system

Abstract

We provide the system. [Solution] A device and means for acquiring and recording construction condition information, A device and means for evaluating information on multiple contractors based on acquired construction condition information and selecting an appropriate contractor, A device that automatically places construction orders with selected contractors, A device that recognizes the worker's operational status in real time and presents the optimal work schedule, A device and means for collecting construction progress data and updating the contractor's capability database, A device and means that provides an interface for displaying the construction progress status on a smartphone and remotely monitoring the project's progress, A system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The problem with the construction order business is that it requires a great deal of time and human resources for complex condition assessment, selection of construction contractors, and adjustment of the ordering process. Furthermore, the inability to appropriately grasp the operating status of construction contractors causes delays and efficiency decreases in the construction schedule. In addition, construction mistakes caused by biases in experience and skills increase the risk of quality degradation and safety. To solve these problems, automation of the ordering process, selection of optimized construction contractors, and effective schedule management are necessary.

Means for Solving the Problems

[0005] This invention streamlines the data collection necessary for ordering by providing means for automatically acquiring and recording construction condition information. Furthermore, by introducing a system that evaluates contractor information and automatically selects the most suitable contractor based on the conditions, it enables the rapid selection of appropriate contractors. In addition, it includes means for automating the ordering of construction work to selected contractors and for recognizing their operational status in real time to propose an optimal schedule. This results in shorter construction periods and more efficient operations. Moreover, by collecting data on the progress of construction and updating the contractor skill database, it contributes to improving the accuracy of selections in future orders.

[0006] "Construction condition information" refers to information that includes detailed conditions necessary for carrying out construction work, such as the scope of work, area, duration, difficulty level, and special requirements.

[0007] "Contractor information" includes data related to the execution of work, such as the contractor's basic information, skill set, experience level, and qualifications.

[0008] "Selection" refers to the process of identifying and deciding on the target that best fits specific criteria from among multiple candidates.

[0009] "Construction order" refers to the business procedure of requesting a construction company to carry out construction work based on specific conditions.

[0010] "Operating status" refers to information indicating the availability and schedule of the construction company or its team.

[0011] A "skills database" refers to a collection of data that systematically records the abilities, qualifications, and past construction achievements of construction companies and individual engineers.

[0012] "Schedule management" refers to management activities that include methods and tools for optimizing the planned time for construction work and ensuring it proceeds on schedule.

[0013] "Means of awarding" refers to methods and systems for evaluating the work performance of construction companies and providing rewards or honors for their achievements. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

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

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0035] This system is designed to efficiently automate construction ordering processes, and its implementation consists of the following elements. First, the system includes an interface where users input construction condition information via a terminal. This information includes details such as the construction content, area, difficulty level, and required skills.

[0036] The server receives construction condition information transmitted from the terminal and compares it with contractor information stored in the database. This comparison process evaluates each contractor's skill set, qualifications, and past construction performance to select the contractor best suited to the conditions. The selection is carried out by an algorithm that chooses the optimal contractor based on skill matching and evaluation scores.

[0037] After selection, the server automatically places orders with the selected contractors, and can adjust the order ratio and amount based on the initial contract. The contractors receive the order details along with the construction schedule information. This schedule is determined by the server managing operational status in real time and allocating optimized time slots.

[0038] Once construction begins, users can monitor the progress of the work via their terminals and make adjustments as needed. Construction progress data is collected by a server, and upon completion, the contractor's skill database is updated. This improves the accuracy of selections for future orders.

[0039] As a concrete example, suppose a user wants to order electrical equipment installation work for a new building. In this case, the user inputs the necessary conditions (such as the need for work at heights or the use of equipment from a specific manufacturer) into a terminal. Based on this, the server automatically selects a contractor with the appropriate skills and qualifications and places the order automatically. During the work, the progress is tracked in real time to ensure smooth completion of the project. This improves the efficiency and safety of ordering construction work.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user enters the detailed conditions of the construction work into the terminal. These conditions include the scope of work, area, duration, difficulty level, required skills, and special requirements. The terminal temporarily stores this information and sends it to the server.

[0043] Step 2:

[0044] The server accesses the database based on the received construction condition information and searches for contractors that meet the conditions. The server evaluates the suitability of each contractor based on their skill set, qualifications, and past performance, and lists the contractors that best meet the conditions.

[0045] Step 3:

[0046] The server monitors the operational status of selected contractors in real time. Based on this, it proposes and adjusts the optimal start date and time for construction. The server then notifies the contractors of the adjusted order details and schedule based on the information obtained.

[0047] Step 4:

[0048] Once the contract for the selected contractor is finalized, the server automatically adjusts the order ratio and amount based on the initial contract and formally sends the order to the contractor. The contractor then receives the order details.

[0049] Step 5:

[0050] Once construction begins, the server monitors the progress and provides real-time information to users via their terminals. Users can check the progress and make adjustments as needed.

[0051] Step 6:

[0052] After the construction is completed, the server aggregates all the project progress data and updates the contractor's skills database. This update ensures that the contractor's track record and skills are up-to-date, which can be used to inform future projects.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] The problem that this invention aims to solve is to improve the efficiency and accuracy of selecting construction specialists in construction ordering operations. Conventional manual processes for selecting construction specialists are time-consuming and labor-intensive, and it was sometimes difficult to select the most suitable specialists. In addition, the lack of real-time monitoring of the progress of construction was a problem, leading to delays in construction progress and a decline in quality.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes means for acquiring and recording construction condition information, means for evaluating construction expert information in a database based on the acquired construction condition information and selecting a construction expert that meets the conditions, and means for collecting work progress data and updating the database of construction experts' capabilities. As a result, the selection of construction experts in construction ordering operations is automated and accuracy is improved, and construction progress can be monitored in real time, significantly improving overall operational efficiency.

[0058] "Construction conditions information" refers to detailed information related to construction work, such as the content of the work, area, difficulty level, and required skills.

[0059] A "construction specialist" refers to a professional who carries out construction work and possesses a specific skill set, qualifications, and past work experience.

[0060] "Contracting work" refers to the process of requesting selected construction specialists to carry out construction work.

[0061] "Operating status" refers to data that shows the current work status and schedule of construction specialists in real time.

[0062] "Work schedule" refers to the timeline and plan of the construction work assigned to the construction specialists.

[0063] A "skills database" refers to a database that records and manages information on the skills and past performance of construction professionals.

[0064] This invention is a system for efficiently automating construction ordering operations, and is composed of user, server, and terminal elements.

[0065] Users input construction-related information via a terminal. The terminal converts this information into a digital format and sends it to the server. The terminal can be implemented using basic computing devices or mobile devices.

[0066] The server uses the construction condition information received from the user to compare it with the construction expert information stored in the database. The database system used here is implemented as a general relational database management system (RDBMS) and efficiently manages the skill sets and past performance of construction experts.

[0067] The server also utilizes a skill matching algorithm to select the construction expert best suited to the input conditions. This process uses a specific machine learning model to evaluate the optimal expert based on skill match and past evaluations. Because this algorithm runs on the server, rapid selection is achieved.

[0068] After selection, the server executes a notification system to automatically place work orders with the selected construction specialists. This notification system transmits order information via email or a dedicated application. Furthermore, the server monitors the work status of the construction specialists in real time and creates an optimal work schedule.

[0069] As the construction progresses, users can monitor the progress via their terminals and make adjustments as needed. The server continuously collects progress data and updates the database of construction specialists' capabilities upon completion of the project. This update process further improves the accuracy of selections when future projects are ordered.

[0070] As a concrete example, consider a case where a user requests interior construction work for an office building. The user inputs conditions such as "interior design," "Tokyo," and "requires high technical skills" into a terminal. Based on this, the server selects construction specialists with the appropriate skills and qualifications and automatically places the order. During the construction process, the user can check the progress in real time on their terminal and make any necessary adjustments.

[0071] An example of a prompt message is: "Please describe an automated system for efficiently carrying out interior construction work on a building. Specifically, please describe the selection and management methods for specialists, given the requirement for high technical expertise and the need for work to be performed in the Kanto area."

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The user uses a terminal to input construction condition information. This information includes the details of the construction, area, difficulty level, and required skills. The entered condition information is converted into a digital format and sent to the server. The input consists of construction condition information specified by the user, and the output is formatted data sent to the server.

[0075] Step 2:

[0076] The server receives construction condition information from the terminal and stores it in the database. The received data serves as the basis for cross-referencing with construction expert information. Specifically, an insert process is performed into the database to ensure accurate storage of the information. The input is construction condition information sent by the user, and the output is the information recorded in the database.

[0077] Step 3:

[0078] The server matches the construction expert information with the construction condition information stored in the database. This process involves querying skill sets, qualifications, and experience information to generate a list of experts that match the criteria. SQL queries are used for the specific data processing. The input is the user's construction condition information, and the output is a list of construction experts that match the criteria.

[0079] Step 4:

[0080] The server uses a generative AI model to select the optimal construction expert from among those who meet the specified criteria. Here, machine learning algorithms are used to calculate skill match and past evaluation scores to select the most suitable expert. The input is a list of construction experts who meet the criteria, and the output is the selection result for the optimal construction expert.

[0081] Step 5:

[0082] The server automatically places work orders with selected construction specialists. Specifically, it creates notification emails and processes notifications through a dedicated application. The order details include construction work content and schedule information. The input is the selection result of the most suitable construction specialist, and the output is an order notification to the construction specialist.

[0083] Step 6:

[0084] The server monitors the operational status of construction specialists in real time and creates an optimal work schedule. Real-time data analysis is performed to optimize each specialist's schedule information. The input is the construction specialists' schedule data, and the output is the optimized work schedule.

[0085] Step 7:

[0086] During construction, users monitor the progress using a terminal and make adjustments as needed. The terminal displays real-time progress data, allowing users to make decisions based on the information. Input is construction progress data, and output is a progress confirmation screen that the user receives.

[0087] Step 8:

[0088] The server analyzes progress data after construction is completed and updates the database of construction specialists' capabilities. This update improves the accuracy of the selection process for future projects. Specifically, it executes database update queries using the progress data. The input is construction progress data, and the output is the updated database of construction specialists' capabilities.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] In modern construction procurement processes, the selection of contractors and facilities, as well as schedule management, are often performed manually, leading to problems such as decreased efficiency and accuracy, and wasted time. Furthermore, there are limited means of monitoring progress in real time, making it difficult to ensure transparency in project management. In this situation, there is a need to develop a system that enables appropriate contractor selection, rapid ordering, and progress monitoring.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes a device for acquiring and recording construction condition information, a device for evaluating multiple contractor information based on the acquired construction condition information and selecting an appropriate contractor, and a device for recognizing the contractor's working status in real time and presenting an optimal work schedule. This enables improved efficiency and accuracy in construction ordering operations, as well as highly transparent project management.

[0094] "Construction condition information" refers to information that describes specific requirements related to construction work, such as the content of the work, location, difficulty level, and necessary technologies.

[0095] "Contractor information" refers to information used to evaluate the capabilities and reliability of a contractor or craftsman in carrying out construction work, including their skills, qualifications, and past performance.

[0096] "Operating status" refers to the operational state of the contractor, including the work they are currently undertaking, its progress, and future work plans.

[0097] A "work schedule" is a plan that outlines the specific steps and timetables formulated to ensure the efficient progress of construction work.

[0098] A "smartphone" is a portable information terminal that, in addition to telephone functionality, also functions as a computer, allowing the use of a variety of applications.

[0099] An "interface" is a framework that describes the connection point or operating environment through which a user and a system exchange information.

[0100] The system that realizes this invention operates primarily around a server. The server receives construction condition information entered by the user via a terminal and stores it in a database at an initial stage. Construction condition information includes the content of the construction, the size of the area, the difficulty of the work, and the required technologies and skills.

[0101] The server automatically performs a procedure to select the most suitable contractor by comparing contractor information stored in the database with the entered construction condition information. Contractor information includes each contractor's skills, qualifications, and past work experience, and the optimal selection is made by evaluating these factors.

[0102] After the selection process is complete, the server automatically places orders with the selected contractors. Contractors can then prepare based on the received order details and work schedule information. The work schedule is determined by the server monitoring the contractors' availability in real time and optimizing time allocation.

[0103] Furthermore, this system also features a user interface that runs on smartphones, allowing users to monitor the progress of construction in real time, even from remote locations. This feature significantly improves the transparency of progress monitoring and enables faster responses.

[0104] For example, when a project manager orders the installation of an air conditioning system for a new commercial facility, they use their smartphone to input conditions such as "installation of an air conditioning system, two stories, using equipment from company XX, start in April." Based on this input, the system quickly selects a suitable contractor and begins the project.

