system

Abstract

We provide the system. [Solution] An information analysis tool that receives construction project data and extracts similar data from a past project database, A selection method that generates an optimal list of elements using a machine learning algorithm with extracted data, An interface means for providing an element list to the user's terminal and collecting feedback from the user, An input processing method that receives on-site input in real time, A learning update method that incorporates collected feedback into the learning model, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 member selection process in base station construction work requires specialized skills and rich experience and depends on personal judgment, so there is a problem that it is difficult to make an efficient and consistent selection. Also, there is a problem that the time and labor required for this process increase, making it difficult to secure resources for new businesses and projects.

Means for Solving the Problems

[0005] This invention provides a system that automates component selection by receiving construction project data, providing data analysis means for extracting similar data from a past project database, and generating an optimal component list using a machine learning algorithm based on the extracted data. Furthermore, it provides means to improve the accuracy and efficiency of component selection by providing the generated component list to a user terminal, collecting user feedback, and incorporating it into the learning model.

[0006] "Construction project data" refers to data containing detailed information about base station construction projects, including information such as installation location, frequency band to be used, construction period, and environmental conditions.

[0007] The "Past Project Database" is a database that stores data on previously completed base station construction projects, including historical information on similar projects.

[0008] "Data analysis means" refers to means that have the function of analyzing received construction project data and extracting similar data from past project databases.

[0009] A "machine learning algorithm" refers to a mathematical technique that learns patterns from past data and uses that learning to make predictions and decisions based on new data.

[0010] "Component selection means" refers to a mechanism for generating an optimal component list using machine learning algorithms based on extracted data.

[0011] "User interface means" refers to an interface that allows users to interact with the system, enabling them to check the parts list and provide feedback.

[0012] A "learning update method" refers to a method that incorporates feedback collected from users into the learning model and supports the improvement of the algorithm's accuracy.

[0013] "Supplier information" refers to information about the source of the components that is attached to the optimized component list. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

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

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

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, 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 invention is a system for streamlining the material selection process in base station construction work, and its main components include a server, terminals, and user interaction. The system as a whole consistently manages the flow from data input and analysis to material selection, result provision, and feedback.

[0036] Users input details of a new base station construction project via a terminal. This includes information such as the project location, frequency band to be used, scale of construction, and environmental conditions. The input data is formatted and then sent to the server via the cloud.

[0037] The server analyzes construction project data stored in the database based on project data received from the terminal. This extracts the implementation history and material usage examples of similar projects, which are then input into a machine learning algorithm.

[0038] The server uses a machine learning algorithm to select the most suitable components for the input data, and the generated component list includes details such as "part number," "specifications," "quantity," and "supplier information." This component list is sent back to the terminal for the user to review.

[0039] Users can use their devices to review the proposed list of components and provide feedback and additional information as needed. This feedback is sent back to the server and used as learning material for future projects. For example, when installing a new base station between buildings in an urban area, the server can automatically select the optimal antenna type and cable specifications based on data from similar past projects and notify the user.

[0040] In this way, the present invention significantly improves the efficiency of material selection in base station construction, thereby increasing the success rate of projects. The system enables efficient allocation of resources, creating a situation where engineers can concentrate on more strategic tasks.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user uses a terminal to input information about a new base station construction project. This information includes the planned installation location, the frequency band to be used, the scale of the construction, and environmental conditions. This input data is formatted into a predetermined format on the terminal before transmission.

[0044] Step 2:

[0045] The terminal sends the formatted project data to the server. This process is conducted via the cloud to ensure secure, real-time data communication.

[0046] Step 3:

[0047] The server analyzes the received project data. First, it identifies related similar projects from the past project database and extracts this data.

[0048] Step 4:

[0049] The server executes a machine learning algorithm based on the extracted data. This algorithm uses patterns learned from similar projects to derive the optimal list of components for the current project.

[0050] Step 5:

[0051] The server adds "part number," "specifications," "quantity," and "supplier information" to the generated parts list and sends it back to the terminal. This list is customized to meet project requirements.

[0052] Step 6:

[0053] The terminal displays the parts list received from the server to the user. The user can review the list and add additional comments or modifications as needed.

[0054] Step 7:

[0055] The user sends feedback from their device to the server after reviewing the product. This feedback is treated as important information for improving the accuracy of component selection.

[0056] Step 8:

[0057] The server analyzes user feedback and uses it to update machine learning algorithms. This will allow for more appropriate component selection in future projects.

[0058] (Example 1)

[0059] 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."

[0060] Traditional base station construction component selection processes rely on experience and individual judgment, leading to challenges in efficiency and accuracy. Furthermore, selecting appropriate components for each project is difficult, hindering optimization. This can result in wasted costs and extended construction periods.

[0061] 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.

[0062] In this invention, the server includes a user interface means for inputting project information from a user terminal and transmitting it to the server via data communication; a data analysis means for analyzing the received construction project information and extracting similar project information from a past database; and a configuration selection means for generating an optimal list of components using a machine learning algorithm. This makes it possible to select components in construction projects efficiently and with high accuracy.

[0063] A "user interface means" is an interface that allows a user to input project information and exchange information with a server via data communication.

[0064] A "data analysis tool" is a tool that analyzes received project information and extracts similar project information from past databases.

[0065] A "configuration selection method" is a means of generating an optimal list of components from data analyzed using a machine learning algorithm.

[0066] An "interface means" is a means of providing a configuration list to the user terminal and collecting feedback from the user.

[0067] "Learning improvement methods" refer to means of continuously updating the learning model and improving its accuracy based on user feedback.

[0068] The "terminal function" is a function that is responsible for appropriately formatting project information entered by the user and sending it to the server.

[0069] The "data addition function" is a feature that adds supplier information to the generated configuration list, making the list more detailed and practical.

[0070] This invention is an innovative system that streamlines the component selection process in base station construction. This system primarily operates through the coordinated interaction of servers, terminals, and users.

[0071] Users input details of new construction projects via a terminal. The terminal formats the entered information into the appropriate data format and transmits it to the server while ensuring security. In this process, a standard computer device is used as the terminal and communicates with the server over the internet. The formatted information includes details such as the project location, the frequency band to be used, the scale of the construction, and environmental conditions.

[0072] The server performs data analysis based on the received data, using a database that holds historical project data. This database contains extensive historical data on past project histories and used components. The server extracts similar project data and uses a machine learning model to generate an optimal component list based on the analysis results. Specific machine learning algorithms are employed in this process to improve performance and maximize accuracy.

