system

Abstract

Provide a system. 【Solution means】 A storage means for storing design information, A sensing means for acquiring three-dimensional information at the work site, An analysis means for comparing the acquired three-dimensional information with the design information, A specifying means for specifying a deviation or problem location based on the comparison result, A generating means for generating feedback regarding the deviation or problem location, A providing means for visually providing the feedback to the user, A visualization means for visualizing the construction progress status, A communication means for transmitting an automatically generated daily report using a communication means, A system including the above.

Description

Technical Field

[0004] , , ,

[0005] , , , ,

[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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the past, the construction quality and progress management at the site were often manually carried out by supervisors and quality control personnel, and such work required a great deal of labor. In addition, it was difficult to detect deviations and problems in the construction state at an early stage and take appropriate measures, which posed risks of construction mistakes and project delays. There is a need for means to efficiently and accurately solve such problems.

Means for Solving the Problems

[0006] "Design data" refers to data containing information that forms the basis for planning buildings and structures at construction sites.

[0007] A "storage device" is a device or system that can store, retain, and retrieve electronic data as needed.

[0008] A "sensor" is a device or system that has the function of observing the physical environment or the state of an object and acquiring that information as electronic data.

[0009] "Three-dimensional data" refers to data that includes three-dimensional spatial information, representing the position and shape of an object using three-dimensional coordinates.

[0010] "Analysis means" refers to a device or system that has the function of processing data and generating evaluations or results according to a specific purpose.

[0011] "Specification means" refers to a device or system equipped with the function of finding necessary information or anomalies from information based on specific conditions or criteria.

[0012] "Generation means" refers to a device or system that has the function of creating or generating necessary information or data through a specific process.

[0013] "Presentation means" refers to a device or system that has the function of displaying or presenting processed information or data in a form that the user can understand. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

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

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

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

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention relates to a system that automatically manages on-site construction quality and progress by combining a camera device equipped with a LiDAR sensor, a server that analyzes the data acquired therefrom, and a terminal that displays the results.

[0036] First, the terminal activates the LiDAR sensor-equipped camera installed at the construction site and configures it to acquire 3D data of the site. Following these instructions, the camera acquires 3D spatial information of the construction site in real time and prepares to send the data to the server.

[0037] Next, the server compares the received 3D data with the design data stored in its storage device. During this analysis, the server uses complex computer algorithms to identify physical deviations and errors from the design drawings based on the actual construction conditions.

[0038] To monitor construction progress, the server calculates the progress rate and compares it with the schedule to evaluate the progress in real time. Based on this information, the server is configured to automatically generate daily reports and construction reports and provide them to managers and on-site personnel.

[0039] Ultimately, the terminal provides the user with feedback on the generated construction status and instructions for corrections. Based on this information, the user can perform the necessary correction work at the construction site.

[0040] For example, if the server assesses the construction progress as 70% and determines there is a 5cm discrepancy in the west wall, the terminal will display this to the user. Based on this instruction, the user can then instruct a specific worker to correct the west wall, thereby improving the accuracy of the construction.

[0041] Thus, the system implementing the present invention can reduce the workload of site supervisors, decrease construction errors, and improve overall efficiency by automatically and efficiently managing the quality and progress of construction.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The terminal activates the LiDAR sensor-equipped camera installed on-site and is configured to collect three-dimensional point cloud data. Furthermore, by performing sensor calibration as needed, it enables the acquisition of highly accurate data.

[0045] Step 2:

[0046] The camera scans the site environment according to instructions and generates three-dimensional point cloud data in real time. This data is then filtered to remove as much noise as possible and sent to the server as high-quality information.

[0047] Step 3:

[0048] The server performs data preprocessing, including denoising and scaling, to compare the received 3D data with the design data stored in the database.

[0049] Step 4:

[0050] The server uses an analysis algorithm to calculate the discrepancies between the 3D data and the design data. In particular, it detects and evaluates positional deviations and inconsistencies in shape during construction.

[0051] Step 5:

[0052] The server calculates the progress rate of the construction based on the detected discrepancies and identifies which areas are experiencing problems.

[0053] Step 6:

[0054] The server generates a feedback report that includes the progress of construction and correction instructions based on the analysis results, and sends it to the terminal.

[0055] Step 7:

[0056] The terminal displays the received feedback report in an easy-to-understand format for the user. Based on this, the user can identify areas for improvement in the construction work.

[0057] Step 8:

[0058] The user instructs on-site workers on specific construction changes according to the provided modification instructions and makes the necessary corrections. This improves the accuracy of the construction.

[0059] (Example 1)

[0060] 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."

[0061] On-site quality control and progress management of construction work typically rely on manual verification and reporting, leading to decreased work efficiency and the potential for inaccurate data due to human error. Furthermore, real-time monitoring of construction progress is difficult, hindering rapid decision-making. Addressing these issues requires accurate and rapid data acquisition and analysis, as well as effective feedback.

[0062] 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.

[0063] In this invention, the server includes a storage means for storing design information, a sensor means for acquiring spatial information at the work site, an analysis means for comparing the acquired spatial information with the design information, and an evaluation means for evaluating the progress status and automatically generating a report. This enables accurate and real-time analysis of the construction status at the site, and allows for efficient quality control and progress management.

[0064] "Design information" refers to standard data such as drawings and specifications related to construction and manufacturing.

[0065] "Memory devices" refer to devices or systems used to store data such as design information and analysis results.

[0066] A "work site" refers to a specific spatial area where construction or manufacturing takes place.

[0067] "Spatial information" refers to information that includes three-dimensional positional data acquired by devices such as LiDAR sensors.

[0068] A "sensor" refers to a device that detects physical states or changes and outputs them as digital data.

[0069] "Analysis means" refers to devices or systems used to process acquired data and evaluate it against specific criteria or indicators.

[0070] "Error" refers to the difference between the actual measured or analyzed value and the ideal or expected value.

[0071] A "discrepancy" refers to a location or part where the acquired data does not match the design information.

[0072] "Identification means" refers to devices or systems used to find specific features or inconsistencies from the results of data analysis.

[0073] A "report" refers to a document or data that summarizes analyzed information and presents it in a way that is easy for stakeholders to understand.

[0074] "Generative means" refers to devices or systems that create new documents or information based on data.

[0075] "Display means" refers to devices or systems that visually present information and convey it to the user.

[0076] "Evaluation tools" refer to devices or systems that perform analysis and judgment based on information according to certain criteria.

[0077] "Communication means" refers to devices and systems used to transmit data and information to other devices or users.

[0078] "Progress status" refers to the state of work or project progress at a specific point in time.

[0079] "Measuring means" refers to devices or systems used to quantitatively measure specific information and obtain the results.

[0080] A "plan" refers to a table or list that shows the schedule and progress plan for a task or project.

[0081] "Means of integration" refers to devices and systems used to incorporate acquired or measured information into existing data and plans.

[0082] This system efficiently manages the quality and progress of construction sites. First, the terminal uses a camera device equipped with a LiDAR sensor to acquire three-dimensional data of the site. This sensor is crucial hardware for accurately capturing spatial information over a wide area. The acquired data is transmitted to the server in real time.

[0083] The server processes the received three-dimensional data. Specifically, the server uses advanced computer algorithms to compare this data with design information previously stored in the system. This process involves analysis to identify errors and discrepancies, enabling highly accurate evaluation. Furthermore, the server evaluates the progress and automatically generates a report. This report clearly shows the current status of the project and supports efficient decision-making.

[0084] The terminal visually displays the generated report to the user, facilitating intuitive understanding. Based on this, the user can quickly instruct necessary corrective actions. For example, if the server evaluates the construction progress as 70% and detects a 5cm deviation at a specific location, the terminal will notify the user, who can then instruct the site to make corrections. In this way, construction management that balances accuracy and efficiency can be achieved.

[0085] Examples of prompts for a generative AI model:

[0086] "Please explain in detail the algorithm for a system that automates progress management at construction sites using specific 3D data analysis techniques."

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The terminal activates a LiDAR sensor-equipped camera installed at the construction site and collects three-dimensional data of the site. During this process, the terminal calibrates the sensor to ensure accurate spatial information is acquired. The input is raw data from the LiDAR sensor, and the output is three-dimensional point cloud data. This data is immediately ready to be transmitted to the server.

[0090] Step 2:

[0091] The server receives point cloud data transmitted from the terminal and compares it with design information previously stored in storage. In this process, the server uses advanced algorithms to compare the received data with the design data and identify discrepancies and inconsistencies. The input is the received 3D data and design information, and the output is data on the location of the discrepancies and the identified errors. The server records this internally to prepare for the next processing step.

[0092] Step 3:

[0093] The server calculates the construction progress rate and automatically generates a report based on the generated analysis data. This calculation also references the project schedule, which serves as a baseline for indicating progress. The inputs are the analysis results and the current project schedule, and the output is a report containing the construction progress rate and a summary of deviations. The server then prepares this information for transmission to administrators and relevant parties.

[0094] Step 4:

[0095] The terminal presents reports received from the server to the user, displaying them in a visually easy-to-understand format. In this step, the terminal uses a graphical user interface to allow users to see discrepancies and progress at a glance. The input is report data from the server, and the output is a display viewable by the user. The user can then use this to give specific field instructions.

[0096] Step 5:

[0097] The user reviews the information presented through the terminal and, if necessary, instructs corrective work at the construction site. This step provides appropriate feedback to the workers to maintain accuracy at the site. The input is the information presented on the terminal, and the output is the feedback and instructions given at the site. This allows for efficient management of the quality and progress of construction.

[0098] (Application Example 1)

[0099] 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."

[0100] Amidst the growing demand for improved construction quality and more efficient progress management on-site, real-time monitoring of construction status and the provision of immediate, responsive feedback are key challenges. In particular, there is a lack of means to identify deviations and errors at construction sites and to accurately measure and display progress rates, which increases the burden on managers and on-site workers.

[0101] 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.

[0102] In this invention, the server includes a storage means for storing design information, a sensing means for acquiring three-dimensional information at the work site, an analysis means for comparing the acquired three-dimensional information with the design information, a visualization means for visualizing the progress of construction, and a communication means for transmitting automatically generated daily reports using a communication means. This makes it possible to identify deviations at the construction site and accurately grasp the progress, enabling efficient construction quality control and the provision of rapid feedback.

[0103] A "storage device" is a data storage device that can store design information and retrieve it as needed.

[0104] "Sensing means" refers to a sensor device used to acquire three-dimensional information at the work site.

[0105] "Analysis means" refers to a device or algorithm that compares acquired three-dimensional information with design information and analyzes discrepancies and errors.

[0106] "Identification means" refers to a device or algorithm that has the function of clearly identifying deviations or problem areas detected by the analysis means.

[0107] "Generating means" refers to a device or program that generates feedback based on deviations or errors clarified by specific means.

[0108] "Means of delivery" refer to displays or applications that visually convey the generated feedback to the user.

[0109] "Visualization means" refers to a display device or program for visually representing the progress of construction work.

[0110] "Communication means" refers to a network connection device or protocol for transmitting the generated daily report to other devices or systems.

[0111] "Reflection means" refers to a device or program that has the function of automatically updating the process plan with progress information obtained by the measurement means.

[0112] The system for realizing this invention consists of a storage means for storing design information, a sensing means for acquiring three-dimensional information at the work site, and a server for comprehensively managing these.

[0113] The server first receives three-dimensional information acquired by the sensing device. The sensing device is a camera device including a LiDAR sensor that acquires spatial data of the site in real time. The acquired data is sent to the server and compared with design information by the analysis device. The hardware used here includes cloud servers and high-performance computers that support advanced processing.

[0114] The server compares the acquired data with design information and identifies discrepancies and problem areas using specific methods. The discrepancy detection algorithm includes a high-precision analysis program to distinguish even minute differences on the 3D model. This makes it possible to detect problems with construction quality at the site early and promote frequent improvements.

[0115] The server further analyzes the progress and provides the results through visualization tools, making the progress visible. The visualized data is provided to the user's terminal, allowing field personnel and managers to issue construction instructions based on it. Generated feedback and daily reports are transmitted promptly via communication tools, enabling responsible parties to respond quickly.

[0116] For example, if the server assesses the construction progress as 70% and determines there is a 5cm discrepancy in the west wall, the terminal will display this to the user. Based on this information, the user can instruct the construction workers to perform the necessary corrections, thereby improving the accuracy of the construction.

[0117] An example of a prompt message for a generated AI model is: "Create a program that analyzes LiDAR data from a construction site, calculates and displays the construction progress and deviations."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The server receives three-dimensional information transmitted from LiDAR-equipped cameras installed on-site. This input data accurately models the space of the construction site. The server temporarily stores this three-dimensional information in its memory, preparing it for subsequent analysis.

[0121] Step 2:

[0122] The server compares the recorded three-dimensional information with the design information. Using analysis tools, it performs data matching and starts calculations to detect discrepancies and errors. The output is a result indicating which parts have discrepancies and to what extent. Specifically, it analyzes 3D point cloud data and evaluates the statistical differences with the design model.

