System

Abstract

A system is provided.SOLUTION: A system comprising: imaging means for capturing an image of a work site; transmitting means for transmitting image data obtained from the imaging means; image recognizing means for analyzing the image data transmitted from the transmitting means; collating means for collating progress data analyzed by the image recognizing means with design document data; report generating means for generating a progress report based on a collation result obtained by the collating means; and payment instructing means for instructing payment of funds based on the progress report generated by the report generating means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In commercial transactions from general contractors to intermediaries and subcontractors, particularly in the construction industry, there is a social problem of long delays in fund deposits, often resulting in cash flow difficulties. One of the causes of this problem is the time it takes to confirm the progress and completion of construction work. The present invention aims to solve this problem by quickly and accurately grasping the progress of construction work and promoting the timely deposit of funds based on this information. [Means for solving the problem]

[0005] The present invention provides a system including a photographing means for capturing images of a construction site, a transmitting means for transmitting the image data obtained from the photographing means, an image recognition means for analyzing the image data transmitted from the transmitting means, a comparison means for comparing the progress data analyzed by the image recognition means with design document data, a report generation means for generating a progress report based on the comparison results obtained by the comparison means, and a payment instruction means for issuing a payment instruction based on the progress report generated by the report generation means, thereby making it possible to efficiently check the progress of construction work and ensure rapid liquidity of funds. This solves the problem of long deposit times and alleviates cash flow problems in the construction industry.

[0006] "Photographing means" refers to a device such as a camera that captures the state of a construction site or building site in real time.

[0007] The "transmission means" refers to a function or device for transmitting the video data acquired by the image capture means to another device such as a server via a network.

[0008] "Image recognition means" refers to AI or algorithms that analyze video data sent from the transmission means and automatically identify specific objects or scenes.

[0009] The "comparison means" is a function or device that compares the progress data obtained by the image recognition means with the design document data registered in advance, and evaluates the degree of agreement and differences.

[0010] The "report generation means" is a function or device that automatically creates a report summarizing the progress and recognized problems based on the data obtained by the collation means.

[0011] The "payment instruction means" is a function or device that instructs the procedure for executing the next step of fund payment based on the progress report created by the report generation means.

[0012] The term "system" refers to the overall structure or mechanism including a photographing means, a transmitting means, an image recognizing means, a verifying means, a report generating means, and a payment instruction means. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

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

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

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

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

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention is a system for grasping the progress of a construction site in real time and realizing flexible deposit of funds based on that progress. Specific embodiments for carrying out the present invention will be described below.

[0035] System Overview

[0036] This system uses surveillance cameras installed at the construction site to capture real-time construction progress and utilizes image recognition AI to evaluate the progress based on the video data. The system provides users with progress reports and payment instructions. The main components of the system are as follows:

[0037] Capture method: This corresponds to a surveillance camera installed on the terminal, which captures video data of the construction site in real time.

[0038] Transmission means: The terminal compresses the captured video data and transmits it to the server via the network.

[0039] Image recognition method: An image recognition AI installed on the server analyzes the transmitted video data and evaluates the progress of the construction work.

[0040] Matching method: The server compares the progress data acquired by the image recognition AI with the design document data registered in advance.

[0041] Report generation means: The server automatically generates a progress report based on the collation results.

[0042] Payment instruction means: The server sends the generated progress report to the user and instructs the user on the next payment procedure based on the report.

[0043] Specific examples of program processing

[0044] 1. Installing surveillance cameras at construction sites and collecting video data

[0045] Terminal: The surveillance camera is installed at the construction site and captures video continuously for 24 hours. The captured video data is sent to the server every 10 minutes.

[0046] 2. Video data analysis and progress check

[0047] Server: The server analyzes the received video data using image recognition AI. As a result of the analysis, progress information such as "foundation work completed" or "steel frame installed" is obtained.

[0048] 3. Verification with design document data

[0049] Server: The server compares the analyzed progress data with the design data registered in advance. The design data contains progress indicators and specifications for each construction stage. By comparing, it is evaluated whether the progress on site matches the design data.

[0050] 4. Progress report and payment instructions

[0051] User: Through a dedicated application, the user checks progress reports from the server, including progress status, issues found, and next steps to take.

[0052] Server: If the progress is as expected, the server instructs the user to proceed with the next payment procedure. The server connects to the payment system and executes instructions to transfer the funds to the specified bank account.

[0053] Usage examples

[0054] For example, at a construction site where steel frame work is underway, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and verifies that the steel frame is being assembled according to design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment stage based on that report. In this way, funds can be deposited in a timely manner in line with the progress of the work.

[0055] This system allows the progress of construction work to be accurately grasped in real time, improves the liquidity of funds, and reduces the risk of construction delays and shortages of funds.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] The device captures images of the construction site using a surveillance camera, which records the image 24 hours a day and generates a captured image every 10 minutes.

[0059] Step 2:

[0060] The device compresses the captured video data and sends it to the server over the network, where it is encrypted for security reasons.

[0061] Step 3:

[0062] The server receives the video data sent from the device, temporarily stores the data, and adds it to a queue to await analysis.

[0063] Step 4:

[0064] The server analyzes the received video data using image recognition AI, which identifies and evaluates specific objects and scenes (e.g., completion of foundation work, installation of steel frames, etc.) to assess the progress of the construction work.

[0065] Step 5:

[0066] The server compares the progress data acquired by the image recognition AI with the design data registered in advance, thereby confirming that progress on-site is proceeding according to the design document.

[0067] Step 6:

[0068] The server automatically generates progress reports based on the results of the comparison with the design data, including progress status, degree of conformance with the design, and identified issues.

[0069] Step 7:

[0070] The server then sends the generated progress report to the user via a dedicated application, who can then check the report and understand the progress of the construction work.

[0071] Step 8:

[0072] The user checks the report and, after confirming that the progress is proceeding as planned, instructs the server to carry out the next payment procedure.

[0073] Step 9:

[0074] The server uses the payment instruction means to issue an instruction to transfer funds to the specified bank account, and the specified amount is transferred in cooperation with the payment system.

[0075] The above is the specific process flow for monitoring construction progress in real time and realizing flexible deposit of funds based on progress.

[0076] Example 1

[0077] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0078] With conventional construction site management systems, it was difficult to grasp the progress of work in real time, and there was a problem that progress-based fund management could not be adequately carried out. As a result, there was an increased risk of construction delays and fund shortages, and an effective means to improve construction efficiency was required.

[0079] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0080] In this invention, the server includes an imaging means for capturing images of the construction site, a transmission means for compressing and transmitting the image data obtained from the imaging means, an image recognition means for analyzing the image data transmitted from the transmission means, a comparison means for comparing the progress data analyzed by the image recognition means with design document data, a report generation means for generating a progress report based on the comparison results obtained by the comparison means, a payment instruction means for issuing a payment instruction for funds based on the progress report generated by the report generation means, and a means for executing payment procedures based on the payment instruction. This makes it possible to accurately grasp the progress of the construction site in real time and to manage funds in a timely manner according to the progress.

[0081] "Photographing means" refers to a device for capturing images of the construction site.

[0082] The "transmission means" is a device or function for compressing the video data obtained from the image capture means and transmitting it to the server.

[0083] The "image recognition means" is software or hardware for analyzing the video data transmitted from the transmission means and extracting construction progress data.

[0084] The "comparison means" is a function for comparing the progress data analyzed by the image recognition means with the design document data registered in advance and evaluating whether they match.

[0085] The "report generation means" is a function for automatically generating a progress report based on the collation results obtained by the collation means.

[0086] The "payment instruction means" is a function for issuing instructions to pay funds based on the progress report generated by the report generation means.

[0087] The "means for executing payment procedures" is a function for transferring funds to an account at a designated financial institution based on the payment instruction means.

[0088] The present invention is a system for grasping the progress of a construction site in real time and realizing flexible deposit of funds based on that progress. Specific embodiments for carrying out the present invention will be described below.

[0089] System Configuration

[0090] The system includes the following major components:

[0091] Recording method: This corresponds to a surveillance camera installed on the terminal, which captures video data of the construction site in real time. The surveillance camera used should be a high-resolution (e.g., 1080p) camera capable of continuous recording.

[0092] Transmission method: The terminal compresses the captured video data and sends it to the server via the network using the H.264 format and the HTTP or FTP protocol.

[0093] Image recognition method: An image recognition AI installed on the server analyzes the transmitted video data. TensorFlow and OpenCV are used for image recognition to extract specific information about the progress of construction work.

[0094] Verification method: The server compares the progress data analyzed by the image recognition method with the design document data registered in advance. The design document data is saved as a PDF or CAD file.

[0095] Report generation: The server automatically generates progress reports based on the results of the checks. The reports are created in PDF or HTML format and include charts and graphs for easy visual understanding.

[0096] Payment instruction means: The server issues a payment instruction to the user based on the generated progress report. The payment instruction is notified through a dedicated application.

[0097] Means of executing payment procedures: Based on the payment instructions, the server connects to the payment system (bank API or payment gateway) and transfers funds to the account of the specified financial institution.

[0098] Specific examples of program processing

[0099] 1. Installing surveillance cameras at construction sites and collecting video data

[0100] Terminal: A surveillance camera is installed at the construction site and captures video continuously for 24 hours. The captured video data is compressed every 10 minutes and sent to the server.

[0101] 2. Video data analysis and progress check

[0102] Server: The server uses image recognition AI to analyze the video data sent from the device. As a result of the analysis, progress information such as "foundation work completed" and "steel frame installed" is obtained.

[0103] 3. Verification with design document data

[0104] Server: The server compares the analyzed progress data with the design data registered in advance. The design data contains progress indicators and specifications for each construction stage. By comparing, it is evaluated whether the progress on site matches the design data.

[0105] 4. Progress report and payment instructions

[0106] User: Through a dedicated application, the user checks progress reports from the server, including progress status, issues found, and next steps to take.

[0107] Server: If the progress is as expected, the server instructs the user to proceed with the next payment procedure. The server then connects to the payment system and executes a transfer instruction to the specified bank account.

[0108] Usage examples

[0109] For example, at a construction site where steel frame work is underway, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and verifies that the steel frame is being assembled according to the design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment stage based on that information.

[0110] Example prompts for generative AI models

[0111] plaintext

[0112] prompt:

[0113] Analyze the following security camera footage and report on the progress of the site. The footage includes the following information: foundation work, steel frame installation, and concrete pouring. Evaluate whether each step has been completed and generate a progress report.

[0114] Video data:

[0115] [Video data link or file name]

[0116] This system allows the progress of construction work to be accurately grasped in real time, improves the liquidity of funds, and reduces the risk of construction delays and shortages of funds.

[0117] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0118] Step 1:

[0119] Installation and initial setup of surveillance cameras at construction sites

[0120] Terminal: Install the surveillance camera in an appropriate location. After installation, set the shooting interval and resolution. For example, set the shooting interval to 10 minutes and the resolution to 1080p.

[0121] Input: installation location, shooting interval, resolution settings.

[0122] Output: Configured security cameras.

[0123] Specific operation: After the setup is complete, the device will put the camera into continuous shooting mode.

[0124] Step 2:

[0125] Video data capture and compression

[0126] Terminal: Capture video at set intervals. For example, capture image data every 10 minutes. Compress the captured data in H.264 format.

[0127] Input: Shooting interval, resolution setting.

[0128] Output: Compressed video data.

[0129] How it works: The device captures video every 10 minutes and compresses it in real time.

[0130] Step 3:

[0131] Video data transmission

[0132] Terminal: Compressed video data is sent to the server using HTTP or FTP protocol.

[0133] Input: Compressed video data.

[0134] Output: Video data sent to the server.

[0135] Specific operation: The device starts transmitting video data to the server in real time.

[0136] Step 4:

[0137] Video data analysis

[0138] Server: Analyzes the received video data using image recognition AI (such as TensorFlow or OpenCV) and extracts information about the progress of construction work.

[0139] Input: Received video data.

[0140] Output: Progress data.

[0141] Specific operation: The server runs an image recognition model to identify the object (e.g., "foundation work completed" or "steel frame installed").

[0142] Step 5:

[0143] Checking progress data against design document data

[0144] Server: Compares the progress data with pre-registered design data (PDF or CAD files) and derives the comparison results.

[0145] Input: Progress data, design document data.

[0146] Output: Matching results.

[0147] What happens: The server compares the progress data with the design document, evaluates the degree of agreement, and generates a result.

[0148] Step 6:

[0149] Generate progress reports

[0150] Server: Generates a progress report based on the collation results, including progress, issues, and next actions.

[0151] Input: Match result.

[0152] Output: Progress report.

[0153] What it does: The server generates progress reports in PDF or HTML format, including charts for easy visual understanding.

[0154] Step 7:

[0155] Progress report notification and payment instructions

[0156] Server: Generates progress reports and sends them to the user, notifying them through a dedicated application.

[0157] Input: Progress report.

[0158] Output: Report and payment instructions sent to user.

[0159] Specific operation: The server uses the notification function to send a report to the user and provide payment instructions.

[0160] Step 8:

[0161] Execute payment procedures

[0162] Server: Based on the payment instructions, the server transfers funds to the financial institution's account using the bank API or payment gateway.

[0163] Input: Payment instructions.

[0164] Output: The completed payment transaction.

[0165] Specific operation: The server executes the transfer instruction to the specified account via API, confirms the completion notification, and records it.

[0166] (Application example 1)

[0167] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0168] Construction and production sites require real-time monitoring of progress and accurate information collection. This allows for smooth material supply and financial management according to progress, resulting in efficient construction and production. However, with current technology, these processes are often carried out manually, consuming a great deal of time and human resources. Furthermore, delays and incorrect reporting in progress management are prone to occur, which also impacts material supply and financial management. A system that can effectively address these issues is needed.

[0169] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0170] In this invention, the server includes an imaging means for capturing images of a construction site or production site, a transmission means for transmitting image data obtained from the imaging means, an image recognition means for analyzing the image data transmitted from the transmission means, a comparison means for comparing progress data analyzed by the image recognition means with design document data or production plan data, a report generation means for generating a progress report based on the comparison results obtained by the comparison means, and an instruction means for issuing instructions for material supply and payment of funds based on the progress report generated by the report generation means. This enables real-time monitoring of progress and realizes timely material supply and fund management.

[0171] A "construction site or production site" is a physical location where construction work or product production takes place and where progress management is required.

[0172] "Photographing means" refers to a device that captures images of the scene in real time, such as a surveillance camera or a video capture device.

[0173] The "transmission means" refers to a communication device or network means for transmitting the video data acquired by the image capture means to the server.

[0174] "Image recognition means" refers to software or AI models that analyze transmitted video data and automatically assess progress on-site.

[0175] "Design document data or production plan data" refers to standard data registered in advance for managing the progress of construction or production, and includes the completion conditions and specifications for each process.

[0176] The "verification means" is software or a system for comparing and collating the progress data analyzed by the image recognition means with the design document data or production plan data.

[0177] The "report generation means" is software or a system for automatically generating a progress report based on the collation results obtained by the collation means.

[0178] An "instruction means" is software or a system for taking actions such as issuing instructions for supplying materials or disbursing funds based on the generated progress report.

[0179] "Materials supply" is the process of automatically replenishing the necessary materials at the site according to progress.

[0180] "Cash management" is the process of depositing and transferring funds according to the progress of construction or production.

[0181] The present invention is a system for monitoring the progress of a construction or production site in real time and automating material supply and financial management based on the progress. Specific embodiments for carrying out the present invention will be described below.

[0182] System Configuration

[0183] The system consists of the following elements:

[0184] 1. Filming methods (surveillance cameras): Cameras installed on-site capture the progress of construction and production in real time. For example, IP cameras are used.

[0185] 2. Transmission method (network communication): Video data captured by the surveillance camera is compressed and sent to the server via the network.

[0186] 3. Image recognition method (image recognition AI): The server uses image recognition software such as TensorFlow or OpenCV's DNN model to analyze this video data.

[0187] 4. Verification means: The server compares the progress data analyzed by the image recognition means with the design data or production plan data. The design data and production plan data are registered in advance and include progress indicators and specifications for each process.

[0188] 5. Report Generation: The server automatically generates a progress report based on the verification results, including the progress status, any issues found, and next steps to take.

[0189] 6. Instruction means: The server issues instructions for supplying materials and paying funds based on the generated progress report. For example, it works with an automatic material ordering system or a fund transfer system between bank accounts.

[0190] Operation overview

[0191] The server uses image recognition AI to process video data sent from surveillance cameras and analyze progress in real time. The analyzed data is compared with design document data or production plan data, and a progress report is generated based on the results of the comparison. The generated report is provided to the user, who then instructs them on the supply of materials and payment of funds required for the next step.

[0192] Typical processing examples

[0193] For example, on a production line, it recognizes when a part on a conveyor belt reaches a designated position. A surveillance camera monitors the position, and image recognition AI confirms that the part has arrived. This triggers the next processing step and reports its progress to a server.

[0194] Prompt example

[0195] Design an AI system that uses real-time video footage from a surveillance camera to recognize parts as they arrive at a specified position and automatically trigger the next machining step. The system will send progress reports with timestamps and image data to a server. The image recognition AI model used should include TensorFlow.

[0196] In this way, the system of the present invention provides real-time monitoring of progress and automation of material supply and cash management.

[0197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0198] Step 1:

[0199] A surveillance camera captures video of a construction or production site in real time. The input is the video data captured through the surveillance camera lens, and the output is the video data.

[0200] Step 2:

[0201] The terminal (computer inside the surveillance camera) compresses the captured video data and sends it to the server via the network. The input is raw video data, which is converted into data for network transmission through data compression, and the output is compressed video data.

[0202] Step 3:

[0203] The server receives the transmitted video data and inputs it into the image recognition AI. The input is compressed video data, which is decoded and preprocessed, and the output is preprocessed data for analysis.

[0204] Step 4:

[0205] The server uses an image recognition AI model to analyze progress information from the preprocessed data. The input is the preprocessed image data, and the AI ​​model extracts progress information. The output is the analyzed progress information.

[0206] Step 5:

[0207] The server compares the analyzed progress information with the design data or production plan data registered in advance. The input is the progress information and design data (or production plan data), and a comparison operation is performed using a matching algorithm. The output is the matching result.

[0208] Step 6:

[0209] The server generates a progress report based on the matching results. The input is the matching results, and the progress report is created by a report generation algorithm. The output is the progress report.

[0210] Step 7:

[0211] The server issues instructions for supplying materials and paying funds based on the generated progress report. The input is the progress report, and instructions are executed in cooperation with the material ordering system and bank transfer system. The output is material ordering information and instructions for transferring funds.

[0212] Step 8:

[0213] The user checks the progress report using a dedicated application. The input is the progress report sent from the server, and the report is displayed through the application. The output is the progress information checked by the user.

[0214] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0215] The present invention combines a system that monitors the progress of a construction site in real time and enables flexible deposit of funds based on that progress with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0216] System Overview

[0217] This system uses surveillance cameras installed at the construction site to capture real-time construction progress and utilizes image recognition AI to evaluate the progress based on the video data.It also has an emotion engine that recognizes the user's emotional state and provides appropriate feedback and adjustments to support user decision-making.The main components of the system are as follows:

[0218] Capture method: This corresponds to a surveillance camera installed on the terminal, which captures video data of the construction site in real time.

[0219] Transmission means: The terminal compresses the captured video data and transmits it to the server via the network.