[0105] An example of a prompt message generated using an AI model is: "Please enter the construction conditions for the installation of an air conditioning system in a commercial facility: Air conditioning system installation, 2 stories, using equipment manufactured by XX company, start in April. Select a suitable contractor and monitor the progress in real time." By using this prompt message, the AI ​​model enables quick and accurate contractor selection and project management.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user uses a terminal to input construction condition information. This input includes construction details, area, difficulty level, and required skills. The terminal sends the information entered by the user to the server.

[0109] Step 2:

[0110] The server stores the received construction condition information in a database. This data is important information that will be used in the subsequent contractor selection process.

[0111] Step 3:

[0112] The server compares contractor information stored in the database with the entered construction condition information. Contractor information includes each contractor's skills, qualifications, and past construction experience. Based on this data, the server selects the appropriate contractor. An algorithm using a generative AI model is utilized for this selection.

[0113] Step 4:

[0114] The server automatically places orders with the selected contractors. Along with the order details, the work schedule is also transmitted. The server monitors the contractors' availability in real time and assigns the most suitable work schedule.

[0115] Step 5:

[0116] Once the contractor begins construction, the server monitors the progress of the work in real time. It collects progress data from the contractor and evaluates whether the construction is progressing according to plan.

[0117] Step 6:

[0118] The server sends the collected progress data to the user's terminal, allowing the user to remotely check the progress of the construction via their smartphone. This enables the user to understand the progress of the construction and issue adjustments as needed.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention is a system that aims to improve the efficiency of construction ordering operations while also providing a better user experience by taking user emotions into consideration. The system mainly consists of the following components.

[0121] First, the user inputs construction condition information using a terminal. This includes the construction details, area, difficulty level, and required skills. As the user inputs the information, the terminal activates an emotion engine to recognize the user's current emotional state. This is done in real time using voice analysis and facial recognition technology.

[0122] The server receives construction condition information and recognized user emotion data transmitted from the terminal. The server searches its database for information on contractors and selects the contractor best suited to the construction conditions. This selection takes into account the contractor's skills, past performance, and the user's emotional state. For example, if the user indicates anxiety, the server will prioritize selecting highly-rated contractors to enhance the user's sense of security.

[0123] Once the selection is complete, the server automatically places the order and notifies the contractor of the optimal schedule. The server adjusts the order ratio and amount based on the order details and communicates them to the contractor. In addition, the emotion engine tracks the user's reactions and monitors whether the user is satisfied throughout the entire ordering process.

[0124] While construction is underway, the server monitors the progress and continuously checks the user's emotional state. The server sends notifications to the terminal as needed and provides feedback to the user to facilitate smooth communication.

[0125] As a concrete example, consider a scenario where a user requests a large-scale renovation project. If this user becomes dissatisfied with the scheduling process, the emotion engine recognizes this change, and the server quickly takes corrective action. The user is then reassured by being immediately provided with the adjusted schedule and additional information about the construction company. This allows the system to optimize both technical efficiency and user experience.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user uses a terminal to input construction condition information. This information includes details such as the nature of the work, location, required skills, and deadline. The terminal collects this information, and during this process, its built-in emotion engine analyzes the user's facial expressions or voice input to infer their current emotional state.

[0129] Step 2:

[0130] The terminal transmits acquired construction condition information and user emotion data to the server. The user's emotion state is categorized into positive, negative, neutral, etc., and transmitted to the server.

[0131] Step 3:

[0132] The server extracts suitable contractors from its database based on the received construction condition information. During this process, it evaluates the contractors' skill sets, qualifications, and past performance to select the most suitable candidates. Furthermore, it considers the user's emotional state; if anxiety or stress is detected, it prioritizes selecting highly-rated contractors.

[0133] Step 4:

[0134] The server automatically places orders with selected contractors, optimizing the order ratio and cost. Contractors are notified of the optimal schedule along with detailed order information for the work. This notification includes prompt confirmation and selection of reliable contractors, addressing user needs.

[0135] Step 5:

[0136] Once construction begins, the server monitors the progress of the work and collects user feedback via the terminal. The emotion engine tracks changes in the user's emotions throughout the construction and reports them to the server. If a user expresses dissatisfaction, the server considers providing additional information or taking prompt action and notifies the user via the terminal.

[0137] Step 6:

[0138] After construction is completed, the server compiles the final construction data and updates the contractor's skill database. This update reflects the success rate of the construction, user feedback, and sentiment data. To improve the user experience, sentiment data is stored as reference data for future use.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0141] In construction ordering processes, conventional systems selected contractors and managed construction progress based on uniform criteria without considering user feelings. This resulted in missed opportunities to alleviate user anxiety and dissatisfaction, making it difficult to provide an optimal user experience. Furthermore, the inability to efficiently select contractors and manage progress hindered the smooth progress of the entire construction project.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] In this invention, the server includes functional means for collecting and recording construction condition information, functional means for recognizing the user's emotional state using voice analysis and facial recognition technology, and functional means for evaluating multiple contractor information and selecting the most suitable contractor based on the collected construction condition information and recognized emotional data. This enables the selection of a contractor that takes the user's emotions into consideration and smooth construction progress management.

[0144] "Construction condition information" refers to specific information related to the construction work, such as the content of the work, the area, the difficulty level, and the required skills.

[0145] "Voice analysis" is a technology that analyzes voice data to understand emotions and intentions conveyed through speech.

[0146] "Facial recognition technology" is a technology that analyzes a user's facial expressions to recognize their emotions and reactions in real time.

[0147] "Emotional state" refers to the emotions and psychological tendencies that a user is experiencing at a given time.

[0148] "Contractor information" refers to information that includes data such as skills, past performance, and evaluations related to the contractor.

[0149] "Ordering ratio" refers to the proportion in which work is distributed among multiple contractors, depending on the scale and nature of the construction project.

[0150] A "capabilities database" is a database that stores and updates information on the skills, track record, and evaluations of construction companies.

[0151] "Feedback" refers to opinions and information provided in response to a user's behavior and emotional state, with the aim of improving the user experience.

[0152] This system is designed to allow users to input construction condition information and place construction orders efficiently and optimally. The terminal receives input from the user regarding construction details, area, difficulty level, and required skills, while simultaneously recognizing the user's emotional state using voice analysis and facial recognition technology. The terminal activates an emotion engine to acquire emotional data in real time.

[0153] Subsequently, the terminal sends the acquired construction condition information and emotional data to the server. The server receives this data, accesses the database, and searches for contractor information. In addition to the contractor's skills, past performance, and ratings, it also considers the user's emotional state to select the most suitable contractor. The selection is performed using a generative AI model and is designed to alleviate the user's anxiety and dissatisfaction.

[0154] Once the selection is complete, the server automatically places the order and notifies the selected contractors of the detailed schedule. The server also notifies the user of the selection results and progress information via their terminal and provides necessary feedback.

[0155] For example, if a user requests a major renovation project and is dissatisfied with the construction schedule, the emotion engine will recognize this change, and the server will quickly adjust the schedule. This can reassure the user. An example of a prompt message would be, "Please explain in detail the criteria for selecting a contractor to alleviate the user's concerns."

[0156] The goal of this system is to maximize the technical efficiency of the ordering process and the user experience.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The user uses a terminal to input construction condition information. The information entered includes the construction details, area, difficulty level, and required skills. The terminal receives this information and prepares it as input data. At the same time, the terminal recognizes the user's emotional state in real time using voice analysis and facial recognition technology. In this process, voice and video data from the user are analyzed and output as emotional data.

[0160] Step 2:

[0161] The terminal sends construction condition information and sentiment data obtained from the user to the server. The server receives this data and starts searching for contractor information by referring to the database. Here, as a data processing step, the construction condition information is matched with the contractor database to generate a list of relevant contractors.

[0162] Step 3:

[0163] The server selects the most suitable contractor based on the generated list of contractors and sentiment data. This selection process uses a generative AI model to evaluate the skills and past performance of contractors while considering the user's emotional state. As a data calculation, an evaluation score is calculated for each contractor, and the final contractor selection is made by weighting these scores according to the user's emotional state.

[0164] Step 4:

[0165] The server automatically places orders for construction work with selected contractors. At this time, it notifies the contractors of the specific work details and schedule. The outputted order details are entered into the contractor's management system, and preparations for construction are completed.

[0166] Step 5:

[0167] During construction, the server monitors the contractor's progress in real time. This information is periodically sent to the server as construction progress data for progress management. Simultaneously, the server continuously monitors the user's emotional state through the terminal, sending notifications and providing feedback as needed.

[0168] Through these steps, the system achieves efficient ordering and an improved user experience.

[0169] (Application Example 2)

[0170] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0171] Traditional construction ordering systems had the problem of not being able to take into account the emotional state of users, resulting in an insufficient optimization of the user experience. As a result, users sometimes felt dissatisfied, and feedback provision and contractor selection were not carried out appropriately. Furthermore, it was difficult to respond to changes in the user's psychological state as life events progressed.

[0172] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0173] In this invention, the server includes means for simultaneously acquiring and analyzing construction condition information and the user's emotional state, means for dynamically selecting an appropriate contractor based on the acquired data, and means for improving the user experience by providing timely feedback that corresponds to the user's emotions. This makes it possible to select a contractor and provide feedback that takes the user's emotional state into consideration, thereby improving the user experience and the accuracy of contractor selection.

[0174] "Construction condition information" refers to specific information related to the construction, such as the content of the work, the area, the difficulty level, and the required skills.

[0175] "User emotional state" refers to data that indicates the user's psychological state, and is obtained through voice analysis and facial recognition.

[0176] "Contractor information" refers to information that includes data such as the skills, past performance, and evaluations of contractors.

[0177] A "construction schedule" refers to a detailed plan of the construction work, presented after considering the working status of the construction company and the progress of the work.

[0178] "Feedback" refers to information provided by the system to the user in the construction ordering process and contractor selection process, reflecting the user's emotional state and opinions.

[0179] A "skills database" refers to a database that systematically compiles information on the capabilities and achievements of construction companies.

[0180] The system for implementing this invention consists of a user terminal, a server running on the cloud, and an emotion engine for data analysis. The user inputs construction condition information using the terminal. To understand the user's emotional state in real time, the terminal uses a camera and microphone to detect facial expressions and voice, which are then analyzed by the emotion engine. This analysis utilizes Google® Cloud Vision API and IBM Watson® emotion analysis API.

[0181] The server receives construction condition information and user emotional state data transmitted from the terminal. Based on this information, a program on the server retrieves information on multiple contractors from a database and selects the most suitable contractor that matches the user's needs and psychological state. In this process, the server also considers the past performance and skill database of contractors. The selected contractor is automatically notified of the construction order and the optimal construction schedule.

[0182] Furthermore, during construction, the server continuously monitors the progress and updates the user's emotional state. This improves the user experience throughout the entire construction process. If an anomaly is detected, the server quickly generates and provides feedback to the user.

[0183] For example, if a user feels anxious during the home renovation process, the system can sense this emotion, and the server will immediately provide information on highly-rated contractors. This allows the user to feel at ease.

[0184] The following is an example of a prompt message.

[0185] User: I want to do a major kitchen renovation, but I'm worried about the budget and the timeframe.

[0186] AI: Don't worry. The contractors we recommend are highly rated and will provide the best plan to stay within your budget.

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The user inputs construction condition information via a terminal. This information includes the details of the construction, area, difficulty level, and required skills. The entered information is temporarily stored on the terminal and prepared for sentiment analysis.

[0190] Step 2:

[0191] The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone in order to obtain the user's emotional state. This data is analyzed through the Google Cloud Vision API and IBM Watson's Sentiment Analysis API to identify the user's emotional state. The analysis results are sent from the device to the server.

[0192] Step 3:

[0193] The server searches its database for contractor information based on the construction conditions information received from the terminal and the user's emotional state. When listing contractors that match the construction details, past performance and user ratings are also considered. In particular, if the user has expressed concerns, contractors with high ratings are selected as a priority. The selection results are then sent back to the terminal.

[0194] Step 4:

[0195] The server automatically places orders for construction work with selected contractors. During this process, the system optimizes the schedule to be as fast and efficient as possible, taking into account the contractors' availability. The resulting schedule information is then communicated to both the contractors and the users' terminals.

[0196] Step 5:

[0197] Once construction begins, the server monitors the progress of the construction and continuously tracks changes in the user's emotional state. If necessary, it sends feedback to the terminal, providing users with up-to-date information to ensure their peace of mind. This feedback process significantly contributes to improving the user experience.

[0198] Step 6:

[0199] After the construction is completed, the server collects the user's final feedback and uses it to update the contractor's skills database. This updated information will be an important indicator in the future contractor selection process.

[0200] The following prompt messages will be used as examples of the interactions at each step.

[0201] User: I want to do a major kitchen renovation, but I'm worried about the budget and the timeframe.

