction site. Based on the data provided by the AI, the server simulates multiple solution scenarios.

[0807] The generated optimal work plan is notified to the worker's information terminal via the network. Smartphones are used here, allowing workers to check the progress on site in real time and adjust their work as needed. For example, if outdoor work is interrupted by rain, they can immediately receive a notification suggesting a shift to indoor work.

[0808] An example of a prompt message is given to the AI ​​model: "Based on the current weather and material supply situation, please suggest the next work step." The entire system works together to dramatically improve the efficiency and adaptability of construction sites.

[0809] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0810] Step 1:

[0811] The server collects diverse data from construction sites, including daily reports, sensor data, camera footage, and weather data. It utilizes a cloud platform to acquire information from various data sources via APIs and other means. The input here is raw data from each information source, while the output is a unified dataset on the cloud.

[0812] Step 2:

[0813] The server converts the collected raw data into a format suitable for analysis. The data standardization process includes unifying date formats and removing outliers. The input is the integrated data obtained in step 1, and the output is clean, consistent, and analysis-ready data.

[0814] Step 3:

[0815] The server analyzes clean data using a generative AI model. Here, it predicts potential problems regarding material arrival forecasts and personnel allocation optimization. The input is pre-processed data, and the output is potential problem and risk information as a result of the analysis.

[0816] Step 4:

[0817] The server generates multiple solution scenarios based on the analysis results and simulates each scenario. The simulations involve optimizing process efficiency and resource allocation. The input is the analysis results, and the output is the optimal process plan.

[0818] Step 5:

[0819] The server notifies the terminal via the network of the optimal process plan. The terminal user can then review this plan as a concrete work procedure and adjust the work as needed. The input is the optimal process plan, and the output is specific action guidelines for the field.

[0820] Step 6:

[0821] The user checks the notification received on their device and adjusts their on-site work based on the suggested tasks. For example, if outdoor work is interrupted by rain, the user can immediately perform the alternative tasks suggested by the system. The input is the notification content displayed on the device, and the output is the new work flow to be performed by the user.

[0822] 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.

[0823] This invention integrates an emotion engine that recognizes user emotions into an efficient management system for construction sites, thereby achieving a user-friendly interface and decision support. The system includes a data acquisition module, a data integration and preprocessing module, an analysis module, a simulation module, a notification and suggestion module, and an emotion engine module.

[0824] \

[0825] \text{1. Data Collection and Integration:}

[0826] \

[0827] The server aggregates diverse data from the field and converts it into a unified format. This data includes sensor information, daily reports, camera footage, and weather data.

[0828] \

[0829] 2. Data Analysis and Simulation:

[0830] \

[0831] The server analyzes the collected data using artificial intelligence. This allows it to predict future problems and risks and generate appropriate solution scenarios. A simulation module evaluates these scenarios and selects the most efficient process plan.

[0832] \

[0833] 3. User recognition using an emotion engine:

[0834] \

[0835] The device uses an emotion engine to recognize the user's emotions in real time from the camera and voice input. This emotion data is reflected in the proposed process plans and interfaces, enabling responses that take into account the user's stress and anxiety.

[0836] \

[0837] 4. Notifications and Suggestions:

[0838] \

[0839] The device adjusts and notifies the user of the optimal process plan based on their emotional state. For example, if the user is feeling stressed, it will respond by providing concise and easy-to-understand language and additional support information.

[0840] \

[0841] Specific example:

[0842] \

[0843] At a construction site, if the weather suddenly changes and could disrupt work, the server analyzes this information and generates an appropriate work plan. The terminal recognizes the site supervisor's emotions, and if the supervisor is feeling anxious, for example, it provides a work plan with emphasized explanations, along with easily accessible support information, thereby reducing unnecessary stress.

[0844] This system enables process management that takes user emotions into consideration, improving efficiency and comfort at construction sites.

[0845] The following describes the processing flow.

[0846] Step 1:

[0847] The server periodically collects data from various sources, such as sensors, cameras, daily reports, and weather data, and sends it to the cloud. This allows for centralized management of the latest information from the field.

[0848] Step 2:

[0849] The server standardizes the collected data and preprocesses it into a format suitable for analysis. By filling in missing data and eliminating outliers, the accuracy of the analysis is improved.

[0850] Step 3:

[0851] The server uses generative artificial intelligence to analyze integrated data and predict potential problems and risks. Based on the analysis results, it detects the possibility of material shortages or work delays predicted in the near future.

[0852] Step 4:

[0853] The server generates multiple solution scenarios based on the analysis results and evaluates them through simulation. It then generates the selected optimal process plan and verifies its feasibility.

[0854] Step 5:

[0855] The device uses an emotion engine to recognize the user's emotions through the camera and microphone, and determines the user's stress level and emotional state.