[0220] Image recognition method: An image recognition AI installed on the server analyzes the transmitted video data and evaluates the progress of the construction work.

[0221] Matching method: The server compares the progress data acquired by the image recognition AI with the design document data registered in advance.

[0222] Report generation means: The server automatically generates a progress report based on the collation results.

[0223] Payment instruction means: The server sends the generated progress report to the user and instructs the user on the next payment procedure based on the report.

[0224] Emotion engine: The server also has an emotion engine that recognizes the user's emotions and adjusts the content of progress reports, notification methods, and payment instruction procedures.

[0225] Specific examples of program processing

[0226] 1. Installing surveillance cameras at construction sites and collecting video data

[0227] Terminal: The surveillance camera is installed at the construction site and captures video continuously for 24 hours. The captured video data is sent to the server every 10 minutes.

[0228] 2. Video data analysis and progress check

[0229] Server: The server analyzes the received video data using image recognition AI. As a result of the analysis, progress information such as "foundation work completed" or "steel frame installed" is obtained.

[0230] 3. Verification with design document data

[0231] Server: The server compares the analyzed progress data with the design data registered in advance. The design data contains progress indicators and specifications for each construction stage. By comparing, it is evaluated whether the progress on site matches the design data.

[0232] 4. Progress report and payment instructions

[0233] User: Through a dedicated application, the user checks progress reports from the server, including progress status, issues found, and next steps to take.

[0234] Server: If the progress is as expected, the server instructs the user to proceed with the next payment procedure. The server connects to the payment system and executes instructions to transfer the funds to the specified bank account.

[0235] 5. User Emotion Recognition and Feedback

[0236] Emotion Engine: The server is equipped with an emotion engine that analyzes feedback (e.g., facial expressions, tone of voice, etc.) when a user views a report. The emotion engine understands the user's emotional state and adjusts the next report and notification method accordingly.

[0237] User: If a user encounters an unexpected problem while viewing a report, the emotion engine will detect the situation and suggest the next course of action to the server.

[0238] Usage examples

[0239] For example, at a construction site where steel frame work is underway, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and verifies that the steel frame is being assembled according to design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment stage based on that report. Furthermore, if the user displays a relieved expression while viewing the report, the emotion engine will set the next report to be in the same format. Conversely, if the user displays a dissatisfied or suspicious expression, the engine will adjust the report to include more detailed information and supplementary explanations.

[0240] This system allows the progress of construction sites to be accurately grasped in real time, improves the liquidity of funds, reduces the risk of construction delays and funding shortages, and emotionally supports the user's decision-making, enabling smoother project management.The above are specific modes for carrying out the present invention.

[0241] The processing flow will be explained below.

[0242] Step 1:

[0243] The device captures images of the construction site using a surveillance camera, which records video continuously 24 hours a day and generates a captured image every 10 minutes.

[0244] Step 2:

[0245] The device compresses the captured video data and transmits it over the network to a server, where the transmission is encrypted for security purposes.

[0246] Step 3:

[0247] The server receives the video data sent from the device, temporarily stores the data, and adds it to a queue to await analysis.

[0248] Step 4:

[0249] The server analyzes the received video data using image recognition AI, where the AI ​​algorithm identifies specific objects and scenes (e.g., foundation work completed, steel frame installation, etc.) to assess the progress of the construction work.

[0250] Step 5:

[0251] The server compares the progress data acquired by the image recognition AI with the design data registered in advance, thereby confirming that progress on-site is proceeding according to the design document.

[0252] Step 6:

[0253] The server automatically generates progress reports based on the results of the comparison with the design data, including progress status, degree of conformance with the design, and identified issues.

[0254] Step 7:

[0255] The server then sends the generated progress report to the user via a dedicated application, who can then check the report and understand the progress of the construction work.

[0256] Step 8:

[0257] The user checks the report and, after confirming that the progress is proceeding as planned, instructs the server to carry out the next payment procedure.

[0258] Step 9:

[0259] The server uses the payment instruction means to issue an instruction to transfer funds to the specified bank account, and the specified amount is transferred in cooperation with the payment system.

[0260] Step 10:

[0261] The emotion engine installed on the server analyzes the user's facial expressions and tone of voice when viewing the report to understand the user's emotional state. For example, if the user shows a relieved expression or tone of voice, the emotion engine records that information.

[0262] Step 11:

[0263] The emotion engine will adjust the content and notification of the next report based on the user's emotional state. For example, if it detects expressions of anxiety or doubt, it will add more details or additional explanations to the next report.

[0264] Step 12:

[0265] The server improves the progress reports and notification methods as appropriate based on feedback from the emotion engine, thereby increasing user satisfaction.

[0266] This series of steps allows the progress of construction to be tracked quickly and accurately, and provides appropriate feedback and support according to the user's emotional state, enabling flexible funding and smooth project management.

[0267] Example 2

[0268] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0269] Construction site progress management is difficult to grasp in real time, which often leads to delays in appropriate fund deposits and plan changes based on progress status. Furthermore, one-sided reports and notifications are given without considering the user's emotional state, which can lead to stress and frustration in user decision-making. A system that solves these problems and improves the efficiency of construction progress management and fund management, as well as user satisfaction, is needed.

[0270] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0271] In this invention, the server includes an imaging means for capturing video of the construction site, a transmission means for transmitting the video data obtained from the imaging means, an image recognition means for analyzing the video data transmitted from the transmission means, a comparison means for comparing the progress data analyzed by the image recognition means with design document data, a report generation means for generating a progress report based on the comparison result obtained by the comparison means, a payment instruction means for issuing a payment instruction based on the progress report generated by the report generation means, an emotion recognition means for recognizing a user's emotion when checking the progress report, and an adjustment means for adjusting the content of the report or the notification method based on the user's emotional state obtained by the emotion recognition means. This makes it possible to accurately grasp the progress of the construction site in real time, improve the liquidity of funds, and enable flexible feedback and decision-making support based on the user's emotional state.

[0272] "Photographing means" refers to a device that captures images of the construction site.

[0273] The "transmission means" is a device that compresses the video data obtained from the image capture means and transmits it to the server via the network.

[0274] The "image recognition means" is a device that uses image recognition technology to analyze received video data and evaluate the progress of construction work.

[0275] The "verification means" is a device or system that verifies the progress data analyzed by the image recognition means with the design document data registered in advance.

[0276] The "report generation means" is a device or system that generates a progress report based on the collation results obtained by the collation means.

[0277] The "payment instruction means" is a device or system that issues instructions for payment of funds based on the progress report generated by the report generation means.

[0278] The "emotion recognition means" is a device or system that analyzes feedback data such as facial expressions and voice in order to recognize the user's emotions.

[0279] The "adjustment means" is a device or system that adjusts the content of the report or the notification method based on the emotional state of the user obtained by the emotion recognition means.

[0280] The present invention combines a system that monitors the progress of a construction site in real time and enables flexible deposit of funds based on that progress with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing the present invention will be described below.

[0281] System Overview

[0282] This system uses surveillance cameras installed at the construction site to capture real-time construction progress and utilizes image recognition AI to evaluate the progress based on the video data.It also has an emotion engine that recognizes the user's emotional state and provides appropriate feedback and adjustments to support user decision-making.The main components of the system are as follows:

[0283] Filming method

[0284] Terminal: Surveillance cameras are installed at the construction site and capture video continuously for 24 hours. For example, a surveillance camera monitors a specific area and collects video data every 10 minutes.

[0285] Transmission method

[0286] Terminal: The acquired video data is compressed using a compression algorithm (e.g., H.264 or HEVC) to reduce the data size and sent to the server via the network.

[0287] Image Recognition Method

[0288] Server: Uses image recognition AI (e.g., TensorFlow or PyTorch) to analyze the transmitted video data. Through analysis, the progress of construction work (e.g., "foundation work completed" or "steel frame installed") is automatically detected.

[0289] Matching method

[0290] Server: Compares progress data analyzed by image recognition with pre-registered design data. The design data includes each construction process and progress indicators, and by comparing them, it evaluates whether on-site progress is consistent with the plan.

[0291] Report Generation Method

[0292] Server: Based on the results of the check, a progress report is automatically generated. The report includes the current construction status, any issues found, and action items for moving to the next stage. The generated report is then sent to the user.

[0293] Payment Instruction Method

[0294] Server: If the progress is as expected, instruct the user to carry out the payment procedure, which involves executing instructions to transfer the funds to the specified account based on the progress report.

[0295] emotion recognition means

[0296] Server: The emotion engine analyzes the feedback (e.g., facial expressions, tone of voice, etc.) of users viewing reports. It analyzes their emotional state and adjusts the content and notification method of the next report.

[0297] Adjustment means

[0298] Server: Adjust the content of the report or notification method based on the user's emotional state obtained through emotion recognition. For example, if the user shows a relieved expression, the same brief report format will be used next time, but if the user shows a dissatisfied expression, a detailed explanation will be added.

[0299] Specific examples

[0300] For example, at a construction site where steel frame work is being carried out, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and confirms that the steel frame is being assembled according to design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment step. Furthermore, if the user shows a relieved expression while viewing the report, the emotion engine will set the next report to be sent in the same format.

[0301] Prompt Sentence Examples

[0302] Here is an example of how to use the prompt:

[0303] plaintext

[0304] Please explain about a system that monitors the progress of a construction site in real time and manages the deposit of funds according to progress. This system also has a function to recognize the user's emotions. Specifically, video of the construction site is captured by a surveillance camera, and the server analyzes the progress using image recognition AI. The analysis results are then compared with the design document data and a progress report is sent to the user. Please also explain how the content of the next report is adjusted based on the user's emotional feedback.

[0305] The above is a specific embodiment for carrying out the present invention.

[0306] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0307] Step 1:

[0308] Surveillance camera footage capture

[0309] Terminal: A surveillance camera installed at the construction site captures video 24 hours a day. Specifically, the camera's sensor monitors a specific area and collects video data every 10 minutes.

[0310] Input: Real-time footage from the scene.

[0311] Output: Video files collected every 10 minutes.

[0312] Step 2:

[0313] Video data compression and transmission

[0314] Terminal: The collected video data is compressed using a compression algorithm (e.g., H.264 or HEVC) to reduce the data size and sent to the server via the network.

[0315] Input: Raw video data (high resolution).

[0316] Output: Compressed video data (low resolution).

[0317] Step 3:

[0318] Analyzing video data and obtaining progress information

[0319] Server: The transmitted video data is analyzed using image recognition AI (e.g., TensorFlow or PyTorch). For example, the progress of construction work can be automatically detected from the images (e.g., "foundation work completed" or "steel frame installed").

[0320] Input: Compressed video data.

[0321] Output: Specific progress information (e.g., steel construction completed).

[0322] Step 4:

[0323] Verification with design document data

[0324] Server: The progress data analyzed by the image recognition method is compared with the design document data registered in advance. The design document contains each construction process and progress indicators, and the server performs the comparison based on this.

[0325] Input: Analyzed progress data. Pre-registered design document data.

[0326] Output: Progress evaluation result (whether it matches the design document or not).

[0327] Step 5:

[0328] Generate and send progress reports

[0329] Server: Generates a progress report based on the results of the check. The report includes the current construction status, any issues found, and action items for moving forward to the next stage. The generated report is sent to the user's application.

[0330] Input: Progress assessment results.

[0331] Output: Progress report.

[0332] Step 6:

[0333] Instructions for disbursement of funds

[0334] Server: Based on the progress report, instructs the user to pay the funds. Specifically, if the progress is as planned, executes instructions to transfer funds to the specified bank account.

[0335] Input: Progress report.

[0336] Output: Instructions for disbursement of funds.

[0337] Step 7:

[0338] User Emotion Recognition and Feedback

[0339] Server: Analyzes the feedback (facial expressions and voice) of users viewing reports using emotion recognition means to recognize their emotional state. For example, facial expression analysis technology or voice analysis technology is used.

[0340] Input: User feedback data (facial expressions, voice, etc.).

[0341] Output: The user's emotional state.

[0342] Step 8:

[0343] Adjustment of reporting content and notification methods

[0344] Server: Based on the emotional state obtained by the emotion recognition means, adjust the content of the next report and notification method. For example, if the user feels relieved, provide a concise report, but if the user feels anxious, provide a detailed explanation.

[0345] Input: The user's emotional state.

[0346] Output: Coordinated reporting and notification methods.

[0347] The above is a specific processing flow of the program of this system.

[0348] (Application example 2)

[0349] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0350] With conventional technologies, it has been difficult to accurately grasp the progress of construction sites in real time or to recognize users' emotions and provide appropriate feedback and adjustments. Furthermore, in physical stores, there is a need for optimization of operations, such as real-time monitoring of store layout and inventory, and adjustment of sales policies based on customers' emotional states. In response to these challenges, the present invention aims to provide a management system that combines progress and emotions at both construction sites and physical stores, thereby optimizing financial management and sales policies.

[0351] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0352] In this invention, the server includes: a photographing means for capturing video of the construction site; a transmitting means for transmitting the video data obtained from the photographing means; an image recognition means for analyzing the video data transmitted from the transmitting means; a comparison means for comparing the progress data analyzed by the image recognition means with design document data; a report generation means for generating a progress report based on the comparison results obtained by the comparison means; a payment instruction means for issuing a payment instruction based on the progress report generated by the report generation means; an emotion engine means for recognizing the user's emotional state and adjusting the content of the progress report, the notification method, and the payment instruction procedure; and a store management means for monitoring the layout and inventory of the physical store in real time and adjusting business policies based on customer emotions. This not only enables real-time monitoring of the progress of the construction site and the operation of the physical store, but also enables optimal feedback and adjustments based on the emotions of users and customers.

[0353] "Photography means" refers to a device used to capture images of construction sites and brick-and-mortar stores.

[0354] The "transmission means" is a device or system for transmitting the video data obtained from the image capture means to the server.

[0355] The "image recognition means" is an artificial intelligence or software that analyzes the video data transmitted from the transmission means and extracts progress data.

[0356] The "comparison means" is a system for comparing the progress data analyzed by the image recognition means with pre-set design document data to confirm a match.

[0357] The "report generation means" is software for automatically generating a progress report based on the results obtained by the collation means.

[0358] The "payment instruction means" is a system for instructing payment of funds based on the progress report generated by the report generation means.

[0359] The "emotion engine means" is an artificial intelligence that recognizes the user's emotional state and adjusts the content and notification method of the progress report.

[0360] "Store management tools" are systems that monitor the layout and inventory of physical stores in real time and adjust sales policies based on customer sentiment.

[0361] This invention is a system for supporting the operation of construction sites and brick-and-mortar stores. The system supports more effective decision-making by combining real-time monitoring of progress status with user emotion recognition.

[0362] System Overview

[0363] The server is implemented using the following hardware and software:

[0364] Capture method: Surveillance cameras installed at construction sites and brick-and-mortar stores capture video data.

[0365] Transmission means: Transmits the video data captured by the imaging means to the server via the network.

[0366] Image recognition means: The server analyzes the received video data using an image processing library such as OpenCV. This analysis process detects progress and product placement.

[0367] Matching method: Progress data is compared with pre-designed data and pre-set targets to determine the degree of agreement.

[0368] Report generation method: Progress reports are automatically generated based on the matching results. The reports are notified to the user and used as information for making decisions about fund management.

[0369] Payment Instructions: Based on progress reports, instructions for the next payment of funds will be issued, including instructions for transfer to bank accounts.

[0370] Emotion engine means: An emotion engine is built in to analyze facial expressions and voice when a user is viewing a report and recognize the user's emotional state, which then adjusts the content of the report and the notification method.

[0371] Store management tools: Monitor store layout and inventory in real time and adjust sales policies based on customer sentiment.

[0372] Example of a system

[0373] 1. Construction site progress management:

[0374] Security cameras installed at the construction site capture images every 10 minutes and send them to a server. The server uses image recognition technology to analyze the progress data and compare it with the data in the design documents. A progress report is generated, and if the work is progressing as planned, instructions are issued for the next payment of funds.

[0375] 2. Physical store operations management:

[0376] Cameras are used to monitor changes to store layouts and inventory checks within physical stores, and an emotion recognition engine is used to analyze customer movements and facial expressions. This allows the company to determine whether customers are interested in or satisfied with products and adjust sales policies accordingly. For example, if a customer is looking with great interest at a new product, the product will be relocated to a more prominent location.

[0377] The specific hardware and software used

[0378] Hardware: Built-in cameras in laptops and smartphones

[0379] Software: OpenCV (image processing library), Scikit-learn (to train the emotion recognition model)

[0380] Prompt Sentence Examples

[0381] Examples of prompts to generate specific AI models are:

[0382] "Create a system that analyzes footage from cameras installed in physical stores and checks whether product placement is complete. Also, explain how to recognize customers' emotional states from their facial expressions in the store and optimize sales feedback."

[0383] In this way, the present invention is a system that enables efficient progress management and improved customer experience both at construction sites and in physical stores.

[0384] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0385] Step 1:

[0386] The terminal captures video data using surveillance cameras installed at construction sites and brick-and-mortar stores. The input is real-time video data captured by the surveillance cameras, and the output is the captured video data. Specifically, the cameras automatically capture video every 10 minutes and store it in local storage.

[0387] Step 2:

[0388] The terminal compresses the captured video data and sends it to the server via the network. The input is the captured video data, and the output is the compressed video data. Specifically, the video data is compressed using a compression algorithm such as H.264 and uploaded to the server via the HTTP protocol.

[0389] Step 3:

[0390] The server receives the transmitted video data and analyzes it using image recognition means. The input is the transmitted compressed video data, and the output is the analyzed progress data or product placement status. Specifically, the server decodes the video data using OpenCV and extracts progress and inventory status using an object detection algorithm.

[0391] Step 4:

[0392] The server compares the progress data obtained by the image recognition means with the design document data and pre-set target data. The input is the analyzed progress data and design document data, and the output is the comparison result. Specifically, it uses a Python data analysis library to compare the progress data with the database and calculate the degree of match.

[0393] Step 5:

[0394] The server generates a progress report based on the matching results. The input is the matching results, and the output is the progress report. Specifically, the server converts the matching results into a text format and automatically generates a report containing the necessary information.

[0395] Step 6:

[0396] The server issues a payment instruction based on the generated progress report. The input is the progress report, and the output is the execution of the payment instruction. Specifically, it works with the bank API to execute a transfer instruction to the specified bank account.

[0397] Step 7:

[0398] The server uses an emotion engine to analyze facial expressions and voice data when the user is viewing a report and recognizes the user's emotional state. The input is the facial expressions and voice data when viewing the report, and the output is the recognized emotional state. Specifically, the emotion engine uses a deep learning model to analyze the input data and output an emotion label.

[0399] Step 8:

[0400] The server adjusts the content and notification method of the next report based on the user's emotional state. The input is the recognized emotional state, and the output is the adjusted content and notification method of the next report. Specifically, if the user expresses dissatisfaction, the server applies a setting to add detailed explanations and supplementary information.

[0401] Step 9:

[0402] In a physical store, the terminal monitors the store layout and inventory and sends the data to a server. The input is video data captured by a surveillance camera inside the store, and the output is compressed video data. Specifically, the camera periodically captures the status of shelves and inventory and sends this data to the server.

[0403] Step 10:

[0404] The server analyzes store layout and inventory data and adjusts sales policies based on customer emotions. The input is in-store video data and customer emotion data, and the output is the adjusted sales policies. Specifically, the emotion engine recognizes emotions from customer facial expressions and determines sales policies such as placing new products around products that show high satisfaction.