[0202] AI: Don't worry. The contractors we recommend are highly rated and will provide the best plan to stay within your budget.

[0203] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0204] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0210] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0212] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0214] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0216] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0219] This system is designed to efficiently automate construction ordering processes, and its implementation consists of the following elements. First, the system includes an interface where users input construction condition information via a terminal. This information includes details such as the construction content, area, difficulty level, and required skills.

[0220] The server receives construction condition information transmitted from the terminal and compares it with contractor information stored in the database. This comparison process evaluates each contractor's skill set, qualifications, and past construction performance to select the contractor best suited to the conditions. The selection is carried out by an algorithm that chooses the optimal contractor based on skill matching and evaluation scores.

[0221] After selection, the server automatically places orders with the selected contractors, and can adjust the order ratio and amount based on the initial contract. The contractors receive the order details along with the construction schedule information. This schedule is determined by the server managing operational status in real time and allocating optimized time slots.

[0222] Once construction begins, users can monitor the progress of the work via their terminals and make adjustments as needed. Construction progress data is collected by a server, and upon completion, the contractor's skill database is updated. This improves the accuracy of selections for future orders.

[0223] As a concrete example, suppose a user wants to order electrical equipment installation work for a new building. In this case, the user inputs the necessary conditions (such as the need for work at heights or the use of equipment from a specific manufacturer) into a terminal. Based on this, the server automatically selects a contractor with the appropriate skills and qualifications and places the order automatically. During the work, the progress is tracked in real time to ensure smooth completion of the project. This improves the efficiency and safety of ordering construction work.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The user enters the detailed conditions of the construction work into the terminal. These conditions include the scope of work, area, duration, difficulty level, required skills, and special requirements. The terminal temporarily stores this information and sends it to the server.

[0227] Step 2:

[0228] The server accesses the database based on the received construction condition information and searches for contractors that meet the conditions. The server evaluates the suitability of each contractor based on their skill set, qualifications, and past performance, and lists the contractors that best meet the conditions.

[0229] Step 3:

[0230] The server monitors the operational status of selected contractors in real time. Based on this, it proposes and adjusts the optimal start date and time for construction. The server then notifies the contractors of the adjusted order details and schedule based on the information obtained.

[0231] Step 4:

[0232] Once the contract for the selected contractor is finalized, the server automatically adjusts the order ratio and amount based on the initial contract and formally sends the order to the contractor. The contractor then receives the order details.

[0233] Step 5:

[0234] Once construction begins, the server monitors the progress and provides real-time information to users via their terminals. Users can check the progress and make adjustments as needed.

[0235] Step 6:

[0236] After the construction is completed, the server aggregates all the project progress data and updates the contractor's skills database. This update ensures that the contractor's track record and skills are up-to-date, which can be used to inform future projects.

[0237] (Example 1)

[0238] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0239] The problem that this invention aims to solve is to improve the efficiency and accuracy of selecting construction specialists in construction ordering operations. Conventional manual processes for selecting construction specialists are time-consuming and labor-intensive, and it was sometimes difficult to select the most suitable specialists. In addition, the lack of real-time monitoring of the progress of construction was a problem, leading to delays in construction progress and a decline in quality.

[0240] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0241] In this invention, the server includes means for acquiring and recording construction condition information, means for evaluating construction expert information in a database based on the acquired construction condition information and selecting a construction expert that meets the conditions, and means for collecting work progress data and updating the database of construction experts' capabilities. As a result, the selection of construction experts in construction ordering operations is automated and accuracy is improved, and construction progress can be monitored in real time, significantly improving overall operational efficiency.

[0242] "Construction conditions information" refers to detailed information related to construction work, such as the content of the work, area, difficulty level, and required skills.

[0243] A "construction specialist" refers to a professional who carries out construction work and possesses a specific skill set, qualifications, and past work experience.

[0244] "Contracting work" refers to the process of requesting selected construction specialists to carry out construction work.

[0245] "Operating status" refers to data that shows the current work status and schedule of construction specialists in real time.

[0246] "Work schedule" refers to the timeline and plan of the construction work assigned to the construction specialists.

[0247] A "skills database" refers to a database that records and manages information on the skills and past performance of construction professionals.

[0248] This invention is a system for efficiently automating construction ordering operations, and is composed of user, server, and terminal elements.

[0249] Users input construction-related information via a terminal. The terminal converts this information into a digital format and sends it to the server. The terminal can be implemented using basic computing devices or mobile devices.

[0250] The server uses the construction condition information received from the user to compare it with the construction expert information stored in the database. The database system used here is implemented as a general relational database management system (RDBMS) and efficiently manages the skill sets and past performance of construction experts.

[0251] The server also utilizes a skill matching algorithm to select the construction expert best suited to the input conditions. This process uses a specific machine learning model to evaluate the optimal expert based on skill match and past evaluations. Because this algorithm runs on the server, rapid selection is achieved.

[0252] After selection, the server executes a notification system to automatically place work orders with the selected construction specialists. This notification system transmits order information via email or a dedicated application. Furthermore, the server monitors the work status of the construction specialists in real time and creates an optimal work schedule.

[0253] As the construction progresses, users can monitor the progress via their terminals and make adjustments as needed. The server continuously collects progress data and updates the database of construction specialists' capabilities upon completion of the project. This update process further improves the accuracy of selections when future projects are ordered.

[0254] As a concrete example, consider a case where a user requests interior construction work for an office building. The user inputs conditions such as "interior design," "Tokyo," and "requires high technical skills" into a terminal. Based on this, the server selects construction specialists with the appropriate skills and qualifications and automatically places the order. During the construction process, the user can check the progress in real time on their terminal and make any necessary adjustments.

[0255] An example of a prompt message is: "Please describe an automated system for efficiently carrying out interior construction work on a building. Specifically, please describe the selection and management methods for specialists, given the requirement for high technical expertise and the need for work to be performed in the Kanto area."

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] The user uses a terminal to input construction condition information. This information includes the details of the construction, area, difficulty level, and required skills. The entered condition information is converted into a digital format and sent to the server. The input consists of construction condition information specified by the user, and the output is formatted data sent to the server.

[0259] Step 2:

[0260] The server receives construction condition information from the terminal and stores it in the database. The received data serves as the basis for cross-referencing with construction expert information. Specifically, an insert process is performed into the database to ensure accurate storage of the information. The input is construction condition information sent by the user, and the output is the information recorded in the database.

[0261] Step 3:

[0262] The server matches the construction expert information with the construction condition information stored in the database. This process involves querying skill sets, qualifications, and experience information to generate a list of experts that match the criteria. SQL queries are used for the specific data processing. The input is the user's construction condition information, and the output is a list of construction experts that match the criteria.

[0263] Step 4:

[0264] The server uses a generative AI model to select the optimal construction expert from among those who meet the specified criteria. Here, machine learning algorithms are used to calculate skill match and past evaluation scores to select the most suitable expert. The input is a list of construction experts who meet the criteria, and the output is the selection result for the optimal construction expert.

[0265] Step 5:

[0266] The server automatically places work orders with selected construction specialists. Specifically, it creates notification emails and processes notifications through a dedicated application. The order details include construction work content and schedule information. The input is the selection result of the most suitable construction specialist, and the output is an order notification to the construction specialist.

[0267] Step 6:

[0268] The server monitors the operational status of construction specialists in real time and creates an optimal work schedule. Real-time data analysis is performed to optimize each specialist's schedule information. The input is the construction specialists' schedule data, and the output is the optimized work schedule.

[0269] Step 7:

[0270] During construction, users monitor the progress using a terminal and make adjustments as needed. The terminal displays real-time progress data, allowing users to make decisions based on the information. Input is construction progress data, and output is a progress confirmation screen that the user receives.

[0271] Step 8:

[0272] The server analyzes progress data after construction is completed and updates the database of construction specialists' capabilities. This update improves the accuracy of the selection process for future projects. Specifically, it executes database update queries using the progress data. The input is construction progress data, and the output is the updated database of construction specialists' capabilities.

[0273] (Application Example 1)

[0274] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0275] In modern construction procurement processes, the selection of contractors and facilities, as well as schedule management, are often performed manually, leading to problems such as decreased efficiency and accuracy, and wasted time. Furthermore, there are limited means of monitoring progress in real time, making it difficult to ensure transparency in project management. In this situation, there is a need to develop a system that enables appropriate contractor selection, rapid ordering, and progress monitoring.

[0276] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0277] In this invention, the server includes a device for acquiring and recording construction condition information, a device for evaluating multiple contractor information based on the acquired construction condition information and selecting an appropriate contractor, and a device for recognizing the contractor's working status in real time and presenting an optimal work schedule. This enables improved efficiency and accuracy in construction ordering operations, as well as highly transparent project management.

[0278] "Construction condition information" refers to information that describes specific requirements related to construction work, such as the content of the work, location, difficulty level, and necessary technologies.

[0279] "Contractor information" refers to information used to evaluate the capabilities and reliability of a contractor or craftsman in carrying out construction work, including their skills, qualifications, and past performance.

[0280] "Operating status" refers to the operational state of the contractor, including the work they are currently undertaking, its progress, and future work plans.

[0281] A "work schedule" is a plan that outlines the specific steps and timetables formulated to ensure the efficient progress of construction work.

[0282] A "smartphone" is a portable information terminal that, in addition to telephone functionality, also functions as a computer, allowing the use of a variety of applications.

[0283] An "interface" is a framework that describes the connection point or operating environment through which a user and a system exchange information.

[0284] The system that realizes this invention operates primarily around a server. The server receives construction condition information entered by the user via a terminal and stores it in a database at an initial stage. Construction condition information includes the content of the construction, the size of the area, the difficulty of the work, and the required technologies and skills.

[0285] The server automatically executes the procedure of comparing the constructor information stored in the database with the input construction condition information and selecting the optimal constructor. The constructor information includes the skills, qualifications, and past work performance of each constructor, and the optimal selection is made by evaluating these.

[0286] After the selection is completed, the server automatically places an order with the selected constructor. The constructor can prepare based on the received order details and work schedule information. The work schedule is determined by the server grasping the operating status of the constructor in real time and performing optimized time allocation.

[0287] Also, this system also has a user interface that operates on a smartphone, and the user can monitor the progress of the construction in real time even from a remote location. This function greatly improves the transparency of progress confirmation and speeds up the response.

[0288] As a specific example, when a project manager places an order for the construction of an air conditioning system in a new commercial facility, conditions such as "installation of an air conditioning system, two-story building, using equipment manufactured by XX company, starting in April" are input using a smartphone. Based on this input, the system quickly selects an appropriate constructor and starts the progress of the project.

[0289] Examples of prompt texts using the generated AI model include forms such as "Please input the construction conditions for the construction of an air conditioning system in a commercial facility: installation of an air conditioning system, two-story building, using equipment manufactured by XX company, starting in April. Select the corresponding constructor and monitor the progress in real time." By using this prompt text, the AI model quickly and accurately selects a constructor and progresses the project.

[0290] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0291] Step 1:

[0292] The user uses a terminal to input construction condition information. This input includes construction details, area, difficulty level, and required skills. The terminal sends the information entered by the user to the server.

[0293] Step 2:

[0294] The server stores the received construction condition information in a database. This data is important information that will be used in the subsequent contractor selection process.

[0295] Step 3:

[0296] The server compares contractor information stored in the database with the entered construction condition information. Contractor information includes each contractor's skills, qualifications, and past construction experience. Based on this data, the server selects the appropriate contractor. An algorithm using a generative AI model is utilized for this selection.

[0297] Step 4:

[0298] The server automatically places orders with the selected contractors. Along with the order details, the work schedule is also transmitted. The server monitors the contractors' availability in real time and assigns the most suitable work schedule.

[0299] Step 5:

[0300] Once the contractor begins construction, the server monitors the progress of the work in real time. It collects progress data from the contractor and evaluates whether the construction is progressing according to plan.

[0301] Step 6:

[0302] By sending the collected progress data to the user's terminal, the server enables the user to remotely check the construction progress through a smartphone. As a result, the user can grasp the construction progress and give adjustment instructions as needed.

[0303] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0304] The present invention is a system that improves the efficiency of the construction ordering business and provides a better user experience by considering the user's emotion. The system mainly consists of the following components.

[0305] First, the user inputs construction condition information using a terminal. This includes construction content, area, difficulty, and required skills. When the user inputs, the terminal activates the emotion engine to recognize the user's current emotional state. This is done in real time by utilizing voice analysis and facial recognition technologies.

[0306] The server receives the construction condition information transmitted from the terminal and the recognized user emotion data. The server searches for information on construction contractors from the database and selects the most suitable contractor for the construction conditions. This selection is made considering the skills and past performance of the construction contractor, as well as the user's emotional state. For example, when the user shows an anxious emotion, the server operates to increase the user's sense of security by preferentially selecting a highly evaluated construction contractor.