[0123] Step 3:

[0124] The server calculates the progress based on the discrepancies and problems identified through comparison. To output the construction progress rate as a percentage, it evaluates the completion status of each process using measurement tools. Specific operations include quantifying the overall progress and analyzing deviations from the progress through comparison with the schedule.

[0125] Step 4:

[0126] The terminal receives evaluation results of deviations and progress transmitted from the server and presents them to the user using visualization tools. Based on the input evaluation information, it generates data output that allows the user to intuitively understand necessary corrections and construction delays. Specifically, it displays a detailed report via a GUI (Graphical User Interface).

[0127] Step 5:

[0128] The user issues necessary construction instructions to workers based on the display results on the terminal. Specific instructions are given for areas requiring correction, enabling on-site workers to respond quickly. As output, an action plan based on the instructions is implemented on-site. Real-time feedback may be provided using communication tools for specific actions.

[0129] 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.

[0130] This invention is a system that combines a LiDAR sensor, a data processing server, a terminal with a user interface, and an emotion engine that recognizes the user's emotions, with the aim of improving work efficiency and ensuring quality at construction sites.

[0131] First, the terminal activates the camera equipped with a LiDAR sensor and is configured to acquire three-dimensional data of the site. The camera collects 3D data of the site and transmits it to the server in real time. This sensor makes it possible to digitize the site conditions with high precision.

[0132] Next, the server compares the received 3D data with the design data. This allows it to identify discrepancies and problem areas in the construction process at the building site. Using a specific algorithm, it performs an analysis to clarify the areas that need correction. Based on these results, the server measures the construction progress rate and generates feedback incorporating the analysis results and progress information.

[0133] The generated feedback is visually displayed to the user through the device. An emotion engine is incorporated, allowing the device to recognize the user's emotions and adapt the content and style of the feedback accordingly. This enables the user interface to respond flexibly to the user's emotional state.

[0134] For example, if the server assesses the construction progress as 80% and detects a 3cm discrepancy in the north wall, the terminal will visually display this information. If the emotion engine detects that the user is experiencing stress, it will soften the tone of the feedback and include more specific correction steps to support the user's understanding and response.

[0135] Thus, the system implementing the present invention not only improves construction efficiency but also reduces stress on site managers and contributes to improved construction accuracy by providing feedback in a way that is sensitive to the user's feelings.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The terminal controls the activation of the LiDAR sensor-equipped camera and performs the necessary settings to acquire 3D data of the site. This allows the camera to automatically start scanning the site.

[0139] Step 2:

[0140] The camera captures 3D point cloud data of the construction site in real time and prepares to send that data to the server. The data may be compressed before being transferred.

[0141] Step 3:

[0142] The server performs initial analysis processes, such as noise reduction and data shaping, to compare the 3D data received from the camera with the design data stored in the database.

[0143] Step 4:

[0144] The server executes an analysis algorithm and compares the 3D data with the design data to identify discrepancies and problem areas in the construction. Specifically, it meticulously detects errors in shape and position and records the results.

[0145] Step 5:

[0146] The server calculates the construction progress rate based on the matching results and uses this information to build feedback. This includes proposed solutions to problems and recommended work procedures.

[0147] Step 6:

[0148] The server sends the generated feedback to the terminal, which receives this information and prepares to display it visually to the user.

[0149] Step 7:

[0150] The emotion engine built into the device recognizes the user's emotions and adjusts the way feedback is displayed and the content accordingly. In this process, if the analyzed emotions indicate stress or anxiety, the display becomes gentler or more detailed explanations are added.

[0151] Step 8:

[0152] The user reviews the feedback and provides specific instructions to on-site workers based on areas where construction corrections are needed. These instructions facilitate quick responses on-site and improve construction accuracy.

[0153] (Example 2)

[0154] 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 will be referred to as the "terminal."

[0155] In construction sites and other construction work, there is a demand for improved work efficiency and quality assurance, but there are problems with quickly and accurately identifying the progress and problem areas on site. Furthermore, it is difficult to provide appropriate feedback while reducing the mental burden on site managers.

[0156] 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.

[0157] In this invention, the server includes a storage means for storing design information, a sensing means for acquiring three-dimensional information at the site, and an analysis means for comparing the acquired three-dimensional information with the design information. This enables efficient acquisition and analysis of site information, allowing for accurate understanding of the construction situation and the provision of flexible feedback.

[0158] "Design information" refers to detailed data related to the design of buildings and structures, including the arrangement, dimensions, and specifications of each component.

[0159] A "memory device" is a device or method for storing information, and functions as the memory or database of a computer.

[0160] "3D information" refers to data that represents the three-dimensional shape and characteristics of objects and the environment at a given site.

[0161] "Sensing means" refers to devices or sensors used to acquire information from objects or the environment, including LiDAR and cameras.

[0162] "Analysis means" refers to the processes and algorithms used to process acquired data and convert it into meaningful information.

[0163] "Deviation" refers to a difference from the target state or design information, and means an error in construction or placement.

[0164] "Generation means" refers to a device or method that creates new information or data based on the analysis results.

[0165] "Display means" refers to a device or screen for visually presenting information, such as a monitor or projector.

[0166] "Adaptive means" refers to a device or method that changes the operation of a system in accordance with the user's state or requirements.

[0167] "Adjustment means" refers to a function that modifies the characteristics of the interface and information provided to suit the user's needs.

[0168] The following system is configured as an embodiment for carrying out this invention.

[0169] The terminal acquires three-dimensional information of the site using a camera equipped with a LiDAR sensor. This camera has the function of scanning the site situation in 3D in real time and acquiring the data in digital format. The acquired data is transmitted to a server using wireless communication technology. Specifically, communication protocols such as Wi-Fi and Bluetooth are used in this process.

[0170] The server compares the received 3D data with design information pre-stored in a memory device. Here, specialized analysis software is used to compare the 3D data and identify deviations and problem areas. This makes it possible to quickly detect discrepancies and errors between the design drawings and the actual site conditions. For example, if a wall of a building under construction is 3 cm off from the design drawing, its specific location can be determined.

[0171] Furthermore, the server generates feedback on the identified issues. This feedback includes specific instructions on what needs to be corrected and how. The generated feedback information is sent to the terminal through an interactive user interface.

[0172] At this time, the device uses its built-in emotion recognition engine to detect the user's emotional state. It determines states such as stress and anxiety from the user's facial expressions and voice, and changes the style of feedback accordingly. This adaptive feedback makes it easier for the user to understand the information and encourages appropriate action.

[0173] For example, if the server analyzes the data and determines that "there is a 3cm discrepancy in the north wall, and the progress is 80%", the terminal will display this information to the user. If the emotion engine determines that the user is stressed, the feedback will be presented in a friendly tone, and specific corrective steps will be provided to support the user.

[0174] An example of a prompt message could be, "Use the LiDAR sensor to acquire 3D data from the site, and display feedback on the user interface based on the analysis results on the server." This prompt allows the system to perform the expected operation and provide appropriate information to the user.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The device activates its LiDAR sensor to acquire three-dimensional information of the site. In this process, the physical environment of the site is 3D scanned and digitized as three-dimensional data. The input is physical environmental information, and the output is digitized three-dimensional data. Specifically, the device's sensing system generates detailed point cloud data using laser ranging and saves it as a data file.

[0178] Step 2:

[0179] The terminal transmits the acquired 3D data to the server. Wi-Fi or wired connections are used for communication. The input is 3D data stored on the terminal, and the output is digital data transmitted to the server. For example, the terminal compresses a data file and transmits it to the server's receiving port using a wireless communication module.

[0180] Step 3:

[0181] The server compares the received 3D data with the design information stored in the memory. The input is the received 3D data and the design information, and the output is the comparison result. In this step, a 3D comparison algorithm is used to overlay the actual field data and the design information and calculate the deviation. Specifically, matching is performed based on similar feature points and the positional deviation is analyzed.

[0182] Step 4:

[0183] The server identifies deviations and problem areas based on the matching results. The input is the matching results, and the output is detailed information about the problem areas. Specifically, the server's analysis software evaluates the numerical deviations and lists the areas that exceed the threshold value. This result forms the basis for feedback generation.

[0184] Step 5:

[0185] The server generates feedback regarding identified deviations and problem areas. The input is information about the identified problem areas, and the output is feedback including correction suggestions. For example, if the north wall is misaligned by 3 cm, feedback will be generated along with correction steps. The generating AI model adds explanations and suggestions to this feedback.

[0186] Step 6:

[0187] The device receives feedback from the server and presents it visually to the user through the user interface. The input is feedback information from the server, and the output is visual information displayed on the user interface. During this process, an emotion engine recognizes the user's emotional state and adjusts the presentation accordingly. For example, the device might display a message in a calm tone that includes encouragement.

[0188] Step 7:

[0189] The user reviews the feedback provided and makes necessary corrections. The input is the feedback information provided by the terminal, and the output is the user's on-site response. Specifically, the user either communicates correction instructions to on-site workers or takes the necessary measures directly on-site.

[0190] (Application Example 2)

[0191] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0192] Improving work efficiency and ensuring quality on-site are crucial challenges. However, on-site conditions and construction progress are often not properly assessed, and the emotional state of site managers can also affect construction efficiency and accuracy. Therefore, a system is needed that accurately analyzes on-site data and provides feedback that takes into account the user's emotions.

[0193] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0194] In this invention, the server includes a storage means for storing design data, a receiving means for acquiring three-dimensional data from the site, and an analysis means for comparing the acquired three-dimensional data with the design data. This makes it possible to accurately grasp the situation on site and provide feedback that is tailored to the user's emotional state.

[0195] A "memory device" is an element that has the function of storing design data and keeping it in a format that can be accessed as needed.

[0196] A "receiving means" is an element equipped with the function of acquiring three-dimensional data at the site and transmitting it to other data processing devices.

[0197] An "analysis tool" is an element that has the function of analyzing acquired three-dimensional data by comparing it with design data and evaluating the situation on site.

[0198] "Identification means" refers to elements that have the function of clearly recognizing problem areas and discrepancies from the comparison results.

[0199] A "creation method" is an element that has the function of generating detailed feedback regarding the identified problem areas.

[0200] A "display means" is an element that has the function of providing the generated feedback to the user visually.

[0201] An "emotional judgment tool" is an element that has the function of recognizing the user's emotions and using that information to provide appropriate feedback.

[0202] This invention aims to improve work efficiency and quality at construction sites by having a server, terminal, and user work together to operate a system. The terminal is equipped with a LiDAR sensor, which is used to acquire three-dimensional data of the construction site. This data is transmitted to the server in real time. The server can analyze the site conditions by comparing the received three-dimensional data with design data. Furthermore, the server recognizes the user's emotions using emotion recognition means and adjusts the content of the feedback based on this. The generated feedback is presented visually to the user through the terminal.

[0203] Specifically, for example, if construction progress is assessed as 80% and a 3cm discrepancy is found in the north wall, the terminal will present this information to the user. If the emotion assessment system detects that the user is experiencing stress, the terminal will adjust the tone of the feedback to be gentler and provide specific correction steps. This allows the user to respond to on-site issues intuitively and calmly.

[0204] An example of a prompt message is, "Based on 3D data of a construction site, please propose a GUI design for an application that analyzes construction progress and problem areas, and provides feedback that takes user emotions into consideration." Using such prompt messages, the generative AI model will be used to design the specific user interface and adjust the feedback content.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The terminal activates its LiDAR sensor to acquire three-dimensional data of the construction site. This data acquisition is performed using cameras and sensors to accurately collect three-dimensional information about the site. The acquired data is then used directly in the next step.

[0208] Step 2:

[0209] The terminal transmits the acquired 3D data to the server in real time. By sending the acquired data as input to the server and having the server receive it, all the spatial information necessary for analysis is aggregated on the server.

[0210] Step 3:

[0211] The server compares the received 3D data with the design data. Using both 3D data and design data as input data, the server identifies differences between this data using an analysis algorithm. This is where discrepancies between the actual site and the design, as well as discrepancies in the production process, are detected.

[0212] Step 4:

[0213] The server identifies the problem areas that need correction based on the matching results and generates feedback. Based on the analyzed data, the identification means clarifies the problem areas, and the creation means generates specific feedback information.

[0214] Step 5:

[0215] The server uses emotion recognition mechanisms to adjust the generated feedback. The tone and content of the feedback are adapted to the user's emotional state. This leads to a deeper understanding of the user and the generation of information that reduces stress.

[0216] Step 6:

[0217] The device visually presents the user with adjusted feedback. This visual display includes specific correction instructions and progress information, allowing the user to take appropriate action at the construction site.

[0218] This series of processes makes it possible to quickly identify problems at the construction site and provide appropriate feedback that takes into account the user's emotional state.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention relates to a system that automatically manages on-site construction quality and progress by combining a camera device equipped with a LiDAR sensor, a server that analyzes the data acquired therefrom, and a terminal that displays the results.