[0405] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0406] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0407] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0408] [Second embodiment]

[0409] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0410] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0411] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0412] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0413] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0414] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0415] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0416] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0417] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0418] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0419] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0420] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0421] The present invention is a system for grasping the progress of a construction site in real time and realizing flexible deposit of funds based on that progress. Specific embodiments for carrying out the present invention will be described below.

[0422] System Overview

[0423] This system uses surveillance cameras installed at the construction site to capture real-time construction progress and utilizes image recognition AI to evaluate the progress based on the video data. The system provides users with progress reports and payment instructions. The main components of the system are as follows:

[0424] Capture method: This corresponds to a surveillance camera installed on the terminal, which captures video data of the construction site in real time.

[0425] Transmission means: The terminal compresses the captured video data and transmits it to the server via the network.

[0426] Image recognition method: An image recognition AI installed on the server analyzes the transmitted video data and evaluates the progress of the construction work.

[0427] Matching method: The server compares the progress data acquired by the image recognition AI with the design document data registered in advance.

[0428] Report generation means: The server automatically generates a progress report based on the collation results.

[0429] Payment instruction means: The server sends the generated progress report to the user and instructs the user on the next payment procedure based on the report.

[0430] Specific examples of program processing

[0431] 1. Installing surveillance cameras at construction sites and collecting video data

[0432] Terminal: The surveillance camera is installed at the construction site and captures video continuously for 24 hours. The captured video data is sent to the server every 10 minutes.

[0433] 2. Video data analysis and progress check

[0434] Server: The server analyzes the received video data using image recognition AI. As a result of the analysis, progress information such as "foundation work completed" or "steel frame installed" is obtained.

[0435] 3. Verification with design document data

[0436] Server: The server compares the analyzed progress data with the design data registered in advance. The design data contains progress indicators and specifications for each construction stage. By comparing, it is evaluated whether the progress on site matches the design data.

[0437] 4. Progress report and payment instructions

[0438] User: Through a dedicated application, the user checks progress reports from the server, including progress status, issues found, and next steps to take.

[0439] Server: If the progress is as expected, the server instructs the user to proceed with the next payment procedure. The server connects to the payment system and executes instructions to transfer the funds to the specified bank account.

[0440] Usage examples

[0441] For example, at a construction site where steel frame work is underway, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and verifies that the steel frame is being assembled according to design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment stage based on that report. In this way, funds can be deposited in a timely manner in line with the progress of the work.

[0442] This system allows the progress of construction work to be accurately grasped in real time, improves the liquidity of funds, and reduces the risk of construction delays and shortages of funds.

[0443] The processing flow will be explained below.

[0444] Step 1:

[0445] The device captures images of the construction site using a surveillance camera, which records the image 24 hours a day and generates a captured image every 10 minutes.

[0446] Step 2:

[0447] The device compresses the captured video data and sends it to the server over the network, where it is encrypted for security reasons.

[0448] Step 3:

[0449] The server receives the video data sent from the device, temporarily stores the data, and adds it to a queue to await analysis.

[0450] Step 4:

[0451] The server analyzes the received video data using image recognition AI, which identifies and evaluates specific objects and scenes (e.g., completion of foundation work, installation of steel frames, etc.) to assess the progress of the construction work.

[0452] Step 5:

[0453] The server compares the progress data acquired by the image recognition AI with the design data registered in advance, thereby confirming that progress on-site is proceeding according to the design document.

[0454] Step 6:

[0455] The server automatically generates progress reports based on the results of the comparison with the design data, including progress status, degree of conformance with the design, and identified issues.

[0456] Step 7:

[0457] The server then sends the generated progress report to the user via a dedicated application, who can then check the report and understand the progress of the construction work.

[0458] Step 8:

[0459] The user checks the report and, after confirming that the progress is proceeding as planned, instructs the server to carry out the next payment procedure.

[0460] Step 9:

[0461] The server uses the payment instruction means to issue an instruction to transfer funds to the specified bank account, and the specified amount is transferred in cooperation with the payment system.

[0462] The above is the specific process flow for monitoring construction progress in real time and realizing flexible deposit of funds based on progress.

[0463] Example 1

[0464] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0465] With conventional construction site management systems, it was difficult to grasp the progress of work in real time, and there was a problem that progress-based fund management could not be adequately carried out. As a result, there was an increased risk of construction delays and fund shortages, and an effective means to improve construction efficiency was required.

[0466] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0467] In this invention, the server includes an imaging means for capturing images of the construction site, a transmission means for compressing and transmitting the image data obtained from the imaging means, an image recognition means for analyzing the image data transmitted from the transmission means, a comparison means for comparing the progress data analyzed by the image recognition means with design document data, a report generation means for generating a progress report based on the comparison results obtained by the comparison means, a payment instruction means for issuing a payment instruction for funds based on the progress report generated by the report generation means, and a means for executing payment procedures based on the payment instruction. This makes it possible to accurately grasp the progress of the construction site in real time and to manage funds in a timely manner according to the progress.

[0468] "Photographing means" refers to a device for capturing images of the construction site.

[0469] The "transmission means" is a device or function for compressing the video data obtained from the image capture means and transmitting it to the server.

[0470] The "image recognition means" is software or hardware for analyzing the video data transmitted from the transmission means and extracting construction progress data.

[0471] The "comparison means" is a function for comparing the progress data analyzed by the image recognition means with the design document data registered in advance and evaluating whether they match.

[0472] The "report generation means" is a function for automatically generating a progress report based on the collation results obtained by the collation means.

[0473] The "payment instruction means" is a function for issuing instructions to pay funds based on the progress report generated by the report generation means.

[0474] The "means for executing payment procedures" is a function for transferring funds to an account at a designated financial institution based on the payment instruction means.

[0475] The present invention is a system for grasping the progress of a construction site in real time and realizing flexible deposit of funds based on that progress. Specific embodiments for carrying out the present invention will be described below.

[0476] System Configuration

[0477] The system includes the following major components:

[0478] Recording method: This corresponds to a surveillance camera installed on the terminal, which captures video data of the construction site in real time. The surveillance camera used should be a high-resolution (e.g., 1080p) camera capable of continuous recording.

[0479] Transmission method: The terminal compresses the captured video data and sends it to the server via the network using the H.264 format and the HTTP or FTP protocol.

[0480] Image recognition method: An image recognition AI installed on the server analyzes the transmitted video data. TensorFlow and OpenCV are used for image recognition to extract specific information about the progress of construction work.

[0481] Verification method: The server compares the progress data analyzed by the image recognition method with the design document data registered in advance. The design document data is saved as a PDF or CAD file.

[0482] Report generation: The server automatically generates progress reports based on the results of the checks. The reports are created in PDF or HTML format and include charts and graphs for easy visual understanding.

[0483] Payment instruction means: The server issues a payment instruction to the user based on the generated progress report. The payment instruction is notified through a dedicated application.

[0484] Means of executing payment procedures: Based on the payment instructions, the server connects to the payment system (bank API or payment gateway) and transfers funds to the account of the specified financial institution.

[0485] Specific examples of program processing

[0486] 1. Installing surveillance cameras at construction sites and collecting video data

[0487] Terminal: A surveillance camera is installed at the construction site and captures video continuously for 24 hours. The captured video data is compressed every 10 minutes and sent to the server.

[0488] 2. Video data analysis and progress check

[0489] Server: The server uses image recognition AI to analyze the video data sent from the device. As a result of the analysis, progress information such as "foundation work completed" and "steel frame installed" is obtained.

[0490] 3. Verification with design document data

[0491] Server: The server compares the analyzed progress data with the design data registered in advance. The design data contains progress indicators and specifications for each construction stage. By comparing, it is evaluated whether the progress on site matches the design data.

[0492] 4. Progress report and payment instructions

[0493] User: Through a dedicated application, the user checks progress reports from the server, including progress status, issues found, and next steps to take.

[0494] Server: If the progress is as expected, the server instructs the user to proceed with the next payment procedure. The server then connects to the payment system and executes a transfer instruction to the specified bank account.

[0495] Usage examples

[0496] For example, at a construction site where steel frame work is underway, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and verifies that the steel frame is being assembled according to the design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment stage based on that information.

[0497] Example prompts for generative AI models

[0498] plaintext

[0499] prompt:

[0500] Analyze the following security camera footage and report on the progress of the site. The footage includes the following information: foundation work, steel frame installation, and concrete pouring. Evaluate whether each step has been completed and generate a progress report.

[0501] Video data:

[0502] [Video data link or file name]

[0503] This system allows the progress of construction work to be accurately grasped in real time, improves the liquidity of funds, and reduces the risk of construction delays and shortages of funds.

[0504] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0505] Step 1:

[0506] Installation and initial setup of surveillance cameras at construction sites

[0507] Terminal: Install the surveillance camera in an appropriate location. After installation, set the shooting interval and resolution. For example, set the shooting interval to 10 minutes and the resolution to 1080p.

[0508] Input: installation location, shooting interval, resolution settings.

[0509] Output: Configured security cameras.

[0510] Specific operation: After the setup is complete, the device will put the camera into continuous shooting mode.

[0511] Step 2:

[0512] Video data capture and compression

[0513] Terminal: Capture video at set intervals. For example, capture image data every 10 minutes. Compress the captured data in H.264 format.

[0514] Input: Shooting interval, resolution setting.

[0515] Output: Compressed video data.

[0516] How it works: The device captures video every 10 minutes and compresses it in real time.

[0517] Step 3:

[0518] Video data transmission

[0519] Terminal: Compressed video data is sent to the server using HTTP or FTP protocol.

[0520] Input: Compressed video data.

[0521] Output: Video data sent to the server.

[0522] Specific operation: The device starts transmitting video data to the server in real time.

[0523] Step 4:

[0524] Video data analysis

[0525] Server: Analyzes the received video data using image recognition AI (such as TensorFlow or OpenCV) and extracts information about the progress of construction work.

[0526] Input: Received video data.

[0527] Output: Progress data.

[0528] Specific operation: The server runs an image recognition model to identify the object (e.g., "foundation work completed" or "steel frame installed").

[0529] Step 5:

[0530] Checking progress data against design document data

[0531] Server: Compares the progress data with pre-registered design data (PDF or CAD files) and derives the comparison results.

[0532] Input: Progress data, design document data.

[0533] Output: Matching results.

[0534] What happens: The server compares the progress data with the design document, evaluates the degree of agreement, and generates a result.

[0535] Step 6:

[0536] Generate progress reports

[0537] Server: Generates a progress report based on the collation results, including progress, issues, and next actions.

[0538] Input: Match result.

[0539] Output: Progress report.

[0540] What it does: The server generates progress reports in PDF or HTML format, including charts for easy visual understanding.

[0541] Step 7:

[0542] Progress report notification and payment instructions

[0543] Server: Generates progress reports and sends them to the user, notifying them through a dedicated application.

[0544] Input: Progress report.

[0545] Output: Report and payment instructions sent to user.

[0546] Specific operation: The server uses the notification function to send a report to the user and provide payment instructions.

[0547] Step 8:

[0548] Execute payment procedures

[0549] Server: Based on the payment instructions, the server transfers funds to the financial institution's account using the bank API or payment gateway.

[0550] Input: Payment instructions.

[0551] Output: The completed payment transaction.

[0552] Specific operation: The server executes the transfer instruction to the specified account via API, confirms the completion notification, and records it.

[0553] (Application example 1)

[0554] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0555] Construction and production sites require real-time monitoring of progress and accurate information collection. This allows for smooth material supply and financial management according to progress, resulting in efficient construction and production. However, with current technology, these processes are often carried out manually, consuming a great deal of time and human resources. Furthermore, delays and incorrect reporting in progress management are prone to occur, which also impacts material supply and financial management. A system that can effectively address these issues is needed.

[0556] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0557] In this invention, the server includes an imaging means for capturing images of a construction site or production site, a transmission means for transmitting image data obtained from the imaging means, an image recognition means for analyzing the image data transmitted from the transmission means, a comparison means for comparing progress data analyzed by the image recognition means with design document data or production plan data, a report generation means for generating a progress report based on the comparison results obtained by the comparison means, and an instruction means for issuing instructions for material supply and payment of funds based on the progress report generated by the report generation means. This enables real-time monitoring of progress and realizes timely material supply and fund management.

[0558] A "construction site or production site" is a physical location where construction work or product production takes place and where progress management is required.

[0559] "Photographing means" refers to a device that captures images of the scene in real time, such as a surveillance camera or a video capture device.

[0560] The "transmission means" refers to a communication device or network means for transmitting the video data acquired by the image capture means to the server.

[0561] "Image recognition means" refers to software or AI models that analyze transmitted video data and automatically assess progress on-site.

[0562] "Design document data or production plan data" refers to standard data registered in advance for managing the progress of construction or production, and includes the completion conditions and specifications for each process.

[0563] The "verification means" is software or a system for comparing and collating the progress data analyzed by the image recognition means with the design document data or production plan data.

[0564] The "report generation means" is software or a system for automatically generating a progress report based on the collation results obtained by the collation means.

[0565] An "instruction means" is software or a system for taking actions such as issuing instructions for supplying materials or disbursing funds based on the generated progress report.

[0566] "Materials supply" is the process of automatically replenishing the necessary materials at the site according to progress.

[0567] "Cash management" is the process of depositing and transferring funds according to the progress of construction or production.

[0568] The present invention is a system for monitoring the progress of a construction or production site in real time and automating material supply and financial management based on the progress. Specific embodiments for carrying out the present invention will be described below.

[0569] System Configuration

[0570] The system consists of the following elements:

[0571] 1. Filming methods (surveillance cameras): Cameras installed on-site capture the progress of construction and production in real time. For example, IP cameras are used.

[0572] 2. Transmission method (network communication): Video data captured by the surveillance camera is compressed and sent to the server via the network.

[0573] 3. Image recognition method (image recognition AI): The server uses image recognition software such as TensorFlow or OpenCV's DNN model to analyze this video data.

[0574] 4. Verification means: The server compares the progress data analyzed by the image recognition means with the design data or production plan data. The design data and production plan data are registered in advance and include progress indicators and specifications for each process.

[0575] 5. Report Generation: The server automatically generates a progress report based on the verification results, including the progress status, any issues found, and next steps to take.

[0576] 6. Instruction means: The server issues instructions for supplying materials and paying funds based on the generated progress report. For example, it works with an automatic material ordering system or a fund transfer system between bank accounts.

[0577] Operation overview

[0578] The server uses image recognition AI to process video data sent from surveillance cameras and analyze progress in real time. The analyzed data is compared with design document data or production plan data, and a progress report is generated based on the results of the comparison. The generated report is provided to the user, who then instructs them on the supply of materials and payment of funds required for the next step.

[0579] Typical processing examples

[0580] For example, on a production line, it recognizes when a part on a conveyor belt reaches a designated position. A surveillance camera monitors the position, and image recognition AI confirms that the part has arrived. This triggers the next processing step and reports its progress to a server.

[0581] Prompt example

[0582] Design an AI system that uses real-time video footage from a surveillance camera to recognize parts as they arrive at a specified position and automatically trigger the next machining step. The system will send progress reports with timestamps and image data to a server. The image recognition AI model used should include TensorFlow.

[0583] In this way, the system of the present invention provides real-time monitoring of progress and automation of material supply and cash management.

[0584] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0585] Step 1:

[0586] A surveillance camera captures video of a construction or production site in real time. The input is the video data captured through the surveillance camera lens, and the output is the video data.

[0587] Step 2:

[0588] The terminal (computer inside the surveillance camera) compresses the captured video data and sends it to the server via the network. The input is raw video data, which is converted into data for network transmission through data compression, and the output is compressed video data.

[0589] Step 3:

[0590] The server receives the transmitted video data and inputs it into the image recognition AI. The input is compressed video data, which is decoded and preprocessed, and the output is preprocessed data for analysis.

[0591] Step 4:

[0592] The server uses an image recognition AI model to analyze progress information from the preprocessed data. The input is the preprocessed image data, and the AI ​​model extracts progress information. The output is the analyzed progress information.

[0593] Step 5:

[0594] The server compares the analyzed progress information with the design data or production plan data registered in advance. The input is the progress information and design data (or production plan data), and a comparison operation is performed using a matching algorithm. The output is the matching result.

[0595] Step 6:

[0596] The server generates a progress report based on the matching results. The input is the matching results, and the progress report is created by a report generation algorithm. The output is the progress report.

[0597] Step 7:

[0598] The server issues instructions for supplying materials and paying funds based on the generated progress report. The input is the progress report, and instructions are executed in cooperation with the material ordering system and bank transfer system. The output is material ordering information and instructions for transferring funds.

[0599] Step 8:

[0600] The user checks the progress report using a dedicated application. The input is the progress report sent from the server, and the report is displayed through the application. The output is the progress information checked by the user.

[0601] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0602] The present invention combines a system that monitors the progress of a construction site in real time and enables flexible deposit of funds based on that progress with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0603] System Overview

[0604] This system uses surveillance cameras installed at the construction site to capture real-time construction progress and utilizes image recognition AI to evaluate the progress based on the video data.It also has an emotion engine that recognizes the user's emotional state and provides appropriate feedback and adjustments to support user decision-making.The main components of the system are as follows:

[0605] Capture method: This corresponds to a surveillance camera installed on the terminal, which captures video data of the construction site in real time.

[0606] Transmission means: The terminal compresses the captured video data and transmits it to the server via the network.

[0607] Image recognition method: An image recognition AI installed on the server analyzes the transmitted video data and evaluates the progress of the construction work.

[0608] Matching method: The server compares the progress data acquired by the image recognition AI with the design document data registered in advance.

[0609] Report generation means: The server automatically generates a progress report based on the collation results.

[0610] Payment instruction means: The server sends the generated progress report to the user and instructs the user on the next payment procedure based on the report.

[0611] Emotion engine: The server also has an emotion engine that recognizes the user's emotions and adjusts the content of progress reports, notification methods, and payment instruction procedures.

[0612] Specific examples of program processing

[0613] 1. Installing surveillance cameras at construction sites and collecting video data

[0614] Terminal: The surveillance camera is installed at the construction site and captures video continuously for 24 hours. The captured video data is sent to the server every 10 minutes.

[0615] 2. Video data analysis and progress check

[0616] Server: The server analyzes the received video data using image recognition AI. As a result of the analysis, progress information such as "foundation work completed" or "steel frame installed" is obtained.

[0617] 3. Verification with design document data

[0618] Server: The server compares the analyzed progress data with the design data registered in advance. The design data contains progress indicators and specifications for each construction stage. By comparing, it is evaluated whether the progress on site matches the design data.

[0619] 4. Progress report and payment instructions

[0620] User: Through a dedicated application, the user checks progress reports from the server, including progress status, issues found, and next steps to take.

[0621] Server: If the progress is as expected, the server instructs the user to proceed with the next payment procedure. The server connects to the payment system and executes instructions to transfer the funds to the specified bank account.

[0622] 5. User Emotion Recognition and Feedback

[0623] Emotion Engine: The server is equipped with an emotion engine that analyzes feedback (e.g., facial expressions, tone of voice, etc.) when a user views a report. The emotion engine understands the user's emotional state and adjusts the next report and notification method accordingly.

[0624] User: If a user encounters an unexpected problem while viewing a report, the emotion engine will detect the situation and suggest the next course of action to the server.