[0307] When the selection is completed, the server automatically places an order and notifies the construction contractor of the optimal schedule. The server adjusts the order ratio and amount in the order details and conveys them to the construction contractor. Also, the emotion engine tracks the user's reaction and monitors whether the user is satisfied throughout the order process.

[0308] While construction is underway, the server monitors the progress and continuously checks the user's emotional state. The server sends notifications to the terminal as needed and provides feedback to the user to facilitate smooth communication.

[0309] As a concrete example, consider a scenario where a user requests a large-scale renovation project. If this user becomes dissatisfied with the scheduling process, the emotion engine recognizes this change, and the server quickly takes corrective action. The user is then reassured by being immediately provided with the adjusted schedule and additional information about the construction company. This allows the system to optimize both technical efficiency and user experience.

[0310] The following describes the processing flow.

[0311] Step 1:

[0312] The user uses a terminal to input construction condition information. This information includes details such as the nature of the work, location, required skills, and deadline. The terminal collects this information, and during this process, its built-in emotion engine analyzes the user's facial expressions or voice input to infer their current emotional state.

[0313] Step 2:

[0314] The terminal transmits acquired construction condition information and user emotion data to the server. The user's emotion state is categorized into positive, negative, neutral, etc., and transmitted to the server.

[0315] Step 3:

[0316] The server extracts suitable contractors from its database based on the received construction condition information. During this process, it evaluates the contractors' skill sets, qualifications, and past performance to select the most suitable candidates. Furthermore, it considers the user's emotional state; if anxiety or stress is detected, it prioritizes selecting highly-rated contractors.

[0317] Step 4:

[0318] The server automatically places orders with selected contractors, optimizing the order ratio and cost. Contractors are notified of the optimal schedule along with detailed order information for the work. This notification includes prompt confirmation and selection of reliable contractors, addressing user needs.

[0319] Step 5:

[0320] Once construction begins, the server monitors the progress of the work and collects user feedback via the terminal. The emotion engine tracks changes in the user's emotions throughout the construction and reports them to the server. If a user expresses dissatisfaction, the server considers providing additional information or taking prompt action and notifies the user via the terminal.

[0321] Step 6:

[0322] After construction is completed, the server compiles the final construction data and updates the contractor's skill database. This update reflects the success rate of the construction, user feedback, and sentiment data. To improve the user experience, sentiment data is stored as reference data for future use.

[0323] (Example 2)

[0324] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0325] In construction ordering processes, conventional systems selected contractors and managed construction progress based on uniform criteria without considering user feelings. This resulted in missed opportunities to alleviate user anxiety and dissatisfaction, making it difficult to provide an optimal user experience. Furthermore, the inability to efficiently select contractors and manage progress hindered the smooth progress of the entire construction project.

[0326] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0327] In this invention, the server includes functional means for collecting and recording construction condition information, functional means for recognizing the user's emotional state using voice analysis and facial recognition technology, and functional means for evaluating multiple contractor information and selecting the most suitable contractor based on the collected construction condition information and recognized emotional data. This enables the selection of a contractor that takes the user's emotions into consideration and smooth construction progress management.

[0328] "Construction condition information" refers to specific information related to the construction work, such as the content of the work, the area, the difficulty level, and the required skills.

[0329] "Voice analysis" is a technology that analyzes voice data to understand emotions and intentions conveyed through speech.

[0330] "Facial recognition technology" is a technology that analyzes a user's facial expressions to recognize their emotions and reactions in real time.

[0331] "Emotional state" refers to the emotions and psychological tendencies that a user is experiencing at a given time.

[0332] "Contractor information" refers to information that includes data such as skills, past performance, and evaluations related to the contractor.

[0333] "Ordering ratio" refers to the proportion in which work is distributed among multiple contractors, depending on the scale and nature of the construction project.

[0334] A "capabilities database" is a database that stores and updates information on the skills, track record, and evaluations of construction companies.

[0335] "Feedback" refers to opinions and information provided in response to a user's behavior and emotional state, with the aim of improving the user experience.

[0336] This system is designed to allow users to input construction condition information and place construction orders efficiently and optimally. The terminal receives input from the user regarding construction details, area, difficulty level, and required skills, while simultaneously recognizing the user's emotional state using voice analysis and facial recognition technology. The terminal activates an emotion engine to acquire emotional data in real time.

[0337] Subsequently, the terminal sends the acquired construction condition information and emotional data to the server. The server receives this data, accesses the database, and searches for contractor information. In addition to the contractor's skills, past performance, and ratings, it also considers the user's emotional state to select the most suitable contractor. The selection is performed using a generative AI model and is designed to alleviate the user's anxiety and dissatisfaction.

[0338] Once the selection is complete, the server automatically places the order and notifies the selected contractors of the detailed schedule. The server also notifies the user of the selection results and progress information via their terminal and provides necessary feedback.

[0339] For example, if a user requests a major renovation project and is dissatisfied with the construction schedule, the emotion engine will recognize this change, and the server will quickly adjust the schedule. This can reassure the user. An example of a prompt message would be, "Please explain in detail the criteria for selecting a contractor to alleviate the user's concerns."

[0340] The goal of this system is to maximize the technical efficiency of the ordering process and the user experience.

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] The user uses a terminal to input construction condition information. The information entered includes the construction details, area, difficulty level, and required skills. The terminal receives this information and prepares it as input data. At the same time, the terminal recognizes the user's emotional state in real time using voice analysis and facial recognition technology. In this process, voice and video data from the user are analyzed and output as emotional data.

[0344] Step 2:

[0345] The terminal sends construction condition information and sentiment data obtained from the user to the server. The server receives this data and starts searching for contractor information by referring to the database. Here, as a data processing step, the construction condition information is matched with the contractor database to generate a list of relevant contractors.

[0346] Step 3:

[0347] The server selects the most suitable contractor based on the generated list of contractors and sentiment data. This selection process uses a generative AI model to evaluate the skills and past performance of contractors while considering the user's emotional state. As a data calculation, an evaluation score is calculated for each contractor, and the final contractor selection is made by weighting these scores according to the user's emotional state.

[0348] Step 4:

[0349] The server automatically places orders for construction work with selected contractors. At this time, it notifies the contractors of the specific work details and schedule. The outputted order details are entered into the contractor's management system, and preparations for construction are completed.

[0350] Step 5:

[0351] During construction, the server monitors the contractor's progress in real time. This information is periodically sent to the server as construction progress data for progress management. Simultaneously, the server continuously monitors the user's emotional state through the terminal, sending notifications and providing feedback as needed.

[0352] Through these steps, the system achieves efficient ordering and an improved user experience.

[0353] (Application Example 2)

[0354] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0355] Traditional construction ordering systems had the problem of not being able to take into account the emotional state of users, resulting in an insufficient optimization of the user experience. As a result, users sometimes felt dissatisfied, and feedback provision and contractor selection were not carried out appropriately. Furthermore, it was difficult to respond to changes in the user's psychological state as life events progressed.

[0356] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0357] In this invention, the server includes means for simultaneously acquiring and analyzing construction condition information and the user's emotional state, means for dynamically selecting an appropriate contractor based on the acquired data, and means for improving the user experience by providing timely feedback that corresponds to the user's emotions. This makes it possible to select a contractor and provide feedback that takes the user's emotional state into consideration, thereby improving the user experience and the accuracy of contractor selection.

[0358] "Construction condition information" refers to specific information related to the construction, such as the content of the work, the area, the difficulty level, and the required skills.

[0359] "User emotional state" refers to data that indicates the user's psychological state, and is obtained through voice analysis and facial recognition.

[0360] "Contractor information" refers to information that includes data such as the skills, past performance, and evaluations of contractors.

[0361] A "construction schedule" refers to a detailed plan of the construction work, presented after considering the working status of the construction company and the progress of the work.

[0362] "Feedback" refers to information provided by the system to the user in the construction ordering process and contractor selection process, reflecting the user's emotional state and opinions.

[0363] A "skills database" refers to a database that systematically compiles information on the capabilities and achievements of construction companies.

[0364] The system for implementing this invention consists of a user terminal, a server running on the cloud, and an emotion engine for data analysis. The user uses the terminal to input construction condition information. To understand the user's emotional state in real time, the terminal uses a camera and microphone to detect facial expressions and voice, which are then analyzed by the emotion engine. This analysis utilizes the Google Cloud Vision API and IBM Watson's emotion analysis API.

[0365] The server receives construction condition information and user emotional state data transmitted from the terminal. Based on this information, a program on the server retrieves information on multiple contractors from a database and selects the most suitable contractor that matches the user's needs and psychological state. In this process, the server also considers the past performance and skill database of contractors. The selected contractor is automatically notified of the construction order and the optimal construction schedule.

[0366] Furthermore, during construction, the server continuously monitors the progress and updates the user's emotional state. This improves the user experience throughout the entire construction process. If an anomaly is detected, the server quickly generates and provides feedback to the user.

[0367] For example, if a user feels anxious during the home renovation process, the system can sense this emotion, and the server will immediately provide information on highly-rated contractors. This allows the user to feel at ease.

[0368] The following is an example of a prompt message.

[0369] User: I want to do a major kitchen renovation, but I'm worried about the budget and the timeframe.

[0370] AI: Don't worry. The contractors we recommend are highly rated and will provide the best plan to stay within your budget.

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] The user inputs construction condition information via a terminal. This information includes the details of the construction, area, difficulty level, and required skills. The entered information is temporarily stored on the terminal and prepared for sentiment analysis.

[0374] Step 2:

[0375] The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone in order to obtain the user's emotional state. This data is analyzed through the Google Cloud Vision API and IBM Watson's Sentiment Analysis API to identify the user's emotional state. The analysis results are sent from the device to the server.

[0376] Step 3:

[0377] The server searches its database for contractor information based on the construction conditions information received from the terminal and the user's emotional state. When listing contractors that match the construction details, past performance and user ratings are also considered. In particular, if the user has expressed concerns, contractors with high ratings are selected as a priority. The selection results are then sent back to the terminal.

[0378] Step 4:

[0379] The server automatically places orders for construction work with selected contractors. During this process, the system optimizes the schedule to be as fast and efficient as possible, taking into account the contractors' availability. The resulting schedule information is then communicated to both the contractors and the users' terminals.

[0380] Step 5:

[0381] Once construction begins, the server monitors the progress of the construction and continuously tracks changes in the user's emotional state. If necessary, it sends feedback to the terminal, providing users with up-to-date information to ensure their peace of mind. This feedback process significantly contributes to improving the user experience.

[0382] Step 6:

[0383] After the construction is completed, the server collects the user's final feedback and uses it to update the contractor's skills database. This updated information will be an important indicator in the future contractor selection process.

[0384] The following prompt messages will be used as examples of the interactions at each step.

[0385] User: I want to do a major kitchen renovation, but I'm worried about the budget and the timeframe.

[0386] AI: Don't worry. The contractors we recommend are highly rated and will provide the best plan to stay within your budget.

[0387] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0388] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0389] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0393] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0394] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0395] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0396] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0397] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0398] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0399] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0400] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0401] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0402] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0403] This system is designed to efficiently automate construction ordering processes, and its implementation consists of the following elements. First, the system includes an interface where users input construction condition information via a terminal. This information includes details such as the construction content, area, difficulty level, and required skills.

[0404] The server receives construction condition information transmitted from the terminal and compares it with contractor information stored in the database. This comparison process evaluates each contractor's skill set, qualifications, and past construction performance to select the contractor best suited to the conditions. The selection is carried out by an algorithm that chooses the optimal contractor based on skill matching and evaluation scores.

[0405] After selection, the server automatically places orders with the selected contractors, and can adjust the order ratio and amount based on the initial contract. The contractors receive the order details along with the construction schedule information. This schedule is determined by the server managing operational status in real time and allocating optimized time slots.

[0406] Once construction begins, users can monitor the progress of the work via their terminals and make adjustments as needed. Construction progress data is collected by a server, and upon completion, the contractor's skill database is updated. This improves the accuracy of selections for future orders.

[0407] As a concrete example, suppose a user wants to order electrical equipment installation work for a new building. In this case, the user inputs the necessary conditions (such as the need for work at heights or the use of equipment from a specific manufacturer) into a terminal. Based on this, the server automatically selects a contractor with the appropriate skills and qualifications and places the order automatically. During the work, the progress is tracked in real time to ensure smooth completion of the project. This improves the efficiency and safety of ordering construction work.

[0408] The following describes the processing flow.