[0073] The generated component list consists of detailed information including "part number," "specifications," "quantity," and "supplier information." This list is sent to the terminal, allowing the user to review it. The user has the opportunity to review the list on the terminal and provide feedback as needed. The feedback is returned to the server and used as training material for the machine learning model, continuously improving the accuracy and efficiency of the entire system.

[0074] For example, when a user plans to install a new wireless base station in an urban area, the server can select the optimal antennas and cables based on past project data from urban environments and notify the user. This significantly streamlines the project planning process and leads to greater success.

[0075] As an example of a prompt, input such as, "Please propose the optimal configuration for a new 5G base station installation project in an urban environment. The operating frequency will be 3.5GHz, and the construction scale will be medium-sized," is possible. Such a configuration is expected to lead to sustained improvements in project outcomes.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user enters project details using a terminal.

[0079] Input: Project location, operating frequency, scale of construction, environmental conditions.

[0080] Operation: The terminal formats the entered information based on the form on the screen.

[0081] Output: Saved as formatted project data.

[0082] Step 2:

[0083] The terminal sends the formatted data to the server.

[0084] Input: Formatted project data.

[0085] Operation: Transfers data to the server according to the data communication protocol.

[0086] Output: The server receives confirmation that the transmission is complete.

[0087] Step 3:

[0088] The server searches the database based on the received data.

[0089] Input: Received project data.

[0090] Operation: Query the past project database and extract similar projects.

[0091] Output: Historical data of similar projects.

[0092] Step 4:

[0093] The server uses machine learning algorithms to generate the optimal components.

[0094] Input: Historical data of similar projects.

[0095] Operation: Input data into the generation AI model and generate the optimal list of components.

[0096] Output: Optimized list of components.

[0097] Step 5:

[0098] The server sends the generated list of components to the terminal.

[0099] Input: Optimized component list.

[0100] Action: Transfers the list to the user's device so they can view it.

[0101] Output: A list of components displayed on the terminal.

[0102] Step 6:

[0103] The user reviews the component list on their device and provides feedback.

[0104] Input: List of components.

[0105] Action: Review the details and write down suggestions for improvement or feedback as needed.

[0106] Output: Provided feedback.

[0107] Step 7:

[0108] The server updates the learned model using the feedback it receives.

[0109] Input: Provided feedback.

[0110] Operation: Improves machine learning models and enhances algorithm accuracy based on feedback.

[0111] Output: Updated machine learning model.

[0112] (Application Example 1)

[0113] 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."

[0114] To improve on-site work efficiency, it is necessary to provide real-time information on the optimal material selection. However, currently, there is no centralized system for this information, making it difficult to immediately reflect on-site feedback. Furthermore, the inability to effectively incorporate user feedback is causing a decline in project efficiency.

[0115] 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.

[0116] In this invention, the server includes information analysis means, selection means, interface means, on-site input processing means, and learning update means. This improves on-site work efficiency and allows for real-time incorporation of user feedback.

[0117] "Information analysis means" refers to a method for receiving construction project data and extracting and analyzing similar information from past project databases.

[0118] "Selection method" refers to a means of generating the optimal list of elements using a machine learning algorithm based on the extracted data.

[0119] An "interface means" is a means for providing the generated list of elements to the user's terminal and for collecting feedback from the user.

[0120] "On-site input processing methods" refer to methods for receiving information entered at a construction site in real time and reflecting it in the system.

[0121] A "learning update method" is a means of incorporating feedback collected from users into the learning model.

[0122] The system that realizes this invention functions by combining a server, terminal, cloud infrastructure, machine learning algorithm, and user interface. The server often runs on a cloud service such as AWS®, where information analysis means, selection means, and learning update means operate. Specifically, the server receives construction project data, refers to past databases to extract similar project data, and then uses a machine learning library such as TENSORFLOW® to select the optimal elements (such as components) and sends the selection results to the terminal.

[0123] The device is either worn by the user or carried as a smartphone. An application developed using React Native runs on it, providing a list of elements via a user interface. Furthermore, it quickly receives input from field workers and sends it to the cloud for feedback collection. This allows users to receive optimal information in real time and react immediately.

[0124] For example, at a construction site around a new city, a user can simply wear smart glasses and operate them to obtain a list of appropriate structural components. This list also includes supply information, significantly streamlining the project's progress.

[0125] By utilizing a generative AI model, it is possible to analyze information through prompt statements and quickly and appropriately select elements. An example of a prompt statement is as follows:

[0126] "We need to select components for a new construction project. The project is located in an urban area. Please select the optimal antenna type and cable specifications based on data from similar past projects."

[0127] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0128] Step 1:

[0129] The terminal provides an interface for the user to input details of a construction project. This step involves entering information such as the project location, the frequency band to be used, and the scale of the construction. The entered data is formatted and prepared for transmission to the server.

[0130] Step 2:

[0131] The terminal sends formatted construction project data to the server via the cloud. This data is used in subsequent processing steps.

[0132] Step 3:

[0133] The server analyzes the received project data and extracts similar past project data from the database. During this process, the server compares past and current projects and performs an analysis to calculate their similarity.

[0134] Step 4:

[0135] The server uses a machine learning algorithm to generate an optimal list of elements based on the extracted similar project data. This algorithm utilizes a generative AI model to process data based on prompt statements and create the list of elements.

[0136] Step 5:

[0137] The server sends the generated element list back to the terminal, making it available to the user. This element list also includes supplier information.

[0138] Step 6:

[0139] The terminal displays the received list of elements to the user, allowing them to use it in their on-site work. The user can input feedback in real time as needed.

[0140] Step 7:

[0141] The server receives the feedback collected from users again and stores it in the database. This feedback is used to update the learning model and contribute to improving the accuracy of future projects.

[0142] 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.

[0143] This invention streamlines the component selection process in base station construction and provides a more user-friendly system by taking into account the user's emotional state. This system mainly consists of server, terminal, and user interaction, with an emotion engine playing a central role.

[0144] The user uses a terminal to input details of the base station construction project. After input, the data is automatically formatted and sent to the server. The terminal is equipped with an emotion engine that recognizes the user's emotions from their voice and facial expressions, and determines the user's emotional state during input. This emotion information is also sent to the server.

[0145] The server extracts similar past data from the database based on the received project data and uses sentiment data provided by the sentiment engine to consider the user's state of mind when selecting components. Then, it uses a machine learning algorithm to generate an optimal component list. This component selection reflects not only the standard technical specifications of the components but also the user's preferences and tendencies based on their emotions.

[0146] The generated parts list includes details such as "part number," "specifications," "quantity," and "supplier information," and is sent back to the terminal for the user to review. When the user reviews the list, the user interface adjusts its design and response based on the user's emotional state to provide a less stressful operating environment.