[0625] Usage examples

[0626] For example, at a construction site where steel frame work is underway, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and verifies that the steel frame is being assembled according to design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment stage based on that report. Furthermore, if the user displays a relieved expression while viewing the report, the emotion engine will set the next report to be in the same format. Conversely, if the user displays a dissatisfied or suspicious expression, the engine will adjust the report to include more detailed information and supplementary explanations.

[0627] This system allows the progress of construction sites to be accurately grasped in real time, improves the liquidity of funds, reduces the risk of construction delays and funding shortages, and emotionally supports the user's decision-making, enabling smoother project management.The above are specific modes for carrying out the present invention.

[0628] The processing flow will be explained below.

[0629] Step 1:

[0630] The device captures images of the construction site using a surveillance camera, which records video continuously 24 hours a day and generates a captured image every 10 minutes.

[0631] Step 2:

[0632] The device compresses the captured video data and transmits it over the network to a server, where the transmission is encrypted for security purposes.

[0633] Step 3:

[0634] The server receives the video data sent from the device, temporarily stores the data, and adds it to a queue to await analysis.

[0635] Step 4:

[0636] The server analyzes the received video data using image recognition AI, where the AI ​​algorithm identifies specific objects and scenes (e.g., foundation work completed, steel frame installation, etc.) to assess the progress of the construction work.

[0637] Step 5:

[0638] The server compares the progress data acquired by the image recognition AI with the design data registered in advance, thereby confirming that progress on-site is proceeding according to the design document.

[0639] Step 6:

[0640] The server automatically generates progress reports based on the results of the comparison with the design data, including progress status, degree of conformance with the design, and identified issues.

[0641] Step 7:

[0642] The server then sends the generated progress report to the user via a dedicated application, who can then check the report and understand the progress of the construction work.

[0643] Step 8:

[0644] The user checks the report and, after confirming that the progress is proceeding as planned, instructs the server to carry out the next payment procedure.

[0645] Step 9:

[0646] The server uses the payment instruction means to issue an instruction to transfer funds to the specified bank account, and the specified amount is transferred in cooperation with the payment system.

[0647] Step 10:

[0648] The emotion engine installed on the server analyzes the user's facial expressions and tone of voice when viewing the report to understand the user's emotional state. For example, if the user shows a relieved expression or tone of voice, the emotion engine records that information.

[0649] Step 11:

[0650] The emotion engine will adjust the content and notification of the next report based on the user's emotional state. For example, if it detects expressions of anxiety or doubt, it will add more details or additional explanations to the next report.

[0651] Step 12:

[0652] The server improves the progress reports and notification methods as appropriate based on feedback from the emotion engine, thereby increasing user satisfaction.

[0653] This series of steps allows the progress of construction to be tracked quickly and accurately, and provides appropriate feedback and support according to the user's emotional state, enabling flexible funding and smooth project management.

[0654] Example 2

[0655] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0656] Construction site progress management is difficult to grasp in real time, which often leads to delays in appropriate fund deposits and plan changes based on progress status. Furthermore, one-sided reports and notifications are given without considering the user's emotional state, which can lead to stress and frustration in user decision-making. A system that solves these problems and improves the efficiency of construction progress management and fund management, as well as user satisfaction, is needed.

[0657] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0658] In this invention, the server includes an imaging means for capturing video of the construction site, a transmission means for transmitting the video data obtained from the imaging means, an image recognition means for analyzing the video data transmitted from the transmission means, a comparison means for comparing the progress data analyzed by the image recognition means with design document data, a report generation means for generating a progress report based on the comparison result obtained by the comparison means, a payment instruction means for issuing a payment instruction based on the progress report generated by the report generation means, an emotion recognition means for recognizing a user's emotion when checking the progress report, and an adjustment means for adjusting the content of the report or the notification method based on the user's emotional state obtained by the emotion recognition means. This makes it possible to accurately grasp the progress of the construction site in real time, improve the liquidity of funds, and enable flexible feedback and decision-making support based on the user's emotional state.

[0659] "Photographing means" refers to a device that captures images of the construction site.

[0660] The "transmission means" is a device that compresses the video data obtained from the image capture means and transmits it to the server via the network.

[0661] The "image recognition means" is a device that uses image recognition technology to analyze received video data and evaluate the progress of construction work.

[0662] The "verification means" is a device or system that verifies the progress data analyzed by the image recognition means with the design document data registered in advance.

[0663] The "report generation means" is a device or system that generates a progress report based on the collation results obtained by the collation means.

[0664] The "payment instruction means" is a device or system that issues instructions for payment of funds based on the progress report generated by the report generation means.

[0665] The "emotion recognition means" is a device or system that analyzes feedback data such as facial expressions and voice in order to recognize the user's emotions.

[0666] The "adjustment means" is a device or system that adjusts the content of the report or the notification method based on the emotional state of the user obtained by the emotion recognition means.

[0667] The present invention combines a system that monitors the progress of a construction site in real time and enables flexible deposit of funds based on that progress with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing the present invention will be described below.

[0668] System Overview

[0669] This system uses surveillance cameras installed at the construction site to capture real-time construction progress and utilizes image recognition AI to evaluate the progress based on the video data.It also has an emotion engine that recognizes the user's emotional state and provides appropriate feedback and adjustments to support user decision-making.The main components of the system are as follows:

[0670] Filming method

[0671] Terminal: Surveillance cameras are installed at the construction site and capture video continuously for 24 hours. For example, a surveillance camera monitors a specific area and collects video data every 10 minutes.

[0672] Transmission method

[0673] Terminal: The acquired video data is compressed using a compression algorithm (e.g., H.264 or HEVC) to reduce the data size and sent to the server via the network.

[0674] Image Recognition Method

[0675] Server: Uses image recognition AI (e.g., TensorFlow or PyTorch) to analyze the transmitted video data. Through analysis, the progress of construction work (e.g., "foundation work completed" or "steel frame installed") is automatically detected.

[0676] Matching method

[0677] Server: Compares progress data analyzed by image recognition with pre-registered design data. The design data includes each construction process and progress indicators, and by comparing them, it evaluates whether on-site progress is consistent with the plan.

[0678] Report Generation Method

[0679] Server: Based on the results of the check, a progress report is automatically generated. The report includes the current construction status, any issues found, and action items for moving to the next stage. The generated report is then sent to the user.

[0680] Payment Instruction Method

[0681] Server: If the progress is as expected, instruct the user to carry out the payment procedure, which involves executing instructions to transfer the funds to the specified account based on the progress report.

[0682] emotion recognition means

[0683] Server: The emotion engine analyzes the feedback (e.g., facial expressions, tone of voice, etc.) of users viewing reports. It analyzes their emotional state and adjusts the content and notification method of the next report.

[0684] Adjustment means

[0685] Server: Adjust the content of the report or notification method based on the user's emotional state obtained through emotion recognition. For example, if the user shows a relieved expression, the same brief report format will be used next time, but if the user shows a dissatisfied expression, a detailed explanation will be added.

[0686] Specific examples

[0687] For example, at a construction site where steel frame work is being carried out, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and confirms that the steel frame is being assembled according to design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment step. Furthermore, if the user shows a relieved expression while viewing the report, the emotion engine will set the next report to be sent in the same format.

[0688] Prompt Sentence Examples

[0689] Here is an example of how to use the prompt:

[0690] plaintext

[0691] Please explain about a system that monitors the progress of a construction site in real time and manages the deposit of funds according to progress. This system also has a function to recognize the user's emotions. Specifically, video of the construction site is captured by a surveillance camera, and the server analyzes the progress using image recognition AI. The analysis results are then compared with the design document data and a progress report is sent to the user. Please also explain how the content of the next report is adjusted based on the user's emotional feedback.

[0692] The above is a specific embodiment for carrying out the present invention.

[0693] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0694] Step 1:

[0695] Surveillance camera footage capture

[0696] Terminal: A surveillance camera installed at the construction site captures video 24 hours a day. Specifically, the camera's sensor monitors a specific area and collects video data every 10 minutes.

[0697] Input: Real-time footage from the scene.

[0698] Output: Video files collected every 10 minutes.

[0699] Step 2:

[0700] Video data compression and transmission

[0701] Terminal: The collected video data is compressed using a compression algorithm (e.g., H.264 or HEVC) to reduce the data size and sent to the server via the network.

[0702] Input: Raw video data (high resolution).

[0703] Output: Compressed video data (low resolution).

[0704] Step 3:

[0705] Analyzing video data and obtaining progress information

[0706] Server: The transmitted video data is analyzed using image recognition AI (e.g., TensorFlow or PyTorch). For example, the progress of construction work can be automatically detected from the images (e.g., "foundation work completed" or "steel frame installed").

[0707] Input: Compressed video data.

[0708] Output: Specific progress information (e.g., steel construction completed).

[0709] Step 4:

[0710] Verification with design document data

[0711] Server: The progress data analyzed by the image recognition method is compared with the design document data registered in advance. The design document contains each construction process and progress indicators, and the server performs the comparison based on this.

[0712] Input: Analyzed progress data. Pre-registered design document data.

[0713] Output: Progress evaluation result (whether it matches the design document or not).

[0714] Step 5:

[0715] Generate and send progress reports

[0716] Server: Generates a progress report based on the results of the check. The report includes the current construction status, any issues found, and action items for moving forward to the next stage. The generated report is sent to the user's application.

[0717] Input: Progress assessment results.

[0718] Output: Progress report.

[0719] Step 6:

[0720] Instructions for disbursement of funds

[0721] Server: Based on the progress report, instructs the user to pay the funds. Specifically, if the progress is as planned, executes instructions to transfer funds to the specified bank account.

[0722] Input: Progress report.

[0723] Output: Instructions for disbursement of funds.

[0724] Step 7:

[0725] User Emotion Recognition and Feedback

[0726] Server: Analyzes the feedback (facial expressions and voice) of users viewing reports using emotion recognition means to recognize their emotional state. For example, facial expression analysis technology or voice analysis technology is used.

[0727] Input: User feedback data (facial expressions, voice, etc.).

[0728] Output: The user's emotional state.

[0729] Step 8:

[0730] Adjustment of reporting content and notification methods

[0731] Server: Based on the emotional state obtained by the emotion recognition means, adjust the content of the next report and notification method. For example, if the user feels relieved, provide a concise report, but if the user feels anxious, provide a detailed explanation.

[0732] Input: The user's emotional state.

[0733] Output: Coordinated reporting and notification methods.

[0734] The above is a specific processing flow of the program of this system.

[0735] (Application example 2)

[0736] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0737] With conventional technologies, it has been difficult to accurately grasp the progress of construction sites in real time or to recognize users' emotions and provide appropriate feedback and adjustments. Furthermore, in physical stores, there is a need for optimization of operations, such as real-time monitoring of store layout and inventory, and adjustment of sales policies based on customers' emotional states. In response to these challenges, the present invention aims to provide a management system that combines progress and emotions at both construction sites and physical stores, thereby optimizing financial management and sales policies.

[0738] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0739] In this invention, the server includes: a photographing means for capturing video of the construction site; a transmitting means for transmitting the video data obtained from the photographing means; an image recognition means for analyzing the video data transmitted from the transmitting means; a comparison means for comparing the progress data analyzed by the image recognition means with design document data; a report generation means for generating a progress report based on the comparison results obtained by the comparison means; a payment instruction means for issuing a payment instruction based on the progress report generated by the report generation means; an emotion engine means for recognizing the user's emotional state and adjusting the content of the progress report, the notification method, and the payment instruction procedure; and a store management means for monitoring the layout and inventory of the physical store in real time and adjusting business policies based on customer emotions. This not only enables real-time monitoring of the progress of the construction site and the operation of the physical store, but also enables optimal feedback and adjustments based on the emotions of users and customers.

[0740] "Photography means" refers to a device used to capture images of construction sites and brick-and-mortar stores.

[0741] The "transmission means" is a device or system for transmitting the video data obtained from the image capture means to the server.

[0742] The "image recognition means" is an artificial intelligence or software that analyzes the video data transmitted from the transmission means and extracts progress data.

[0743] The "comparison means" is a system for comparing the progress data analyzed by the image recognition means with pre-set design document data to confirm a match.

[0744] The "report generation means" is software for automatically generating a progress report based on the results obtained by the collation means.

[0745] The "payment instruction means" is a system for instructing payment of funds based on the progress report generated by the report generation means.

[0746] The "emotion engine means" is an artificial intelligence that recognizes the user's emotional state and adjusts the content and notification method of the progress report.

[0747] "Store management tools" are systems that monitor the layout and inventory of physical stores in real time and adjust sales policies based on customer sentiment.

[0748] This invention is a system for supporting the operation of construction sites and brick-and-mortar stores. The system supports more effective decision-making by combining real-time monitoring of progress status with user emotion recognition.

[0749] System Overview

[0750] The server is implemented using the following hardware and software:

[0751] Capture method: Surveillance cameras installed at construction sites and brick-and-mortar stores capture video data.

[0752] Transmission means: Transmits the video data captured by the imaging means to the server via the network.

[0753] Image recognition means: The server analyzes the received video data using an image processing library such as OpenCV. This analysis process detects progress and product placement.

[0754] Matching method: Progress data is compared with pre-designed data and pre-set targets to determine the degree of agreement.

[0755] Report generation method: Progress reports are automatically generated based on the matching results. The reports are notified to the user and used as information for making decisions about fund management.

[0756] Payment Instructions: Based on progress reports, instructions for the next payment of funds will be issued, including instructions for transfer to bank accounts.

[0757] Emotion engine means: An emotion engine is built in to analyze facial expressions and voice when a user is viewing a report and recognize the user's emotional state, which then adjusts the content of the report and the notification method.

[0758] Store management tools: Monitor store layout and inventory in real time and adjust sales policies based on customer sentiment.

[0759] Example of a system

[0760] 1. Construction site progress management:

[0761] Security cameras installed at the construction site capture images every 10 minutes and send them to a server. The server uses image recognition technology to analyze the progress data and compare it with the data in the design documents. A progress report is generated, and if the work is progressing as planned, instructions are issued for the next payment of funds.

[0762] 2. Physical store operations management:

[0763] Cameras are used to monitor changes to store layouts and inventory checks within physical stores, and an emotion recognition engine is used to analyze customer movements and facial expressions. This allows the company to determine whether customers are interested in or satisfied with products and adjust sales policies accordingly. For example, if a customer is looking with great interest at a new product, the product will be relocated to a more prominent location.

[0764] The specific hardware and software used

[0765] Hardware: Built-in cameras in laptops and smartphones

[0766] Software: OpenCV (image processing library), Scikit-learn (to train the emotion recognition model)

[0767] Prompt Sentence Examples

[0768] Examples of prompts to generate specific AI models are:

[0769] "Create a system that analyzes footage from cameras installed in physical stores and checks whether product placement is complete. Also, explain how to recognize customers' emotional states from their facial expressions in the store and optimize sales feedback."

[0770] In this way, the present invention is a system that enables efficient progress management and improved customer experience both at construction sites and in physical stores.

[0771] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0772] Step 1:

[0773] The terminal captures video data using surveillance cameras installed at construction sites and brick-and-mortar stores. The input is real-time video data captured by the surveillance cameras, and the output is the captured video data. Specifically, the cameras automatically capture video every 10 minutes and store it in local storage.

[0774] Step 2:

[0775] The terminal compresses the captured video data and sends it to the server via the network. The input is the captured video data, and the output is the compressed video data. Specifically, the video data is compressed using a compression algorithm such as H.264 and uploaded to the server via the HTTP protocol.

[0776] Step 3:

[0777] The server receives the transmitted video data and analyzes it using image recognition means. The input is the transmitted compressed video data, and the output is the analyzed progress data or product placement status. Specifically, the server decodes the video data using OpenCV and extracts progress and inventory status using an object detection algorithm.

[0778] Step 4:

[0779] The server compares the progress data obtained by the image recognition means with the design document data and pre-set target data. The input is the analyzed progress data and design document data, and the output is the comparison result. Specifically, it uses a Python data analysis library to compare the progress data with the database and calculate the degree of match.

[0780] Step 5:

[0781] The server generates a progress report based on the matching results. The input is the matching results, and the output is the progress report. Specifically, the server converts the matching results into a text format and automatically generates a report containing the necessary information.

[0782] Step 6:

[0783] The server issues a payment instruction based on the generated progress report. The input is the progress report, and the output is the execution of the payment instruction. Specifically, it works with the bank API to execute a transfer instruction to the specified bank account.

[0784] Step 7:

[0785] The server uses an emotion engine to analyze facial expressions and voice data when the user is viewing a report and recognizes the user's emotional state. The input is the facial expressions and voice data when viewing the report, and the output is the recognized emotional state. Specifically, the emotion engine uses a deep learning model to analyze the input data and output an emotion label.

[0786] Step 8:

[0787] The server adjusts the content and notification method of the next report based on the user's emotional state. The input is the recognized emotional state, and the output is the adjusted content and notification method of the next report. Specifically, if the user expresses dissatisfaction, the server applies a setting to add detailed explanations and supplementary information.

[0788] Step 9:

[0789] In a physical store, the terminal monitors the store layout and inventory and sends the data to a server. The input is video data captured by a surveillance camera inside the store, and the output is compressed video data. Specifically, the camera periodically captures the status of shelves and inventory and sends this data to the server.

[0790] Step 10:

[0791] The server analyzes store layout and inventory data and adjusts sales policies based on customer emotions. The input is in-store video data and customer emotion data, and the output is the adjusted sales policies. Specifically, the emotion engine recognizes emotions from customer facial expressions and determines sales policies such as placing new products around products that show high satisfaction.

[0792] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0793] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0794] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0795] [Third embodiment]

[0796] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0797] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0798] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0799] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0800] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0801] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0802] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0803] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0804] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0805] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0806] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0807] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0808] The present invention is a system for grasping the progress of a construction site in real time and realizing flexible deposit of funds based on that progress. Specific embodiments for carrying out the present invention will be described below.

[0809] System Overview

[0810] This system uses surveillance cameras installed at the construction site to capture real-time construction progress and utilizes image recognition AI to evaluate the progress based on the video data. The system provides users with progress reports and payment instructions. The main components of the system are as follows:

[0811] Capture method: This corresponds to a surveillance camera installed on the terminal, which captures video data of the construction site in real time.

[0812] Transmission means: The terminal compresses the captured video data and transmits it to the server via the network.

[0813] Image recognition method: An image recognition AI installed on the server analyzes the transmitted video data and evaluates the progress of the construction work.

[0814] Matching method: The server compares the progress data acquired by the image recognition AI with the design document data registered in advance.

[0815] Report generation means: The server automatically generates a progress report based on the collation results.

[0816] Payment instruction means: The server sends the generated progress report to the user and instructs the user on the next payment procedure based on the report.

[0817] Specific examples of program processing

[0818] 1. Installing surveillance cameras at construction sites and collecting video data

[0819] Terminal: The surveillance camera is installed at the construction site and captures video continuously for 24 hours. The captured video data is sent to the server every 10 minutes.

[0820] 2. Video data analysis and progress check

[0821] Server: The server analyzes the received video data using image recognition AI. As a result of the analysis, progress information such as "foundation work completed" or "steel frame installed" is obtained.

[0822] 3. Verification with design document data

[0823] Server: The server compares the analyzed progress data with the design data registered in advance. The design data contains progress indicators and specifications for each construction stage. By comparing, it is evaluated whether the progress on site matches the design data.