[0409] Step 1:

[0410] The user enters the detailed conditions of the construction work into the terminal. These conditions include the scope of work, area, duration, difficulty level, required skills, and special requirements. The terminal temporarily stores this information and sends it to the server.

[0411] Step 2:

[0412] The server accesses the database based on the received construction condition information and searches for contractors that meet the conditions. The server evaluates the suitability of each contractor based on their skill set, qualifications, and past performance, and lists the contractors that best meet the conditions.

[0413] Step 3:

[0414] The server monitors the operational status of selected contractors in real time. Based on this, it proposes and adjusts the optimal start date and time for construction. The server then notifies the contractors of the adjusted order details and schedule based on the information obtained.

[0415] Step 4:

[0416] Once the contract for the selected contractor is finalized, the server automatically adjusts the order ratio and amount based on the initial contract and formally sends the order to the contractor. The contractor then receives the order details.

[0417] Step 5:

[0418] Once construction begins, the server monitors the progress and provides real-time information to users via their terminals. Users can check the progress and make adjustments as needed.

[0419] Step 6:

[0420] After the construction is completed, the server aggregates all the project progress data and updates the contractor's skills database. This update ensures that the contractor's track record and skills are up-to-date, which can be used to inform future projects.

[0421] (Example 1)

[0422] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0423] The problem that this invention aims to solve is to improve the efficiency and accuracy of selecting construction specialists in construction ordering operations. Conventional manual processes for selecting construction specialists are time-consuming and labor-intensive, and it was sometimes difficult to select the most suitable specialists. In addition, the lack of real-time monitoring of the progress of construction was a problem, leading to delays in construction progress and a decline in quality.

[0424] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0425] In this invention, the server includes means for acquiring and recording construction condition information, means for evaluating construction expert information in a database based on the acquired construction condition information and selecting a construction expert that meets the conditions, and means for collecting work progress data and updating the database of construction experts' capabilities. As a result, the selection of construction experts in construction ordering operations is automated and accuracy is improved, and construction progress can be monitored in real time, significantly improving overall operational efficiency.

[0426] "Construction conditions information" refers to detailed information related to construction work, such as the content of the work, area, difficulty level, and required skills.

[0427] A "construction specialist" refers to a professional who carries out construction work and possesses a specific skill set, qualifications, and past work experience.

[0428] "Contracting work" refers to the process of requesting selected construction specialists to carry out construction work.

[0429] "Operating status" refers to data that shows the current work status and schedule of construction specialists in real time.

[0430] "Work schedule" refers to the timeline and plan of the construction work assigned to the construction specialists.

[0431] A "skills database" refers to a database that records and manages information on the skills and past performance of construction professionals.

[0432] This invention is a system for efficiently automating construction ordering operations, and is composed of user, server, and terminal elements.

[0433] Users input construction-related information via a terminal. The terminal converts this information into a digital format and sends it to the server. The terminal can be implemented using basic computing devices or mobile devices.

[0434] The server uses the construction condition information received from the user to compare it with the construction expert information stored in the database. The database system used here is implemented as a general relational database management system (RDBMS) and efficiently manages the skill sets and past performance of construction experts.

[0435] The server also utilizes a skill matching algorithm to select the construction expert best suited to the input conditions. This process uses a specific machine learning model to evaluate the optimal expert based on skill match and past evaluations. Because this algorithm runs on the server, rapid selection is achieved.

[0436] After selection, the server executes a notification system to automatically place work orders with the selected construction specialists. This notification system transmits order information via email or a dedicated application. Furthermore, the server monitors the work status of the construction specialists in real time and creates an optimal work schedule.

[0437] As the construction progresses, users can monitor the progress via their terminals and make adjustments as needed. The server continuously collects progress data and updates the database of construction specialists' capabilities upon completion of the project. This update process further improves the accuracy of selections when future projects are ordered.

[0438] As a concrete example, consider a case where a user requests interior construction work for an office building. The user inputs conditions such as "interior design," "Tokyo," and "requires high technical skills" into a terminal. Based on this, the server selects construction specialists with the appropriate skills and qualifications and automatically places the order. During the construction process, the user can check the progress in real time on their terminal and make any necessary adjustments.

[0439] An example of a prompt message is: "Please describe an automated system for efficiently carrying out interior construction work on a building. Specifically, please describe the selection and management methods for specialists, given the requirement for high technical expertise and the need for work to be performed in the Kanto area."

[0440] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0441] Step 1:

[0442] The user uses a terminal to input construction condition information. This information includes the details of the construction, area, difficulty level, and required skills. The entered condition information is converted into a digital format and sent to the server. The input consists of construction condition information specified by the user, and the output is formatted data sent to the server.

[0443] Step 2:

[0444] The server receives construction condition information from the terminal and stores it in the database. The received data serves as the basis for cross-referencing with construction expert information. Specifically, an insert process is performed into the database to ensure accurate storage of the information. The input is construction condition information sent by the user, and the output is the information recorded in the database.

[0445] Step 3:

[0446] The server matches the construction expert information with the construction condition information stored in the database. This process involves querying skill sets, qualifications, and experience information to generate a list of experts that match the criteria. SQL queries are used for the specific data processing. The input is the user's construction condition information, and the output is a list of construction experts that match the criteria.

[0447] Step 4:

[0448] The server uses a generative AI model to select the optimal construction expert from among those who meet the specified criteria. Here, machine learning algorithms are used to calculate skill match and past evaluation scores to select the most suitable expert. The input is a list of construction experts who meet the criteria, and the output is the selection result for the optimal construction expert.

[0449] Step 5:

[0450] The server automatically places work orders with selected construction specialists. Specifically, it creates notification emails and processes notifications through a dedicated application. The order details include construction work content and schedule information. The input is the selection result of the most suitable construction specialist, and the output is an order notification to the construction specialist.

[0451] Step 6:

[0452] The server monitors the operational status of construction specialists in real time and creates an optimal work schedule. Real-time data analysis is performed to optimize each specialist's schedule information. The input is the construction specialists' schedule data, and the output is the optimized work schedule.

[0453] Step 7:

[0454] During construction, users monitor the progress using a terminal and make adjustments as needed. The terminal displays real-time progress data, allowing users to make decisions based on the information. Input is construction progress data, and output is a progress confirmation screen that the user receives.

[0455] Step 8:

[0456] The server analyzes progress data after construction is completed and updates the database of construction specialists' capabilities. This update improves the accuracy of the selection process for future projects. Specifically, it executes database update queries using the progress data. The input is construction progress data, and the output is the updated database of construction specialists' capabilities.

[0457] (Application Example 1)

[0458] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0459] In modern construction procurement processes, the selection of contractors and facilities, as well as schedule management, are often performed manually, leading to problems such as decreased efficiency and accuracy, and wasted time. Furthermore, there are limited means of monitoring progress in real time, making it difficult to ensure transparency in project management. In this situation, there is a need to develop a system that enables appropriate contractor selection, rapid ordering, and progress monitoring.

[0460] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0461] In this invention, the server includes a device for acquiring and recording construction condition information, a device for evaluating multiple contractor information based on the acquired construction condition information and selecting an appropriate contractor, and a device for recognizing the contractor's working status in real time and presenting an optimal work schedule. This enables improved efficiency and accuracy in construction ordering operations, as well as highly transparent project management.

[0462] "Construction condition information" refers to information that describes specific requirements related to construction work, such as the content of the work, location, difficulty level, and necessary technologies.

[0463] "Contractor information" refers to information used to evaluate the capabilities and reliability of a contractor or craftsman in carrying out construction work, including their skills, qualifications, and past performance.

[0464] "Operating status" refers to the operational state of the contractor, including the work they are currently undertaking, its progress, and future work plans.

[0465] A "work schedule" is a plan that outlines the specific steps and timetables formulated to ensure the efficient progress of construction work.

[0466] A "smartphone" is a portable information terminal that, in addition to telephone functionality, also functions as a computer, allowing the use of a variety of applications.

[0467] An "interface" is a framework that describes the connection point or operating environment through which a user and a system exchange information.

[0468] The system that realizes this invention operates primarily around a server. The server receives construction condition information entered by the user via a terminal and stores it in a database at an initial stage. Construction condition information includes the content of the construction, the size of the area, the difficulty of the work, and the required technologies and skills.

[0469] The server automatically performs a procedure to select the most suitable contractor by comparing contractor information stored in the database with the entered construction condition information. Contractor information includes each contractor's skills, qualifications, and past work experience, and the optimal selection is made by evaluating these factors.

[0470] After the selection process is complete, the server automatically places orders with the selected contractors. Contractors can then prepare based on the received order details and work schedule information. The work schedule is determined by the server monitoring the contractors' availability in real time and optimizing time allocation.

[0471] Furthermore, this system also features a user interface that runs on smartphones, allowing users to monitor the progress of construction in real time, even from remote locations. This feature significantly improves the transparency of progress monitoring and enables faster responses.

[0472] For example, when a project manager orders the installation of an air conditioning system for a new commercial facility, they use their smartphone to input conditions such as "installation of an air conditioning system, two stories, using equipment from company XX, start in April." Based on this input, the system quickly selects a suitable contractor and begins the project.

[0473] An example of a prompt message generated using an AI model is: "Please enter the construction conditions for the installation of an air conditioning system in a commercial facility: Air conditioning system installation, 2 stories, using equipment manufactured by XX company, start in April. Select a suitable contractor and monitor the progress in real time." By using this prompt message, the AI ​​model enables quick and accurate contractor selection and project management.

[0474] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0475] Step 1:

[0476] The user uses a terminal to input construction condition information. This input includes construction details, area, difficulty level, and required skills. The terminal sends the information entered by the user to the server.

[0477] Step 2:

[0478] The server stores the received construction condition information in a database. This data is important information that will be used in the subsequent contractor selection process.

[0479] Step 3:

[0480] The server compares contractor information stored in the database with the entered construction condition information. Contractor information includes each contractor's skills, qualifications, and past construction experience. Based on this data, the server selects the appropriate contractor. An algorithm using a generative AI model is utilized for this selection.

[0481] Step 4:

[0482] The server automatically places orders with the selected contractors. Along with the order details, the work schedule is also transmitted. The server monitors the contractors' availability in real time and assigns the most suitable work schedule.

[0483] Step 5:

[0484] Once the contractor begins construction, the server monitors the progress of the work in real time. It collects progress data from the contractor and evaluates whether the construction is progressing according to plan.

[0485] Step 6:

[0486] The server sends the collected progress data to the user's terminal, allowing the user to remotely check the progress of the construction via their smartphone. This enables the user to understand the progress of the construction and issue adjustments as needed.

[0487] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0488] This invention is a system that aims to improve the efficiency of construction ordering operations while also providing a better user experience by taking user emotions into consideration. The system mainly consists of the following components.

[0489] First, the user inputs construction condition information using a terminal. This includes the construction details, area, difficulty level, and required skills. As the user inputs the information, the terminal activates an emotion engine to recognize the user's current emotional state. This is done in real time using voice analysis and facial recognition technology.

[0490] The server receives construction condition information and recognized user emotion data transmitted from the terminal. The server searches its database for information on contractors and selects the contractor best suited to the construction conditions. This selection takes into account the contractor's skills, past performance, and the user's emotional state. For example, if the user indicates anxiety, the server will prioritize selecting highly-rated contractors to enhance the user's sense of security.

[0491] Once the selection is complete, the server automatically places the order and notifies the contractor of the optimal schedule. The server adjusts the order ratio and amount based on the order details and communicates them to the contractor. In addition, the emotion engine tracks the user's reactions and monitors whether the user is satisfied throughout the entire ordering process.

[0492] While construction is underway, the server monitors the progress and continuously checks the user's emotional state. The server sends notifications to the terminal as needed and provides feedback to the user to facilitate smooth communication.

[0493] As a concrete example, consider a scenario where a user requests a large-scale renovation project. If this user becomes dissatisfied with the scheduling process, the emotion engine recognizes this change, and the server quickly takes corrective action. The user is then reassured by being immediately provided with the adjusted schedule and additional information about the construction company. This allows the system to optimize both technical efficiency and user experience.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] The user uses a terminal to input construction condition information. This information includes details such as the nature of the work, location, required skills, and deadline. The terminal collects this information, and during this process, its built-in emotion engine analyzes the user's facial expressions or voice input to infer their current emotional state.

[0497] Step 2:

[0498] The terminal transmits acquired construction condition information and user emotion data to the server. The user's emotion state is categorized into positive, negative, neutral, etc., and transmitted to the server.

[0499] Step 3:

[0500] The server extracts suitable contractors from its database based on the received construction condition information. During this process, it evaluates the contractors' skill sets, qualifications, and past performance to select the most suitable candidates. Furthermore, it considers the user's emotional state; if anxiety or stress is detected, it prioritizes selecting highly-rated contractors.