[0147] For example, if a user feels anxiety or dissatisfaction with a particular piece of equipment, the emotion engine detects this state, and the server selects components that reflect that emotion. Furthermore, if a user expresses positive emotions towards a component list, that feedback is stored in the server and used in future selection processes.

[0148] This system allows users to select components more comfortably and efficiently, and to receive appropriate support that takes their feelings into consideration.

[0149] The following describes the processing flow.

[0150] Step 1:

[0151] The user inputs detailed information about the base station construction project via a terminal. This information includes the installation location, the frequency to be used, and the construction period. The terminal formats this data into a predetermined format.

[0152] Step 2:

[0153] The device collects emotional data from the user's facial expressions and voice, along with the input information, using its built-in emotion engine. The emotion engine then analyzes the recognized emotions to send them to the server.

[0154] Step 3:

[0155] The device sends formatted project data and emotional data to the server. This enables personalization based on the user's emotional state.

[0156] Step 4:

[0157] The server analyzes the received project data and extracts data from similar past projects from the database. It also considers the received sentiment data to understand the user's current state.

[0158] Step 5:

[0159] The server uses machine learning algorithms to generate an optimal list of components based on project requirements. This list is then refined using sentiment data to reflect the user's interests and concerns.

[0160] Step 6:

[0161] The server adds detailed information such as "model number," "specifications," and "supplier information" to the generated parts list and sends it back to the terminal.

[0162] Step 7:

[0163] The terminal displays data from the server to the user and dynamically adjusts the user interface according to the user's emotions. For example, if the user expresses dissatisfaction, the color scheme and animations are changed to improve visibility.

[0164] Step 8:

[0165] The user reviews the parts list via their device and provides additional feedback if necessary. This feedback is then sent back from the device to the server and incorporated into the system's learning model.

[0166] This process allows the system to select components with greater accuracy while taking user emotions into consideration.

[0167] (Example 2)

[0168] 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".

[0169] In large-scale and complex projects such as base station construction, the material selection process is typically based solely on technical requirements, neglecting user emotions and ease of use. This can make the selection process stressful and inefficient for users. Furthermore, the inability to effectively utilize emotional factors and past feedback can lead to insufficient project optimization.

[0170] 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.

[0171] In this invention, the server includes information analysis means, selection means, user interface means, and model update means. This makes it possible to integrate user input data and emotional data to select the optimal materials and further improve the user experience.

[0172] "Information analysis means" refers to devices or processes that receive information related to construction activities and extract similar past data from a database.

[0173] "Selection method" refers to a device or process for integrating user emotional data with information related to construction activities to select the optimal material.

[0174] A "user interface means" is an interface system that provides selected information to a user device and adjusts the response according to the user's emotional state.

[0175] A "model update means" is a device or process for collecting user responses and integrating those collected responses into a learning model.

[0176] "Device function" refers to the function of a device that automatically formats and transmits data provided by the user.

[0177] "Information addition means" refers to devices or processes for adding source information to selected material information.

[0178] This invention is a system that streamlines the material selection process in base station construction projects and provides a more user-friendly experience by taking user emotions into consideration. The embodiments for carrying out this invention are described in detail below.

[0179] The user uses a terminal to input detailed information about the base station construction project. The terminal is equipped with an emotion engine that analyzes voice and facial expressions to determine the user's emotional state in real time. This emotional information and project data are formatted using an automated formatting tool and then securely transmitted to the server.

[0180] The server extracts data from a database of similar past projects based on the received data. Database management systems and SQL queries are used for information analysis. Furthermore, the server uses the received sentiment data to create a list of materials optimized for the user. Here, machine learning algorithms, including sentiment data, are applied to select materials that reflect the user's emotions in addition to technical aspects.

[0181] The generated material list is sent back to the terminal for user review. The terminal adjusts the user interface based on the user's emotional state to provide a more comfortable operating environment. For example, if the user expresses concern about a particular material, a safety-oriented alternative is provided that addresses the reason for the concern.

[0182] For example, if a user feels uneasy about a particular piece of equipment, the emotion engine detects that emotional state, and the server selects materials that reflect that emotion. This allows the user to feel more confident in the material list. Furthermore, if the user provides positive feedback on the material list, this is saved on the server and used in future selection processes.

[0183] An example of a prompt message is: "Consider the detailed information of the base station construction project and user sentiment data to plan the optimal list of materials."

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] The user enters detailed information about the base station construction project into the terminal. Based on the data entered by the user, the terminal uses a dedicated formatting tool to format the data. This input data includes the scale of the project, required materials, and deadlines. The formatted data is then processed into a format that ensures consistency necessary for subsequent processing.

[0187] Step 2:

[0188] The device analyzes the user's emotional state using an emotion engine. Input includes user voice data and facial expression data, which the device processes in real time. The emotional data generated through analysis is classified as categories such as "anxiety" and "satisfaction," and output as metadata representing the user's emotional state.

[0189] Step 3:

[0190] The terminal sends pre-formatted project data and generated sentiment data as a set to the server. Data security is ensured during transmission through encryption technology. The input data received by the server includes pre-formatted project information and user sentiment metadata.

[0191] Step 4:

[0192] The server references the database and extracts historical similar project data using SQL queries. The input here is data that reflects the characteristics and requirements of the project. Based on this, the server selects historical data that meets the criteria and outputs it as a similar dataset.

[0193] Step 5:

[0194] The server generates an optimal list of materials using a machine learning algorithm that takes user sentiment data into account. This process uses similar datasets and sentiment data as input. Based on this data, the algorithm selects the materials best suited to the user's preferences and actual technical requirements, and outputs a list of materials as a result.

[0195] Step 6:

[0196] The generated material list is sent back from the server to the terminal, where the user reviews its contents. During this process, the screen design and operational feedback are adjusted, taking into account the impact the terminal receives from the user's emotional state. Specifically, the color scheme and layout of the displayed content are adjusted to provide the user with a sense of security.

[0197] Step 7:

[0198] Users input feedback on the material list, and this data is sent back to the server via their terminal. The server receives this feedback as new input data and integrates it into a learning model. As a result, the feedback is accumulated as valuable historical data that will be reflected in future material selection processes.

[0199] (Application Example 2)

[0200] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0201] In modern factories, parts selection is a complex process for many workers, often leading to stress and anxiety. Such emotional burdens can result in decreased work efficiency and selection errors, highlighting the need for a flexible system that considers emotional well-being. Furthermore, user feedback and emotional states must be taken into account to create a more user-friendly and optimal parts selection process.

[0202] 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.