[0856] Step 6:

[0857] The device adjusts the process plan according to the user's emotions, and notifies them with improved presentation and additional information. For example, it adds supportive information to reassure users who are feeling anxious.

[0858] Step 7:

[0859] Users check notifications from their devices and modify actual processes and personnel allocations based on the proposed adjustments. This ensures efficient and stress-free project execution.

[0860] (Example 2)

[0861] 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".

[0862] At construction sites and other work environments, there is a need to efficiently collect data from various sources to enable early problem detection and the development of optimal project plans. However, the complexity and diverse formats of the data hinder real-time analysis and decision-making. Furthermore, while it is desirable to take into account the emotions of on-site workers to enable more flexible and appropriate responses, conventional systems do not adequately achieve this.

[0863] 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.

[0864] In this invention, the server includes means for collecting data from multiple sources, means for integrating and transforming the collected data on a data processing infrastructure, and means for analyzing the data using a trained model to predict problems in the work site. This enables the development of flexible process plans that take into account the user's emotional state and provides rapid decision-making support.

[0865] "Information sources" refer to a variety of devices and systems that transmit data, including sensors, reports, video equipment, and weather information systems.

[0866] "Data processing infrastructure" refers to computing resources and platforms for storing, integrating, transforming, and analyzing collected data, and typically consists of computer networks and cloud environments.

[0867] A "pre-trained model" refers to an algorithm that has been trained in advance using machine learning or artificial intelligence techniques, and is used for data analysis and prediction.

[0868] A "work site" refers to a place where physical work such as construction or manufacturing takes place, and where there is a need to improve operational efficiency and safety.

[0869] "Means of predicting problems" refers to techniques and methods used to identify potential risks and obstacles in advance through data analysis.

[0870] A "process plan" refers to a work plan or procedure manual that has been formulated with consideration for work efficiency and safety.

[0871] "Developing flexible process plans" refers to the process of creating optimal work procedures in real time based on data analysis, tailored to the specific situation.

[0872] "Decision support" refers to technologies and methods that provide information and schedules to enable optimal and effective choices in tasks and operations.

[0873] This invention is a system that enables efficient work management at construction sites and other locations. It primarily involves a server and terminals working together to provide the user with the most suitable work plan. The details are described below.

[0874] The server collects data from the field through IoT devices and data collection platforms. Specifically, it aggregates sensor information, daily reports, camera footage, weather data, etc., integrates this data on a cloud infrastructure, and converts it to JSON format as needed. This standardizes the data so that it can be used for data analysis and forecasting.

[0875] The server then analyzes the collected data using machine learning frameworks such as TensorFlow. Here, historical and real-time data are combined to predict future risks and problems. This analysis allows for the early detection of issues such as the risk of work interruptions under specific weather conditions or material shortages.

[0876] The user's device receives data sent from the server and utilizes facial recognition technology and voice analysis tools (e.g., Microsoft's Emotion API) to analyze the user's emotions in real time. It senses stress and anxiety from the user's facial expressions and voice, and incorporates corresponding feedback into the process plan.

[0877] This system offers a concrete example of flexible process management in response to weather fluctuations. For instance, if the weather suddenly deteriorates during the day, the server analyzes weather data and suggests temporary work suspensions or shift changes. The terminal determines whether the site supervisor is feeling anxious and notifies them of specific support measures as needed.

[0878] An example of a prompt message is, "Based on the latest work site data, analyze the site supervisor's emotions and generate suggestions to reduce stress," which allows the system to provide suggestions tailored to the user.

[0879] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0880] Step 1:

[0881] The server collects sensor data, daily reports, camera footage, and weather data from construction sites. This information is input via IoT devices and data collection platforms. The server converts the data, provided in different formats, into a unified format (e.g., JSON). This standardizes the data, making it ready for later analysis.

[0882] Step 2:

[0883] The server analyzes unified data using a machine learning framework (e.g., TensorFlow). The input data is combined with historical and real-time data and processed by a generative AI model. This analysis process predicts future risks and problems, such as the risk of work interruptions due to weather changes. The analysis results are output as a risk assessment and proposed countermeasures.

[0884] Step 3:

[0885] The server generates multiple process plans based on the analysis results and selects the optimal plan through simulation. The input data analysis results are evaluated by the simulation module. This results in the output of efficient work schedules and resource allocation plans.

[0886] Step 4:

[0887] The terminal notifies the user of the optimal process plan received from the server. During this process, real-time emotional data obtained from the user's camera and microphone is input. The terminal uses facial recognition technology and voice analysis tools to evaluate the user's emotional state. If the user is experiencing high stress, the terminal outputs and displays a process plan that emphasizes specific support and advice.