[0236] First, the terminal activates the LiDAR sensor-equipped camera installed at the construction site and configures it to acquire 3D data of the site. Following these instructions, the camera acquires 3D spatial information of the construction site in real time and prepares to send the data to the server.

[0237] Next, the server compares the received 3D data with the design data stored in its storage device. During this analysis, the server uses complex computer algorithms to identify physical deviations and errors from the design drawings based on the actual construction conditions.

[0238] To monitor construction progress, the server calculates the progress rate and compares it with the schedule to evaluate the progress in real time. Based on this information, the server is configured to automatically generate daily reports and construction reports and provide them to managers and on-site personnel.

[0239] Ultimately, the terminal provides the user with feedback on the generated construction status and instructions for corrections. Based on this information, the user can perform the necessary correction work at the construction site.

[0240] For example, if the server assesses the construction progress as 70% and determines there is a 5cm discrepancy in the west wall, the terminal will display this to the user. Based on this instruction, the user can then instruct a specific worker to correct the west wall, thereby improving the accuracy of the construction.

[0241] Thus, the system implementing the present invention can reduce the workload of site supervisors, decrease construction errors, and improve overall efficiency by automatically and efficiently managing the quality and progress of construction.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The terminal activates the LiDAR sensor-equipped camera installed on-site and is configured to collect three-dimensional point cloud data. Furthermore, by performing sensor calibration as needed, it enables the acquisition of highly accurate data.

[0245] Step 2:

[0246] The camera scans the site environment according to instructions and generates three-dimensional point cloud data in real time. This data is then filtered to remove as much noise as possible and sent to the server as high-quality information.

[0247] Step 3:

[0248] The server performs data preprocessing, including denoising and scaling, to compare the received 3D data with the design data stored in the database.

[0249] Step 4:

[0250] The server uses an analysis algorithm to calculate the discrepancies between the 3D data and the design data. In particular, it detects and evaluates positional deviations and inconsistencies in shape during construction.

[0251] Step 5:

[0252] The server calculates the progress rate of the construction based on the detected discrepancies and identifies which areas are experiencing problems.

[0253] Step 6:

[0254] The server generates a feedback report that includes the progress of construction and correction instructions based on the analysis results, and sends it to the terminal.

[0255] Step 7:

[0256] The terminal displays the received feedback report in an easy-to-understand format for the user. Based on this, the user can identify areas for improvement in the construction work.

[0257] Step 8:

[0258] The user instructs on-site workers on specific construction changes according to the provided modification instructions and makes the necessary corrections. This improves the accuracy of the construction.

[0259] (Example 1)

[0260] 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".

[0261] On-site quality control and progress management of construction work typically rely on manual verification and reporting, leading to decreased work efficiency and the potential for inaccurate data due to human error. Furthermore, real-time monitoring of construction progress is difficult, hindering rapid decision-making. Addressing these issues requires accurate and rapid data acquisition and analysis, as well as effective feedback.

[0262] 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.

[0263] In this invention, the server includes a storage means for storing design information, a sensor means for acquiring spatial information at the work site, an analysis means for comparing the acquired spatial information with the design information, and an evaluation means for evaluating the progress status and automatically generating a report. This enables accurate and real-time analysis of the construction status at the site, and allows for efficient quality control and progress management.

[0264] "Design information" refers to standard data such as drawings and specifications related to construction and manufacturing.

[0265] "Memory devices" refer to devices or systems used to store data such as design information and analysis results.

[0266] A "work site" refers to a specific spatial area where construction or manufacturing takes place.

[0267] "Spatial information" refers to information that includes three-dimensional positional data acquired by devices such as LiDAR sensors.

[0268] A "sensor" refers to a device that detects physical states or changes and outputs them as digital data.

[0269] "Analysis means" refers to devices or systems used to process acquired data and evaluate it against specific criteria or indicators.

[0270] "Error" refers to the difference between the actual measured or analyzed value and the ideal or expected value.

[0271] A "discrepancy" refers to a location or part where the acquired data does not match the design information.

[0272] "Identification means" refers to devices or systems used to find specific features or inconsistencies from the results of data analysis.

[0273] A "report" refers to a document or data that summarizes analyzed information and presents it in a way that is easy for stakeholders to understand.

[0274] "Generative means" refers to devices or systems that create new documents or information based on data.

[0275] "Display means" refers to devices or systems that visually present information and convey it to the user.

[0276] "Evaluation tools" refer to devices or systems that perform analysis and judgment based on information according to certain criteria.

[0277] "Communication means" refers to devices and systems used to transmit data and information to other devices or users.

[0278] "Progress status" refers to the state of work or project progress at a specific point in time.

[0279] The "measurement means" refers to a device or system for quantitatively measuring a certain specific information and obtaining the result.

[0280] The "schedule" refers to a table or list showing the schedule and progress plan of work or a project.

[0281] The "reflection means" refers to a device or system for incorporating the acquired or measured information into existing data or plans.

[0282] This system efficiently manages the quality and progress of the construction site. First, the terminal uses a camera device equipped with a LiDAR sensor to acquire three-dimensional data of the site. This sensor is an important hardware for accurately capturing spatial information over a wide range. The acquired data is transmitted to the server in real time.

[0283] The server processes the received three-dimensional data. Specifically, when the server collates this data with the design information stored in the system in advance, it uses advanced computer algorithms. This process performs an analysis to identify errors and inconsistencies, enabling a highly accurate evaluation. Furthermore, the server evaluates the progress status and automatically generates a report. This report clearly shows the current status of the project and supports efficient decision-making.

[0284] The terminal visually displays the generated report to the user, facilitating intuitive understanding. Based on this, the user can quickly instruct the necessary corrective work. As a specific example, when the server evaluates the construction progress as 70% and detects a 5 cm deviation at a specific location, the terminal notifies the user, and the user instructs the correction at the site. In this way, construction management that achieves both accuracy and efficiency can be realized.

[0285] Examples of prompt sentences for the generated AI model:

[0286] "Please explain in detail the algorithm of the system that automates the progress management of the construction site using specific 3D data analysis methods."

[0287] The flow of the specific process in Example 1 will be described using FIG. 11.

[0288] Step 1:

[0289] The terminal activates the camera with a LiDAR sensor installed at the construction site and collects three-dimensional data of the site. In this process, the terminal calibrates the sensor and sets it so that accurate spatial information can be obtained. The input is raw data from the LiDAR sensor, and the output is three-dimensional point cloud data. This data is prepared to be immediately transmitted to the server.

[0290] Step 2:

[0291] The server receives the point cloud data transmitted from the terminal and compares it with the design information previously stored in the storage. In this process, the server uses an advanced algorithm to compare the received data with the design data and identify deviations and inconsistencies. The input is the received three-dimensional data and design information, and the output is the position information of the deviation and the data of the identified error. The server records this internally in preparation for the next process.

[0292] Step 3:

[0293] The server calculates the construction progress rate and automatically creates a report based on the generated analysis data. In this calculation, the process schedule used as a reference for indicating progress is also referred to. The input is the analysis result and the current process schedule, and the output is a report including the construction progress rate and an overview of the deviation. The server prepares to transmit this information to the administrator and related parties.

[0294] Step 4:

[0295] The terminal presents reports received from the server to the user, displaying them in a visually easy-to-understand format. In this step, the terminal uses a graphical user interface to allow users to see discrepancies and progress at a glance. The input is report data from the server, and the output is a display viewable by the user. The user can then use this to give specific field instructions.

[0296] Step 5:

[0297] The user reviews the information presented through the terminal and, if necessary, instructs corrective work at the construction site. This step provides appropriate feedback to the workers to maintain accuracy at the site. The input is the information presented on the terminal, and the output is the feedback and instructions given at the site. This allows for efficient management of the quality and progress of construction.

[0298] (Application Example 1)

[0299] 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."

[0300] Amidst the growing demand for improved construction quality and more efficient progress management on-site, real-time monitoring of construction status and the provision of immediate, responsive feedback are key challenges. In particular, there is a lack of means to identify deviations and errors at construction sites and to accurately measure and display progress rates, which increases the burden on managers and on-site workers.

[0301] 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.

[0302] In this invention, the server includes a storage means for storing design information, a sensing means for acquiring three-dimensional information at the work site, an analysis means for comparing the acquired three-dimensional information with the design information, a visualization means for visualizing the construction progress status, and a communication means for transmitting the automatically generated daily report using the communication means. Thereby, it becomes possible to identify the deviation at the construction site and accurately grasp the progress, and it becomes possible to perform efficient construction quality management and provide rapid feedback.

[0303] The "storage means" is a data storage device that stores design information and can retrieve it as needed.

[0304] The "sensing means" is a sensor device for acquiring three-dimensional information at the work site.

[0305] The "analysis means" is a device or algorithm that compares the acquired three-dimensional information with the design information and analyzes deviations and errors.

[0306] The "identification means" is a device or algorithm having a function of clearly identifying the deviations and problem areas detected by the analysis means.

[0307] The "generation means" is a device or program that generates feedback based on the deviations and errors clarified by the identification means.

[0308] The "providing means" is a display or application for visually conveying the generated feedback to the user.

[0309] The "visualization means" is a display device or program for visually expressing the construction progress status.

[0310] The "communication means" is a network connection device or protocol for transmitting the generated daily report to other devices or systems.

[0311] "Reflection means" refers to a device or program that has the function of automatically updating the process plan with progress information obtained by the measurement means.

[0312] The system for realizing this invention consists of a storage means for storing design information, a sensing means for acquiring three-dimensional information at the work site, and a server for comprehensively managing these.

[0313] The server first receives three-dimensional information acquired by the sensing device. The sensing device is a camera device including a LiDAR sensor that acquires spatial data of the site in real time. The acquired data is sent to the server and compared with design information by the analysis device. The hardware used here includes cloud servers and high-performance computers that support advanced processing.

[0314] The server compares the acquired data with design information and identifies discrepancies and problem areas using specific methods. The discrepancy detection algorithm includes a high-precision analysis program to distinguish even minute differences on the 3D model. This makes it possible to detect problems with construction quality at the site early and promote frequent improvements.

[0315] The server further analyzes the progress and provides the results through visualization tools, making the progress visible. The visualized data is provided to the user's terminal, allowing field personnel and managers to issue construction instructions based on it. Generated feedback and daily reports are transmitted promptly via communication tools, enabling responsible parties to respond quickly.

[0316] For example, if the server assesses the construction progress as 70% and determines there is a 5cm discrepancy in the west wall, the terminal will display this to the user. Based on this information, the user can instruct the construction workers to perform the necessary corrections, thereby improving the accuracy of the construction.

[0317] An example of a prompt message for a generated AI model is: "Create a program that analyzes LiDAR data from a construction site, calculates and displays the construction progress and deviations."

[0318] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0319] Step 1:

[0320] The server receives three-dimensional information transmitted from LiDAR-equipped cameras installed on-site. This input data accurately models the space of the construction site. The server temporarily stores this three-dimensional information in its memory, preparing it for subsequent analysis.

[0321] Step 2:

[0322] The server compares the recorded three-dimensional information with the design information. Using analysis tools, it performs data matching and starts calculations to detect discrepancies and errors. The output is a result indicating which parts have discrepancies and to what extent. Specifically, it analyzes 3D point cloud data and evaluates the statistical differences with the design model.

[0323] Step 3:

[0324] The server calculates the progress based on the discrepancies and problems identified through comparison. To output the construction progress rate as a percentage, it evaluates the completion status of each process using measurement tools. Specific operations include quantifying the overall progress and analyzing deviations from the progress through comparison with the schedule.

[0325] Step 4:

[0326] The terminal receives evaluation results of deviations and progress transmitted from the server and presents them to the user using visualization tools. Based on the input evaluation information, it generates data output that allows the user to intuitively understand necessary corrections and construction delays. Specifically, it displays a detailed report via a GUI (Graphical User Interface).

[0327] Step 5:

[0328] The user issues necessary construction instructions to workers based on the display results on the terminal. Specific instructions are given for areas requiring correction, enabling on-site workers to respond quickly. As output, an action plan based on the instructions is implemented on-site. Real-time feedback may be provided using communication tools for specific actions.

[0329] 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.

[0330] This invention is a system that combines a LiDAR sensor, a data processing server, a terminal with a user interface, and an emotion engine that recognizes the user's emotions, with the aim of improving work efficiency and ensuring quality at construction sites.

[0331] First, the terminal activates the camera equipped with a LiDAR sensor and is configured to acquire three-dimensional data of the site. The camera collects 3D data of the site and transmits it to the server in real time. This sensor makes it possible to digitize the site conditions with high precision.

[0332] Next, the server compares the received 3D data with the design data. This allows it to identify discrepancies and problem areas in the construction process at the building site. Using a specific algorithm, it performs an analysis to clarify the areas that need correction. Based on these results, the server measures the construction progress rate and generates feedback incorporating the analysis results and progress information.