[0203] In this invention, the server includes data analysis means, component selection means using machine learning algorithms, user interface means for collecting user feedback, and emotion analysis means for detecting the worker's emotional state in real time. This makes it possible to select components while taking the worker's emotions into consideration.

[0204] "Data analysis means" refers to a device or method that receives construction project data and has the function of extracting similar data from a database of past projects.

[0205] "Component selection means" refers to an apparatus or method that uses extracted data and a machine learning algorithm to create an optimal component list.

[0206] "User interface means" refers to an interface function that provides the generated component list to the user terminal and collects feedback obtained from it.

[0207] A "learning update means" is a device or method that has the function of updating a learning model based on feedback collected from users, thereby improving the accuracy of component selection in subsequent instances.

[0208] "Emotional analysis means" refers to a device or method for detecting the emotional state of an operator in real time and adjusting the operating environment based on that.

[0209] The embodiment for carrying out the invention describes a method for constructing a system that provides appropriate support to workers in the parts selection process in a factory, tailored to their emotional state. This system functions by combining data analysis, material selection, user interface, learning and updating, and sentiment analysis.

[0210] The server receives data related to the factory construction project and uses data analysis techniques to extract similar information from historical databases. MySQL® is used as the database management system for data comparison and matching.

[0211] The terminal uses a smartphone or smart glasses equipped with a camera and microphone to analyze the worker's emotions in real time. In this process, Microsoft® Azure® Emotion API is used as the emotion recognition library to identify the emotional state based on voice and facial expression data.

[0212] Machine learning algorithms (e.g., Scikit-learn) generate optimal component lists based on the received data. This enables selections that consider not only the technical specifications of the parts typically required, but also the worker's emotions.

[0213] The user interface presents the worker with a list of materials and collects feedback. The collected feedback is then used to inform future selections through a learning update mechanism.

[0214] For example, if a worker picking parts in a factory feels anxious about the task, the system can detect this and provide selection options and reference information on the screen to reduce stress. Further improvements in prediction accuracy can be achieved by inputting a prompt such as, "We want to create a simple and accurate parts selection list based on the sentiment data of the work line. What is the recommended approach?" into the generating AI model.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] The terminal uses the camera and microphone of a smartphone or smart glasses to acquire emotional data when a worker begins a parts selection task. The input consists of the worker's facial expressions and voice, which are analyzed using the Emotion API to detect the worker's emotional state in real time. Emotional state data is generated as output.

[0218] Step 2:

[0219] The user inputs information about the parts they wish to select into the terminal. The terminal formats the input data and transfers it to the server. Here, the input is the specification information of the parts the user needs, and the output is the formatted data.

[0220] Step 3:

[0221] The server uses the received part specification data to extract data from a database (MySQL) of similar past projects. The input is formatted part data, and the output is data from similar projects. Database queries are performed during this process.

[0222] Step 4:

[0223] The server uses extracted data and sentiment data to apply a machine learning algorithm (Scikit-learn) and generate the optimal component list. The input is historical project data and sentiment data, and the output is a component list. Data analysis and the application of a predictive model are performed here.

[0224] Step 5:

[0225] The server sends the generated parts list to the terminal. The user checks the parts list on the terminal and provides feedback. The input is the parts list, and the output is the user's feedback.

[0226] Step 6:

[0227] The device sends feedback to the server. The server uses a learning update mechanism to incorporate the feedback into the machine learning model. The input is the feedback data, and the output is the updated learning model.

[0228] Step 7:

[0229] The user inputs prompt statements into an AI model to obtain approaches and ideas for further improving the parts selection process. For example, input such as "I want to create a simple and accurate parts selection list based on sentiment data from the work line. What is your recommended approach?" will result in suggestions from the AI ​​model as output.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] [Second Embodiment]

[0234] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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".

[0246] This invention is a system for streamlining the material selection process in base station construction work, and its main components include a server, terminals, and user interaction. The system as a whole consistently manages the flow from data input and analysis to material selection, result provision, and feedback.

[0247] Users input details of a new base station construction project via a terminal. This includes information such as the project location, frequency band to be used, scale of construction, and environmental conditions. The input data is formatted and then sent to the server via the cloud.

[0248] The server analyzes construction project data stored in the database based on project data received from the terminal. This extracts the implementation history and material usage examples of similar projects, which are then input into a machine learning algorithm.

[0249] The server uses a machine learning algorithm to select the most suitable components for the input data, and the generated component list includes details such as "part number," "specifications," "quantity," and "supplier information." This component list is sent back to the terminal for the user to review.

[0250] Users can use their devices to review the proposed list of components and provide feedback and additional information as needed. This feedback is sent back to the server and used as learning material for future projects. For example, when installing a new base station between buildings in an urban area, the server can automatically select the optimal antenna type and cable specifications based on data from similar past projects and notify the user.

[0251] In this way, the present invention significantly improves the efficiency of material selection in base station construction, thereby increasing the success rate of projects. The system enables efficient allocation of resources, creating a situation where engineers can concentrate on more strategic tasks.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] The user uses a terminal to input information about a new base station construction project. This information includes the planned installation location, the frequency band to be used, the scale of the construction, and environmental conditions. This input data is formatted into a predetermined format on the terminal before transmission.

[0255] Step 2:

[0256] The terminal sends the formatted project data to the server. This process is conducted via the cloud to ensure secure, real-time data communication.

[0257] Step 3:

[0258] The server analyzes the received project data. First, it identifies related similar projects from the past project database and extracts this data.

[0259] Step 4:

[0260] The server executes a machine learning algorithm based on the extracted data. This algorithm uses patterns learned from similar projects to derive the optimal list of components for the current project.

[0261] Step 5:

[0262] The server adds "part number," "specifications," "quantity," and "supplier information" to the generated parts list and sends it back to the terminal. This list is customized to meet project requirements.

[0263] Step 6:

[0264] The terminal displays the parts list received from the server to the user. The user can review the list and add additional comments or modifications as needed.

[0265] Step 7:

[0266] The user sends feedback from their device to the server after reviewing the product. This feedback is treated as important information for improving the accuracy of component selection.

[0267] Step 8:

[0268] The server analyzes user feedback and uses it to update machine learning algorithms. This will allow for more appropriate component selection in future projects.

[0269] (Example 1)

[0270] 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".

[0271] Traditional base station construction component selection processes rely on experience and individual judgment, leading to challenges in efficiency and accuracy. Furthermore, selecting appropriate components for each project is difficult, hindering optimization. This can result in wasted costs and extended construction periods.

[0272] 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.

[0273] In this invention, the server includes a user interface means for inputting project information from a user terminal and transmitting it to the server via data communication; a data analysis means for analyzing the received construction project information and extracting similar project information from a past database; and a configuration selection means for generating an optimal list of components using a machine learning algorithm. This makes it possible to select components in construction projects efficiently and with high accuracy.