[0824] 4. Progress report and payment instructions

[0825] User: Through a dedicated application, the user checks progress reports from the server, including progress status, issues found, and next steps to take.

[0826] Server: If the progress is as expected, the server instructs the user to proceed with the next payment procedure. The server connects to the payment system and executes instructions to transfer the funds to the specified bank account.

[0827] Usage examples

[0828] For example, at a construction site where steel frame work is underway, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and verifies that the steel frame is being assembled according to design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment stage based on that report. In this way, funds can be deposited in a timely manner in line with the progress of the work.

[0829] This system allows the progress of construction work to be accurately grasped in real time, improves the liquidity of funds, and reduces the risk of construction delays and shortages of funds.

[0830] The processing flow will be explained below.

[0831] Step 1:

[0832] The device captures images of the construction site using a surveillance camera, which records the image 24 hours a day and generates a captured image every 10 minutes.

[0833] Step 2:

[0834] The device compresses the captured video data and sends it to the server over the network, where it is encrypted for security reasons.

[0835] Step 3:

[0836] The server receives the video data sent from the device, temporarily stores the data, and adds it to a queue to await analysis.

[0837] Step 4:

[0838] The server analyzes the received video data using image recognition AI, which identifies and evaluates specific objects and scenes (e.g., completion of foundation work, installation of steel frames, etc.) to assess the progress of the construction work.

[0839] Step 5:

[0840] The server compares the progress data acquired by the image recognition AI with the design data registered in advance, thereby confirming that progress on-site is proceeding according to the design document.

[0841] Step 6:

[0842] The server automatically generates progress reports based on the results of the comparison with the design data, including progress status, degree of conformance with the design, and identified issues.

[0843] Step 7:

[0844] The server then sends the generated progress report to the user via a dedicated application, who can then check the report and understand the progress of the construction work.

[0845] Step 8:

[0846] The user checks the report and, after confirming that the progress is proceeding as planned, instructs the server to carry out the next payment procedure.

[0847] Step 9:

[0848] The server uses the payment instruction means to issue an instruction to transfer funds to the specified bank account, and the specified amount is transferred in cooperation with the payment system.

[0849] The above is the specific process flow for monitoring construction progress in real time and realizing flexible deposit of funds based on progress.

[0850] Example 1

[0851] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0852] With conventional construction site management systems, it was difficult to grasp the progress of work in real time, and there was a problem that progress-based fund management could not be adequately carried out. As a result, there was an increased risk of construction delays and fund shortages, and an effective means to improve construction efficiency was required.

[0853] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0854] In this invention, the server includes an imaging means for capturing images of the construction site, a transmission means for compressing and transmitting the image data obtained from the imaging means, an image recognition means for analyzing the image data transmitted from the transmission means, a comparison means for comparing the progress data analyzed by the image recognition means with design document data, a report generation means for generating a progress report based on the comparison results obtained by the comparison means, a payment instruction means for issuing a payment instruction for funds based on the progress report generated by the report generation means, and a means for executing payment procedures based on the payment instruction. This makes it possible to accurately grasp the progress of the construction site in real time and to manage funds in a timely manner according to the progress.

[0855] "Photographing means" refers to a device for capturing images of the construction site.

[0856] The "transmission means" is a device or function for compressing the video data obtained from the image capture means and transmitting it to the server.

[0857] The "image recognition means" is software or hardware for analyzing the video data transmitted from the transmission means and extracting construction progress data.

[0858] The "comparison means" is a function for comparing the progress data analyzed by the image recognition means with the design document data registered in advance and evaluating whether they match.

[0859] The "report generation means" is a function for automatically generating a progress report based on the collation results obtained by the collation means.

[0860] The "payment instruction means" is a function for issuing instructions to pay funds based on the progress report generated by the report generation means.

[0861] The "means for executing payment procedures" is a function for transferring funds to an account at a designated financial institution based on the payment instruction means.

[0862] The present invention is a system for grasping the progress of a construction site in real time and realizing flexible deposit of funds based on that progress. Specific embodiments for carrying out the present invention will be described below.

[0863] System Configuration

[0864] The system includes the following major components:

[0865] Recording method: This corresponds to a surveillance camera installed on the terminal, which captures video data of the construction site in real time. The surveillance camera used should be a high-resolution (e.g., 1080p) camera capable of continuous recording.

[0866] Transmission method: The terminal compresses the captured video data and sends it to the server via the network using the H.264 format and the HTTP or FTP protocol.

[0867] Image recognition method: An image recognition AI installed on the server analyzes the transmitted video data. TensorFlow and OpenCV are used for image recognition to extract specific information about the progress of construction work.

[0868] Verification method: The server compares the progress data analyzed by the image recognition method with the design document data registered in advance. The design document data is saved as a PDF or CAD file.

[0869] Report generation: The server automatically generates progress reports based on the results of the checks. The reports are created in PDF or HTML format and include charts and graphs for easy visual understanding.

[0870] Payment instruction means: The server issues a payment instruction to the user based on the generated progress report. The payment instruction is notified through a dedicated application.

[0871] Means of executing payment procedures: Based on the payment instructions, the server connects to the payment system (bank API or payment gateway) and transfers funds to the account of the specified financial institution.

[0872] Specific examples of program processing

[0873] 1. Installing surveillance cameras at construction sites and collecting video data

[0874] Terminal: A surveillance camera is installed at the construction site and captures video continuously for 24 hours. The captured video data is compressed every 10 minutes and sent to the server.

[0875] 2. Video data analysis and progress check

[0876] Server: The server uses image recognition AI to analyze the video data sent from the device. As a result of the analysis, progress information such as "foundation work completed" and "steel frame installed" is obtained.

[0877] 3. Verification with design document data

[0878] Server: The server compares the analyzed progress data with the design data registered in advance. The design data contains progress indicators and specifications for each construction stage. By comparing, it is evaluated whether the progress on site matches the design data.

[0879] 4. Progress report and payment instructions

[0880] User: Through a dedicated application, the user checks progress reports from the server, including progress status, issues found, and next steps to take.

[0881] Server: If the progress is as expected, the server instructs the user to proceed with the next payment procedure. The server then connects to the payment system and executes a transfer instruction to the specified bank account.

[0882] Usage examples

[0883] For example, at a construction site where steel frame work is underway, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and verifies that the steel frame is being assembled according to the design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment stage based on that information.

[0884] Example prompts for generative AI models

[0885] plaintext

[0886] prompt:

[0887] Analyze the following security camera footage and report on the progress of the site. The footage includes the following information: foundation work, steel frame installation, and concrete pouring. Evaluate whether each step has been completed and generate a progress report.

[0888] Video data:

[0889] [Video data link or file name]

[0890] This system allows the progress of construction work to be accurately grasped in real time, improves the liquidity of funds, and reduces the risk of construction delays and shortages of funds.

[0891] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0892] Step 1:

[0893] Installation and initial setup of surveillance cameras at construction sites

[0894] Terminal: Install the surveillance camera in an appropriate location. After installation, set the shooting interval and resolution. For example, set the shooting interval to 10 minutes and the resolution to 1080p.

[0895] Input: installation location, shooting interval, resolution settings.

[0896] Output: Configured security cameras.

[0897] Specific operation: After the setup is complete, the device will put the camera into continuous shooting mode.

[0898] Step 2:

[0899] Video data capture and compression

[0900] Terminal: Capture video at set intervals. For example, capture image data every 10 minutes. Compress the captured data in H.264 format.

[0901] Input: Shooting interval, resolution setting.

[0902] Output: Compressed video data.

[0903] How it works: The device captures video every 10 minutes and compresses it in real time.

[0904] Step 3:

[0905] Video data transmission

[0906] Terminal: Compressed video data is sent to the server using HTTP or FTP protocol.

[0907] Input: Compressed video data.

[0908] Output: Video data sent to the server.

[0909] Specific operation: The device starts transmitting video data to the server in real time.

[0910] Step 4:

[0911] Video data analysis

[0912] Server: Analyzes the received video data using image recognition AI (such as TensorFlow or OpenCV) and extracts information about the progress of construction work.

[0913] Input: Received video data.

[0914] Output: Progress data.

[0915] Specific operation: The server runs an image recognition model to identify the object (e.g., "foundation work completed" or "steel frame installed").

[0916] Step 5:

[0917] Checking progress data against design document data

[0918] Server: Compares the progress data with pre-registered design data (PDF or CAD files) and derives the comparison results.

[0919] Input: Progress data, design document data.

[0920] Output: Matching results.

[0921] What happens: The server compares the progress data with the design document, evaluates the degree of agreement, and generates a result.

[0922] Step 6:

[0923] Generate progress reports

[0924] Server: Generates a progress report based on the collation results, including progress, issues, and next actions.

[0925] Input: Match result.

[0926] Output: Progress report.

[0927] What it does: The server generates progress reports in PDF or HTML format, including charts for easy visual understanding.

[0928] Step 7:

[0929] Progress report notification and payment instructions

[0930] Server: Generates progress reports and sends them to the user, notifying them through a dedicated application.

[0931] Input: Progress report.

[0932] Output: Report and payment instructions sent to user.

[0933] Specific operation: The server uses the notification function to send a report to the user and provide payment instructions.

[0934] Step 8:

[0935] Execute payment procedures

[0936] Server: Based on the payment instructions, the server transfers funds to the financial institution's account using the bank API or payment gateway.

[0937] Input: Payment instructions.

[0938] Output: The completed payment transaction.

[0939] Specific operation: The server executes the transfer instruction to the specified account via API, confirms the completion notification, and records it.

[0940] (Application example 1)

[0941] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0942] Construction and production sites require real-time monitoring of progress and accurate information collection. This allows for smooth material supply and financial management according to progress, resulting in efficient construction and production. However, with current technology, these processes are often carried out manually, consuming a great deal of time and human resources. Furthermore, delays and incorrect reporting in progress management are prone to occur, which also impacts material supply and financial management. A system that can effectively address these issues is needed.

[0943] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0944] In this invention, the server includes an imaging means for capturing images of a construction site or production site, a transmission means for transmitting image data obtained from the imaging means, an image recognition means for analyzing the image data transmitted from the transmission means, a comparison means for comparing progress data analyzed by the image recognition means with design document data or production plan data, a report generation means for generating a progress report based on the comparison results obtained by the comparison means, and an instruction means for issuing instructions for material supply and payment of funds based on the progress report generated by the report generation means. This enables real-time monitoring of progress and realizes timely material supply and fund management.

[0945] A "construction site or production site" is a physical location where construction work or product production takes place and where progress management is required.

[0946] "Photographing means" refers to a device that captures images of the scene in real time, such as a surveillance camera or a video capture device.

[0947] The "transmission means" refers to a communication device or network means for transmitting the video data acquired by the image capture means to the server.

[0948] "Image recognition means" refers to software or AI models that analyze transmitted video data and automatically assess progress on-site.

[0949] "Design document data or production plan data" refers to standard data registered in advance for managing the progress of construction or production, and includes the completion conditions and specifications for each process.

[0950] The "verification means" is software or a system for comparing and collating the progress data analyzed by the image recognition means with the design document data or production plan data.

[0951] The "report generation means" is software or a system for automatically generating a progress report based on the collation results obtained by the collation means.

[0952] An "instruction means" is software or a system for taking actions such as issuing instructions for supplying materials or disbursing funds based on the generated progress report.

[0953] "Materials supply" is the process of automatically replenishing the necessary materials at the site according to progress.

[0954] "Cash management" is the process of depositing and transferring funds according to the progress of construction or production.

[0955] The present invention is a system for monitoring the progress of a construction or production site in real time and automating material supply and financial management based on the progress. Specific embodiments for carrying out the present invention will be described below.

[0956] System Configuration

[0957] The system consists of the following elements:

[0958] 1. Filming methods (surveillance cameras): Cameras installed on-site capture the progress of construction and production in real time. For example, IP cameras are used.

[0959] 2. Transmission method (network communication): Video data captured by the surveillance camera is compressed and sent to the server via the network.

[0960] 3. Image recognition method (image recognition AI): The server uses image recognition software such as TensorFlow or OpenCV's DNN model to analyze this video data.

[0961] 4. Verification means: The server compares the progress data analyzed by the image recognition means with the design data or production plan data. The design data and production plan data are registered in advance and include progress indicators and specifications for each process.

[0962] 5. Report Generation: The server automatically generates a progress report based on the verification results, including the progress status, any issues found, and next steps to take.

[0963] 6. Instruction means: The server issues instructions for supplying materials and paying funds based on the generated progress report. For example, it works with an automatic material ordering system or a fund transfer system between bank accounts.

[0964] Operation overview

[0965] The server uses image recognition AI to process video data sent from surveillance cameras and analyze progress in real time. The analyzed data is compared with design document data or production plan data, and a progress report is generated based on the results of the comparison. The generated report is provided to the user, who then instructs them on the supply of materials and payment of funds required for the next step.

[0966] Typical processing examples

[0967] For example, on a production line, it recognizes when a part on a conveyor belt reaches a designated position. A surveillance camera monitors the position, and image recognition AI confirms that the part has arrived. This triggers the next processing step and reports its progress to a server.

[0968] Prompt example

[0969] Design an AI system that uses real-time video footage from a surveillance camera to recognize parts as they arrive at a specified position and automatically trigger the next machining step. The system will send progress reports with timestamps and image data to a server. The image recognition AI model used should include TensorFlow.

[0970] In this way, the system of the present invention provides real-time monitoring of progress and automation of material supply and cash management.

[0971] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0972] Step 1:

[0973] A surveillance camera captures video of a construction or production site in real time. The input is the video data captured through the surveillance camera lens, and the output is the video data.

[0974] Step 2:

[0975] The terminal (computer inside the surveillance camera) compresses the captured video data and sends it to the server via the network. The input is raw video data, which is converted into data for network transmission through data compression, and the output is compressed video data.

[0976] Step 3:

[0977] The server receives the transmitted video data and inputs it into the image recognition AI. The input is compressed video data, which is decoded and preprocessed, and the output is preprocessed data for analysis.

[0978] Step 4:

[0979] The server uses an image recognition AI model to analyze progress information from the preprocessed data. The input is the preprocessed image data, and the AI ​​model extracts progress information. The output is the analyzed progress information.

[0980] Step 5:

[0981] The server compares the analyzed progress information with the design data or production plan data registered in advance. The input is the progress information and design data (or production plan data), and a comparison operation is performed using a matching algorithm. The output is the matching result.

[0982] Step 6:

[0983] The server generates a progress report based on the matching results. The input is the matching results, and the progress report is created by a report generation algorithm. The output is the progress report.

[0984] Step 7:

[0985] The server issues instructions for supplying materials and paying funds based on the generated progress report. The input is the progress report, and instructions are executed in cooperation with the material ordering system and bank transfer system. The output is material ordering information and instructions for transferring funds.

[0986] Step 8:

[0987] The user checks the progress report using a dedicated application. The input is the progress report sent from the server, and the report is displayed through the application. The output is the progress information checked by the user.

[0988] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0989] The present invention combines a system that monitors the progress of a construction site in real time and enables flexible deposit of funds based on that progress with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0990] System Overview

[0991] This system uses surveillance cameras installed at the construction site to capture real-time construction progress and utilizes image recognition AI to evaluate the progress based on the video data.It also has an emotion engine that recognizes the user's emotional state and provides appropriate feedback and adjustments to support user decision-making.The main components of the system are as follows:

[0992] Capture method: This corresponds to a surveillance camera installed on the terminal, which captures video data of the construction site in real time.

[0993] Transmission means: The terminal compresses the captured video data and transmits it to the server via the network.

[0994] Image recognition method: An image recognition AI installed on the server analyzes the transmitted video data and evaluates the progress of the construction work.

[0995] Matching method: The server compares the progress data acquired by the image recognition AI with the design document data registered in advance.

[0996] Report generation means: The server automatically generates a progress report based on the collation results.

[0997] Payment instruction means: The server sends the generated progress report to the user and instructs the user on the next payment procedure based on the report.

[0998] Emotion engine: The server also has an emotion engine that recognizes the user's emotions and adjusts the content of progress reports, notification methods, and payment instruction procedures.

[0999] Specific examples of program processing

[1000] 1. Installing surveillance cameras at construction sites and collecting video data

[1001] Terminal: The surveillance camera is installed at the construction site and captures video continuously for 24 hours. The captured video data is sent to the server every 10 minutes.

[1002] 2. Video data analysis and progress check

[1003] Server: The server analyzes the received video data using image recognition AI. As a result of the analysis, progress information such as "foundation work completed" or "steel frame installed" is obtained.

[1004] 3. Verification with design document data

[1005] Server: The server compares the analyzed progress data with the design data registered in advance. The design data contains progress indicators and specifications for each construction stage. By comparing, it is evaluated whether the progress on site matches the design data.

[1006] 4. Progress report and payment instructions

[1007] User: Through a dedicated application, the user checks progress reports from the server, including progress status, issues found, and next steps to take.

[1008] Server: If the progress is as expected, the server instructs the user to proceed with the next payment procedure. The server connects to the payment system and executes instructions to transfer the funds to the specified bank account.

[1009] 5. User Emotion Recognition and Feedback

[1010] Emotion Engine: The server is equipped with an emotion engine that analyzes feedback (e.g., facial expressions, tone of voice, etc.) when a user views a report. The emotion engine understands the user's emotional state and adjusts the next report and notification method accordingly.

[1011] User: If a user encounters an unexpected problem while viewing a report, the emotion engine will detect the situation and suggest the next course of action to the server.

[1012] Usage examples

[1013] For example, at a construction site where steel frame work is underway, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and verifies that the steel frame is being assembled according to design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment stage based on that report. Furthermore, if the user displays a relieved expression while viewing the report, the emotion engine will set the next report to be in the same format. Conversely, if the user displays a dissatisfied or suspicious expression, the engine will adjust the report to include more detailed information and supplementary explanations.

[1014] This system allows the progress of construction sites to be accurately grasped in real time, improves the liquidity of funds, reduces the risk of construction delays and funding shortages, and emotionally supports the user's decision-making, enabling smoother project management.The above are specific modes for carrying out the present invention.

[1015] The processing flow will be explained below.

[1016] Step 1:

[1017] The device captures images of the construction site using a surveillance camera, which records video continuously 24 hours a day and generates a captured image every 10 minutes.

[1018] Step 2:

[1019] The device compresses the captured video data and transmits it over the network to a server, where the transmission is encrypted for security purposes.

[1020] Step 3:

[1021] The server receives the video data sent from the device, temporarily stores the data, and adds it to a queue to await analysis.

[1022] Step 4:

[1023] The server analyzes the received video data using image recognition AI, where the AI ​​algorithm identifies specific objects and scenes (e.g., foundation work completed, steel frame installation, etc.) to assess the progress of the construction work.

[1024] Step 5:

[1025] The server compares the progress data acquired by the image recognition AI with the design data registered in advance, thereby confirming that progress on-site is proceeding according to the design document.

[1026] Step 6:

[1027] The server automatically generates progress reports based on the results of the comparison with the design data, including progress status, degree of conformance with the design, and identified issues.

[1028] Step 7:

[1029] The server then sends the generated progress report to the user via a dedicated application, who can then check the report and understand the progress of the construction work.

[1030] Step 8:

[1031] The user checks the report and, after confirming that the progress is proceeding as planned, instructs the server to carry out the next payment procedure.