[0888] Step 5:

[0889] Based on the provided process plan and supporting information, users make on-site decisions. User feedback and new data are returned to Step 1 and incorporated into the data collection process. This allows the system to continuously update and optimize information, enabling it to continue providing suggestions that meet user needs.

[0890] (Application Example 2)

[0891] 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".

[0892] Construction site management processes are complex and involve a variety of intertwined factors, making traditional statistical data insufficient. On-site leadership and the mental state of workers significantly impact management efficiency. Furthermore, rapid responses to unexpected situations and sudden environmental changes are required. Therefore, a management system capable of flexible process proposals that take workers' emotions into consideration is necessary.

[0893] 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.

[0894] In this invention, the server includes means for collecting data from multiple information sources, means for integrating and processing the collected data on the cloud, means for analyzing the data using generative artificial intelligence to predict and detect problems at construction sites, means for simulating multiple solution scenarios based on the analysis results and generating an optimal process plan, means for recognizing the user's emotions and adjusting the process plan based on their emotional state, and means for notifying the worker's mobile terminal of the adjusted optimal process plan via the network. This enables flexible and effective process management that takes into account the correlation between understanding the situation at the site and emotions.

[0895] A "source of information" refers to any basis or origin for providing data or information, including devices such as sensors, cameras, and audio devices.

[0896] "Cloud" refers to a distributed data storage and computing resource located on the internet, providing an environment for data storage and processing.

[0897] "Generative artificial intelligence" refers to a system that utilizes machine learning algorithms to extract patterns from data and has the ability to make predictions and judgments.

[0898] "Analysis" is the process of thoroughly examining data and extracting information and knowledge.

[0899] "Simulation" is a technique that simulates real-world systems and situations to predict the outcomes of different scenarios.

[0900] "User emotions" refer to the psychological sensations and states experienced by the user, and are inferred from voice and facial expressions.

[0901] A "project plan" is a plan or scenario that outlines the steps and schedule of a task or process.

[0902] A "network" is a communication infrastructure used to send and receive data between different devices.

[0903] A "mobile device" is a portable device capable of performing information processing, and includes devices such as smartphones and tablets.

[0904] The system for realizing this invention mainly consists of a server and terminals. The server is equipped with hardware and software environments for collecting data from various sources and integrating and processing it on the cloud. Specifically, it uses cloud platforms such as Amazon Web Services (AWS) and Microsoft Azure, and performs data analysis using programming languages ​​such as Python and R for data processing.

[0905] The server uses generative artificial intelligence models to analyze data in real time. This analysis detects problems at construction sites and simulates solutions. Machine learning libraries such as TensorFlow and PyTorch are particularly utilized.

[0906] The device utilizes input devices such as cameras and microphones to recognize the user's emotions. By using Microsoft's Azure Face API and Google Cloud's Natural Language API, it infers emotions from the user's facial expressions and voice, and sends this information to the server. This information is then used to adjust the process plan.

[0907] The server sends the adjusted project plan to the terminal, and the mobile device notifies the user. This notification is optimized according to the user's emotional state.

[0908] As a concrete example, consider a situation at a construction site where a sudden change in weather affects the work schedule. The server analyzes weather data and generates an alternative schedule. If the user expresses concern, the terminal provides reassurance by sending a notification containing a detailed explanation.

[0909] An example of a prompt message is, "Based on the sentiment analysis of the current situation, please generate alternative work plans to reduce stress."

[0910] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0911] Step 1:

[0912] The server collects various information from construction sites through sensors, cameras, microphones, and other means. Inputs include daily site reports, video data, audio data, and weather data. This data is uploaded to the server and undergoes data integration processing to convert it into a unified format. The output is an integrated dataset.

[0913] Step 2:

[0914] The server performs analysis using a generative AI model. It receives an integrated dataset as input and analyzes the data using machine learning algorithms. Through this analysis, it predicts problems and risks in the work. As output, it generates analysis results and a risk assessment report.

[0915] Step 3:

[0916] The server performs scenario simulations based on the analysis results. Using the analysis results as input, it generates multiple possible solution scenarios. The simulation selects the most efficient process plan. The selected process plan is generated as output.

[0917] Step 4:

[0918] The device recognizes the user's emotions in real time using the camera and microphone. It acquires the user's video and audio data as input. This data is analyzed using an emotion recognition engine to evaluate the user's emotional state. As output, it generates recognized emotion data.

[0919] Step 5:

[0920] The server adjusts the process plan considering the user's emotional data. It uses the selected process plan and recognized emotional data as input. Based on prompts, it optimizes the process plan to reduce user stress. The adjusted process plan is generated as output.

[0921] Step 6:

[0922] The terminal notifies the user of the optimized process plan. It receives the adjusted process plan as input. It presents it clearly to the user via the notification system, adding supplementary explanations to alleviate concerns. As output, it sends a notification to the user containing details of the adjusted plan.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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."