[0274] A "user interface means" is an interface that allows a user to input project information and exchange information with a server via data communication.

[0275] A "data analysis tool" is a tool that analyzes received project information and extracts similar project information from past databases.

[0276] A "configuration selection method" is a means of generating an optimal list of components from data analyzed using a machine learning algorithm.

[0277] An "interface means" is a means of providing a configuration list to the user terminal and collecting feedback from the user.

[0278] "Learning improvement methods" refer to means of continuously updating the learning model and improving its accuracy based on user feedback.

[0279] The "terminal function" is a function that is responsible for appropriately formatting project information entered by the user and sending it to the server.

[0280] The "data addition function" is a feature that adds supplier information to the generated configuration list, making the list more detailed and practical.

[0281] This invention is an innovative system that streamlines the component selection process in base station construction. This system primarily operates through the coordinated interaction of servers, terminals, and users.

[0282] Users input details of new construction projects via a terminal. The terminal formats the entered information into the appropriate data format and transmits it to the server while ensuring security. In this process, a standard computer device is used as the terminal and communicates with the server over the internet. The formatted information includes details such as the project location, the frequency band to be used, the scale of the construction, and environmental conditions.

[0283] Based on the received data, the server performs data analysis using a database that holds past project data. The database stores large-scale historical data on past project histories and used components. The server extracts similar project data and generates an optimal component list based on the analysis results using a machine learning model. A specific machine learning algorithm is used for this process to improve performance and maximize accuracy.

[0284] The generated component list consists of detailed information including "model number", "specifications", "quantity", "supplier information", etc. This list is sent to the terminal so that the user can view it. The user has the opportunity to view the list on the terminal and provide feedback if necessary. The feedback is sent back to the server and used as learning material for the machine learning model, thereby continuously improving the accuracy and efficiency of the entire system.

[0285] As a specific example, when a user intends to install a new wireless base station in an urban area, the server can select an optimal antenna and cable based on past project data in the urban environment and notify the user. This significantly improves the project planning work and leads to success.

[0286] As an example of the prompt text, inputs such as "Please propose the optimal components for a new 5G base station installation project in an urban environment. The operating frequency is 3.5 GHz and the construction scale is medium." are possible. With such a configuration, continuous improvement of project results can be expected.

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

[0288] Step 1:

[0289] The user inputs the detailed information of the project using the terminal.

[0290] Input: Project location, operating frequency, scale of construction, environmental conditions.

[0291] Operation: The terminal formats the entered information based on the form on the screen.

[0292] Output: Saved as formatted project data.

[0293] Step 2:

[0294] The terminal sends the formatted data to the server.

[0295] Input: Formatted project data.

[0296] Operation: Transfers data to the server according to the data communication protocol.

[0297] Output: The server receives confirmation that the transmission is complete.

[0298] Step 3:

[0299] The server searches the database based on the received data.

[0300] Input: Received project data.

[0301] Operation: Query the past project database and extract similar projects.

[0302] Output: Historical data of similar projects.

[0303] Step 4:

[0304] The server uses machine learning algorithms to generate the optimal components.

[0305] Input: Historical data of similar projects.

[0306] Action: Input data into the generated AI model and generate an optimal component list.

[0307] Output: Optimized component list.

[0308] Step 5:

[0309] The server sends the generated component list to the terminal.

[0310] Input: Optimized component list.

[0311] Action: Transfer the list to the terminal so that the user can view it.

[0312] Output: Component list displayed on the terminal.

[0313] Step 6:

[0314] The user checks the component list on the terminal and provides feedback.

[0315] Input: Component list.​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​ (Application Example 1)

[0324] 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."

[0325] To improve on-site work efficiency, it is necessary to provide real-time information on the optimal material selection. However, currently, there is no centralized system for this information, making it difficult to immediately reflect on-site feedback. Furthermore, the inability to effectively incorporate user feedback is causing a decline in project efficiency.

[0326] 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.

[0327] In this invention, the server includes information analysis means, selection means, interface means, on-site input processing means, and learning update means. This improves on-site work efficiency and allows for real-time incorporation of user feedback.

[0328] "Information analysis means" refers to a method for receiving construction project data and extracting and analyzing similar information from past project databases.

[0329] "Selection method" refers to a means of generating the optimal list of elements using a machine learning algorithm based on the extracted data.

[0330] An "interface means" is a means for providing the generated list of elements to the user's terminal and for collecting feedback from the user.

[0331] "On-site input processing methods" refer to methods for receiving information entered at a construction site in real time and reflecting it in the system.

[0332] A "learning update method" is a means of incorporating feedback collected from users into the learning model.

[0333] The system that realizes this invention functions by combining a server, terminal, cloud infrastructure, machine learning algorithm, and user interface. The server often runs on a cloud service such as AWS, and it is here that the information analysis means, selection means, and learning update means operate. Specifically, the server receives construction project data, refers to a past database to extract similar project data, and then uses a machine learning library such as TensorFlow to select the optimal elements (such as components) and sends the selection results to the terminal.

[0334] The device is either worn by the user or carried as a smartphone. An application developed using React Native runs on it, providing a list of elements via a user interface. Furthermore, it quickly receives input from field workers and sends it to the cloud for feedback collection. This allows users to receive optimal information in real time and react immediately.

[0335] For example, at a construction site around a new city, a user can simply wear smart glasses and operate them to obtain a list of appropriate structural components. This list also includes supply information, significantly streamlining the project's progress.

[0336] By utilizing a generative AI model, it is possible to analyze information through prompt statements and quickly and appropriately select elements. An example of a prompt statement is as follows:

[0337] "We need to select components for a new construction project. The project is located in an urban area. Please select the optimal antenna type and cable specifications based on data from similar past projects."

[0338] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0339] Step 1:

[0340] The terminal provides an interface for the user to input details of a construction project. This step involves entering information such as the project location, the frequency band to be used, and the scale of the construction. The entered data is formatted and prepared for transmission to the server.

[0341] Step 2:

[0342] The terminal sends formatted construction project data to the server via the cloud. This data is used in subsequent processing steps.

[0343] Step 3:

[0344] The server analyzes the received project data and extracts similar past project data from the database. During this process, the server compares past and current projects and performs an analysis to calculate their similarity.

[0345] Step 4:

[0346] The server uses a machine learning algorithm to generate an optimal list of elements based on the extracted similar project data. This algorithm utilizes a generative AI model to process data based on prompt statements and create the list of elements.