[1032] Step 9:

[1033] The server uses the payment instruction means to issue an instruction to transfer funds to the specified bank account, and the specified amount is transferred in cooperation with the payment system.

[1034] Step 10:

[1035] The emotion engine installed on the server analyzes the user's facial expressions and tone of voice when viewing the report to understand the user's emotional state. For example, if the user shows a relieved expression or tone of voice, the emotion engine records that information.

[1036] Step 11:

[1037] The emotion engine will adjust the content and notification of the next report based on the user's emotional state. For example, if it detects expressions of anxiety or doubt, it will add more details or additional explanations to the next report.

[1038] Step 12:

[1039] The server improves the progress reports and notification methods as appropriate based on feedback from the emotion engine, thereby increasing user satisfaction.

[1040] This series of steps allows the progress of construction to be tracked quickly and accurately, and provides appropriate feedback and support according to the user's emotional state, enabling flexible funding and smooth project management.

[1041] Example 2

[1042] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1043] Construction site progress management is difficult to grasp in real time, which often leads to delays in appropriate fund deposits and plan changes based on progress status. Furthermore, one-sided reports and notifications are given without considering the user's emotional state, which can lead to stress and frustration in user decision-making. A system that solves these problems and improves the efficiency of construction progress management and fund management, as well as user satisfaction, is needed.

[1044] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1045] In this invention, the server includes an imaging means for capturing video of the construction site, a transmission means for transmitting the video data obtained from the imaging means, an image recognition means for analyzing the video data transmitted from the transmission means, a comparison means for comparing the progress data analyzed by the image recognition means with design document data, a report generation means for generating a progress report based on the comparison result obtained by the comparison means, a payment instruction means for issuing a payment instruction based on the progress report generated by the report generation means, an emotion recognition means for recognizing a user's emotion when checking the progress report, and an adjustment means for adjusting the content of the report or the notification method based on the user's emotional state obtained by the emotion recognition means. This makes it possible to accurately grasp the progress of the construction site in real time, improve the liquidity of funds, and enable flexible feedback and decision-making support based on the user's emotional state.

[1046] "Photographing means" refers to a device that captures images of the construction site.

[1047] The "transmission means" is a device that compresses the video data obtained from the image capture means and transmits it to the server via the network.

[1048] The "image recognition means" is a device that uses image recognition technology to analyze received video data and evaluate the progress of construction work.

[1049] The "verification means" is a device or system that verifies the progress data analyzed by the image recognition means with the design document data registered in advance.

[1050] The "report generation means" is a device or system that generates a progress report based on the collation results obtained by the collation means.

[1051] The "payment instruction means" is a device or system that issues instructions for payment of funds based on the progress report generated by the report generation means.

[1052] The "emotion recognition means" is a device or system that analyzes feedback data such as facial expressions and voice in order to recognize the user's emotions.

[1053] The "adjustment means" is a device or system that adjusts the content of the report or the notification method based on the emotional state of the user obtained by the emotion recognition means.

[1054] The present invention combines a system that monitors the progress of a construction site in real time and enables flexible deposit of funds based on that progress with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing the present invention will be described below.

[1055] System Overview

[1056] This system uses surveillance cameras installed at the construction site to capture real-time construction progress and utilizes image recognition AI to evaluate the progress based on the video data.It also has an emotion engine that recognizes the user's emotional state and provides appropriate feedback and adjustments to support user decision-making.The main components of the system are as follows:

[1057] Filming method

[1058] Terminal: Surveillance cameras are installed at the construction site and capture video continuously for 24 hours. For example, a surveillance camera monitors a specific area and collects video data every 10 minutes.

[1059] Transmission method

[1060] Terminal: The acquired video data is compressed using a compression algorithm (e.g., H.264 or HEVC) to reduce the data size and sent to the server via the network.

[1061] Image Recognition Method

[1062] Server: Uses image recognition AI (e.g., TensorFlow or PyTorch) to analyze the transmitted video data. Through analysis, the progress of construction work (e.g., "foundation work completed" or "steel frame installed") is automatically detected.

[1063] Matching method

[1064] Server: Compares progress data analyzed by image recognition with pre-registered design data. The design data includes each construction process and progress indicators, and by comparing them, it evaluates whether on-site progress is consistent with the plan.

[1065] Report Generation Method

[1066] Server: Based on the results of the check, a progress report is automatically generated. The report includes the current construction status, any issues found, and action items for moving to the next stage. The generated report is then sent to the user.

[1067] Payment Instruction Method

[1068] Server: If the progress is as expected, instruct the user to carry out the payment procedure, which involves executing instructions to transfer the funds to the specified account based on the progress report.

[1069] emotion recognition means

[1070] Server: The emotion engine analyzes the feedback (e.g., facial expressions, tone of voice, etc.) of users viewing reports. It analyzes their emotional state and adjusts the content and notification method of the next report.

[1071] Adjustment means

[1072] Server: Adjust the content of the report or notification method based on the user's emotional state obtained through emotion recognition. For example, if the user shows a relieved expression, the same brief report format will be used next time, but if the user shows a dissatisfied expression, a detailed explanation will be added.

[1073] Specific examples

[1074] For example, at a construction site where steel frame work is being carried out, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and confirms that the steel frame is being assembled according to design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment step. Furthermore, if the user shows a relieved expression while viewing the report, the emotion engine will set the next report to be sent in the same format.

[1075] Prompt Sentence Examples

[1076] Here is an example of how to use the prompt:

[1077] plaintext

[1078] Please explain about a system that monitors the progress of a construction site in real time and manages the deposit of funds according to progress. This system also has a function to recognize the user's emotions. Specifically, video of the construction site is captured by a surveillance camera, and the server analyzes the progress using image recognition AI. The analysis results are then compared with the design document data and a progress report is sent to the user. Please also explain how the content of the next report is adjusted based on the user's emotional feedback.

[1079] The above is a specific embodiment for carrying out the present invention.

[1080] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1081] Step 1:

[1082] Surveillance camera footage capture

[1083] Terminal: A surveillance camera installed at the construction site captures video 24 hours a day. Specifically, the camera's sensor monitors a specific area and collects video data every 10 minutes.

[1084] Input: Real-time footage from the scene.

[1085] Output: Video files collected every 10 minutes.

[1086] Step 2:

[1087] Video data compression and transmission

[1088] Terminal: The collected video data is compressed using a compression algorithm (e.g., H.264 or HEVC) to reduce the data size and sent to the server via the network.

[1089] Input: Raw video data (high resolution).

[1090] Output: Compressed video data (low resolution).

[1091] Step 3:

[1092] Analyzing video data and obtaining progress information

[1093] Server: The transmitted video data is analyzed using image recognition AI (e.g., TensorFlow or PyTorch). For example, the progress of construction work can be automatically detected from the images (e.g., "foundation work completed" or "steel frame installed").

[1094] Input: Compressed video data.

[1095] Output: Specific progress information (e.g., steel construction completed).

[1096] Step 4:

[1097] Verification with design document data

[1098] Server: The progress data analyzed by the image recognition method is compared with the design document data registered in advance. The design document contains each construction process and progress indicators, and the server performs the comparison based on this.

[1099] Input: Analyzed progress data. Pre-registered design document data.

[1100] Output: Progress evaluation result (whether it matches the design document or not).

[1101] Step 5:

[1102] Generate and send progress reports

[1103] Server: Generates a progress report based on the results of the check. The report includes the current construction status, any issues found, and action items for moving forward to the next stage. The generated report is sent to the user's application.

[1104] Input: Progress assessment results.

[1105] Output: Progress report.

[1106] Step 6:

[1107] Instructions for disbursement of funds

[1108] Server: Based on the progress report, instructs the user to pay the funds. Specifically, if the progress is as planned, executes instructions to transfer funds to the specified bank account.

[1109] Input: Progress report.

[1110] Output: Instructions for disbursement of funds.

[1111] Step 7:

[1112] User Emotion Recognition and Feedback

[1113] Server: Analyzes the feedback (facial expressions and voice) of users viewing reports using emotion recognition means to recognize their emotional state. For example, facial expression analysis technology or voice analysis technology is used.

[1114] Input: User feedback data (facial expressions, voice, etc.).

[1115] Output: The user's emotional state.

[1116] Step 8:

[1117] Adjustment of reporting content and notification methods

[1118] Server: Based on the emotional state obtained by the emotion recognition means, adjust the content of the next report and notification method. For example, if the user feels relieved, provide a concise report, but if the user feels anxious, provide a detailed explanation.

[1119] Input: The user's emotional state.

[1120] Output: Coordinated reporting and notification methods.

[1121] The above is a specific processing flow of the program of this system.

[1122] (Application example 2)

[1123] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1124] With conventional technologies, it has been difficult to accurately grasp the progress of construction sites in real time or to recognize users' emotions and provide appropriate feedback and adjustments. Furthermore, in physical stores, there is a need for optimization of operations, such as real-time monitoring of store layout and inventory, and adjustment of sales policies based on customers' emotional states. In response to these challenges, the present invention aims to provide a management system that combines progress and emotions at both construction sites and physical stores, thereby optimizing financial management and sales policies.

[1125] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1126] In this invention, the server includes: a photographing means for capturing video of the construction site; a transmitting means for transmitting the video data obtained from the photographing means; an image recognition means for analyzing the video data transmitted from the transmitting means; a comparison means for comparing the progress data analyzed by the image recognition means with design document data; a report generation means for generating a progress report based on the comparison results obtained by the comparison means; a payment instruction means for issuing a payment instruction based on the progress report generated by the report generation means; an emotion engine means for recognizing the user's emotional state and adjusting the content of the progress report, the notification method, and the payment instruction procedure; and a store management means for monitoring the layout and inventory of the physical store in real time and adjusting business policies based on customer emotions. This not only enables real-time monitoring of the progress of the construction site and the operation of the physical store, but also enables optimal feedback and adjustments based on the emotions of users and customers.

[1127] "Photography means" refers to a device used to capture images of construction sites and brick-and-mortar stores.

[1128] The "transmission means" is a device or system for transmitting the video data obtained from the image capture means to the server.

[1129] The "image recognition means" is an artificial intelligence or software that analyzes the video data transmitted from the transmission means and extracts progress data.

[1130] The "comparison means" is a system for comparing the progress data analyzed by the image recognition means with pre-set design document data to confirm a match.

[1131] The "report generation means" is software for automatically generating a progress report based on the results obtained by the collation means.

[1132] The "payment instruction means" is a system for instructing payment of funds based on the progress report generated by the report generation means.

[1133] The "emotion engine means" is an artificial intelligence that recognizes the user's emotional state and adjusts the content and notification method of the progress report.

[1134] "Store management tools" are systems that monitor the layout and inventory of physical stores in real time and adjust sales policies based on customer sentiment.

[1135] This invention is a system for supporting the operation of construction sites and brick-and-mortar stores. The system supports more effective decision-making by combining real-time monitoring of progress status with user emotion recognition.

[1136] System Overview

[1137] The server is implemented using the following hardware and software:

[1138] Capture method: Surveillance cameras installed at construction sites and brick-and-mortar stores capture video data.

[1139] Transmission means: Transmits the video data captured by the imaging means to the server via the network.

[1140] Image recognition means: The server analyzes the received video data using an image processing library such as OpenCV. This analysis process detects progress and product placement.

[1141] Matching method: Progress data is compared with pre-designed data and pre-set targets to determine the degree of agreement.

[1142] Report generation method: Progress reports are automatically generated based on the matching results. The reports are notified to the user and used as information for making decisions about fund management.

[1143] Payment Instructions: Based on progress reports, instructions for the next payment of funds will be issued, including instructions for transfer to bank accounts.

[1144] Emotion engine means: An emotion engine is built in to analyze facial expressions and voice when a user is viewing a report and recognize the user's emotional state, which then adjusts the content of the report and the notification method.

[1145] Store management tools: Monitor store layout and inventory in real time and adjust sales policies based on customer sentiment.

[1146] Example of a system

[1147] 1. Construction site progress management:

[1148] Security cameras installed at the construction site capture images every 10 minutes and send them to a server. The server uses image recognition technology to analyze the progress data and compare it with the data in the design documents. A progress report is generated, and if the work is progressing as planned, instructions are issued for the next payment of funds.

[1149] 2. Physical store operations management:

[1150] Cameras are used to monitor changes to store layouts and inventory checks within physical stores, and an emotion recognition engine is used to analyze customer movements and facial expressions. This allows the company to determine whether customers are interested in or satisfied with products and adjust sales policies accordingly. For example, if a customer is looking with great interest at a new product, the product will be relocated to a more prominent location.

[1151] The specific hardware and software used

[1152] Hardware: Built-in cameras in laptops and smartphones

[1153] Software: OpenCV (image processing library), Scikit-learn (to train the emotion recognition model)

[1154] Prompt Sentence Examples

[1155] Examples of prompts to generate specific AI models are:

[1156] "Create a system that analyzes footage from cameras installed in physical stores and checks whether product placement is complete. Also, explain how to recognize customers' emotional states from their facial expressions in the store and optimize sales feedback."

[1157] In this way, the present invention is a system that enables efficient progress management and improved customer experience both at construction sites and in physical stores.

[1158] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1159] Step 1:

[1160] The terminal captures video data using surveillance cameras installed at construction sites and brick-and-mortar stores. The input is real-time video data captured by the surveillance cameras, and the output is the captured video data. Specifically, the cameras automatically capture video every 10 minutes and store it in local storage.

[1161] Step 2:

[1162] The terminal compresses the captured video data and sends it to the server via the network. The input is the captured video data, and the output is the compressed video data. Specifically, the video data is compressed using a compression algorithm such as H.264 and uploaded to the server via the HTTP protocol.

[1163] Step 3:

[1164] The server receives the transmitted video data and analyzes it using image recognition means. The input is the transmitted compressed video data, and the output is the analyzed progress data or product placement status. Specifically, the server decodes the video data using OpenCV and extracts progress and inventory status using an object detection algorithm.

[1165] Step 4:

[1166] The server compares the progress data obtained by the image recognition means with the design document data and pre-set target data. The input is the analyzed progress data and design document data, and the output is the comparison result. Specifically, it uses a Python data analysis library to compare the progress data with the database and calculate the degree of match.

[1167] Step 5:

[1168] The server generates a progress report based on the matching results. The input is the matching results, and the output is the progress report. Specifically, the server converts the matching results into a text format and automatically generates a report containing the necessary information.

[1169] Step 6:

[1170] The server issues a payment instruction based on the generated progress report. The input is the progress report, and the output is the execution of the payment instruction. Specifically, it works with the bank API to execute a transfer instruction to the specified bank account.

[1171] Step 7:

[1172] The server uses an emotion engine to analyze facial expressions and voice data when the user is viewing a report and recognizes the user's emotional state. The input is the facial expressions and voice data when viewing the report, and the output is the recognized emotional state. Specifically, the emotion engine uses a deep learning model to analyze the input data and output an emotion label.

[1173] Step 8:

[1174] The server adjusts the content and notification method of the next report based on the user's emotional state. The input is the recognized emotional state, and the output is the adjusted content and notification method of the next report. Specifically, if the user expresses dissatisfaction, the server applies a setting to add detailed explanations and supplementary information.

[1175] Step 9:

[1176] In a physical store, the terminal monitors the store layout and inventory and sends the data to a server. The input is video data captured by a surveillance camera inside the store, and the output is compressed video data. Specifically, the camera periodically captures the status of shelves and inventory and sends this data to the server.

[1177] Step 10:

[1178] The server analyzes store layout and inventory data and adjusts sales policies based on customer emotions. The input is in-store video data and customer emotion data, and the output is the adjusted sales policies. Specifically, the emotion engine recognizes emotions from customer facial expressions and determines sales policies such as placing new products around products that show high satisfaction.

[1179] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1180] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1181] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1182] [Fourth embodiment]

[1183] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1184] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1185] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1186] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1187] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1189] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1190] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1191] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1192] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1193] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1194] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1195] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1196] The present invention is a system for grasping the progress of a construction site in real time and realizing flexible deposit of funds based on that progress. Specific embodiments for carrying out the present invention will be described below.

[1197] System Overview

[1198] This system uses surveillance cameras installed at the construction site to capture real-time construction progress and utilizes image recognition AI to evaluate the progress based on the video data. The system provides users with progress reports and payment instructions. The main components of the system are as follows:

[1199] Capture method: This corresponds to a surveillance camera installed on the terminal, which captures video data of the construction site in real time.

[1200] Transmission means: The terminal compresses the captured video data and transmits it to the server via the network.

[1201] Image recognition method: An image recognition AI installed on the server analyzes the transmitted video data and evaluates the progress of the construction work.

[1202] Matching method: The server compares the progress data acquired by the image recognition AI with the design document data registered in advance.

[1203] Report generation means: The server automatically generates a progress report based on the collation results.

[1204] Payment instruction means: The server sends the generated progress report to the user and instructs the user on the next payment procedure based on the report.

[1205] Specific examples of program processing

[1206] 1. Installing surveillance cameras at construction sites and collecting video data

[1207] Terminal: The surveillance camera is installed at the construction site and captures video continuously for 24 hours. The captured video data is sent to the server every 10 minutes.

[1208] 2. Video data analysis and progress check

[1209] Server: The server analyzes the received video data using image recognition AI. As a result of the analysis, progress information such as "foundation work completed" or "steel frame installed" is obtained.

[1210] 3. Verification with design document data

[1211] Server: The server compares the analyzed progress data with the design data registered in advance. The design data contains progress indicators and specifications for each construction stage. By comparing, it is evaluated whether the progress on site matches the design data.

[1212] 4. Progress report and payment instructions

[1213] User: Through a dedicated application, the user checks progress reports from the server, including progress status, issues found, and next steps to take.

[1214] Server: If the progress is as expected, the server instructs the user to proceed with the next payment procedure. The server connects to the payment system and executes instructions to transfer the funds to the specified bank account.

[1215] Usage examples

[1216] For example, at a construction site where steel frame work is underway, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and verifies that the steel frame is being assembled according to design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment stage based on that report. In this way, funds can be deposited in a timely manner in line with the progress of the work.

[1217] This system allows the progress of construction work to be accurately grasped in real time, improves the liquidity of funds, and reduces the risk of construction delays and shortages of funds.

[1218] The processing flow will be explained below.

[1219] Step 1:

[1220] The device captures images of the construction site using a surveillance camera, which records the image 24 hours a day and generates a captured image every 10 minutes.

[1221] Step 2:

[1222] The device compresses the captured video data and sends it to the server over the network, where it is encrypted for security reasons.

[1223] Step 3:

[1224] The server receives the video data sent from the device, temporarily stores the data, and adds it to a queue to await analysis.

[1225] Step 4:

[1226] The server analyzes the received video data using image recognition AI, which identifies and evaluates specific objects and scenes (e.g., completion of foundation work, installation of steel frames, etc.) to assess the progress of the construction work.

[1227] Step 5:

[1228] The server compares the progress data acquired by the image recognition AI with the design data registered in advance, thereby confirming that progress on-site is proceeding according to the design document.

[1229] Step 6:

[1230] The server automatically generates progress reports based on the results of the comparison with the design data, including progress status, degree of conformance with the design, and identified issues.

[1231] Step 7:

[1232] The server then sends the generated progress report to the user via a dedicated application, who can then check the report and understand the progress of the construction work.

[1233] Step 8:

[1234] The user checks the report and, after confirming that the progress is proceeding as planned, instructs the server to carry out the next payment procedure.