[0501] Step 4:

[0502] The server automatically places orders with selected contractors, optimizing the order ratio and cost. Contractors are notified of the optimal schedule along with detailed order information for the work. This notification includes prompt confirmation and selection of reliable contractors, addressing user needs.

[0503] Step 5:

[0504] Once construction begins, the server monitors the progress of the work and collects user feedback via the terminal. The emotion engine tracks changes in the user's emotions throughout the construction and reports them to the server. If a user expresses dissatisfaction, the server considers providing additional information or taking prompt action and notifies the user via the terminal.

[0505] Step 6:

[0506] After construction is completed, the server compiles the final construction data and updates the contractor's skill database. This update reflects the success rate of the construction, user feedback, and sentiment data. To improve the user experience, sentiment data is stored as reference data for future use.

[0507] (Example 2)

[0508] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0509] In construction ordering processes, conventional systems selected contractors and managed construction progress based on uniform criteria without considering user feelings. This resulted in missed opportunities to alleviate user anxiety and dissatisfaction, making it difficult to provide an optimal user experience. Furthermore, the inability to efficiently select contractors and manage progress hindered the smooth progress of the entire construction project.

[0510] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0511] In this invention, the server includes functional means for collecting and recording construction condition information, functional means for recognizing the user's emotional state using voice analysis and facial recognition technology, and functional means for evaluating multiple contractor information and selecting the most suitable contractor based on the collected construction condition information and recognized emotional data. This enables the selection of a contractor that takes the user's emotions into consideration and smooth construction progress management.

[0512] "Construction condition information" refers to specific information related to the construction work, such as the content of the work, the area, the difficulty level, and the required skills.

[0513] "Voice analysis" is a technology that analyzes voice data to understand emotions and intentions conveyed through speech.

[0514] "Facial recognition technology" is a technology that analyzes a user's facial expressions to recognize their emotions and reactions in real time.

[0515] "Emotional state" refers to the emotions and psychological tendencies that a user is experiencing at a given time.

[0516] "Contractor information" refers to information that includes data such as skills, past performance, and evaluations related to the contractor.

[0517] "Ordering ratio" refers to the proportion in which work is distributed among multiple contractors, depending on the scale and nature of the construction project.

[0518] A "capabilities database" is a database that stores and updates information on the skills, track record, and evaluations of construction companies.

[0519] "Feedback" refers to opinions and information provided in response to a user's behavior and emotional state, with the aim of improving the user experience.

[0520] This system is designed to allow users to input construction condition information and place construction orders efficiently and optimally. The terminal receives input from the user regarding construction details, area, difficulty level, and required skills, while simultaneously recognizing the user's emotional state using voice analysis and facial recognition technology. The terminal activates an emotion engine to acquire emotional data in real time.

[0521] Subsequently, the terminal sends the acquired construction condition information and emotional data to the server. The server receives this data, accesses the database, and searches for contractor information. In addition to the contractor's skills, past performance, and ratings, it also considers the user's emotional state to select the most suitable contractor. The selection is performed using a generative AI model and is designed to alleviate the user's anxiety and dissatisfaction.

[0522] Once the selection is complete, the server automatically places the order and notifies the selected contractors of the detailed schedule. The server also notifies the user of the selection results and progress information via their terminal and provides necessary feedback.

[0523] For example, if a user requests a major renovation project and is dissatisfied with the construction schedule, the emotion engine will recognize this change, and the server will quickly adjust the schedule. This can reassure the user. An example of a prompt message would be, "Please explain in detail the criteria for selecting a contractor to alleviate the user's concerns."

[0524] The goal of this system is to maximize the technical efficiency of the ordering process and the user experience.

[0525] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0526] Step 1:

[0527] The user uses a terminal to input construction condition information. The information entered includes the construction details, area, difficulty level, and required skills. The terminal receives this information and prepares it as input data. At the same time, the terminal recognizes the user's emotional state in real time using voice analysis and facial recognition technology. In this process, voice and video data from the user are analyzed and output as emotional data.

[0528] Step 2:

[0529] The terminal sends construction condition information and sentiment data obtained from the user to the server. The server receives this data and starts searching for contractor information by referring to the database. Here, as a data processing step, the construction condition information is matched with the contractor database to generate a list of relevant contractors.

[0530] Step 3:

[0531] The server selects the most suitable contractor based on the generated list of contractors and sentiment data. This selection process uses a generative AI model to evaluate the skills and past performance of contractors while considering the user's emotional state. As a data calculation, an evaluation score is calculated for each contractor, and the final contractor selection is made by weighting these scores according to the user's emotional state.

[0532] Step 4:

[0533] The server automatically places orders for construction work with selected contractors. At this time, it notifies the contractors of the specific work details and schedule. The outputted order details are entered into the contractor's management system, and preparations for construction are completed.

[0534] Step 5:

[0535] During construction, the server monitors the contractor's progress in real time. This information is periodically sent to the server as construction progress data for progress management. Simultaneously, the server continuously monitors the user's emotional state through the terminal, sending notifications and providing feedback as needed.

[0536] Through these steps, the system achieves efficient ordering and an improved user experience.

[0537] (Application Example 2)

[0538] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0539] Traditional construction ordering systems had the problem of not being able to take into account the emotional state of users, resulting in an insufficient optimization of the user experience. As a result, users sometimes felt dissatisfied, and feedback provision and contractor selection were not carried out appropriately. Furthermore, it was difficult to respond to changes in the user's psychological state as life events progressed.

[0540] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0541] In this invention, the server includes means for simultaneously acquiring and analyzing construction condition information and the user's emotional state, means for dynamically selecting an appropriate contractor based on the acquired data, and means for improving the user experience by providing timely feedback that corresponds to the user's emotions. This makes it possible to select a contractor and provide feedback that takes the user's emotional state into consideration, thereby improving the user experience and the accuracy of contractor selection.

[0542] "Construction condition information" refers to specific information related to the construction, such as the content of the work, the area, the difficulty level, and the required skills.

[0543] "User emotional state" refers to data that indicates the user's psychological state, and is obtained through voice analysis and facial recognition.

[0544] "Contractor information" refers to information that includes data such as the skills, past performance, and evaluations of contractors.

[0545] A "construction schedule" refers to a detailed plan of the construction work, presented after considering the working status of the construction company and the progress of the work.

[0546] "Feedback" refers to information provided by the system to the user in the construction ordering process and contractor selection process, reflecting the user's emotional state and opinions.

[0547] A "skills database" refers to a database that systematically compiles information on the capabilities and achievements of construction companies.

[0548] The system for implementing this invention consists of a user terminal, a server running on the cloud, and an emotion engine for data analysis. The user uses the terminal to input construction condition information. To understand the user's emotional state in real time, the terminal uses a camera and microphone to detect facial expressions and voice, which are then analyzed by the emotion engine. This analysis utilizes the Google Cloud Vision API and IBM Watson's emotion analysis API.

[0549] The server receives construction condition information and user emotional state data transmitted from the terminal. Based on this information, a program on the server retrieves information on multiple contractors from a database and selects the most suitable contractor that matches the user's needs and psychological state. In this process, the server also considers the past performance and skill database of contractors. The selected contractor is automatically notified of the construction order and the optimal construction schedule.

[0550] Furthermore, during construction, the server continuously monitors the progress and updates the user's emotional state. This improves the user experience throughout the entire construction process. If an anomaly is detected, the server quickly generates and provides feedback to the user.

[0551] For example, if a user feels anxious during the home renovation process, the system can sense this emotion, and the server will immediately provide information on highly-rated contractors. This allows the user to feel at ease.

[0552] The following is an example of a prompt message.

[0553] User: I want to do a major kitchen renovation, but I'm worried about the budget and the timeframe.

[0554] AI: Don't worry. The contractors we recommend are highly rated and will provide the best plan to stay within your budget.

[0555] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0556] Step 1:

[0557] The user inputs construction condition information via a terminal. This information includes the details of the construction, area, difficulty level, and required skills. The entered information is temporarily stored on the terminal and prepared for sentiment analysis.

[0558] Step 2:

[0559] The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone in order to obtain the user's emotional state. This data is analyzed through the Google Cloud Vision API and IBM Watson's Sentiment Analysis API to identify the user's emotional state. The analysis results are sent from the device to the server.

[0560] Step 3:

[0561] The server searches its database for contractor information based on the construction conditions information received from the terminal and the user's emotional state. When listing contractors that match the construction details, past performance and user ratings are also considered. In particular, if the user has expressed concerns, contractors with high ratings are selected as a priority. The selection results are then sent back to the terminal.

[0562] Step 4:

[0563] The server automatically places orders for construction work with selected contractors. During this process, the system optimizes the schedule to be as fast and efficient as possible, taking into account the contractors' availability. The resulting schedule information is then communicated to both the contractors and the users' terminals.

[0564] Step 5:

[0565] Once construction begins, the server monitors the progress of the construction and continuously tracks changes in the user's emotional state. If necessary, it sends feedback to the terminal, providing users with up-to-date information to ensure their peace of mind. This feedback process significantly contributes to improving the user experience.

[0566] Step 6:

[0567] After the construction is completed, the server collects the user's final feedback and uses it to update the contractor's skills database. This updated information will be an important indicator in the future contractor selection process.

[0568] The following prompt messages will be used as examples of the interactions at each step.

[0569] User: I want to do a major kitchen renovation, but I'm worried about the budget and the timeframe.

[0570] AI: Don't worry. The contractors we recommend are highly rated and will provide the best plan to stay within your budget.

[0571] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0572] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0573] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0574] [Fourth Embodiment]

[0575] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0576] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0577] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0578] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0579] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0580] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0581] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0582] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0583] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0584] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0585] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0586] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0587] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0588] This system is designed to efficiently automate construction ordering processes, and its implementation consists of the following elements. First, the system includes an interface where users input construction condition information via a terminal. This information includes details such as the construction content, area, difficulty level, and required skills.

[0589] The server receives construction condition information transmitted from the terminal and compares it with contractor information stored in the database. This comparison process evaluates each contractor's skill set, qualifications, and past construction performance to select the contractor best suited to the conditions. The selection is carried out by an algorithm that chooses the optimal contractor based on skill matching and evaluation scores.

[0590] After selection, the server automatically places orders with the selected contractors, and can adjust the order ratio and amount based on the initial contract. The contractors receive the order details along with the construction schedule information. This schedule is determined by the server managing operational status in real time and allocating optimized time slots.

[0591] Once construction begins, users can monitor the progress of the work via their terminals and make adjustments as needed. Construction progress data is collected by a server, and upon completion, the contractor's skill database is updated. This improves the accuracy of selections for future orders.

[0592] As a concrete example, suppose a user wants to order electrical equipment installation work for a new building. In this case, the user inputs the necessary conditions (such as the need for work at heights or the use of equipment from a specific manufacturer) into a terminal. Based on this, the server automatically selects a contractor with the appropriate skills and qualifications and places the order automatically. During the work, the progress is tracked in real time to ensure smooth completion of the project. This improves the efficiency and safety of ordering construction work.

[0593] The following describes the processing flow.

[0594] Step 1:

[0595] The user enters the detailed conditions of the construction work into the terminal. These conditions include the scope of work, area, duration, difficulty level, required skills, and special requirements. The terminal temporarily stores this information and sends it to the server.

[0596] Step 2:

[0597] The server accesses the database based on the received construction condition information and searches for contractors that meet the conditions. The server evaluates the suitability of each contractor based on their skill set, qualifications, and past performance, and lists the contractors that best meet the conditions.

[0598] Step 3:

[0599] The server monitors the operational status of selected contractors in real time. Based on this, it proposes and adjusts the optimal start date and time for construction. The server then notifies the contractors of the adjusted order details and schedule based on the information obtained.

[0600] Step 4:

[0601] Once the contract for the selected contractor is finalized, the server automatically adjusts the order ratio and amount based on the initial contract and formally sends the order to the contractor. The contractor then receives the order details.

[0602] Step 5:

[0603] Once construction begins, the server monitors the progress and provides real-time information to users via their terminals. Users can check the progress and make adjustments as needed.

[0604] Step 6:

[0605] After the construction is completed, the server aggregates all the project progress data and updates the contractor's skills database. This update ensures that the contractor's track record and skills are up-to-date, which can be used to inform future projects.

[0606] (Example 1)

[0607] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0608] The problem that this invention aims to solve is to improve the efficiency and accuracy of selecting construction specialists in construction ordering operations. Conventional manual processes for selecting construction specialists are time-consuming and labor-intensive, and it was sometimes difficult to select the most suitable specialists. In addition, the lack of real-time monitoring of the progress of construction was a problem, leading to delays in construction progress and a decline in quality.