[0347] Step 5:

[0348] The server sends the generated element list back to the terminal, making it available to the user. This element list also includes supplier information.

[0349] Step 6:

[0350] The terminal displays the received list of elements to the user, allowing them to use it in their on-site work. The user can input feedback in real time as needed.

[0351] Step 7:

[0352] The server receives the feedback collected from users again and stores it in the database. This feedback is used to update the learning model and contribute to improving the accuracy of future projects.

[0353] 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.

[0354] This invention streamlines the component selection process in base station construction and provides a more user-friendly system by taking into account the user's emotional state. This system mainly consists of server, terminal, and user interaction, with an emotion engine playing a central role.

[0355] The user uses a terminal to input details of the base station construction project. After input, the data is automatically formatted and sent to the server. The terminal is equipped with an emotion engine that recognizes the user's emotions from their voice and facial expressions, and determines the user's emotional state during input. This emotion information is also sent to the server.

[0356] The server extracts similar past data from the database based on the received project data and uses sentiment data provided by the sentiment engine to consider the user's state of mind when selecting components. Then, it uses a machine learning algorithm to generate an optimal component list. This component selection reflects not only the standard technical specifications of the components but also the user's preferences and tendencies based on their emotions.

[0357] The generated parts list includes details such as "part number," "specifications," "quantity," and "supplier information," and is sent back to the terminal for the user to review. When the user reviews the list, the user interface adjusts its design and response based on the user's emotional state to provide a less stressful operating environment.

[0358] For example, if a user feels anxiety or dissatisfaction with a particular piece of equipment, the emotion engine detects this state, and the server selects components that reflect that emotion. Furthermore, if a user expresses positive emotions towards a component list, that feedback is stored in the server and used in future selection processes.

[0359] This system allows users to select components more comfortably and efficiently, and to receive appropriate support that takes their feelings into consideration.

[0360] The following describes the processing flow.

[0361] Step 1:

[0362] The user inputs detailed information about the base station construction project via a terminal. This information includes the installation location, the frequency to be used, and the construction period. The terminal formats this data into a predetermined format.

[0363] Step 2:

[0364] The device collects emotional data from the user's facial expressions and voice, along with the input information, using its built-in emotion engine. The emotion engine then analyzes the recognized emotions to send them to the server.

[0365] Step 3:

[0366] The device sends formatted project data and emotional data to the server. This enables personalization based on the user's emotional state.

[0367] Step 4:

[0368] The server analyzes the received project data and extracts data from similar past projects from the database. It also considers the received sentiment data to understand the user's current state.

[0369] Step 5:

[0370] The server uses machine learning algorithms to generate an optimal list of components based on project requirements. This list is then refined using sentiment data to reflect the user's interests and concerns.

[0371] Step 6:

[0372] The server adds detailed information such as "model number," "specifications," and "supplier information" to the generated parts list and sends it back to the terminal.

[0373] Step 7:

[0374] The terminal displays data from the server to the user and dynamically adjusts the user interface according to the user's emotions. For example, if the user expresses dissatisfaction, the color scheme and animations are changed to improve visibility.

[0375] Step 8:

[0376] The user reviews the parts list via their device and provides additional feedback if necessary. This feedback is then sent back from the device to the server and incorporated into the system's learning model.

[0377] This process allows the system to select components with greater accuracy while taking user emotions into consideration.

[0378] (Example 2)

[0379] 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".

[0380] In large-scale and complex projects such as base station construction, the material selection process is typically based solely on technical requirements, neglecting user emotions and ease of use. This can make the selection process stressful and inefficient for users. Furthermore, the inability to effectively utilize emotional factors and past feedback can lead to insufficient project optimization.

[0381] 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.

[0382] In this invention, the server includes information analysis means, selection means, user interface means, and model update means. This makes it possible to integrate user input data and emotional data to select the optimal materials and further improve the user experience.

[0383] "Information analysis means" refers to devices or processes that receive information related to construction activities and extract similar past data from a database.

[0384] "Selection method" refers to a device or process for integrating user emotional data with information related to construction activities to select the optimal material.

[0385] A "user interface means" is an interface system that provides selected information to a user device and adjusts the response according to the user's emotional state.

[0386] A "model update means" is a device or process for collecting user responses and integrating those collected responses into a learning model.

[0387] "Device function" refers to the function of a device that automatically formats and transmits data provided by the user.

[0388] "Information addition means" refers to devices or processes for adding source information to selected material information.

[0389] This invention is a system that streamlines the material selection process in base station construction projects and provides a more user-friendly experience by taking user emotions into consideration. The embodiments for carrying out this invention are described in detail below.

[0390] The user uses a terminal to input detailed information about the base station construction project. The terminal is equipped with an emotion engine that analyzes voice and facial expressions to determine the user's emotional state in real time. This emotional information and project data are formatted using an automated formatting tool and then securely transmitted to the server.

[0391] The server extracts data from a database of similar past projects based on the received data. Database management systems and SQL queries are used for information analysis. Furthermore, the server uses the received sentiment data to create a list of materials optimized for the user. Here, machine learning algorithms, including sentiment data, are applied to select materials that reflect the user's emotions in addition to technical aspects.

[0392] The generated material list is sent back to the terminal for user review. The terminal adjusts the user interface based on the user's emotional state to provide a more comfortable operating environment. For example, if the user expresses concern about a particular material, a safety-oriented alternative is provided that addresses the reason for the concern.

[0393] For example, if a user feels uneasy about a particular piece of equipment, the emotion engine detects that emotional state, and the server selects materials that reflect that emotion. This allows the user to feel more confident in the material list. Furthermore, if the user provides positive feedback on the material list, this is saved on the server and used in future selection processes.

[0394] An example of a prompt message is: "Consider the detailed information of the base station construction project and user sentiment data to plan the optimal list of materials."

[0395] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0396] Step 1:

[0397] The user enters detailed information about the base station construction project into the terminal. Based on the data entered by the user, the terminal uses a dedicated formatting tool to format the data. This input data includes the scale of the project, required materials, and deadlines. The formatted data is then processed into a format that ensures consistency necessary for subsequent processing.

[0398] Step 2:

[0399] The device analyzes the user's emotional state using an emotion engine. Input includes user voice data and facial expression data, which the device processes in real time. The emotional data generated through analysis is classified as categories such as "anxiety" and "satisfaction," and output as metadata representing the user's emotional state.

[0400] Step 3:

[0401] The terminal sends pre-formatted project data and generated sentiment data as a set to the server. Data security is ensured during transmission through encryption technology. The input data received by the server includes pre-formatted project information and user sentiment metadata.