[0333] The generated feedback is visually displayed to the user through the device. An emotion engine is incorporated, allowing the device to recognize the user's emotions and adapt the content and style of the feedback accordingly. This enables the user interface to respond flexibly to the user's emotional state.

[0334] For example, if the server assesses the construction progress as 80% and detects a 3cm discrepancy in the north wall, the terminal will visually display this information. If the emotion engine detects that the user is experiencing stress, it will soften the tone of the feedback and include more specific correction steps to support the user's understanding and response.

[0335] Thus, the system implementing the present invention not only improves construction efficiency but also reduces stress on site managers and contributes to improved construction accuracy by providing feedback in a way that is sensitive to the user's feelings.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The terminal controls the activation of the LiDAR sensor-equipped camera and performs the necessary settings to acquire 3D data of the site. This allows the camera to automatically start scanning the site.

[0339] Step 2:

[0340] The camera captures 3D point cloud data of the construction site in real time and prepares to send that data to the server. The data may be compressed before being transferred.

[0341] Step 3:

[0342] The server performs initial analysis processes, such as noise reduction and data shaping, to compare the 3D data received from the camera with the design data stored in the database.

[0343] Step 4:

[0344] The server executes an analysis algorithm and compares the 3D data with the design data to identify discrepancies and problem areas in the construction. Specifically, it meticulously detects errors in shape and position and records the results.

[0345] Step 5:

[0346] The server calculates the construction progress rate based on the matching results and uses this information to build feedback. This includes proposed solutions to problems and recommended work procedures.

[0347] Step 6:

[0348] The server sends the generated feedback to the terminal, which receives this information and prepares to display it visually to the user.

[0349] Step 7:

[0350] The emotion engine built into the device recognizes the user's emotions and adjusts the way feedback is displayed and the content accordingly. In this process, if the analyzed emotions indicate stress or anxiety, the display becomes gentler or more detailed explanations are added.

[0351] Step 8:

[0352] The user reviews the feedback and provides specific instructions to on-site workers based on areas where construction corrections are needed. These instructions facilitate quick responses on-site and improve construction accuracy.

[0353] (Example 2)

[0354] 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".

[0355] In construction sites and other construction work, there is a demand for improved work efficiency and quality assurance, but there are problems with quickly and accurately identifying the progress and problem areas on site. Furthermore, it is difficult to provide appropriate feedback while reducing the mental burden on site managers.

[0356] 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.

[0357] In this invention, the server includes a storage means for storing design information, a sensing means for acquiring three-dimensional information at the site, and an analysis means for comparing the acquired three-dimensional information with the design information. This enables efficient acquisition and analysis of site information, allowing for accurate understanding of the construction situation and the provision of flexible feedback.

[0358] "Design information" refers to detailed data related to the design of buildings and structures, including the arrangement, dimensions, and specifications of each component.

[0359] A "memory device" is a device or method for storing information, and functions as the memory or database of a computer.

[0360] "3D information" refers to data that represents the three-dimensional shape and characteristics of objects and the environment at a given site.

[0361] "Sensing means" refers to devices or sensors used to acquire information from objects or the environment, including LiDAR and cameras.

[0362] "Analysis means" refers to the processes and algorithms used to process acquired data and convert it into meaningful information.

[0363] "Deviation" refers to a difference from the target state or design information, and means an error in construction or placement.

[0364] "Generation means" refers to a device or method that creates new information or data based on the analysis results.

[0365] "Display means" refers to a device or screen for visually presenting information, such as a monitor or projector.

[0366] "Adaptive means" refers to a device or method that changes the operation of a system in accordance with the user's state or requirements.

[0367] "Adjustment means" refers to a function that modifies the characteristics of the interface and information provided to suit the user's needs.

[0368] The following system is configured as an embodiment for carrying out this invention.

[0369] The terminal acquires three-dimensional information of the site using a camera equipped with a LiDAR sensor. This camera has the function of scanning the site situation in 3D in real time and acquiring the data in digital format. The acquired data is transmitted to a server using wireless communication technology. Specifically, communication protocols such as Wi-Fi and Bluetooth are used in this process.

[0370] The server compares the received 3D data with design information pre-stored in a memory device. Here, specialized analysis software is used to compare the 3D data and identify deviations and problem areas. This makes it possible to quickly detect discrepancies and errors between the design drawings and the actual site conditions. For example, if a wall of a building under construction is 3 cm off from the design drawing, its specific location can be determined.

[0371] Furthermore, the server generates feedback on the identified issues. This feedback includes specific instructions on what needs to be corrected and how. The generated feedback information is sent to the terminal through an interactive user interface.

[0372] At this time, the device uses its built-in emotion recognition engine to detect the user's emotional state. It determines states such as stress and anxiety from the user's facial expressions and voice, and changes the style of feedback accordingly. This adaptive feedback makes it easier for the user to understand the information and encourages appropriate action.

[0373] For example, if the server analyzes the data and determines that "there is a 3cm discrepancy in the north wall, and the progress is 80%", the terminal will display this information to the user. If the emotion engine determines that the user is stressed, the feedback will be presented in a friendly tone, and specific corrective steps will be provided to support the user.

[0374] An example of a prompt message could be, "Use the LiDAR sensor to acquire 3D data from the site, and display feedback on the user interface based on the analysis results on the server." This prompt allows the system to perform the expected operation and provide appropriate information to the user.

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1:

[0377] The device activates its LiDAR sensor to acquire three-dimensional information of the site. In this process, the physical environment of the site is 3D scanned and digitized as three-dimensional data. The input is physical environmental information, and the output is digitized three-dimensional data. Specifically, the device's sensing system generates detailed point cloud data using laser ranging and saves it as a data file.

[0378] Step 2:

[0379] The terminal transmits the acquired 3D data to the server. Wi-Fi or wired connections are used for communication. The input is 3D data stored on the terminal, and the output is digital data transmitted to the server. For example, the terminal compresses a data file and transmits it to the server's receiving port using a wireless communication module.

[0380] Step 3:

[0381] The server compares the received 3D data with the design information stored in the memory. The input is the received 3D data and the design information, and the output is the comparison result. In this step, a 3D comparison algorithm is used to overlay the actual field data and the design information and calculate the deviation. Specifically, matching is performed based on similar feature points and the positional deviation is analyzed.

[0382] Step 4:

[0383] The server identifies deviations and problem areas based on the matching results. The input is the matching results, and the output is detailed information about the problem areas. Specifically, the server's analysis software evaluates the numerical deviations and lists the areas that exceed the threshold value. This result forms the basis for feedback generation.

[0384] Step 5:

[0385] The server generates feedback regarding identified deviations and problem areas. The input is information about the identified problem areas, and the output is feedback including correction suggestions. For example, if the north wall is misaligned by 3 cm, feedback will be generated along with correction steps. The generating AI model adds explanations and suggestions to this feedback.

[0386] Step 6:

[0387] The device receives feedback from the server and presents it visually to the user through the user interface. The input is feedback information from the server, and the output is visual information displayed on the user interface. During this process, an emotion engine recognizes the user's emotional state and adjusts the presentation accordingly. For example, the device might display a message in a calm tone that includes encouragement.

[0388] Step 7:

[0389] The user reviews the feedback provided and makes necessary corrections. The input is the feedback information provided by the terminal, and the output is the user's on-site response. Specifically, the user either communicates correction instructions to on-site workers or takes the necessary measures directly on-site.

[0390] (Application Example 2)

[0391] 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 as the "terminal".

[0392] Improving work efficiency and ensuring quality on-site are crucial challenges. However, on-site conditions and construction progress are often not properly assessed, and the emotional state of site managers can also affect construction efficiency and accuracy. Therefore, a system is needed that accurately analyzes on-site data and provides feedback that takes into account the user's emotions.

[0393] 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.

[0394] In this invention, the server includes a storage means for storing design data, a receiving means for acquiring three-dimensional data from the site, and an analysis means for comparing the acquired three-dimensional data with the design data. This makes it possible to accurately grasp the situation on site and provide feedback that is tailored to the user's emotional state.

[0395] A "memory device" is an element that has the function of storing design data and keeping it in a format that can be accessed as needed.

[0396] A "receiving means" is an element equipped with the function of acquiring three-dimensional data at the site and transmitting it to other data processing devices.

[0397] An "analysis tool" is an element that has the function of analyzing acquired three-dimensional data by comparing it with design data and evaluating the situation on site.

[0398] "Identification means" refers to elements that have the function of clearly recognizing problem areas and discrepancies from the comparison results.

[0399] A "creation method" is an element that has the function of generating detailed feedback regarding the identified problem areas.

[0400] A "display means" is an element that has the function of providing the generated feedback to the user visually.

[0401] An "emotional judgment tool" is an element that has the function of recognizing the user's emotions and using that information to provide appropriate feedback.

[0402] This invention aims to improve work efficiency and quality at construction sites by having a server, terminal, and user work together to operate a system. The terminal is equipped with a LiDAR sensor, which is used to acquire three-dimensional data of the construction site. This data is transmitted to the server in real time. The server can analyze the site conditions by comparing the received three-dimensional data with design data. Furthermore, the server recognizes the user's emotions using emotion recognition means and adjusts the content of the feedback based on this. The generated feedback is presented visually to the user through the terminal.

[0403] Specifically, for example, if construction progress is assessed as 80% and a 3cm discrepancy is found in the north wall, the terminal will present this information to the user. If the emotion assessment system detects that the user is experiencing stress, the terminal will adjust the tone of the feedback to be gentler and provide specific correction steps. This allows the user to respond to on-site issues intuitively and calmly.

[0404] An example of a prompt message is, "Based on 3D data of a construction site, please propose a GUI design for an application that analyzes construction progress and problem areas, and provides feedback that takes user emotions into consideration." Using such prompt messages, the generative AI model will be used to design the specific user interface and adjust the feedback content.

[0405] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0406] Step 1:

[0407] The terminal activates its LiDAR sensor to acquire three-dimensional data of the construction site. This data acquisition is performed using cameras and sensors to accurately collect three-dimensional information about the site. The acquired data is then used directly in the next step.

[0408] Step 2:

[0409] The terminal transmits the acquired 3D data to the server in real time. By sending the acquired data as input to the server and having the server receive it, all the spatial information necessary for analysis is aggregated on the server.

[0410] Step 3:

[0411] The server compares the received 3D data with the design data. Using both 3D data and design data as input data, the server identifies differences between this data using an analysis algorithm. This is where discrepancies between the actual site and the design, as well as discrepancies in the production process, are detected.

[0412] Step 4:

[0413] The server identifies the problem areas that need correction based on the matching results and generates feedback. Based on the analyzed data, the identification means clarifies the problem areas, and the creation means generates specific feedback information.

[0414] Step 5:

[0415] The server uses emotion recognition mechanisms to adjust the generated feedback. The tone and content of the feedback are adapted to the user's emotional state. This leads to a deeper understanding of the user and the generation of information that reduces stress.

[0416] Step 6:

[0417] The device visually presents the user with adjusted feedback. This visual display includes specific correction instructions and progress information, allowing the user to take appropriate action at the construction site.

[0418] This series of processes makes it possible to quickly identify problems at the construction site and provide appropriate feedback that takes into account the user's emotional state.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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".

[0435] This invention relates to a system that automatically manages on-site construction quality and progress by combining a camera device equipped with a LiDAR sensor, a server that analyzes the data acquired therefrom, and a terminal that displays the results.

[0436] First, the terminal activates the LiDAR sensor-equipped camera installed at the construction site and configures it to acquire 3D data of the site. Following these instructions, the camera acquires 3D spatial information of the construction site in real time and prepares to send the data to the server.

[0437] Next, the server compares the received 3D data with the design data stored in its storage device. During this analysis, the server uses complex computer algorithms to identify physical deviations and errors from the design drawings based on the actual construction conditions.

[0438] To monitor construction progress, the server calculates the progress rate and compares it with the schedule to evaluate the progress in real time. Based on this information, the server is configured to automatically generate daily reports and construction reports and provide them to managers and on-site personnel.

[0439] Ultimately, the terminal provides the user with feedback on the generated construction status and instructions for corrections. Based on this information, the user can perform the necessary correction work at the construction site.

[0440] For example, if the server assesses the construction progress as 70% and determines there is a 5cm discrepancy in the west wall, the terminal will display this to the user. Based on this instruction, the user can then instruct a specific worker to correct the west wall, thereby improving the accuracy of the construction.

[0441] Thus, the system implementing the present invention can reduce the workload of site supervisors, decrease construction errors, and improve overall efficiency by automatically and efficiently managing the quality and progress of construction.

[0442] The following describes the processing flow.

[0443] Step 1:

[0444] The terminal activates the LiDAR sensor-equipped camera installed on-site and is configured to collect three-dimensional point cloud data. Furthermore, by performing sensor calibration as needed, it enables the acquisition of highly accurate data.

[0445] Step 2:

[0446] The camera scans the site environment according to instructions and generates three-dimensional point cloud data in real time. This data is then filtered to remove as much noise as possible and sent to the server as high-quality information.