[0609] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0610] In this invention, the server includes means for acquiring and recording construction condition information, means for evaluating construction expert information in a database based on the acquired construction condition information and selecting a construction expert that meets the conditions, and means for collecting work progress data and updating the database of construction experts' capabilities. As a result, the selection of construction experts in construction ordering operations is automated and accuracy is improved, and construction progress can be monitored in real time, significantly improving overall operational efficiency.

[0611] "Construction conditions information" refers to detailed information related to construction work, such as the content of the work, area, difficulty level, and required skills.

[0612] A "construction specialist" refers to a professional who carries out construction work and possesses a specific skill set, qualifications, and past work experience.

[0613] "Contracting work" refers to the process of requesting selected construction specialists to carry out construction work.

[0614] "Operating status" refers to data that shows the current work status and schedule of construction specialists in real time.

[0615] "Work schedule" refers to the timeline and plan of the construction work assigned to the construction specialists.

[0616] A "skills database" refers to a database that records and manages information on the skills and past performance of construction professionals.

[0617] This invention is a system for efficiently automating construction ordering operations, and is composed of user, server, and terminal elements.

[0618] Users input construction-related information via a terminal. The terminal converts this information into a digital format and sends it to the server. The terminal can be implemented using basic computing devices or mobile devices.

[0619] The server uses the construction condition information received from the user to compare it with the construction expert information stored in the database. The database system used here is implemented as a general relational database management system (RDBMS) and efficiently manages the skill sets and past performance of construction experts.

[0620] The server also utilizes a skill matching algorithm to select the construction expert best suited to the input conditions. This process uses a specific machine learning model to evaluate the optimal expert based on skill match and past evaluations. Because this algorithm runs on the server, rapid selection is achieved.

[0621] After selection, the server executes a notification system to automatically place work orders with the selected construction specialists. This notification system transmits order information via email or a dedicated application. Furthermore, the server monitors the work status of the construction specialists in real time and creates an optimal work schedule.

[0622] As the construction progresses, users can monitor the progress via their terminals and make adjustments as needed. The server continuously collects progress data and updates the database of construction specialists' capabilities upon completion of the project. This update process further improves the accuracy of selections when future projects are ordered.

[0623] As a concrete example, consider a case where a user requests interior construction work for an office building. The user inputs conditions such as "interior design," "Tokyo," and "requires high technical skills" into a terminal. Based on this, the server selects construction specialists with the appropriate skills and qualifications and automatically places the order. During the construction process, the user can check the progress in real time on their terminal and make any necessary adjustments.

[0624] An example of a prompt message is: "Please describe an automated system for efficiently carrying out interior construction work on a building. Specifically, please describe the selection and management methods for specialists, given the requirement for high technical expertise and the need for work to be performed in the Kanto area."

[0625] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0626] Step 1:

[0627] The user uses a terminal to input construction condition information. This information includes the details of the construction, area, difficulty level, and required skills. The entered condition information is converted into a digital format and sent to the server. The input consists of construction condition information specified by the user, and the output is formatted data sent to the server.

[0628] Step 2:

[0629] The server receives construction condition information from the terminal and stores it in the database. The received data serves as the basis for cross-referencing with construction expert information. Specifically, an insert process is performed into the database to ensure accurate storage of the information. The input is construction condition information sent by the user, and the output is the information recorded in the database.

[0630] Step 3:

[0631] The server matches the construction expert information with the construction condition information stored in the database. This process involves querying skill sets, qualifications, and experience information to generate a list of experts that match the criteria. SQL queries are used for the specific data processing. The input is the user's construction condition information, and the output is a list of construction experts that match the criteria.

[0632] Step 4:

[0633] The server uses a generative AI model to select the optimal construction expert from among those who meet the specified criteria. Here, machine learning algorithms are used to calculate skill match and past evaluation scores to select the most suitable expert. The input is a list of construction experts who meet the criteria, and the output is the selection result for the optimal construction expert.

[0634] Step 5:

[0635] The server automatically places work orders with selected construction specialists. Specifically, it creates notification emails and processes notifications through a dedicated application. The order details include construction work content and schedule information. The input is the selection result of the most suitable construction specialist, and the output is an order notification to the construction specialist.

[0636] Step 6:

[0637] The server monitors the operational status of construction specialists in real time and creates an optimal work schedule. Real-time data analysis is performed to optimize each specialist's schedule information. The input is the construction specialists' schedule data, and the output is the optimized work schedule.

[0638] Step 7:

[0639] During construction, users monitor the progress using a terminal and make adjustments as needed. The terminal displays real-time progress data, allowing users to make decisions based on the information. Input is construction progress data, and output is a progress confirmation screen that the user receives.

[0640] Step 8:

[0641] The server analyzes progress data after construction is completed and updates the database of construction specialists' capabilities. This update improves the accuracy of the selection process for future projects. Specifically, it executes database update queries using the progress data. The input is construction progress data, and the output is the updated database of construction specialists' capabilities.

[0642] (Application Example 1)

[0643] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0644] In modern construction procurement processes, the selection of contractors and facilities, as well as schedule management, are often performed manually, leading to problems such as decreased efficiency and accuracy, and wasted time. Furthermore, there are limited means of monitoring progress in real time, making it difficult to ensure transparency in project management. In this situation, there is a need to develop a system that enables appropriate contractor selection, rapid ordering, and progress monitoring.

[0645] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0646] In this invention, the server includes a device for acquiring and recording construction condition information, a device for evaluating multiple contractor information based on the acquired construction condition information and selecting an appropriate contractor, and a device for recognizing the contractor's working status in real time and presenting an optimal work schedule. This enables improved efficiency and accuracy in construction ordering operations, as well as highly transparent project management.

[0647] "Construction condition information" refers to information that describes specific requirements related to construction work, such as the content of the work, location, difficulty level, and necessary technologies.

[0648] "Contractor information" refers to information used to evaluate the capabilities and reliability of a contractor or craftsman in carrying out construction work, including their skills, qualifications, and past performance.

[0649] "Operating status" refers to the operational state of the contractor, including the work they are currently undertaking, its progress, and future work plans.

[0650] A "work schedule" is a plan that outlines the specific steps and timetables formulated to ensure the efficient progress of construction work.

[0651] A "smartphone" is a portable information terminal that, in addition to telephone functionality, also functions as a computer, allowing the use of a variety of applications.

[0652] An "interface" is a framework that describes the connection point or operating environment through which a user and a system exchange information.

[0653] The system that realizes this invention operates primarily around a server. The server receives construction condition information entered by the user via a terminal and stores it in a database at an initial stage. Construction condition information includes the content of the construction, the size of the area, the difficulty of the work, and the required technologies and skills.

[0654] The server automatically performs a procedure to select the most suitable contractor by comparing contractor information stored in the database with the entered construction condition information. Contractor information includes each contractor's skills, qualifications, and past work experience, and the optimal selection is made by evaluating these factors.

[0655] After the selection process is complete, the server automatically places orders with the selected contractors. Contractors can then prepare based on the received order details and work schedule information. The work schedule is determined by the server monitoring the contractors' availability in real time and optimizing time allocation.

[0656] Furthermore, this system also features a user interface that runs on smartphones, allowing users to monitor the progress of construction in real time, even from remote locations. This feature significantly improves the transparency of progress monitoring and enables faster responses.

[0657] For example, when a project manager orders the installation of an air conditioning system for a new commercial facility, they use their smartphone to input conditions such as "installation of an air conditioning system, two stories, using equipment from company XX, start in April." Based on this input, the system quickly selects a suitable contractor and begins the project.

[0658] An example of a prompt message generated using an AI model is: "Please enter the construction conditions for the installation of an air conditioning system in a commercial facility: Air conditioning system installation, 2 stories, using equipment manufactured by XX company, start in April. Select a suitable contractor and monitor the progress in real time." By using this prompt message, the AI ​​model enables quick and accurate contractor selection and project management.

[0659] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0660] Step 1:

[0661] The user uses a terminal to input construction condition information. This input includes construction details, area, difficulty level, and required skills. The terminal sends the information entered by the user to the server.

[0662] Step 2:

[0663] The server stores the received construction condition information in a database. This data is important information that will be used in the subsequent contractor selection process.

[0664] Step 3:

[0665] The server compares contractor information stored in the database with the entered construction condition information. Contractor information includes each contractor's skills, qualifications, and past construction experience. Based on this data, the server selects the appropriate contractor. An algorithm using a generative AI model is utilized for this selection.

[0666] Step 4:

[0667] The server automatically places orders with the selected contractors. Along with the order details, the work schedule is also transmitted. The server monitors the contractors' availability in real time and assigns the most suitable work schedule.

[0668] Step 5:

[0669] Once the contractor begins construction, the server monitors the progress of the work in real time. It collects progress data from the contractor and evaluates whether the construction is progressing according to plan.

[0670] Step 6:

[0671] The server sends the collected progress data to the user's terminal, allowing the user to remotely check the progress of the construction via their smartphone. This enables the user to understand the progress of the construction and issue adjustments as needed.

[0672] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0673] This invention is a system that aims to improve the efficiency of construction ordering operations while also providing a better user experience by taking user emotions into consideration. The system mainly consists of the following components.

[0674] First, the user inputs construction condition information using a terminal. This includes the construction details, area, difficulty level, and required skills. As the user inputs the information, the terminal activates an emotion engine to recognize the user's current emotional state. This is done in real time using voice analysis and facial recognition technology.

[0675] The server receives construction condition information and recognized user emotion data transmitted from the terminal. The server searches its database for information on contractors and selects the contractor best suited to the construction conditions. This selection takes into account the contractor's skills, past performance, and the user's emotional state. For example, if the user indicates anxiety, the server will prioritize selecting highly-rated contractors to enhance the user's sense of security.

[0676] Once the selection is complete, the server automatically places the order and notifies the contractor of the optimal schedule. The server adjusts the order ratio and amount based on the order details and communicates them to the contractor. In addition, the emotion engine tracks the user's reactions and monitors whether the user is satisfied throughout the entire ordering process.

[0677] While construction is underway, the server monitors the progress and continuously checks the user's emotional state. The server sends notifications to the terminal as needed and provides feedback to the user to facilitate smooth communication.

[0678] As a concrete example, consider a scenario where a user requests a large-scale renovation project. If this user becomes dissatisfied with the scheduling process, the emotion engine recognizes this change, and the server quickly takes corrective action. The user is then reassured by being immediately provided with the adjusted schedule and additional information about the construction company. This allows the system to optimize both technical efficiency and user experience.

[0679] The following describes the processing flow.

[0680] Step 1:

[0681] The user uses a terminal to input construction condition information. This information includes details such as the nature of the work, location, required skills, and deadline. The terminal collects this information, and during this process, its built-in emotion engine analyzes the user's facial expressions or voice input to infer their current emotional state.

[0682] Step 2:

[0683] The terminal transmits acquired construction condition information and user emotion data to the server. The user's emotion state is categorized into positive, negative, neutral, etc., and transmitted to the server.

[0684] Step 3:

[0685] The server extracts suitable contractors from its database based on the received construction condition information. During this process, it evaluates the contractors' skill sets, qualifications, and past performance to select the most suitable candidates. Furthermore, it considers the user's emotional state; if anxiety or stress is detected, it prioritizes selecting highly-rated contractors.

[0686] Step 4:

[0687] The server automatically places orders with selected contractors, optimizing the order ratio and cost. Contractors are notified of the optimal schedule along with detailed order information for the work. This notification includes prompt confirmation and selection of reliable contractors, addressing user needs.

[0688] Step 5:

[0689] Once construction begins, the server monitors the progress of the work and collects user feedback via the terminal. The emotion engine tracks changes in the user's emotions throughout the construction and reports them to the server. If a user expresses dissatisfaction, the server considers providing additional information or taking prompt action and notifies the user via the terminal.

[0690] Step 6:

[0691] After construction is completed, the server compiles the final construction data and updates the contractor's skill database. This update reflects the success rate of the construction, user feedback, and sentiment data. To improve the user experience, sentiment data is stored as reference data for future use.

[0692] (Example 2)

[0693] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0694] In construction ordering processes, conventional systems selected contractors and managed construction progress based on uniform criteria without considering user feelings. This resulted in missed opportunities to alleviate user anxiety and dissatisfaction, making it difficult to provide an optimal user experience. Furthermore, the inability to efficiently select contractors and manage progress hindered the smooth progress of the entire construction project.

[0695] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0696] In this invention, the server includes functional means for collecting and recording construction condition information, functional means for recognizing the user's emotional state using voice analysis and facial recognition technology, and functional means for evaluating multiple contractor information and selecting the most suitable contractor based on the collected construction condition information and recognized emotional data. This enables the selection of a contractor that takes the user's emotions into consideration and smooth construction progress management.