[0402] Step 4:

[0403] The server references the database and extracts historical similar project data using SQL queries. The input here is data that reflects the characteristics and requirements of the project. Based on this, the server selects historical data that meets the criteria and outputs it as a similar dataset.

[0404] Step 5:

[0405] The server generates an optimal list of materials using a machine learning algorithm that takes user sentiment data into account. This process uses similar datasets and sentiment data as input. Based on this data, the algorithm selects the materials best suited to the user's preferences and actual technical requirements, and outputs a list of materials as a result.

[0406] Step 6:

[0407] The generated material list is sent back from the server to the terminal, where the user reviews its contents. During this process, the screen design and operational feedback are adjusted, taking into account the impact the terminal receives from the user's emotional state. Specifically, the color scheme and layout of the displayed content are adjusted to provide the user with a sense of security.

[0408] Step 7:

[0409] Users input feedback on the material list, and this data is sent back to the server via their terminal. The server receives this feedback as new input data and integrates it into a learning model. As a result, the feedback is accumulated as valuable historical data that will be reflected in future material selection processes.

[0410] (Application Example 2)

[0411] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0412] In modern factories, parts selection is a complex process for many workers, often leading to stress and anxiety. Such emotional burdens can result in decreased work efficiency and selection errors, highlighting the need for a flexible system that considers emotional well-being. Furthermore, user feedback and emotional states must be taken into account to create a more user-friendly and optimal parts selection process.

[0413] 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.

[0414] In this invention, the server includes data analysis means, component selection means using machine learning algorithms, user interface means for collecting user feedback, and emotion analysis means for detecting the worker's emotional state in real time. This makes it possible to select components while taking the worker's emotions into consideration.

[0415] "Data analysis means" refers to a device or method that receives construction project data and has the function of extracting similar data from a database of past projects.

[0416] "Component selection means" refers to an apparatus or method that uses extracted data and a machine learning algorithm to create an optimal component list.

[0417] "User interface means" refers to an interface function that provides the generated component list to the user terminal and collects feedback obtained from it.

[0418] A "learning update means" is a device or method that has the function of updating a learning model based on feedback collected from users, thereby improving the accuracy of component selection in subsequent instances.

[0419] "Emotional analysis means" refers to a device or method for detecting the emotional state of an operator in real time and adjusting the operating environment based on that.

[0420] The embodiment for carrying out the invention describes a method for constructing a system that provides appropriate support to workers in the parts selection process in a factory, tailored to their emotional state. This system functions by combining data analysis, material selection, user interface, learning and updating, and sentiment analysis.

[0421] The server receives data related to the factory construction project and uses data analysis techniques to extract similar information from historical databases. MySQL is used as the database management system for data comparison and matching.

[0422] The terminal uses a smartphone or smart glasses equipped with a camera and microphone to analyze the worker's emotions in real time. In this process, Microsoft Azure's Emotion API is utilized as an emotion recognition library to identify emotional states based on voice and facial expression data.

[0423] Machine learning algorithms (e.g., Scikit-learn) generate optimal component lists based on the received data. This enables selections that consider not only the technical specifications of the parts typically required, but also the worker's emotions.

[0424] The user interface presents the worker with a list of materials and collects feedback. The collected feedback is then used to inform future selections through a learning update mechanism.

[0425] For example, if a worker picking parts in a factory feels anxious about the task, the system can detect this and provide selection options and reference information on the screen to reduce stress. Further improvements in prediction accuracy can be achieved by inputting a prompt such as, "We want to create a simple and accurate parts selection list based on the sentiment data of the work line. What is the recommended approach?" into the generating AI model.

[0426] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0427] Step 1:

[0428] The terminal uses the camera and microphone of a smartphone or smart glasses to acquire emotional data when a worker begins a parts selection task. The input consists of the worker's facial expressions and voice, which are analyzed using the Emotion API to detect the worker's emotional state in real time. Emotional state data is generated as output.

[0429] Step 2:

[0430] The user inputs information about the parts they wish to select into the terminal. The terminal formats the input data and transfers it to the server. Here, the input is the specification information of the parts the user needs, and the output is the formatted data.

[0431] Step 3:

[0432] The server uses the received part specification data to extract data from a database (MySQL) of similar past projects. The input is formatted part data, and the output is data from similar projects. Database queries are performed during this process.

[0433] Step 4:

[0434] The server uses extracted data and sentiment data to apply a machine learning algorithm (Scikit-learn) and generate the optimal component list. The input is historical project data and sentiment data, and the output is a component list. Data analysis and the application of a predictive model are performed here.

[0435] Step 5:

[0436] The server sends the generated parts list to the terminal. The user checks the parts list on the terminal and provides feedback. The input is the parts list, and the output is the user's feedback.

[0437] Step 6:

[0438] The device sends feedback to the server. The server uses a learning update mechanism to incorporate the feedback into the machine learning model. The input is the feedback data, and the output is the updated learning model.

[0439] Step 7:

[0440] The user inputs prompt statements into an AI model to obtain approaches and ideas for further improving the parts selection process. For example, input such as "I want to create a simple and accurate parts selection list based on sentiment data from the work line. What is your recommended approach?" will result in suggestions from the AI ​​model as output.

[0441] 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.

[0442] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0443] 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.

[0444] [Third Embodiment]

[0445] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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).

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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".

[0457] This invention is a system for streamlining the material selection process in base station construction work, and its main components include a server, terminals, and user interaction. The system as a whole consistently manages the flow from data input and analysis to material selection, result provision, and feedback.

[0458] Users input details of a new base station construction project via a terminal. This includes information such as the project location, frequency band to be used, scale of construction, and environmental conditions. The input data is formatted and then sent to the server via the cloud.

[0459] The server analyzes construction project data stored in the database based on project data received from the terminal. This extracts the implementation history and material usage examples of similar projects, which are then input into a machine learning algorithm.

[0460] The server uses a machine learning algorithm to select the most suitable components for the input data, and the generated component list includes details such as "part number," "specifications," "quantity," and "supplier information." This component list is sent back to the terminal for the user to review.

[0461] Users can use their devices to review the proposed list of components and provide feedback and additional information as needed. This feedback is sent back to the server and used as learning material for future projects. For example, when installing a new base station between buildings in an urban area, the server can automatically select the optimal antenna type and cable specifications based on data from similar past projects and notify the user.

[0462] In this way, the present invention significantly improves the efficiency of material selection in base station construction, thereby increasing the success rate of projects. The system enables efficient allocation of resources, creating a situation where engineers can concentrate on more strategic tasks.

[0463] The following describes the processing flow.