[0447] Step 3:

[0448] The server performs data preprocessing, including denoising and scaling, to compare the received 3D data with the design data stored in the database.

[0449] Step 4:

[0450] The server uses an analysis algorithm to calculate the discrepancies between the 3D data and the design data. In particular, it detects and evaluates positional deviations and inconsistencies in shape during construction.

[0451] Step 5:

[0452] The server calculates the progress rate of the construction based on the detected discrepancies and identifies which areas are experiencing problems.

[0453] Step 6:

[0454] The server generates a feedback report that includes the progress of construction and correction instructions based on the analysis results, and sends it to the terminal.

[0455] Step 7:

[0456] The terminal displays the received feedback report in an easy-to-understand format for the user. Based on this, the user can identify areas for improvement in the construction work.

[0457] Step 8:

[0458] The user instructs on-site workers on specific construction changes according to the provided modification instructions and makes the necessary corrections. This improves the accuracy of the construction.

[0459] (Example 1)

[0460] 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."

[0461] On-site quality control and progress management of construction work typically rely on manual verification and reporting, leading to decreased work efficiency and the potential for inaccurate data due to human error. Furthermore, real-time monitoring of construction progress is difficult, hindering rapid decision-making. Addressing these issues requires accurate and rapid data acquisition and analysis, as well as effective feedback.

[0462] 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.

[0463] In this invention, the server includes a storage means for storing design information, a sensor means for acquiring spatial information at the work site, an analysis means for comparing the acquired spatial information with the design information, and an evaluation means for evaluating the progress status and automatically generating a report. This enables accurate and real-time analysis of the construction status at the site, and allows for efficient quality control and progress management.

[0464] "Design information" refers to standard data such as drawings and specifications related to construction and manufacturing.

[0465] "Memory devices" refer to devices or systems used to store data such as design information and analysis results.

[0466] A "work site" refers to a specific spatial area where construction or manufacturing takes place.

[0467] "Spatial information" refers to information that includes three-dimensional positional data acquired by devices such as LiDAR sensors.

[0468] A "sensor" refers to a device that detects physical states or changes and outputs them as digital data.

[0469] "Analysis means" refers to devices or systems used to process acquired data and evaluate it against specific criteria or indicators.

[0470] "Error" refers to the difference between the actual measured or analyzed value and the ideal or expected value.

[0471] A "discrepancy" refers to a location or part where the acquired data does not match the design information.

[0472] "Identification means" refers to devices or systems used to find specific features or inconsistencies from the results of data analysis.

[0473] A "report" refers to a document or data that summarizes analyzed information and presents it in a way that is easy for stakeholders to understand.

[0474] "Generative means" refers to devices or systems that create new documents or information based on data.

[0475] "Display means" refers to devices or systems that visually present information and convey it to the user.

[0476] "Evaluation tools" refer to devices or systems that perform analysis and judgment based on information according to certain criteria.

[0477] "Communication means" refers to devices and systems used to transmit data and information to other devices or users.

[0478] "Progress status" refers to the state of work or project progress at a specific point in time.

[0479] "Measuring means" refers to devices or systems used to quantitatively measure specific information and obtain the results.

[0480] A "plan" refers to a table or list that shows the schedule and progress plan for a task or project.

[0481] "Means of integration" refers to devices and systems used to incorporate acquired or measured information into existing data and plans.

[0482] This system efficiently manages the quality and progress of construction sites. First, the terminal uses a camera device equipped with a LiDAR sensor to acquire three-dimensional data of the site. This sensor is crucial hardware for accurately capturing spatial information over a wide area. The acquired data is transmitted to the server in real time.

[0483] The server processes the received three-dimensional data. Specifically, the server uses advanced computer algorithms to compare this data with design information previously stored in the system. This process involves analysis to identify errors and discrepancies, enabling highly accurate evaluation. Furthermore, the server evaluates the progress and automatically generates a report. This report clearly shows the current status of the project and supports efficient decision-making.

[0484] The terminal visually displays the generated report to the user, facilitating intuitive understanding. Based on this, the user can quickly instruct necessary corrective actions. For example, if the server evaluates the construction progress as 70% and detects a 5cm deviation at a specific location, the terminal will notify the user, who can then instruct the site to make corrections. In this way, construction management that balances accuracy and efficiency can be achieved.

[0485] Examples of prompts for a generative AI model:

[0486] "Please explain in detail the algorithm for a system that automates progress management at construction sites using specific 3D data analysis techniques."

[0487] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0488] Step 1:

[0489] The terminal activates a LiDAR sensor-equipped camera installed at the construction site and collects three-dimensional data of the site. During this process, the terminal calibrates the sensor to ensure accurate spatial information is acquired. The input is raw data from the LiDAR sensor, and the output is three-dimensional point cloud data. This data is immediately ready to be transmitted to the server.

[0490] Step 2:

[0491] The server receives point cloud data transmitted from the terminal and compares it with design information previously stored in storage. In this process, the server uses advanced algorithms to compare the received data with the design data and identify discrepancies and inconsistencies. The input is the received 3D data and design information, and the output is data on the location of the discrepancies and the identified errors. The server records this internally to prepare for the next processing step.

[0492] Step 3:

[0493] The server calculates the construction progress rate and automatically generates a report based on the generated analysis data. This calculation also references the project schedule, which serves as a baseline for indicating progress. The inputs are the analysis results and the current project schedule, and the output is a report containing the construction progress rate and a summary of deviations. The server then prepares this information for transmission to administrators and relevant parties.

[0494] Step 4:

[0495] The terminal presents reports received from the server to the user, displaying them in a visually easy-to-understand format. In this step, the terminal uses a graphical user interface to allow users to see discrepancies and progress at a glance. The input is report data from the server, and the output is a display viewable by the user. The user can then use this to give specific field instructions.

[0496] Step 5:

[0497] The user reviews the information presented through the terminal and, if necessary, instructs corrective work at the construction site. This step provides appropriate feedback to the workers to maintain accuracy at the site. The input is the information presented on the terminal, and the output is the feedback and instructions given at the site. This allows for efficient management of the quality and progress of construction.

[0498] (Application Example 1)

[0499] 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."

[0500] Amidst the growing demand for improved construction quality and more efficient progress management on-site, real-time monitoring of construction status and the provision of immediate, responsive feedback are key challenges. In particular, there is a lack of means to identify deviations and errors at construction sites and to accurately measure and display progress rates, which increases the burden on managers and on-site workers.

[0501] 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.

[0502] In this invention, the server includes a storage means for storing design information, a sensing means for acquiring three-dimensional information at the work site, an analysis means for comparing the acquired three-dimensional information with the design information, a visualization means for visualizing the progress of construction, and a communication means for transmitting automatically generated daily reports using a communication means. This makes it possible to identify deviations at the construction site and accurately grasp the progress, enabling efficient construction quality control and the provision of rapid feedback.

[0503] A "storage device" is a data storage device that can store design information and retrieve it as needed.

[0504] "Sensing means" refers to a sensor device used to acquire three-dimensional information at the work site.

[0505] "Analysis means" refers to a device or algorithm that compares acquired three-dimensional information with design information and analyzes discrepancies and errors.

[0506] "Identification means" refers to a device or algorithm that has the function of clearly identifying deviations or problem areas detected by the analysis means.

[0507] "Generating means" refers to a device or program that generates feedback based on deviations or errors clarified by specific means.

[0508] "Means of delivery" refer to displays or applications that visually convey the generated feedback to the user.

[0509] "Visualization means" refers to a display device or program for visually representing the progress of construction work.

[0510] "Communication means" refers to a network connection device or protocol for transmitting the generated daily report to other devices or systems.

[0511] "Reflection means" refers to a device or program that has the function of automatically updating the process plan with progress information obtained by the measurement means.

[0512] The system for realizing this invention consists of a storage means for storing design information, a sensing means for acquiring three-dimensional information at the work site, and a server for comprehensively managing these.

[0513] The server first receives three-dimensional information acquired by the sensing device. The sensing device is a camera device including a LiDAR sensor that acquires spatial data of the site in real time. The acquired data is sent to the server and compared with design information by the analysis device. The hardware used here includes cloud servers and high-performance computers that support advanced processing.

[0514] The server compares the acquired data with design information and identifies discrepancies and problem areas using specific methods. The discrepancy detection algorithm includes a high-precision analysis program to distinguish even minute differences on the 3D model. This makes it possible to detect problems with construction quality at the site early and promote frequent improvements.

[0515] The server further analyzes the progress and provides the results through visualization tools, making the progress visible. The visualized data is provided to the user's terminal, allowing field personnel and managers to issue construction instructions based on it. Generated feedback and daily reports are transmitted promptly via communication tools, enabling responsible parties to respond quickly.

[0516] For example, if the server assesses the construction progress as 70% and determines there is a 5cm discrepancy in the west wall, the terminal will display this to the user. Based on this information, the user can instruct the construction workers to perform the necessary corrections, thereby improving the accuracy of the construction.

[0517] An example of a prompt message for a generated AI model is: "Create a program that analyzes LiDAR data from a construction site, calculates and displays the construction progress and deviations."

[0518] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0519] Step 1:

[0520] The server receives three-dimensional information transmitted from LiDAR-equipped cameras installed on-site. This input data accurately models the space of the construction site. The server temporarily stores this three-dimensional information in its memory, preparing it for subsequent analysis.

[0521] Step 2:

[0522] The server compares the recorded three-dimensional information with the design information. Using analysis tools, it performs data matching and starts calculations to detect discrepancies and errors. The output is a result indicating which parts have discrepancies and to what extent. Specifically, it analyzes 3D point cloud data and evaluates the statistical differences with the design model.

[0523] Step 3:

[0524] The server calculates the progress based on the discrepancies and problems identified through comparison. To output the construction progress rate as a percentage, it evaluates the completion status of each process using measurement tools. Specific operations include quantifying the overall progress and analyzing deviations from the progress through comparison with the schedule.

[0525] Step 4:

[0526] The terminal receives evaluation results of deviations and progress transmitted from the server and presents them to the user using visualization tools. Based on the input evaluation information, it generates data output that allows the user to intuitively understand necessary corrections and construction delays. Specifically, it displays a detailed report via a GUI (Graphical User Interface).

[0527] Step 5:

[0528] The user issues necessary construction instructions to workers based on the display results on the terminal. Specific instructions are given for areas requiring correction, enabling on-site workers to respond quickly. As output, an action plan based on the instructions is implemented on-site. Real-time feedback may be provided using communication tools for specific actions.

[0529] 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.

[0530] This invention is a system that combines a LiDAR sensor, a data processing server, a terminal with a user interface, and an emotion engine that recognizes the user's emotions, with the aim of improving work efficiency and ensuring quality at construction sites.

[0531] First, the terminal activates the camera equipped with a LiDAR sensor and is configured to acquire three-dimensional data of the site. The camera collects 3D data of the site and transmits it to the server in real time. This sensor makes it possible to digitize the site conditions with high precision.

[0532] Next, the server compares the received 3D data with the design data. This allows it to identify discrepancies and problem areas in the construction process at the building site. Using a specific algorithm, it performs an analysis to clarify the areas that need correction. Based on these results, the server measures the construction progress rate and generates feedback incorporating the analysis results and progress information.

[0533] The generated feedback is visually displayed to the user through the device. An emotion engine is incorporated, allowing the device to recognize the user's emotions and adapt the content and style of the feedback accordingly. This enables the user interface to respond flexibly to the user's emotional state.

[0534] For example, if the server assesses the construction progress as 80% and detects a 3cm discrepancy in the north wall, the terminal will visually display this information. If the emotion engine detects that the user is experiencing stress, it will soften the tone of the feedback and include more specific correction steps to support the user's understanding and response.

[0535] Thus, the system implementing the present invention not only improves construction efficiency but also reduces stress on site managers and contributes to improved construction accuracy by providing feedback in a way that is sensitive to the user's feelings.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The terminal controls the activation of the LiDAR sensor-equipped camera and performs the necessary settings to acquire 3D data of the site. This allows the camera to automatically start scanning the site.

[0539] Step 2:

[0540] The camera captures 3D point cloud data of the construction site in real time and prepares to send that data to the server. The data may be compressed before being transferred.

[0541] Step 3:

[0542] The server performs initial analysis processes, such as noise reduction and data shaping, to compare the 3D data received from the camera with the design data stored in the database.

[0543] Step 4:

[0544] The server executes an analysis algorithm and compares the 3D data with the design data to identify discrepancies and problem areas in the construction. Specifically, it meticulously detects errors in shape and position and records the results.

[0545] Step 5:

[0546] The server calculates the construction progress rate based on the matching results and uses this information to build feedback. This includes proposed solutions to problems and recommended work procedures.

[0547] Step 6:

[0548] The server sends the generated feedback to the terminal, which receives this information and prepares to display it visually to the user.