[0932] 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.

[0933] 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.

[0934] 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.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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.

[0940] 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.

[0941] 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.

[0942] 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.

[0943] 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 to be incorporated by reference.

[0944] The following is further disclosed regarding the embodiments described above.

[0945] (Claim 1)

[0946] Means of collecting data from multiple sources,

[0947] A means of integrating and processing the collected data on the cloud,

[0948] A means for analyzing data using generative artificial intelligence to predict and detect problems at construction sites,

[0949] A means for simulating multiple solution scenarios based on analysis results and generating the optimal process plan,

[0950] A means of notifying workers' terminals of the optimal process plan via the network,

[0951] A system that includes this.

[0952] (Claim 2)

[0953] The system according to claim 1, comprising means for predicting future risks using past data and proposing preventive measures in advance.

[0954] (Claim 3)

[0955] The system according to claim 1, further comprising preprocessing means for detecting and removing anomalies from integrated data.

[0956] "Example 1"

[0957] (Claim 1)

[0958] Means of collecting digital information from multiple sources,

[0959] A means for integrating and processing the collected information on a network,

[0960] A means for analyzing information using a generative artificial intelligence model to predict and detect problems in the work site,

[0961] A means for modeling multiple solution scenarios based on analysis results and generating the optimal procedure,

[0962] A means of notifying the worker's device of the optimal procedure via a communication means,

[0963] A system that includes this.

[0964] (Claim 2)

[0965] The system according to claim 1, comprising means for predicting future challenges using past information and proposing preventive measures.

[0966] (Claim 3)

[0967] The system according to claim 1, further comprising preprocessing means for detecting and removing abnormal data from integrated information.

[0968] "Application Example 1"

[0969] (Claim 1)

[0970] Means of collecting data from multiple sources,

[0971] A means of integrating and processing the collected data on the cloud,

[0972] A means for analyzing data using generative artificial intelligence to predict and detect problems at construction sites,

[0973] A means for simulating multiple solution scenarios based on analysis results and generating the optimal process plan,

[0974] A means of notifying workers of the optimal process plan via the network to their information terminals,

[0975] A means for workers to check the progress on site in real time and adjust their work according to changes in the situation,

[0976] A system that includes this.

[0977] (Claim 2)

[0978] The system according to claim 1, comprising means for predicting future risks using past data and proposing preventive measures in advance.

[0979] (Claim 3)

[0980] The system according to claim 1, further comprising preprocessing means for detecting and removing anomalies from integrated data.

[0981] "Example 2 of combining an emotion engine"

[0982] (Claim 1)

[0983] Means of collecting data from multiple sources,

[0984] Means for integrating and transforming the collected data on a data processing infrastructure,

[0985] A method for analyzing data using a pre-trained model and predicting problems in the work site,

[0986] A means of evaluating multiple process options based on analysis results and formulating the optimal work plan,

[0987] A means to recognize the user's emotional state in real time and reflect it in the process plan,

[0988] A means of transmitting the optimal process plan to work management equipment via an information network,

[0989] A system that includes this.

[0990] (Claim 2)

[0991] The system according to claim 1, comprising means for predicting future challenges using past data and providing appropriate countermeasures in advance.

[0992] (Claim 3)

[0993] The system according to claim 1, further comprising preprocessing means for detecting and removing abnormal items from integrated data.

[0994] "Application example 2 when combining with an emotional engine"

[0995] (Claim 1)

[0996] Means of collecting data from multiple sources,

[0997] A means of integrating and processing the collected data on the cloud,

[0998] A means for analyzing data using generative artificial intelligence to predict and detect problems at construction sites,

[0999] A means for simulating multiple solution scenarios based on analysis results and generating the optimal process plan,

[1000] A means of recognizing the user's emotions and adjusting the process plan based on that emotional state,

[1001] A means of notifying workers of the adjusted optimal process plan via the network to their mobile devices,

[1002] A system that includes this.

[1003] (Claim 2)

[1004] The system according to claim 1, comprising means for predicting future risks using past data and proposing preventive measures in advance.

[1005] (Claim 3)

[1006] The system according to claim 1, further comprising preprocessing means for detecting and removing anomalies from integrated data. [Explanation of symbols]

[1007] 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. Means of collecting data from multiple sources, A means of integrating and processing the collected data on the cloud, A means for analyzing data using generative artificial intelligence to predict and detect problems at construction sites, A means for simulating multiple solution scenarios based on analysis results and generating the optimal process plan, A means of notifying workers' terminals of the optimal process plan via the network, A system that includes this.

2. The system according to claim 1, comprising means for predicting future risks using past data and proposing preventive measures in advance.

3. The system according to claim 1, further comprising preprocessing means for detecting and removing anomalies from integrated data.

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