[1235] Step 9:

[1236] The server uses the payment instruction means to issue an instruction to transfer funds to the specified bank account, and the specified amount is transferred in cooperation with the payment system.

[1237] The above is the specific process flow for monitoring construction progress in real time and realizing flexible deposit of funds based on progress.

[1238] Example 1

[1239] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1240] With conventional construction site management systems, it was difficult to grasp the progress of work in real time, and there was a problem that progress-based fund management could not be adequately carried out. As a result, there was an increased risk of construction delays and fund shortages, and an effective means to improve construction efficiency was required.

[1241] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1242] In this invention, the server includes an imaging means for capturing images of the construction site, a transmission means for compressing and transmitting the image data obtained from the imaging means, an image recognition means for analyzing the image data transmitted from the transmission means, a comparison means for comparing the progress data analyzed by the image recognition means with design document data, a report generation means for generating a progress report based on the comparison results obtained by the comparison means, a payment instruction means for issuing a payment instruction for funds based on the progress report generated by the report generation means, and a means for executing payment procedures based on the payment instruction. This makes it possible to accurately grasp the progress of the construction site in real time and to manage funds in a timely manner according to the progress.

[1243] "Photographing means" refers to a device for capturing images of the construction site.

[1244] The "transmission means" is a device or function for compressing the video data obtained from the image capture means and transmitting it to the server.

[1245] The "image recognition means" is software or hardware for analyzing the video data transmitted from the transmission means and extracting construction progress data.

[1246] The "comparison means" is a function for comparing the progress data analyzed by the image recognition means with the design document data registered in advance and evaluating whether they match.

[1247] The "report generation means" is a function for automatically generating a progress report based on the collation results obtained by the collation means.

[1248] The "payment instruction means" is a function for issuing instructions to pay funds based on the progress report generated by the report generation means.

[1249] The "means for executing payment procedures" is a function for transferring funds to an account at a designated financial institution based on the payment instruction means.

[1250] The present invention is a system for grasping the progress of a construction site in real time and realizing flexible deposit of funds based on that progress. Specific embodiments for carrying out the present invention will be described below.

[1251] System Configuration

[1252] The system includes the following major components:

[1253] Recording method: This corresponds to a surveillance camera installed on the terminal, which captures video data of the construction site in real time. The surveillance camera used should be a high-resolution (e.g., 1080p) camera capable of continuous recording.

[1254] Transmission method: The terminal compresses the captured video data and sends it to the server via the network using the H.264 format and the HTTP or FTP protocol.

[1255] Image recognition method: An image recognition AI installed on the server analyzes the transmitted video data. TensorFlow and OpenCV are used for image recognition to extract specific information about the progress of construction work.

[1256] Verification method: The server compares the progress data analyzed by the image recognition method with the design document data registered in advance. The design document data is saved as a PDF or CAD file.

[1257] Report generation: The server automatically generates progress reports based on the results of the checks. The reports are created in PDF or HTML format and include charts and graphs for easy visual understanding.

[1258] Payment instruction means: The server issues a payment instruction to the user based on the generated progress report. The payment instruction is notified through a dedicated application.

[1259] Means of executing payment procedures: Based on the payment instructions, the server connects to the payment system (bank API or payment gateway) and transfers funds to the account of the specified financial institution.

[1260] Specific examples of program processing

[1261] 1. Installing surveillance cameras at construction sites and collecting video data

[1262] Terminal: A surveillance camera is installed at the construction site and captures video continuously for 24 hours. The captured video data is compressed every 10 minutes and sent to the server.

[1263] 2. Video data analysis and progress check

[1264] Server: The server uses image recognition AI to analyze the video data sent from the device. As a result of the analysis, progress information such as "foundation work completed" and "steel frame installed" is obtained.

[1265] 3. Verification with design document data

[1266] Server: The server compares the analyzed progress data with the design data registered in advance. The design data contains progress indicators and specifications for each construction stage. By comparing, it is evaluated whether the progress on site matches the design data.

[1267] 4. Progress report and payment instructions

[1268] User: Through a dedicated application, the user checks progress reports from the server, including progress status, issues found, and next steps to take.

[1269] Server: If the progress is as expected, the server instructs the user to proceed with the next payment procedure. The server then connects to the payment system and executes a transfer instruction to the specified bank account.

[1270] Usage examples

[1271] For example, at a construction site where steel frame work is underway, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and verifies that the steel frame is being assembled according to the design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment stage based on that information.

[1272] Example prompts for generative AI models

[1273] plaintext

[1274] prompt:

[1275] Analyze the following security camera footage and report on the progress of the site. The footage includes the following information: foundation work, steel frame installation, and concrete pouring. Evaluate whether each step has been completed and generate a progress report.

[1276] Video data:

[1277] [Video data link or file name]

[1278] This system allows the progress of construction work to be accurately grasped in real time, improves the liquidity of funds, and reduces the risk of construction delays and shortages of funds.

[1279] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1280] Step 1:

[1281] Installation and initial setup of surveillance cameras at construction sites

[1282] Terminal: Install the surveillance camera in an appropriate location. After installation, set the shooting interval and resolution. For example, set the shooting interval to 10 minutes and the resolution to 1080p.

[1283] Input: installation location, shooting interval, resolution settings.

[1284] Output: Configured security cameras.

[1285] Specific operation: After the setup is complete, the device will put the camera into continuous shooting mode.

[1286] Step 2:

[1287] Video data capture and compression

[1288] Terminal: Capture video at set intervals. For example, capture image data every 10 minutes. Compress the captured data in H.264 format.

[1289] Input: Shooting interval, resolution setting.

[1290] Output: Compressed video data.

[1291] How it works: The device captures video every 10 minutes and compresses it in real time.

[1292] Step 3:

[1293] Video data transmission

[1294] Terminal: Compressed video data is sent to the server using HTTP or FTP protocol.

[1295] Input: Compressed video data.

[1296] Output: Video data sent to the server.

[1297] Specific operation: The device starts transmitting video data to the server in real time.

[1298] Step 4:

[1299] Video data analysis

[1300] Server: Analyzes the received video data using image recognition AI (such as TensorFlow or OpenCV) and extracts information about the progress of construction work.

[1301] Input: Received video data.

[1302] Output: Progress data.

[1303] Specific operation: The server runs an image recognition model to identify the object (e.g., "foundation work completed" or "steel frame installed").

[1304] Step 5:

[1305] Checking progress data against design document data

[1306] Server: Compares the progress data with pre-registered design data (PDF or CAD files) and derives the comparison results.

[1307] Input: Progress data, design document data.

[1308] Output: Matching results.

[1309] What happens: The server compares the progress data with the design document, evaluates the degree of agreement, and generates a result.

[1310] Step 6:

[1311] Generate progress reports

[1312] Server: Generates a progress report based on the collation results, including progress, issues, and next actions.

[1313] Input: Match result.

[1314] Output: Progress report.

[1315] What it does: The server generates progress reports in PDF or HTML format, including charts for easy visual understanding.

[1316] Step 7:

[1317] Progress report notification and payment instructions

[1318] Server: Generates progress reports and sends them to the user, notifying them through a dedicated application.

[1319] Input: Progress report.

[1320] Output: Report and payment instructions sent to user.

[1321] Specific operation: The server uses the notification function to send a report to the user and provide payment instructions.

[1322] Step 8:

[1323] Execute payment procedures

[1324] Server: Based on the payment instructions, the server transfers funds to the financial institution's account using the bank API or payment gateway.

[1325] Input: Payment instructions.

[1326] Output: The completed payment transaction.

[1327] Specific operation: The server executes the transfer instruction to the specified account via API, confirms the completion notification, and records it.

[1328] (Application example 1)

[1329] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1330] Construction and production sites require real-time monitoring of progress and accurate information collection. This allows for smooth material supply and financial management according to progress, resulting in efficient construction and production. However, with current technology, these processes are often carried out manually, consuming a great deal of time and human resources. Furthermore, delays and incorrect reporting in progress management are prone to occur, which also impacts material supply and financial management. A system that can effectively address these issues is needed.

[1331] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1332] In this invention, the server includes an imaging means for capturing images of a construction site or production site, a transmission means for transmitting image data obtained from the imaging means, an image recognition means for analyzing the image data transmitted from the transmission means, a comparison means for comparing progress data analyzed by the image recognition means with design document data or production plan data, a report generation means for generating a progress report based on the comparison results obtained by the comparison means, and an instruction means for issuing instructions for material supply and payment of funds based on the progress report generated by the report generation means. This enables real-time monitoring of progress and realizes timely material supply and fund management.

[1333] A "construction site or production site" is a physical location where construction work or product production takes place and where progress management is required.

[1334] "Photographing means" refers to a device that captures images of the scene in real time, such as a surveillance camera or a video capture device.

[1335] The "transmission means" refers to a communication device or network means for transmitting the video data acquired by the image capture means to the server.

[1336] "Image recognition means" refers to software or AI models that analyze transmitted video data and automatically assess progress on-site.

[1337] "Design document data or production plan data" refers to standard data registered in advance for managing the progress of construction or production, and includes the completion conditions and specifications for each process.

[1338] The "verification means" is software or a system for comparing and collating the progress data analyzed by the image recognition means with the design document data or production plan data.

[1339] The "report generation means" is software or a system for automatically generating a progress report based on the collation results obtained by the collation means.

[1340] An "instruction means" is software or a system for taking actions such as issuing instructions for supplying materials or disbursing funds based on the generated progress report.

[1341] "Materials supply" is the process of automatically replenishing the necessary materials at the site according to progress.

[1342] "Cash management" is the process of depositing and transferring funds according to the progress of construction or production.

[1343] The present invention is a system for monitoring the progress of a construction or production site in real time and automating material supply and financial management based on the progress. Specific embodiments for carrying out the present invention will be described below.

[1344] System Configuration

[1345] The system consists of the following elements:

[1346] 1. Filming methods (surveillance cameras): Cameras installed on-site capture the progress of construction and production in real time. For example, IP cameras are used.

[1347] 2. Transmission method (network communication): Video data captured by the surveillance camera is compressed and sent to the server via the network.

[1348] 3. Image recognition method (image recognition AI): The server uses image recognition software such as TensorFlow or OpenCV's DNN model to analyze this video data.

[1349] 4. Verification means: The server compares the progress data analyzed by the image recognition means with the design data or production plan data. The design data and production plan data are registered in advance and include progress indicators and specifications for each process.

[1350] 5. Report Generation: The server automatically generates a progress report based on the verification results, including the progress status, any issues found, and next steps to take.

[1351] 6. Instruction means: The server issues instructions for supplying materials and paying funds based on the generated progress report. For example, it works with an automatic material ordering system or a fund transfer system between bank accounts.

[1352] Operation overview

[1353] The server uses image recognition AI to process video data sent from surveillance cameras and analyze progress in real time. The analyzed data is compared with design document data or production plan data, and a progress report is generated based on the results of the comparison. The generated report is provided to the user, who then instructs them on the supply of materials and payment of funds required for the next step.

[1354] Typical processing examples

[1355] For example, on a production line, it recognizes when a part on a conveyor belt reaches a designated position. A surveillance camera monitors the position, and image recognition AI confirms that the part has arrived. This triggers the next processing step and reports its progress to a server.

[1356] Prompt example

[1357] Design an AI system that uses real-time video footage from a surveillance camera to recognize parts as they arrive at a specified position and automatically trigger the next machining step. The system will send progress reports with timestamps and image data to a server. The image recognition AI model used should include TensorFlow.

[1358] In this way, the system of the present invention provides real-time monitoring of progress and automation of material supply and cash management.

[1359] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1360] Step 1:

[1361] A surveillance camera captures video of a construction or production site in real time. The input is the video data captured through the surveillance camera lens, and the output is the video data.

[1362] Step 2:

[1363] The terminal (computer inside the surveillance camera) compresses the captured video data and sends it to the server via the network. The input is raw video data, which is converted into data for network transmission through data compression, and the output is compressed video data.

[1364] Step 3:

[1365] The server receives the transmitted video data and inputs it into the image recognition AI. The input is compressed video data, which is decoded and preprocessed, and the output is preprocessed data for analysis.

[1366] Step 4:

[1367] The server uses an image recognition AI model to analyze progress information from the preprocessed data. The input is the preprocessed image data, and the AI ​​model extracts progress information. The output is the analyzed progress information.

[1368] Step 5:

[1369] The server compares the analyzed progress information with the design data or production plan data registered in advance. The input is the progress information and design data (or production plan data), and a comparison operation is performed using a matching algorithm. The output is the matching result.

[1370] Step 6:

[1371] The server generates a progress report based on the matching results. The input is the matching results, and the progress report is created by a report generation algorithm. The output is the progress report.

[1372] Step 7:

[1373] The server issues instructions for supplying materials and paying funds based on the generated progress report. The input is the progress report, and instructions are executed in cooperation with the material ordering system and bank transfer system. The output is material ordering information and instructions for transferring funds.

[1374] Step 8:

[1375] The user checks the progress report using a dedicated application. The input is the progress report sent from the server, and the report is displayed through the application. The output is the progress information checked by the user.

[1376] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1377] The present invention combines a system that monitors the progress of a construction site in real time and enables flexible deposit of funds based on that progress with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1378] System Overview

[1379] This system uses surveillance cameras installed at the construction site to capture real-time construction progress and utilizes image recognition AI to evaluate the progress based on the video data.It also has an emotion engine that recognizes the user's emotional state and provides appropriate feedback and adjustments to support user decision-making.The main components of the system are as follows:

[1380] Capture method: This corresponds to a surveillance camera installed on the terminal, which captures video data of the construction site in real time.

[1381] Transmission means: The terminal compresses the captured video data and transmits it to the server via the network.

[1382] Image recognition method: An image recognition AI installed on the server analyzes the transmitted video data and evaluates the progress of the construction work.

[1383] Matching method: The server compares the progress data acquired by the image recognition AI with the design document data registered in advance.

[1384] Report generation means: The server automatically generates a progress report based on the collation results.

[1385] Payment instruction means: The server sends the generated progress report to the user and instructs the user on the next payment procedure based on the report.

[1386] Emotion engine: The server also has an emotion engine that recognizes the user's emotions and adjusts the content of progress reports, notification methods, and payment instruction procedures.

[1387] Specific examples of program processing

[1388] 1. Installing surveillance cameras at construction sites and collecting video data

[1389] Terminal: The surveillance camera is installed at the construction site and captures video continuously for 24 hours. The captured video data is sent to the server every 10 minutes.

[1390] 2. Video data analysis and progress check

[1391] Server: The server analyzes the received video data using image recognition AI. As a result of the analysis, progress information such as "foundation work completed" or "steel frame installed" is obtained.

[1392] 3. Verification with design document data

[1393] Server: The server compares the analyzed progress data with the design data registered in advance. The design data contains progress indicators and specifications for each construction stage. By comparing, it is evaluated whether the progress on site matches the design data.

[1394] 4. Progress report and payment instructions

[1395] User: Through a dedicated application, the user checks progress reports from the server, including progress status, issues found, and next steps to take.

[1396] Server: If the progress is as expected, the server instructs the user to proceed with the next payment procedure. The server connects to the payment system and executes instructions to transfer the funds to the specified bank account.

[1397] 5. User Emotion Recognition and Feedback

[1398] Emotion Engine: The server is equipped with an emotion engine that analyzes feedback (e.g., facial expressions, tone of voice, etc.) when a user views a report. The emotion engine understands the user's emotional state and adjusts the next report and notification method accordingly.

[1399] User: If a user encounters an unexpected problem while viewing a report, the emotion engine will detect the situation and suggest the next course of action to the server.

[1400] Usage examples

[1401] For example, at a construction site where steel frame work is underway, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and verifies that the steel frame is being assembled according to design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment stage based on that report. Furthermore, if the user displays a relieved expression while viewing the report, the emotion engine will set the next report to be in the same format. Conversely, if the user displays a dissatisfied or suspicious expression, the engine will adjust the report to include more detailed information and supplementary explanations.

[1402] This system allows the progress of construction sites to be accurately grasped in real time, improves the liquidity of funds, reduces the risk of construction delays and funding shortages, and emotionally supports the user's decision-making, enabling smoother project management.The above are specific modes for carrying out the present invention.

[1403] The processing flow will be explained below.

[1404] Step 1:

[1405] The device captures images of the construction site using a surveillance camera, which records video continuously 24 hours a day and generates a captured image every 10 minutes.

[1406] Step 2:

[1407] The device compresses the captured video data and transmits it over the network to a server, where the transmission is encrypted for security purposes.

[1408] Step 3:

[1409] The server receives the video data sent from the device, temporarily stores the data, and adds it to a queue to await analysis.

[1410] Step 4:

[1411] The server analyzes the received video data using image recognition AI, where the AI ​​algorithm identifies specific objects and scenes (e.g., foundation work completed, steel frame installation, etc.) to assess the progress of the construction work.

[1412] Step 5:

[1413] The server compares the progress data acquired by the image recognition AI with the design data registered in advance, thereby confirming that progress on-site is proceeding according to the design document.

[1414] Step 6:

[1415] The server automatically generates progress reports based on the results of the comparison with the design data, including progress status, degree of conformance with the design, and identified issues.

[1416] Step 7:

[1417] The server then sends the generated progress report to the user via a dedicated application, who can then check the report and understand the progress of the construction work.

[1418] Step 8:

[1419] The user checks the report and, after confirming that the progress is proceeding as planned, instructs the server to carry out the next payment procedure.

[1420] Step 9:

[1421] The server uses the payment instruction means to issue an instruction to transfer funds to the specified bank account, and the specified amount is transferred in cooperation with the payment system.

[1422] Step 10:

[1423] The emotion engine installed on the server analyzes the user's facial expressions and tone of voice when viewing the report to understand the user's emotional state. For example, if the user shows a relieved expression or tone of voice, the emotion engine records that information.

[1424] Step 11:

[1425] The emotion engine will adjust the content and notification of the next report based on the user's emotional state. For example, if it detects expressions of anxiety or doubt, it will add more details or additional explanations to the next report.

[1426] Step 12:

[1427] The server improves the progress reports and notification methods as appropriate based on feedback from the emotion engine, thereby increasing user satisfaction.

[1428] This series of steps allows the progress of construction to be tracked quickly and accurately, and provides appropriate feedback and support according to the user's emotional state, enabling flexible funding and smooth project management.

[1429] Example 2

[1430] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1431] Construction site progress management is difficult to grasp in real time, which often leads to delays in appropriate fund deposits and plan changes based on progress status. Furthermore, one-sided reports and notifications are given without considering the user's emotional state, which can lead to stress and frustration in user decision-making. A system that solves these problems and improves the efficiency of construction progress management and fund management, as well as user satisfaction, is needed.

[1432] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1433] In this invention, the server includes an imaging means for capturing video of the construction site, a transmission means for transmitting the video data obtained from the imaging means, an image recognition means for analyzing the video data transmitted from the transmission means, a comparison means for comparing the progress data analyzed by the image recognition means with design document data, a report generation means for generating a progress report based on the comparison result obtained by the comparison means, a payment instruction means for issuing a payment instruction based on the progress report generated by the report generation means, an emotion recognition means for recognizing a user's emotion when checking the progress report, and an adjustment means for adjusting the content of the report or the notification method based on the user's emotional state obtained by the emotion recognition means. This makes it possible to accurately grasp the progress of the construction site in real time, improve the liquidity of funds, and enable flexible feedback and decision-making support based on the user's emotional state.