[0549] Step 7:

[0550] The emotion engine built into the device recognizes the user's emotions and adjusts the way feedback is displayed and the content accordingly. In this process, if the analyzed emotions indicate stress or anxiety, the display becomes gentler or more detailed explanations are added.

[0551] Step 8:

[0552] The user reviews the feedback and provides specific instructions to on-site workers based on areas where construction corrections are needed. These instructions facilitate quick responses on-site and improve construction accuracy.

[0553] (Example 2)

[0554] 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."

[0555] In construction sites and other construction work, there is a demand for improved work efficiency and quality assurance, but there are problems with quickly and accurately identifying the progress and problem areas on site. Furthermore, it is difficult to provide appropriate feedback while reducing the mental burden on site managers.

[0556] 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.

[0557] In this invention, the server includes a storage means for storing design information, a sensing means for acquiring three-dimensional information at the site, and an analysis means for comparing the acquired three-dimensional information with the design information. This enables efficient acquisition and analysis of site information, allowing for accurate understanding of the construction situation and the provision of flexible feedback.

[0558] "Design information" refers to detailed data related to the design of buildings and structures, including the arrangement, dimensions, and specifications of each component.

[0559] A "memory device" is a device or method for storing information, and functions as the memory or database of a computer.

[0560] "3D information" refers to data that represents the three-dimensional shape and characteristics of objects and the environment at a given site.

[0561] "Sensing means" refers to devices or sensors used to acquire information from objects or the environment, including LiDAR and cameras.

[0562] "Analysis means" refers to the processes and algorithms used to process acquired data and convert it into meaningful information.

[0563] "Deviation" refers to a difference from the target state or design information, and means an error in construction or placement.

[0564] "Generation means" refers to a device or method that creates new information or data based on the analysis results.

[0565] "Display means" refers to a device or screen for visually presenting information, such as a monitor or projector.

[0566] "Adaptive means" refers to a device or method that changes the operation of a system in accordance with the user's state or requirements.

[0567] "Adjustment means" refers to a function that modifies the characteristics of the interface and information provided to suit the user's needs.

[0568] The following system is configured as an embodiment for carrying out this invention.

[0569] The terminal acquires three-dimensional information of the site using a camera equipped with a LiDAR sensor. This camera has the function of scanning the site situation in 3D in real time and acquiring the data in digital format. The acquired data is transmitted to a server using wireless communication technology. Specifically, communication protocols such as Wi-Fi and Bluetooth are used in this process.

[0570] The server compares the received 3D data with design information pre-stored in a memory device. Here, specialized analysis software is used to compare the 3D data and identify deviations and problem areas. This makes it possible to quickly detect discrepancies and errors between the design drawings and the actual site conditions. For example, if a wall of a building under construction is 3 cm off from the design drawing, its specific location can be determined.

[0571] Furthermore, the server generates feedback on the identified issues. This feedback includes specific instructions on what needs to be corrected and how. The generated feedback information is sent to the terminal through an interactive user interface.

[0572] At this time, the device uses its built-in emotion recognition engine to detect the user's emotional state. It determines states such as stress and anxiety from the user's facial expressions and voice, and changes the style of feedback accordingly. This adaptive feedback makes it easier for the user to understand the information and encourages appropriate action.

[0573] For example, if the server analyzes the data and determines that "there is a 3cm discrepancy in the north wall, and the progress is 80%", the terminal will display this information to the user. If the emotion engine determines that the user is stressed, the feedback will be presented in a friendly tone, and specific corrective steps will be provided to support the user.

[0574] An example of a prompt message could be, "Use the LiDAR sensor to acquire 3D data from the site, and display feedback on the user interface based on the analysis results on the server." This prompt allows the system to perform the expected operation and provide appropriate information to the user.

[0575] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0576] Step 1:

[0577] The device activates its LiDAR sensor to acquire three-dimensional information of the site. In this process, the physical environment of the site is 3D scanned and digitized as three-dimensional data. The input is physical environmental information, and the output is digitized three-dimensional data. Specifically, the device's sensing system generates detailed point cloud data using laser ranging and saves it as a data file.

[0578] Step 2:

[0579] The terminal transmits the acquired 3D data to the server. Wi-Fi or wired connections are used for communication. The input is 3D data stored on the terminal, and the output is digital data transmitted to the server. For example, the terminal compresses a data file and transmits it to the server's receiving port using a wireless communication module.

[0580] Step 3:

[0581] The server compares the received 3D data with the design information stored in the memory. The input is the received 3D data and the design information, and the output is the comparison result. In this step, a 3D comparison algorithm is used to overlay the actual field data and the design information and calculate the deviation. Specifically, matching is performed based on similar feature points and the positional deviation is analyzed.

[0582] Step 4:

[0583] The server identifies deviations and problem areas based on the matching results. The input is the matching results, and the output is detailed information about the problem areas. Specifically, the server's analysis software evaluates the numerical deviations and lists the areas that exceed the threshold value. This result forms the basis for feedback generation.

[0584] Step 5:

[0585] The server generates feedback regarding identified deviations and problem areas. The input is information about the identified problem areas, and the output is feedback including correction suggestions. For example, if the north wall is misaligned by 3 cm, feedback will be generated along with correction steps. The generating AI model adds explanations and suggestions to this feedback.

[0586] Step 6:

[0587] The device receives feedback from the server and presents it visually to the user through the user interface. The input is feedback information from the server, and the output is visual information displayed on the user interface. During this process, an emotion engine recognizes the user's emotional state and adjusts the presentation accordingly. For example, the device might display a message in a calm tone that includes encouragement.

[0588] Step 7:

[0589] The user reviews the feedback provided and makes necessary corrections. The input is the feedback information provided by the terminal, and the output is the user's on-site response. Specifically, the user either communicates correction instructions to on-site workers or takes the necessary measures directly on-site.

[0590] (Application Example 2)

[0591] 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."

[0592] Improving work efficiency and ensuring quality on-site are crucial challenges. However, on-site conditions and construction progress are often not properly assessed, and the emotional state of site managers can also affect construction efficiency and accuracy. Therefore, a system is needed that accurately analyzes on-site data and provides feedback that takes into account the user's emotions.

[0593] 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.

[0594] In this invention, the server includes a storage means for storing design data, a receiving means for acquiring three-dimensional data from the site, and an analysis means for comparing the acquired three-dimensional data with the design data. This makes it possible to accurately grasp the situation on site and provide feedback that is tailored to the user's emotional state.

[0595] A "memory device" is an element that has the function of storing design data and keeping it in a format that can be accessed as needed.

[0596] A "receiving means" is an element equipped with the function of acquiring three-dimensional data at the site and transmitting it to other data processing devices.

[0597] An "analysis tool" is an element that has the function of analyzing acquired three-dimensional data by comparing it with design data and evaluating the situation on site.

[0598] "Identification means" refers to elements that have the function of clearly recognizing problem areas and discrepancies from the comparison results.

[0599] A "creation method" is an element that has the function of generating detailed feedback regarding the identified problem areas.

[0600] A "display means" is an element that has the function of providing the generated feedback to the user visually.

[0601] An "emotional judgment tool" is an element that has the function of recognizing the user's emotions and using that information to provide appropriate feedback.

[0602] This invention aims to improve work efficiency and quality at construction sites by having a server, terminal, and user work together to operate a system. The terminal is equipped with a LiDAR sensor, which is used to acquire three-dimensional data of the construction site. This data is transmitted to the server in real time. The server can analyze the site conditions by comparing the received three-dimensional data with design data. Furthermore, the server recognizes the user's emotions using emotion recognition means and adjusts the content of the feedback based on this. The generated feedback is presented visually to the user through the terminal.

[0603] Specifically, for example, if construction progress is assessed as 80% and a 3cm discrepancy is found in the north wall, the terminal will present this information to the user. If the emotion assessment system detects that the user is experiencing stress, the terminal will adjust the tone of the feedback to be gentler and provide specific correction steps. This allows the user to respond to on-site issues intuitively and calmly.

[0604] An example of a prompt message is, "Based on 3D data of a construction site, please propose a GUI design for an application that analyzes construction progress and problem areas, and provides feedback that takes user emotions into consideration." Using such prompt messages, the generative AI model will be used to design the specific user interface and adjust the feedback content.

[0605] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0606] Step 1:

[0607] The terminal activates its LiDAR sensor to acquire three-dimensional data of the construction site. This data acquisition is performed using cameras and sensors to accurately collect three-dimensional information about the site. The acquired data is then used directly in the next step.

[0608] Step 2:

[0609] The terminal transmits the acquired 3D data to the server in real time. By sending the acquired data as input to the server and having the server receive it, all the spatial information necessary for analysis is aggregated on the server.

[0610] Step 3:

[0611] The server compares the received 3D data with the design data. Using both 3D data and design data as input data, the server identifies differences between this data using an analysis algorithm. This is where discrepancies between the actual site and the design, as well as discrepancies in the production process, are detected.

[0612] Step 4:

[0613] The server identifies the problem areas that need correction based on the matching results and generates feedback. Based on the analyzed data, the identification means clarifies the problem areas, and the creation means generates specific feedback information.

[0614] Step 5:

[0615] The server uses emotion recognition mechanisms to adjust the generated feedback. The tone and content of the feedback are adapted to the user's emotional state. This leads to a deeper understanding of the user and the generation of information that reduces stress.

[0616] Step 6:

[0617] The device visually presents the user with adjusted feedback. This visual display includes specific correction instructions and progress information, allowing the user to take appropriate action at the construction site.

[0618] This series of processes makes it possible to quickly identify problems at the construction site and provide appropriate feedback that takes into account the user's emotional state.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] [Fourth Embodiment]

[0623] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0624] 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.

[0625] 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).

[0626] 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.

[0627] 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.

[0628] 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).

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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".

[0636] This invention relates to a system that automatically manages on-site construction quality and progress by combining a camera device equipped with a LiDAR sensor, a server that analyzes the data acquired therefrom, and a terminal that displays the results.

[0637] First, the terminal activates the LiDAR sensor-equipped camera installed at the construction site and configures it to acquire 3D data of the site. Following these instructions, the camera acquires 3D spatial information of the construction site in real time and prepares to send the data to the server.

[0638] Next, the server compares the received 3D data with the design data stored in its storage device. During this analysis, the server uses complex computer algorithms to identify physical deviations and errors from the design drawings based on the actual construction conditions.

[0639] To monitor construction progress, the server calculates the progress rate and compares it with the schedule to evaluate the progress in real time. Based on this information, the server is configured to automatically generate daily reports and construction reports and provide them to managers and on-site personnel.

[0640] Ultimately, the terminal provides the user with feedback on the generated construction status and instructions for corrections. Based on this information, the user can perform the necessary correction work at the construction site.

[0641] For example, if the server assesses the construction progress as 70% and determines there is a 5cm discrepancy in the west wall, the terminal will display this to the user. Based on this instruction, the user can then instruct a specific worker to correct the west wall, thereby improving the accuracy of the construction.

[0642] Thus, the system implementing the present invention can reduce the workload of site supervisors, decrease construction errors, and improve overall efficiency by automatically and efficiently managing the quality and progress of construction.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] The terminal activates the LiDAR sensor-equipped camera installed on-site and is configured to collect three-dimensional point cloud data. Furthermore, by performing sensor calibration as needed, it enables the acquisition of highly accurate data.

[0646] Step 2:

[0647] The camera scans the site environment according to instructions and generates three-dimensional point cloud data in real time. This data is then filtered to remove as much noise as possible and sent to the server as high-quality information.

[0648] Step 3:

[0649] The server performs data preprocessing, including denoising and scaling, to compare the received 3D data with the design data stored in the database.

[0650] Step 4:

[0651] The server uses an analysis algorithm to calculate the discrepancies between the 3D data and the design data. In particular, it detects and evaluates positional deviations and inconsistencies in shape during construction.

[0652] Step 5:

[0653] The server calculates the progress rate of the construction based on the detected discrepancies and identifies which areas are experiencing problems.

[0654] Step 6:

[0655] The server generates a feedback report that includes the progress of construction and correction instructions based on the analysis results, and sends it to the terminal.

[0656] Step 7:

[0657] The terminal displays the received feedback report in an easy-to-understand format for the user. Based on this, the user can identify areas for improvement in the construction work.

[0658] Step 8:

[0659] The user instructs on-site workers on specific construction changes according to the provided modification instructions and makes the necessary corrections. This improves the accuracy of the construction.

[0660] (Example 1)

[0661] 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".

[0662] On-site quality control and progress management of construction work typically rely on manual verification and reporting, leading to decreased work efficiency and the potential for inaccurate data due to human error. Furthermore, real-time monitoring of construction progress is difficult, hindering rapid decision-making. Addressing these issues requires accurate and rapid data acquisition and analysis, as well as effective feedback.

[0663] 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.

[0664] In this invention, the server includes a storage means for storing design information, a sensor means for acquiring spatial information at the work site, an analysis means for comparing the acquired spatial information with the design information, and an evaluation means for evaluating the progress status and automatically generating a report. This enables accurate and real-time analysis of the construction status at the site, and allows for efficient quality control and progress management.