[0697] "Construction condition information" refers to specific information related to the construction work, such as the content of the work, the area, the difficulty level, and the required skills.

[0698] "Voice analysis" is a technology that analyzes voice data to understand emotions and intentions conveyed through speech.

[0699] "Facial recognition technology" is a technology that analyzes a user's facial expressions to recognize their emotions and reactions in real time.

[0700] "Emotional state" refers to the emotions and psychological tendencies that a user is experiencing at a given time.

[0701] "Contractor information" refers to information that includes data such as skills, past performance, and evaluations related to the contractor.

[0702] "Ordering ratio" refers to the proportion in which work is distributed among multiple contractors, depending on the scale and nature of the construction project.

[0703] A "capabilities database" is a database that stores and updates information on the skills, track record, and evaluations of construction companies.

[0704] "Feedback" refers to opinions and information provided in response to a user's behavior and emotional state, with the aim of improving the user experience.

[0705] This system is designed to allow users to input construction condition information and place construction orders efficiently and optimally. The terminal receives input from the user regarding construction details, area, difficulty level, and required skills, while simultaneously recognizing the user's emotional state using voice analysis and facial recognition technology. The terminal activates an emotion engine to acquire emotional data in real time.

[0706] Subsequently, the terminal sends the acquired construction condition information and emotional data to the server. The server receives this data, accesses the database, and searches for contractor information. In addition to the contractor's skills, past performance, and ratings, it also considers the user's emotional state to select the most suitable contractor. The selection is performed using a generative AI model and is designed to alleviate the user's anxiety and dissatisfaction.

[0707] Once the selection is complete, the server automatically places the order and notifies the selected contractors of the detailed schedule. The server also notifies the user of the selection results and progress information via their terminal and provides necessary feedback.

[0708] For example, if a user requests a major renovation project and is dissatisfied with the construction schedule, the emotion engine will recognize this change, and the server will quickly adjust the schedule. This can reassure the user. An example of a prompt message would be, "Please explain in detail the criteria for selecting a contractor to alleviate the user's concerns."

[0709] The goal of this system is to maximize the technical efficiency of the ordering process and the user experience.

[0710] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0711] Step 1:

[0712] The user uses a terminal to input construction condition information. The information entered includes the construction details, area, difficulty level, and required skills. The terminal receives this information and prepares it as input data. At the same time, the terminal recognizes the user's emotional state in real time using voice analysis and facial recognition technology. In this process, voice and video data from the user are analyzed and output as emotional data.

[0713] Step 2:

[0714] The terminal sends construction condition information and sentiment data obtained from the user to the server. The server receives this data and starts searching for contractor information by referring to the database. Here, as a data processing step, the construction condition information is matched with the contractor database to generate a list of relevant contractors.

[0715] Step 3:

[0716] The server selects the most suitable contractor based on the generated list of contractors and sentiment data. This selection process uses a generative AI model to evaluate the skills and past performance of contractors while considering the user's emotional state. As a data calculation, an evaluation score is calculated for each contractor, and the final contractor selection is made by weighting these scores according to the user's emotional state.

[0717] Step 4:

[0718] The server automatically places orders for construction work with selected contractors. At this time, it notifies the contractors of the specific work details and schedule. The outputted order details are entered into the contractor's management system, and preparations for construction are completed.

[0719] Step 5:

[0720] During construction, the server monitors the contractor's progress in real time. This information is periodically sent to the server as construction progress data for progress management. Simultaneously, the server continuously monitors the user's emotional state through the terminal, sending notifications and providing feedback as needed.

[0721] Through these steps, the system achieves efficient ordering and an improved user experience.

[0722] (Application Example 2)

[0723] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0724] Traditional construction ordering systems had the problem of not being able to take into account the emotional state of users, resulting in an insufficient optimization of the user experience. As a result, users sometimes felt dissatisfied, and feedback provision and contractor selection were not carried out appropriately. Furthermore, it was difficult to respond to changes in the user's psychological state as life events progressed.

[0725] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0726] In this invention, the server includes means for simultaneously acquiring and analyzing construction condition information and the user's emotional state, means for dynamically selecting an appropriate contractor based on the acquired data, and means for improving the user experience by providing timely feedback that corresponds to the user's emotions. This makes it possible to select a contractor and provide feedback that takes the user's emotional state into consideration, thereby improving the user experience and the accuracy of contractor selection.

[0727] "Construction condition information" refers to specific information related to the construction, such as the content of the work, the area, the difficulty level, and the required skills.

[0728] "User emotional state" refers to data that indicates the user's psychological state, and is obtained through voice analysis and facial recognition.

[0729] "Contractor information" refers to information that includes data such as the skills, past performance, and evaluations of contractors.

[0730] A "construction schedule" refers to a detailed plan of the construction work, presented after considering the working status of the construction company and the progress of the work.

[0731] "Feedback" refers to information provided by the system to the user in the construction ordering process and contractor selection process, reflecting the user's emotional state and opinions.

[0732] A "skills database" refers to a database that systematically compiles information on the capabilities and achievements of construction companies.

[0733] The system for implementing this invention consists of a user terminal, a server running on the cloud, and an emotion engine for data analysis. The user uses the terminal to input construction condition information. To understand the user's emotional state in real time, the terminal uses a camera and microphone to detect facial expressions and voice, which are then analyzed by the emotion engine. This analysis utilizes the Google Cloud Vision API and IBM Watson's emotion analysis API.

[0734] The server receives construction condition information and user emotional state data transmitted from the terminal. Based on this information, a program on the server retrieves information on multiple contractors from a database and selects the most suitable contractor that matches the user's needs and psychological state. In this process, the server also considers the past performance and skill database of contractors. The selected contractor is automatically notified of the construction order and the optimal construction schedule.

[0735] Furthermore, during construction, the server continuously monitors the progress and updates the user's emotional state. This improves the user experience throughout the entire construction process. If an anomaly is detected, the server quickly generates and provides feedback to the user.

[0736] For example, if a user feels anxious during the home renovation process, the system can sense this emotion, and the server will immediately provide information on highly-rated contractors. This allows the user to feel at ease.

[0737] The following is an example of a prompt message.

[0738] User: I want to do a major kitchen renovation, but I'm worried about the budget and the timeframe.

[0739] AI: Don't worry. The contractors we recommend are highly rated and will provide the best plan to stay within your budget.

[0740] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0741] Step 1:

[0742] The user inputs construction condition information via a terminal. This information includes the details of the construction, area, difficulty level, and required skills. The entered information is temporarily stored on the terminal and prepared for sentiment analysis.

[0743] Step 2:

[0744] The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone in order to obtain the user's emotional state. This data is analyzed through the Google Cloud Vision API and IBM Watson's Sentiment Analysis API to identify the user's emotional state. The analysis results are sent from the device to the server.

[0745] Step 3:

[0746] The server searches its database for contractor information based on the construction conditions information received from the terminal and the user's emotional state. When listing contractors that match the construction details, past performance and user ratings are also considered. In particular, if the user has expressed concerns, contractors with high ratings are selected as a priority. The selection results are then sent back to the terminal.

[0747] Step 4:

[0748] The server automatically places orders for construction work with selected contractors. During this process, the system optimizes the schedule to be as fast and efficient as possible, taking into account the contractors' availability. The resulting schedule information is then communicated to both the contractors and the users' terminals.

[0749] Step 5:

[0750] Once construction begins, the server monitors the progress of the construction and continuously tracks changes in the user's emotional state. If necessary, it sends feedback to the terminal, providing users with up-to-date information to ensure their peace of mind. This feedback process significantly contributes to improving the user experience.

[0751] Step 6:

[0752] After the construction is completed, the server collects the user's final feedback and uses it to update the contractor's skills database. This updated information will be an important indicator in the future contractor selection process.

[0753] The following prompt messages will be used as examples of the interactions at each step.

[0754] User: I want to do a major kitchen renovation, but I'm worried about the budget and the timeframe.

[0755] AI: Don't worry. The contractors we recommend are highly rated and will provide the best plan to stay within your budget.

[0756] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0757] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0758] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0759] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0760] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0761] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0762] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0763] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0764] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0765] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0766] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0767] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0768] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0769] 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.

[0770] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0771] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0772] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0773] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0774] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0775] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0776] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0777] The following is further disclosed regarding the embodiments described above.

[0778] (Claim 1)

[0779] A means of acquiring and recording construction condition information,

[0780] A means of evaluating information on multiple contractors based on acquired construction condition information and selecting an appropriate contractor,

[0781] A method for automatically placing construction orders with selected contractors,

[0782] A means to recognize the operational status of construction contractors in real time and propose the optimal construction schedule,

[0783] A means of collecting construction progress data and updating the skills database of construction contractors,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, which provides means for automatically adjusting the order ratio and amount based on acquired construction condition information.

[0787] (Claim 3)

[0788] A system according to claim 1, which provides a means for awarding appropriate contractors based on their skills and past construction track record.

[0789] "Example 1"

[0790] (Claim 1)

[0791] A means of acquiring and recording construction condition information,

[0792] A means of evaluating the information of construction experts in the database based on the acquired construction condition information and selecting a construction expert that meets the conditions,

[0793] A means of automatically placing work orders with selected construction specialists,

[0794] A means to recognize the operational status of construction specialists in real time and propose the optimal work schedule,

[0795] A means of collecting project progress data and updating the database of construction specialists' capabilities,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, which provides means for automatically adjusting the order ratio and amount based on acquired construction condition information.

[0799] (Claim 3)

[0800] A system according to claim 1, which provides a means for awarding appropriate construction specialists, taking into consideration the capabilities and past work performance of the construction specialists.

[0801] "Application Example 1"

[0802] (Claim 1)

[0803] A device and means for acquiring and recording construction condition information,

[0804] A device and means for evaluating information on multiple contractors based on acquired construction condition information and selecting an appropriate contractor,

[0805] A device that automatically places construction orders with selected contractors,

[0806] A device that recognizes the worker's operational status in real time and presents the optimal work schedule,

[0807] A device and means for collecting construction progress data and updating the contractor's capability database,

[0808] A device and means that provides an interface for displaying the construction progress status on a smartphone and remotely monitoring the project's progress,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] A device for automatically adjusting the order ratio and amount based on acquired construction condition information, as described in claim 1.

[0812] (Claim 3)

[0813] A system according to claim 1 for awarding appropriate contractors based on their abilities and past construction track record.

[0814] "Example 2 of combining an emotion engine"

[0815] (Claim 1)

[0816] A functional means for collecting and recording construction condition information,

[0817] A functional means for recognizing the user's emotional state using voice analysis and facial expression recognition technology,

[0818] A functional means for evaluating information on multiple construction companies and selecting the most suitable company based on collected construction condition information and recognized sentiment data,

[0819] A functional means for automatically placing orders with selected construction companies,

[0820] A functional means to monitor the progress of the construction company's work in real time and propose the optimal schedule,

[0821] A functional means for aggregating construction progress data and updating the capabilities database of construction companies,

[0822] A functional means for monitoring the user's emotional response and providing feedback,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, which has a function to automatically adjust the order ratio and amount.

[0826] (Claim 3)

[0827] A system according to claim 1 that performs an appropriate evaluation considering the capabilities and past work history of the construction company.

[0828] "Application example 2 when combining with an emotional engine"

[0829] (Claim 1)

[0830] A means of acquiring and recording construction condition information,

[0831] A means for evaluating information on multiple contractors based on acquired construction condition information and the user's emotional state, and selecting an appropriate contractor,

[0832] A method for automatically placing construction orders with selected contractors,

[0833] A means to recognize the operational status of construction companies and the emotional state of users in real time and propose the optimal construction schedule,

[0834] A means of collecting construction progress data and user feedback, and updating the contractor's skills database,

[0835] A means of providing users with emotional state-based feedback and improving the user experience throughout the entire ordering process,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, which provides means for automatically adjusting the order ratio and amount according to acquired construction condition information and the user's emotional state.

[0839] (Claim 3)

[0840] A system according to claim 1, which provides means for evaluating an appropriate contractor by taking into account the contractor's skills, past construction performance, and user sentiment data. [Explanation of Symbols]

[0841] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device and means for acquiring and recording construction condition information, A device and means for evaluating information on multiple contractors based on acquired construction condition information and selecting an appropriate contractor, A device that automatically places construction orders with selected contractors, A device that recognizes the worker's operational status in real time and presents the optimal work schedule, A device and means for collecting construction progress data and updating the contractor's capability database, A device and means that provides an interface for displaying the construction progress status on a smartphone and remotely monitoring the project's progress, A system that includes this.

2. A device for automatically adjusting the order ratio and amount based on acquired construction condition information, as described in claim 1.

3. A system according to claim 1 for awarding appropriate contractors based on their abilities and past construction track record.

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