[1434] "Photographing means" refers to a device that captures images of the construction site.

[1435] The "transmission means" is a device that compresses the video data obtained from the image capture means and transmits it to the server via the network.

[1436] The "image recognition means" is a device that uses image recognition technology to analyze received video data and evaluate the progress of construction work.

[1437] The "verification means" is a device or system that verifies the progress data analyzed by the image recognition means with the design document data registered in advance.

[1438] The "report generation means" is a device or system that generates a progress report based on the collation results obtained by the collation means.

[1439] The "payment instruction means" is a device or system that issues instructions for payment of funds based on the progress report generated by the report generation means.

[1440] The "emotion recognition means" is a device or system that analyzes feedback data such as facial expressions and voice in order to recognize the user's emotions.

[1441] The "adjustment means" is a device or system that adjusts the content of the report or the notification method based on the emotional state of the user obtained by the emotion recognition means.

[1442] The present invention combines a system that monitors the progress of a construction site in real time and enables flexible deposit of funds based on that progress with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing the present invention will be described below.

[1443] System Overview

[1444] This system uses surveillance cameras installed at the construction site to capture real-time construction progress and utilizes image recognition AI to evaluate the progress based on the video data.It also has an emotion engine that recognizes the user's emotional state and provides appropriate feedback and adjustments to support user decision-making.The main components of the system are as follows:

[1445] Filming method

[1446] Terminal: Surveillance cameras are installed at the construction site and capture video continuously for 24 hours. For example, a surveillance camera monitors a specific area and collects video data every 10 minutes.

[1447] Transmission method

[1448] Terminal: The acquired video data is compressed using a compression algorithm (e.g., H.264 or HEVC) to reduce the data size and sent to the server via the network.

[1449] Image Recognition Method

[1450] Server: Uses image recognition AI (e.g., TensorFlow or PyTorch) to analyze the transmitted video data. Through analysis, the progress of construction work (e.g., "foundation work completed" or "steel frame installed") is automatically detected.

[1451] Matching method

[1452] Server: Compares progress data analyzed by image recognition with pre-registered design data. The design data includes each construction process and progress indicators, and by comparing them, it evaluates whether on-site progress is consistent with the plan.

[1453] Report Generation Method

[1454] Server: Based on the results of the check, a progress report is automatically generated. The report includes the current construction status, any issues found, and action items for moving to the next stage. The generated report is then sent to the user.

[1455] Payment Instruction Method

[1456] Server: If the progress is as expected, instruct the user to carry out the payment procedure, which involves executing instructions to transfer the funds to the specified account based on the progress report.

[1457] emotion recognition means

[1458] Server: The emotion engine analyzes the feedback (e.g., facial expressions, tone of voice, etc.) of users viewing reports. It analyzes their emotional state and adjusts the content and notification method of the next report.

[1459] Adjustment means

[1460] Server: Adjust the content of the report or notification method based on the user's emotional state obtained through emotion recognition. For example, if the user shows a relieved expression, the same brief report format will be used next time, but if the user shows a dissatisfied expression, a detailed explanation will be added.

[1461] Specific examples

[1462] For example, at a construction site where steel frame work is being carried out, surveillance cameras capture footage every 10 minutes and send it to a server. Image recognition AI installed on the server analyzes the footage and confirms that the steel frame is being assembled according to design. If the server determines that progress is normal, it sends a report to the user and instructs them on the next payment step. Furthermore, if the user shows a relieved expression while viewing the report, the emotion engine will set the next report to be sent in the same format.

[1463] Prompt Sentence Examples

[1464] Here is an example of how to use the prompt:

[1465] plaintext

[1466] Please explain about a system that monitors the progress of a construction site in real time and manages the deposit of funds according to progress. This system also has a function to recognize the user's emotions. Specifically, video of the construction site is captured by a surveillance camera, and the server analyzes the progress using image recognition AI. The analysis results are then compared with the design document data and a progress report is sent to the user. Please also explain how the content of the next report is adjusted based on the user's emotional feedback.

[1467] The above is a specific embodiment for carrying out the present invention.

[1468] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1469] Step 1:

[1470] Surveillance camera footage capture

[1471] Terminal: A surveillance camera installed at the construction site captures video 24 hours a day. Specifically, the camera's sensor monitors a specific area and collects video data every 10 minutes.

[1472] Input: Real-time footage from the scene.

[1473] Output: Video files collected every 10 minutes.

[1474] Step 2:

[1475] Video data compression and transmission

[1476] Terminal: The collected video data is compressed using a compression algorithm (e.g., H.264 or HEVC) to reduce the data size and sent to the server via the network.

[1477] Input: Raw video data (high resolution).

[1478] Output: Compressed video data (low resolution).

[1479] Step 3:

[1480] Analyzing video data and obtaining progress information

[1481] Server: The transmitted video data is analyzed using image recognition AI (e.g., TensorFlow or PyTorch). For example, the progress of construction work can be automatically detected from the images (e.g., "foundation work completed" or "steel frame installed").

[1482] Input: Compressed video data.

[1483] Output: Specific progress information (e.g., steel construction completed).

[1484] Step 4:

[1485] Verification with design document data

[1486] Server: The progress data analyzed by the image recognition method is compared with the design document data registered in advance. The design document contains each construction process and progress indicators, and the server performs the comparison based on this.

[1487] Input: Analyzed progress data. Pre-registered design document data.

[1488] Output: Progress evaluation result (whether it matches the design document or not).

[1489] Step 5:

[1490] Generate and send progress reports

[1491] Server: Generates a progress report based on the results of the check. The report includes the current construction status, any issues found, and action items for moving forward to the next stage. The generated report is sent to the user's application.

[1492] Input: Progress assessment results.

[1493] Output: Progress report.

[1494] Step 6:

[1495] Instructions for disbursement of funds

[1496] Server: Based on the progress report, instructs the user to pay the funds. Specifically, if the progress is as planned, executes instructions to transfer funds to the specified bank account.

[1497] Input: Progress report.

[1498] Output: Instructions for disbursement of funds.

[1499] Step 7:

[1500] User Emotion Recognition and Feedback

[1501] Server: Analyzes the feedback (facial expressions and voice) of users viewing reports using emotion recognition means to recognize their emotional state. For example, facial expression analysis technology or voice analysis technology is used.

[1502] Input: User feedback data (facial expressions, voice, etc.).

[1503] Output: The user's emotional state.

[1504] Step 8:

[1505] Adjustment of reporting content and notification methods

[1506] Server: Based on the emotional state obtained by the emotion recognition means, adjust the content of the next report and notification method. For example, if the user feels relieved, provide a concise report, but if the user feels anxious, provide a detailed explanation.

[1507] Input: The user's emotional state.

[1508] Output: Coordinated reporting and notification methods.

[1509] The above is a specific processing flow of the program of this system.

[1510] (Application example 2)

[1511] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1512] With conventional technologies, it has been difficult to accurately grasp the progress of construction sites in real time or to recognize users' emotions and provide appropriate feedback and adjustments. Furthermore, in physical stores, there is a need for optimization of operations, such as real-time monitoring of store layout and inventory, and adjustment of sales policies based on customers' emotional states. In response to these challenges, the present invention aims to provide a management system that combines progress and emotions at both construction sites and physical stores, thereby optimizing financial management and sales policies.

[1513] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1514] In this invention, the server includes: a photographing means for capturing video of the construction site; a transmitting means for transmitting the video data obtained from the photographing means; an image recognition means for analyzing the video data transmitted from the transmitting means; a comparison means for comparing the progress data analyzed by the image recognition means with design document data; a report generation means for generating a progress report based on the comparison results obtained by the comparison means; a payment instruction means for issuing a payment instruction based on the progress report generated by the report generation means; an emotion engine means for recognizing the user's emotional state and adjusting the content of the progress report, the notification method, and the payment instruction procedure; and a store management means for monitoring the layout and inventory of the physical store in real time and adjusting business policies based on customer emotions. This not only enables real-time monitoring of the progress of the construction site and the operation of the physical store, but also enables optimal feedback and adjustments based on the emotions of users and customers.

[1515] "Photography means" refers to a device used to capture images of construction sites and brick-and-mortar stores.

[1516] The "transmission means" is a device or system for transmitting the video data obtained from the image capture means to the server.

[1517] The "image recognition means" is an artificial intelligence or software that analyzes the video data transmitted from the transmission means and extracts progress data.

[1518] The "comparison means" is a system for comparing the progress data analyzed by the image recognition means with pre-set design document data to confirm a match.

[1519] The "report generation means" is software for automatically generating a progress report based on the results obtained by the collation means.

[1520] The "payment instruction means" is a system for instructing payment of funds based on the progress report generated by the report generation means.

[1521] The "emotion engine means" is an artificial intelligence that recognizes the user's emotional state and adjusts the content and notification method of the progress report.

[1522] "Store management tools" are systems that monitor the layout and inventory of physical stores in real time and adjust sales policies based on customer sentiment.

[1523] This invention is a system for supporting the operation of construction sites and brick-and-mortar stores. The system supports more effective decision-making by combining real-time monitoring of progress status with user emotion recognition.

[1524] System Overview

[1525] The server is implemented using the following hardware and software:

[1526] Capture method: Surveillance cameras installed at construction sites and brick-and-mortar stores capture video data.

[1527] Transmission means: Transmits the video data captured by the imaging means to the server via the network.

[1528] Image recognition means: The server analyzes the received video data using an image processing library such as OpenCV. This analysis process detects progress and product placement.

[1529] Matching method: Progress data is compared with pre-designed data and pre-set targets to determine the degree of agreement.

[1530] Report generation method: Progress reports are automatically generated based on the matching results. The reports are notified to the user and used as information for making decisions about fund management.

[1531] Payment Instructions: Based on progress reports, instructions for the next payment of funds will be issued, including instructions for transfer to bank accounts.

[1532] Emotion engine means: An emotion engine is built in to analyze facial expressions and voice when a user is viewing a report and recognize the user's emotional state, which then adjusts the content of the report and the notification method.

[1533] Store management tools: Monitor store layout and inventory in real time and adjust sales policies based on customer sentiment.

[1534] Example of a system

[1535] 1. Construction site progress management:

[1536] Security cameras installed at the construction site capture images every 10 minutes and send them to a server. The server uses image recognition technology to analyze the progress data and compare it with the data in the design documents. A progress report is generated, and if the work is progressing as planned, instructions are issued for the next payment of funds.

[1537] 2. Physical store operations management:

[1538] Cameras are used to monitor changes to store layouts and inventory checks within physical stores, and an emotion recognition engine is used to analyze customer movements and facial expressions. This allows the company to determine whether customers are interested in or satisfied with products and adjust sales policies accordingly. For example, if a customer is looking with great interest at a new product, the product will be relocated to a more prominent location.

[1539] The specific hardware and software used

[1540] Hardware: Built-in cameras in laptops and smartphones

[1541] Software: OpenCV (image processing library), Scikit-learn (to train the emotion recognition model)

[1542] Prompt Sentence Examples

[1543] Examples of prompts to generate specific AI models are:

[1544] "Create a system that analyzes footage from cameras installed in physical stores and checks whether product placement is complete. Also, explain how to recognize customers' emotional states from their facial expressions in the store and optimize sales feedback."

[1545] In this way, the present invention is a system that enables efficient progress management and improved customer experience both at construction sites and in physical stores.

[1546] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1547] Step 1:

[1548] The terminal captures video data using surveillance cameras installed at construction sites and brick-and-mortar stores. The input is real-time video data captured by the surveillance cameras, and the output is the captured video data. Specifically, the cameras automatically capture video every 10 minutes and store it in local storage.

[1549] Step 2:

[1550] The terminal compresses the captured video data and sends it to the server via the network. The input is the captured video data, and the output is the compressed video data. Specifically, the video data is compressed using a compression algorithm such as H.264 and uploaded to the server via the HTTP protocol.

[1551] Step 3:

[1552] The server receives the transmitted video data and analyzes it using image recognition means. The input is the transmitted compressed video data, and the output is the analyzed progress data or product placement status. Specifically, the server decodes the video data using OpenCV and extracts progress and inventory status using an object detection algorithm.

[1553] Step 4:

[1554] The server compares the progress data obtained by the image recognition means with the design document data and pre-set target data. The input is the analyzed progress data and design document data, and the output is the comparison result. Specifically, it uses a Python data analysis library to compare the progress data with the database and calculate the degree of match.

[1555] Step 5:

[1556] The server generates a progress report based on the matching results. The input is the matching results, and the output is the progress report. Specifically, the server converts the matching results into a text format and automatically generates a report containing the necessary information.

[1557] Step 6:

[1558] The server issues a payment instruction based on the generated progress report. The input is the progress report, and the output is the execution of the payment instruction. Specifically, it works with the bank API to execute a transfer instruction to the specified bank account.

[1559] Step 7:

[1560] The server uses an emotion engine to analyze facial expressions and voice data when the user is viewing a report and recognizes the user's emotional state. The input is the facial expressions and voice data when viewing the report, and the output is the recognized emotional state. Specifically, the emotion engine uses a deep learning model to analyze the input data and output an emotion label.

[1561] Step 8:

[1562] The server adjusts the content and notification method of the next report based on the user's emotional state. The input is the recognized emotional state, and the output is the adjusted content and notification method of the next report. Specifically, if the user expresses dissatisfaction, the server applies a setting to add detailed explanations and supplementary information.

[1563] Step 9:

[1564] In a physical store, the terminal monitors the store layout and inventory and sends the data to a server. The input is video data captured by a surveillance camera inside the store, and the output is compressed video data. Specifically, the camera periodically captures the status of shelves and inventory and sends this data to the server.

[1565] Step 10:

[1566] The server analyzes store layout and inventory data and adjusts sales policies based on customer emotions. The input is in-store video data and customer emotion data, and the output is the adjusted sales policies. Specifically, the emotion engine recognizes emotions from customer facial expressions and determines sales policies such as placing new products around products that show high satisfaction.

[1567] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1568] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1569] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1570] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1571] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1572] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1573] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1574] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1575] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1576] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1577] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1578] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1579] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1580] 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.

[1581] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1582] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1583] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1584] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1585] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1586] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1587] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1588] The following is further disclosed regarding the above embodiment.

[1589] (Claim 1)

[1590] a means for capturing footage of the construction site;

[1591] a transmitting means for transmitting the video data obtained from the photographing means;

[1592] image recognition means for analyzing the video data transmitted from the transmission means;

[1593] a comparison means for comparing the progress data analyzed by the image recognition means with design document data;

[1594] report generation means for generating a progress report based on the collation result obtained by the collation means;

[1595] a payment instruction means for issuing a payment instruction for funds based on the progress report generated by the report generation means;

[1596] A system including:

[1597] (Claim 2)

[1598] 2. The system according to claim 1, wherein the collation means evaluates the progress of a specific process at a construction site.

[1599] (Claim 3)

[1600] 2. The system of claim 1, wherein the payment instruction means transfers funds to a designated bank account based on the progress report.

[1601] "Example 1"

[1602] (Claim 1)

[1603] a means for capturing footage of the construction site;

[1604] a transmitting means for compressing and transmitting the video data obtained from the imaging means;

[1605] image recognition means for analyzing the video data transmitted from the transmission means;

[1606] a comparison means for comparing the progress data analyzed by the image recognition means with design document data;

[1607] report generation means for generating a progress report based on the collation result obtained by the collation means;

[1608] a payment instruction means for issuing a payment instruction for funds based on the progress report generated by the report generation means;

[1609] means for executing a payment procedure based on the payment instruction;

[1610] A system including:

[1611] (Claim 2)

[1612] 2. The system according to claim 1, wherein the collation means evaluates the progress of a plurality of processes.

[1613] (Claim 3)

[1614] 2. The system according to claim 1, wherein the payment instruction means transfers funds to a designated account at a financial institution based on the progress report.

[1615] "Application Example 1"

[1616] (Claim 1)

[1617] a means for capturing images of a construction or production site;

[1618] a transmitting means for transmitting the video data obtained from the photographing means;

[1619] image recognition means for analyzing the video data transmitted from the transmission means;

[1620] a comparison means for comparing the progress data analyzed by the image recognition means with design document data or production plan data;

[1621] report generation means for generating a progress report based on the collation result obtained by the collation means;

[1622] an instruction means for issuing instructions for supplying materials and paying funds based on the progress report generated by the report generation means;

[1623] A system including:

[1624] (Claim 2)

[1625] 2. The system according to claim 1, wherein the collation means evaluates the progress of a specific process at a construction site or a production site.

[1626] (Claim 3)

[1627] 2. The system according to claim 1, wherein the instruction means transfers funds to a designated bank account or supplies materials to a designated location based on the progress report.

[1628] "Example 2: Combining Emotion Engines"

[1629] (Claim 1)

[1630] a means for capturing footage of the construction site;

[1631] a transmitting means for transmitting the video data obtained from the photographing means;

[1632] image recognition means for analyzing the video data transmitted from the transmission means;

[1633] a comparison means for comparing the progress data analyzed by the image recognition means with design document data;

[1634] report generation means for generating a progress report based on the collation result obtained by the collation means;

[1635] a payment instruction means for issuing a payment instruction for funds based on the progress report generated by the report generation means;

[1636] emotion recognition means for recognizing the emotion of the user when checking the progress report;

[1637] an adjustment means for adjusting the content of the report or the notification method based on the emotional state of the user obtained by the emotion recognition means;

[1638] A system including:

[1639] (Claim 2)

[1640] 2. The system according to claim 1, wherein the emotion recognition means analyzes the user's facial expression and voice to evaluate the user's emotional state.

[1641] (Claim 3)

[1642] 2. The system of claim 1, wherein the payment instruction means transfers funds to a designated account based on the progress report.

[1643] "Application example 2 when combining emotion engines"

[1644] (Claim 1)

[1645] a means for capturing footage of the construction site;

[1646] a transmitting means for transmitting the video data obtained from the photographing means;

[1647] image recognition means for analyzing the video data transmitted from the transmission means;

[1648] a comparison means for comparing the progress data analyzed by the image recognition means with design document data;

[1649] report generation means for generating a progress report based on the collation result obtained by the collation means;

[1650] a payment instruction means for issuing a payment instruction for funds based on the progress report generated by the report generation means;

[1651] an emotion engine means for recognizing the user's emotional state and adjusting the content of progress reports, notification methods, and payment instruction procedures;

[1652] A store management tool that monitors store layout and inventory in real time and adjusts sales policies based on customer sentiment.

[1653] A system including:

[1654] (Claim 2)

[1655] 2. The system according to claim 1, wherein the collation means evaluates the progress of a specific process at a construction site.

[1656] (Claim 3)

[1657] 2. The system of claim 1, wherein the payment instruction means transfers funds to a designated bank account based on the progress report. [Explanation of symbols]

[1658] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for capturing footage of the construction site; a transmitting means for transmitting the video data obtained from the photographing means; image recognition means for analyzing the video data transmitted from the transmission means; a comparison means for comparing the progress data analyzed by the image recognition means with design document data; report generation means for generating a progress report based on the collation result obtained by the collation means; a payment instruction means for issuing a payment instruction for funds based on the progress report generated by the report generation means; A system including:

2. 2. The system according to claim 1, wherein the checking means evaluates the progress of a specific process at a construction site.

3. 2. The system of claim 1, wherein said payment instruction means transfers funds to a designated bank account based on the progress report.

Images