[0665] "Design information" refers to standard data such as drawings and specifications related to construction and manufacturing.

[0666] "Memory devices" refer to devices or systems used to store data such as design information and analysis results.

[0667] A "work site" refers to a specific spatial area where construction or manufacturing takes place.

[0668] "Spatial information" refers to information that includes three-dimensional positional data acquired by devices such as LiDAR sensors.

[0669] A "sensor" refers to a device that detects physical states or changes and outputs them as digital data.

[0670] "Analysis means" refers to devices or systems used to process acquired data and evaluate it against specific criteria or indicators.

[0671] "Error" refers to the difference between the actual measured or analyzed value and the ideal or expected value.

[0672] A "discrepancy" refers to a location or part where the acquired data does not match the design information.

[0673] "Identification means" refers to devices or systems used to find specific features or inconsistencies from the results of data analysis.

[0674] A "report" refers to a document or data that summarizes analyzed information and presents it in a way that is easy for stakeholders to understand.

[0675] "Generative means" refers to devices or systems that create new documents or information based on data.

[0676] "Display means" refers to devices or systems that visually present information and convey it to the user.

[0677] "Evaluation tools" refer to devices or systems that perform analysis and judgment based on information according to certain criteria.

[0678] "Communication means" refers to devices and systems used to transmit data and information to other devices or users.

[0679] "Progress status" refers to the state of work or project progress at a specific point in time.

[0680] "Measuring means" refers to devices or systems used to quantitatively measure specific information and obtain the results.

[0681] A "plan" refers to a table or list that shows the schedule and progress plan for a task or project.

[0682] "Means of integration" refers to devices and systems used to incorporate acquired or measured information into existing data and plans.

[0683] This system efficiently manages the quality and progress of construction sites. First, the terminal uses a camera device equipped with a LiDAR sensor to acquire three-dimensional data of the site. This sensor is crucial hardware for accurately capturing spatial information over a wide area. The acquired data is transmitted to the server in real time.

[0684] The server processes the received three-dimensional data. Specifically, the server uses advanced computer algorithms to compare this data with design information previously stored in the system. This process involves analysis to identify errors and discrepancies, enabling highly accurate evaluation. Furthermore, the server evaluates the progress and automatically generates a report. This report clearly shows the current status of the project and supports efficient decision-making.

[0685] The terminal visually displays the generated report to the user, facilitating intuitive understanding. Based on this, the user can quickly instruct necessary corrective actions. For example, if the server evaluates the construction progress as 70% and detects a 5cm deviation at a specific location, the terminal will notify the user, who can then instruct the site to make corrections. In this way, construction management that balances accuracy and efficiency can be achieved.

[0686] Examples of prompts for a generative AI model:

[0687] "Please explain in detail the algorithm for a system that automates progress management at construction sites using specific 3D data analysis techniques."

[0688] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0689] Step 1:

[0690] The terminal activates a LiDAR sensor-equipped camera installed at the construction site and collects three-dimensional data of the site. During this process, the terminal calibrates the sensor to ensure accurate spatial information is acquired. The input is raw data from the LiDAR sensor, and the output is three-dimensional point cloud data. This data is immediately ready to be transmitted to the server.

[0691] Step 2:

[0692] The server receives point cloud data transmitted from the terminal and compares it with design information previously stored in storage. In this process, the server uses advanced algorithms to compare the received data with the design data and identify discrepancies and inconsistencies. The input is the received 3D data and design information, and the output is data on the location of the discrepancies and the identified errors. The server records this internally to prepare for the next processing step.

[0693] Step 3:

[0694] The server calculates the construction progress rate and automatically generates a report based on the generated analysis data. This calculation also references the project schedule, which serves as a baseline for indicating progress. The inputs are the analysis results and the current project schedule, and the output is a report containing the construction progress rate and a summary of deviations. The server then prepares this information for transmission to administrators and relevant parties.

[0695] Step 4:

[0696] The terminal presents reports received from the server to the user, displaying them in a visually easy-to-understand format. In this step, the terminal uses a graphical user interface to allow users to see discrepancies and progress at a glance. The input is report data from the server, and the output is a display viewable by the user. The user can then use this to give specific field instructions.

[0697] Step 5:

[0698] The user reviews the information presented through the terminal and, if necessary, instructs corrective work at the construction site. This step provides appropriate feedback to the workers to maintain accuracy at the site. The input is the information presented on the terminal, and the output is the feedback and instructions given at the site. This allows for efficient management of the quality and progress of construction.

[0699] (Application Example 1)

[0700] 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".

[0701] Amidst the growing demand for improved construction quality and more efficient progress management on-site, real-time monitoring of construction status and the provision of immediate, responsive feedback are key challenges. In particular, there is a lack of means to identify deviations and errors at construction sites and to accurately measure and display progress rates, which increases the burden on managers and on-site workers.

[0702] 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.

[0703] In this invention, the server includes a storage means for storing design information, a sensing means for acquiring three-dimensional information at the work site, an analysis means for comparing the acquired three-dimensional information with the design information, a visualization means for visualizing the progress of construction, and a communication means for transmitting automatically generated daily reports using a communication means. This makes it possible to identify deviations at the construction site and accurately grasp the progress, enabling efficient construction quality control and the provision of rapid feedback.

[0704] A "storage device" is a data storage device that can store design information and retrieve it as needed.

[0705] "Sensing means" refers to a sensor device used to acquire three-dimensional information at the work site.

[0706] "Analysis means" refers to a device or algorithm that compares acquired three-dimensional information with design information and analyzes discrepancies and errors.

[0707] "Identification means" refers to a device or algorithm that has the function of clearly identifying deviations or problem areas detected by the analysis means.

[0708] "Generating means" refers to a device or program that generates feedback based on deviations or errors clarified by specific means.

[0709] "Means of delivery" refer to displays or applications that visually convey the generated feedback to the user.

[0710] "Visualization means" refers to a display device or program for visually representing the progress of construction work.

[0711] "Communication means" refers to a network connection device or protocol for transmitting the generated daily report to other devices or systems.

[0712] "Reflection means" refers to a device or program that has the function of automatically updating the process plan with progress information obtained by the measurement means.

[0713] The system for realizing this invention consists of a storage means for storing design information, a sensing means for acquiring three-dimensional information at the work site, and a server for comprehensively managing these.

[0714] The server first receives three-dimensional information acquired by the sensing device. The sensing device is a camera device including a LiDAR sensor that acquires spatial data of the site in real time. The acquired data is sent to the server and compared with design information by the analysis device. The hardware used here includes cloud servers and high-performance computers that support advanced processing.

[0715] The server compares the acquired data with design information and identifies discrepancies and problem areas using specific methods. The discrepancy detection algorithm includes a high-precision analysis program to distinguish even minute differences on the 3D model. This makes it possible to detect problems with construction quality at the site early and promote frequent improvements.

[0716] The server further analyzes the progress and provides the results through visualization tools, making the progress visible. The visualized data is provided to the user's terminal, allowing field personnel and managers to issue construction instructions based on it. Generated feedback and daily reports are transmitted promptly via communication tools, enabling responsible parties to respond quickly.

[0717] For example, if the server assesses the construction progress as 70% and determines there is a 5cm discrepancy in the west wall, the terminal will display this to the user. Based on this information, the user can instruct the construction workers to perform the necessary corrections, thereby improving the accuracy of the construction.

[0718] An example of a prompt message for a generated AI model is: "Create a program that analyzes LiDAR data from a construction site, calculates and displays the construction progress and deviations."

[0719] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0720] Step 1:

[0721] The server receives three-dimensional information transmitted from LiDAR-equipped cameras installed on-site. This input data accurately models the space of the construction site. The server temporarily stores this three-dimensional information in its memory, preparing it for subsequent analysis.

[0722] Step 2:

[0723] The server compares the recorded three-dimensional information with the design information. Using analysis tools, it performs data matching and starts calculations to detect discrepancies and errors. The output is a result indicating which parts have discrepancies and to what extent. Specifically, it analyzes 3D point cloud data and evaluates the statistical differences with the design model.

[0724] Step 3:

[0725] The server calculates the progress based on the discrepancies and problems identified through comparison. To output the construction progress rate as a percentage, it evaluates the completion status of each process using measurement tools. Specific operations include quantifying the overall progress and analyzing deviations from the progress through comparison with the schedule.

[0726] Step 4:

[0727] The terminal receives evaluation results of deviations and progress transmitted from the server and presents them to the user using visualization tools. Based on the input evaluation information, it generates data output that allows the user to intuitively understand necessary corrections and construction delays. Specifically, it displays a detailed report via a GUI (Graphical User Interface).

[0728] Step 5:

[0729] The user issues necessary construction instructions to workers based on the display results on the terminal. Specific instructions are given for areas requiring correction, enabling on-site workers to respond quickly. As output, an action plan based on the instructions is implemented on-site. Real-time feedback may be provided using communication tools for specific actions.

[0730] 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.

[0731] This invention is a system that combines a LiDAR sensor, a data processing server, a terminal with a user interface, and an emotion engine that recognizes the user's emotions, with the aim of improving work efficiency and ensuring quality at construction sites.

[0732] First, the terminal activates the camera equipped with a LiDAR sensor and is configured to acquire three-dimensional data of the site. The camera collects 3D data of the site and transmits it to the server in real time. This sensor makes it possible to digitize the site conditions with high precision.

[0733] Next, the server compares the received 3D data with the design data. This allows it to identify discrepancies and problem areas in the construction process at the building site. Using a specific algorithm, it performs an analysis to clarify the areas that need correction. Based on these results, the server measures the construction progress rate and generates feedback incorporating the analysis results and progress information.

[0734] The generated feedback is visually displayed to the user through the device. An emotion engine is incorporated, allowing the device to recognize the user's emotions and adapt the content and style of the feedback accordingly. This enables the user interface to respond flexibly to the user's emotional state.

[0735] For example, if the server assesses the construction progress as 80% and detects a 3cm discrepancy in the north wall, the terminal will visually display this information. If the emotion engine detects that the user is experiencing stress, it will soften the tone of the feedback and include more specific correction steps to support the user's understanding and response.

[0736] Thus, the system implementing the present invention not only improves construction efficiency but also reduces stress on site managers and contributes to improved construction accuracy by providing feedback in a way that is sensitive to the user's feelings.

[0737] The following describes the processing flow.

[0738] Step 1:

[0739] The terminal controls the activation of the LiDAR sensor-equipped camera and performs the necessary settings to acquire 3D data of the site. This allows the camera to automatically start scanning the site.

[0740] Step 2:

[0741] The camera captures 3D point cloud data of the construction site in real time and prepares to send that data to the server. The data may be compressed before being transferred.

[0742] Step 3:

[0743] The server performs initial analysis processes, such as noise reduction and data shaping, to compare the 3D data received from the camera with the design data stored in the database.

[0744] Step 4:

[0745] The server executes an analysis algorithm and compares the 3D data with the design data to identify discrepancies and problem areas in the construction. Specifically, it meticulously detects errors in shape and position and records the results.

[0746] Step 5:

[0747] The server calculates the construction progress rate based on the matching results and uses this information to build feedback. This includes proposed solutions to problems and recommended work procedures.

[0748] Step 6:

[0749] The server sends the generated feedback to the terminal, which receives this information and prepares to display it visually to the user.

[0750] Step 7:

[0751] The emotion engine built into the device recognizes the user's emotions and adjusts the way feedback is displayed and the content accordingly. In this process, if the analyzed emotions indicate stress or anxiety, the display becomes gentler or more detailed explanations are added.

[0752] Step 8:

[0753] The user reviews the feedback and provides specific instructions to on-site workers based on areas where construction corrections are needed. These instructions facilitate quick responses on-site and improve construction accuracy.

[0754] (Example 2)

[0755] 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".

[0756] In construction sites and other construction work, there is a demand for improved work efficiency and quality assurance, but there are problems with quickly and accurately identifying the progress and problem areas on site. Furthermore, it is difficult to provide appropriate feedback while reducing the mental burden on site managers.

[0757] 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.

[0758] In this invention, the server includes a storage means for storing design information, a sensing means for acquiring three-dimensional information at the site, and an analysis means for comparing the acquired three-dimensional information with the design information. This enables efficient acquisition and analysis of site information, allowing for accurate understanding of the construction situation and the provision of flexible feedback.

[0759] "Design information" refers to detailed data related to the design of buildings and structures, including the arrangement, dimensions, and specifications of each component.

[0760] A "memory device" is a device or method for storing information, and functions as the memory or database of a computer.

[0761] "3D information" refers to data that represents the three-dimensional shape and characteristics of objects and the environment at a given site.

[0762] "Sensing means" refers to devices or sensors used to acquire information from objects or the environment, including LiDAR and cameras.

[0763] "Analysis means" refers to the processes and algorithms used to process acquired data and convert it into meaningful information.

[0764] "Deviation" refers to a difference from the target state or design information, and means an error in construction or placement.

[0765] "Generation means" refers to a device or method that creates new information or data based on the analysis results.

[0766] "Display means" refers to a device or screen for visually presenting information, such as a monitor or projector.

[0767] "Adaptive means" refers to a device or method that changes the operation of a system in accordance with the user's state or requirements.

[0768] "Adjustment means" refers to a function that modifies the characteristics of the interface and information provided to suit the user's needs.

[0769] The following system is configured as an embodiment for carrying out this invention.

[0770] The terminal acquires three-dimensional information of the site using a camera equipped with a LiDAR sensor. This camera has the function of scanning the site situation in 3D in real time and acquiring the data in digital format. The acquired data is transmitted to a server using wireless communication technology. Specifically, communication protocols such as Wi-Fi and Bluetooth are used in this process.

[0771] The server compares the received 3D data with design information pre-stored in a memory device. Here, specialized analysis software is used to compare the 3D data and identify deviations and problem areas. This makes it possible to quickly detect discrepancies and errors between the design drawings and the actual site conditions. For example, if a wall of a building under construction is 3 cm off from the design drawing, its specific location can be determined.

[0772] Furthermore, the server generates feedback on the identified issues. This feedback includes specific instructions on what needs to be corrected and how. The generated feedback information is sent to the terminal through an interactive user interface.

[0773] At this time, the device uses its built-in emotion recognition engine to detect the user's emotional state. It determines states such as stress and anxiety from the user's facial expressions and voice, and changes the style of feedback accordingly. This adaptive feedback makes it easier for the user to understand the information and encourages appropriate action.

[0774] For example, if the server analyzes the data and determines that "there is a 3cm discrepancy in the north wall, and the progress is 80%", the terminal will display this information to the user. If the emotion engine determines that the user is stressed, the feedback will be presented in a friendly tone, and specific corrective steps will be provided to support the user.

[0775] An example of a prompt message could be, "Use the LiDAR sensor to acquire 3D data from the site, and display feedback on the user interface based on the analysis results on the server." This prompt allows the system to perform the expected operation and provide appropriate information to the user.

[0776] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0777] Step 1:

[0778] The device activates its LiDAR sensor to acquire three-dimensional information of the site. In this process, the physical environment of the site is 3D scanned and digitized as three-dimensional data. The input is physical environmental information, and the output is digitized three-dimensional data. Specifically, the device's sensing system generates detailed point cloud data using laser ranging and saves it as a data file.

[0779] Step 2:

[0780] The terminal transmits the acquired 3D data to the server. Wi-Fi or wired connections are used for communication. The input is 3D data stored on the terminal, and the output is digital data transmitted to the server. For example, the terminal compresses a data file and transmits it to the server's receiving port using a wireless communication module.

[0781] Step 3:

[0782] The server compares the received 3D data with the design information stored in the memory. The input is the received 3D data and the design information, and the output is the comparison result. In this step, a 3D comparison algorithm is used to overlay the actual field data and the design information and calculate the deviation. Specifically, matching is performed based on similar feature points and the positional deviation is analyzed.

[0783] Step 4:

[0784] The server identifies deviations and problem areas based on the matching results. The input is the matching results, and the output is detailed information about the problem areas. Specifically, the server's analysis software evaluates the numerical deviations and lists the areas that exceed the threshold value. This result forms the basis for feedback generation.

[0785] Step 5:

[0786] The server generates feedback regarding identified deviations and problem areas. The input is information about the identified problem areas, and the output is feedback including correction suggestions. For example, if the north wall is misaligned by 3 cm, feedback will be generated along with correction steps. The generating AI model adds explanations and suggestions to this feedback.

[0787] Step 6:

[0788] The device receives feedback from the server and presents it visually to the user through the user interface. The input is feedback information from the server, and the output is visual information displayed on the user interface. During this process, an emotion engine recognizes the user's emotional state and adjusts the presentation accordingly. For example, the device might display a message in a calm tone that includes encouragement.

[0789] Step 7:

[0790] The user reviews the feedback provided and makes necessary corrections. The input is the feedback information provided by the terminal, and the output is the user's on-site response. Specifically, the user either communicates correction instructions to on-site workers or takes the necessary measures directly on-site.

[0791] (Application Example 2)

[0792] 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".

[0793] Improving work efficiency and ensuring quality on-site are crucial challenges. However, on-site conditions and construction progress are often not properly assessed, and the emotional state of site managers can also affect construction efficiency and accuracy. Therefore, a system is needed that accurately analyzes on-site data and provides feedback that takes into account the user's emotions.

[0794] 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.

[0795] In this invention, the server includes a storage means for storing design data, a receiving means for acquiring three-dimensional data from the site, and an analysis means for comparing the acquired three-dimensional data with the design data. This makes it possible to accurately grasp the situation on site and provide feedback that is tailored to the user's emotional state.

[0796] A "memory device" is an element that has the function of storing design data and keeping it in a format that can be accessed as needed.

[0797] A "receiving means" is an element equipped with the function of acquiring three-dimensional data at the site and transmitting it to other data processing devices.

[0798] An "analysis tool" is an element that has the function of analyzing acquired three-dimensional data by comparing it with design data and evaluating the situation on site.

[0799] "Identification means" refers to elements that have the function of clearly recognizing problem areas and discrepancies from the comparison results.

[0800] A "creation method" is an element that has the function of generating detailed feedback regarding the identified problem areas.

[0801] A "display means" is an element that has the function of providing the generated feedback to the user visually.

[0802] An "emotional judgment tool" is an element that has the function of recognizing the user's emotions and using that information to provide appropriate feedback.

[0803] This invention aims to improve work efficiency and quality at construction sites by having a server, terminal, and user work together to operate a system. The terminal is equipped with a LiDAR sensor, which is used to acquire three-dimensional data of the construction site. This data is transmitted to the server in real time. The server can analyze the site conditions by comparing the received three-dimensional data with design data. Furthermore, the server recognizes the user's emotions using emotion recognition means and adjusts the content of the feedback based on this. The generated feedback is presented visually to the user through the terminal.

[0804] Specifically, for example, if construction progress is assessed as 80% and a 3cm discrepancy is found in the north wall, the terminal will present this information to the user. If the emotion assessment system detects that the user is experiencing stress, the terminal will adjust the tone of the feedback to be gentler and provide specific correction steps. This allows the user to respond to on-site issues intuitively and calmly.

[0805] An example of a prompt message is, "Based on 3D data of a construction site, please propose a GUI design for an application that analyzes construction progress and problem areas, and provides feedback that takes user emotions into consideration." Using such prompt messages, the generative AI model will be used to design the specific user interface and adjust the feedback content.

[0806] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0807] Step 1:

[0808] The terminal activates its LiDAR sensor to acquire three-dimensional data of the construction site. This data acquisition is performed using cameras and sensors to accurately collect three-dimensional information about the site. The acquired data is then used directly in the next step.

[0809] Step 2:

[0810] The terminal transmits the acquired 3D data to the server in real time. By sending the acquired data as input to the server and having the server receive it, all the spatial information necessary for analysis is aggregated on the server.

[0811] Step 3:

[0812] The server compares the received 3D data with the design data. Using both 3D data and design data as input data, the server identifies differences between this data using an analysis algorithm. This is where discrepancies between the actual site and the design, as well as discrepancies in the production process, are detected.

[0813] Step 4:

[0814] The server identifies the problem areas that need correction based on the matching results and generates feedback. Based on the analyzed data, the identification means clarifies the problem areas, and the creation means generates specific feedback information.

[0815] Step 5:

[0816] The server uses emotion recognition mechanisms to adjust the generated feedback. The tone and content of the feedback are adapted to the user's emotional state. This leads to a deeper understanding of the user and the generation of information that reduces stress.

[0817] Step 6:

[0818] The device visually presents the user with adjusted feedback. This visual display includes specific correction instructions and progress information, allowing the user to take appropriate action at the construction site.

[0819] This series of processes makes it possible to quickly identify problems at the construction site and provide appropriate feedback that takes into account the user's emotional state.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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."

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] The following is further disclosed regarding the embodiments described above.

[0842] (Claim 1)

[0843] A storage means for storing design data,

[0844] A sensor means for acquiring three-dimensional data at the site,

[0845] An analysis means for comparing the acquired three-dimensional data with the design data,

[0846] A means for identifying discrepancies or problem areas based on the aforementioned comparison results,

[0847] A generation means for generating feedback regarding the aforementioned misalignment or problem area,

[0848] A system including a means for visually presenting the aforementioned feedback to the user.

[0849] (Claim 2)

[0850] The system according to claim 1, further comprising a measurement means for calculating the construction progress rate based on the three-dimensional data.

[0851] (Claim 3)

[0852] The system according to claim 1, further comprising a reflection means for automatically reflecting the progress rate data obtained by the measurement means into a process chart.

[0853] "Example 1"

[0854] (Claim 1)

[0855] A storage means for storing design information,

[0856] A sensor means for acquiring spatial information at the work site,

[0857] An analysis means for comparing the acquired spatial information with the design information,

[0858] An identification means for identifying errors or discrepancies based on the aforementioned comparison results,

[0859] A generating means for creating a report on the aforementioned errors or discrepancies,

[0860] A display means for visually displaying the aforementioned report to the operator,

[0861] An evaluation method that assesses the progress status and automatically generates reports,

[0862] A system including means of communication for distributing the aforementioned report to relevant parties.

[0863] (Claim 2)

[0864] The system according to claim 1, further comprising a measuring means for calculating the work progress based on the spatial information.

[0865] (Claim 3)

[0866] The system according to claim 1, further comprising a reflection means for automatically reflecting progress data obtained by the measurement means into a schedule.

[0867] "Application Example 1"

[0868] (Claim 1)

[0869] A storage means for storing design information,

[0870] A sensing means for acquiring three-dimensional information at the work site,

[0871] An analysis means for comparing the acquired three-dimensional information with the design information,

[0872] A means for identifying discrepancies or problem areas based on the aforementioned comparison results,

[0873] A generation means for generating feedback regarding the aforementioned misalignment or problem area,

[0874] A means of providing the aforementioned feedback to the user visually,

[0875] A visualization tool for visualizing the progress of construction,

[0876] A communication means that transmits automatically generated daily reports using a communication means,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, further comprising a measurement means for calculating progress based on the three-dimensional information.

[0880] (Claim 3)

[0881] The system according to claim 1, further comprising a reflection means for automatically reflecting progress information obtained by the measurement means into the process plan.

[0882] "Example 2 of combining an emotion engine"

[0883] (Claim 1)

[0884] A storage means for storing design information,

[0885] A sensing means for acquiring three-dimensional information at the site,

[0886] Analysis means for comparing the acquired three-dimensional information with the design information,

[0887] A means for identifying deviations or problem areas based on the aforementioned comparison results,

[0888] A generation means for generating information about the aforementioned deviation or problem area,

[0889] A display means for visually displaying the aforementioned information to the user,

[0890] A system that includes adaptive means to detect the user's emotional state and adjust the displayed content and style accordingly.

[0891] (Claim 2)

[0892] The system according to claim 1, further comprising a measurement means for calculating the progress rate based on the three-dimensional information.

[0893] (Claim 3)

[0894] The system according to claim 1, further comprising an adjustment means for adjusting the style of feedback generated by the adaptation means to match the user's emotional state.

[0895] "Application example 2 when combining with an emotional engine"

[0896] (Claim 1)

[0897] A storage means for storing design data,

[0898] A receiving means for acquiring three-dimensional data at the site,

[0899] An analysis means for comparing the acquired three-dimensional data with the design data,

[0900] A means for identifying discrepancies or problem areas based on the aforementioned comparison results,

[0901] A means for generating feedback regarding the aforementioned discrepancy or problem area,

[0902] A display means for adjusting and visually presenting the aforementioned feedback according to the user's emotional state,

[0903] A system that includes a means of recognizing user emotions.

[0904] (Claim 2)

[0905] The system according to claim 1, further comprising a measurement means for calculating the construction progress rate based on the three-dimensional data.

[0906] (Claim 3)

[0907] The system according to claim 1, further comprising a reflection means for automatically reflecting the progress rate data obtained by the measurement means into a process chart. [Explanation of symbols]

[0908] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A storage means for storing design information, A sensing means for acquiring three-dimensional information at the work site, An analysis means for comparing the acquired three-dimensional information with the design information, A means for identifying discrepancies or problem areas based on the aforementioned comparison results, A generation means for generating feedback regarding the aforementioned misalignment or problem area, A means of providing the aforementioned feedback to the user visually, A visualization tool for visualizing the progress of construction, A communication means that transmits automatically generated daily reports using a communication means, A system that includes this.

2. The system according to claim 1, further comprising a measurement means for calculating progress based on the three-dimensional information.

3. The system according to claim 1, further comprising a reflection means for automatically reflecting progress information obtained by the measurement means into the process plan.

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