[0464] Step 1:

[0465] The user uses a terminal to input information about a new base station construction project. This information includes the planned installation location, the frequency band to be used, the scale of the construction, and environmental conditions. This input data is formatted into a predetermined format on the terminal before transmission.

[0466] Step 2:

[0467] The terminal sends the formatted project data to the server. This process is conducted via the cloud to ensure secure, real-time data communication.

[0468] Step 3:

[0469] The server analyzes the received project data. First, it identifies related similar projects from the past project database and extracts this data.

[0470] Step 4:

[0471] The server executes a machine learning algorithm based on the extracted data. This algorithm uses patterns learned from similar projects to derive the optimal list of components for the current project.

[0472] Step 5:

[0473] The server adds "part number," "specifications," "quantity," and "supplier information" to the generated parts list and sends it back to the terminal. This list is customized to meet project requirements.

[0474] Step 6:

[0475] The terminal displays the parts list received from the server to the user. The user can review the list and add additional comments or modifications as needed.

[0476] Step 7:

[0477] The user sends feedback from their device to the server after reviewing the product. This feedback is treated as important information for improving the accuracy of component selection.

[0478] Step 8:

[0479] The server analyzes user feedback and uses it to update machine learning algorithms. This will allow for more appropriate component selection in future projects.

[0480] (Example 1)

[0481] 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."

[0482] Traditional base station construction component selection processes rely on experience and individual judgment, leading to challenges in efficiency and accuracy. Furthermore, selecting appropriate components for each project is difficult, hindering optimization. This can result in wasted costs and extended construction periods.

[0483] 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.

[0484] In this invention, the server includes a user interface means for inputting project information from a user terminal and transmitting it to the server via data communication; a data analysis means for analyzing the received construction project information and extracting similar project information from a past database; and a configuration selection means for generating an optimal list of components using a machine learning algorithm. This makes it possible to select components in construction projects efficiently and with high accuracy.

[0485] A "user interface means" is an interface that allows a user to input project information and exchange information with a server via data communication.

[0486] A "data analysis tool" is a tool that analyzes received project information and extracts similar project information from past databases.

[0487] A "configuration selection method" is a means of generating an optimal list of components from data analyzed using a machine learning algorithm.

[0488] An "interface means" is a means of providing a configuration list to the user terminal and collecting feedback from the user.

[0489] "Learning improvement methods" refer to means of continuously updating the learning model and improving its accuracy based on user feedback.

[0490] The "terminal function" is a function that is responsible for appropriately formatting project information entered by the user and sending it to the server.

[0491] The "data addition function" is a feature that adds supplier information to the generated configuration list, making the list more detailed and practical.

[0492] This invention is an innovative system that streamlines the component selection process in base station construction. This system primarily operates through the coordinated interaction of servers, terminals, and users.

[0493] Users input details of new construction projects via a terminal. The terminal formats the entered information into the appropriate data format and transmits it to the server while ensuring security. In this process, a standard computer device is used as the terminal and communicates with the server over the internet. The formatted information includes details such as the project location, the frequency band to be used, the scale of the construction, and environmental conditions.

[0494] The server performs data analysis based on the received data, using a database that holds historical project data. This database contains extensive historical data on past project histories and used components. The server extracts similar project data and uses a machine learning model to generate an optimal component list based on the analysis results. Specific machine learning algorithms are employed in this process to improve performance and maximize accuracy.

[0495] The generated component list consists of detailed information including "part number," "specifications," "quantity," and "supplier information." This list is sent to the terminal, allowing the user to review it. The user has the opportunity to review the list on the terminal and provide feedback as needed. The feedback is returned to the server and used as training material for the machine learning model, continuously improving the accuracy and efficiency of the entire system.

[0496] For example, when a user plans to install a new wireless base station in an urban area, the server can select the optimal antennas and cables based on past project data from urban environments and notify the user. This significantly streamlines the project planning process and leads to greater success.

[0497] As an example of a prompt, input such as, "Please propose the optimal configuration for a new 5G base station installation project in an urban environment. The operating frequency will be 3.5GHz, and the construction scale will be medium-sized," is possible. Such a configuration is expected to lead to sustained improvements in project outcomes.

[0498] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0499] Step 1:

[0500] The user enters project details using a terminal.

[0501] Input: Project location, operating frequency, scale of construction, environmental conditions.

[0502] Operation: The terminal formats the entered information based on the form on the screen.

[0503] Output: Saved as formatted project data.

[0504] Step 2:

[0505] The terminal sends the formatted data to the server.

[0506] Input: Formatted project data.

[0507] Operation: Transfers data to the server according to the data communication protocol.

[0508] Output: The server receives confirmation that the transmission is complete.

[0509] Step 3:

[0510] The server searches the database based on the received data.

[0511] Input: Received project data.

[0512] Operation: Query the past project database and extract similar projects.

[0513] Output: Historical data of similar projects.

[0514] Step 4:

[0515] The server uses machine learning algorithms to generate the optimal components.

[0516] Input: Historical data of similar projects.

[0517] Operation: Input data into the generation AI model and generate the optimal list of components.

[0518] Output: Optimized list of components.

[0519] Step 5:

[0520] The server sends the generated list of components to the terminal.

[0521] Input: Optimized component list.

[0522] Action: Transfers the list to the user's device so they can view it.

[0523] Output: A list of components displayed on the terminal.

[0524] Step 6:

[0525] The user reviews the component list on their device and provides feedback.

[0526] Input: List of components.

[0527] Action: Review the details and write down suggestions for improvement or feedback as needed.

[0528] Output: Provided feedback.

[0529] Step 7:

[0530] The server updates the learned model using the feedback it receives.

[0531] Input: Provided feedback.

[0532] Operation: Improves machine learning models and enhances algorithm accuracy based on feedback.

[0533] Output: Updated machine learning model.

[0534] (Application Example 1)

[0535] 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."

[0536] To improve on-site work efficiency, it is necessary to provide real-time information on the optimal material selection. However, currently, there is no centralized system for this information, making it difficult to immediately reflect on-site feedback. Furthermore, the inability to effectively incorporate user feedback is causing a decline in project efficiency.

[0537] 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.

[0538] In this invention, the server includes information analysis means, selection means, interface means, on-site input processing means, and learning update means. This improves on-site work efficiency and allows for real-time incorporation of user feedback.

[0539] "Information analysis means" refers to a method for receiving construction project data and extracting and analyzing similar information from past project databases.

[0540] "Selection method" refers to a means of generating the optimal list of elements using a machine learning algorithm based on the extracted data.

[0541] An "interface means" is a means for providing the generated list of elements to the user's terminal and for collecting feedback from the user.

[0542] "On-site input processing methods" refer to methods for receiving information entered at a construction site in real time and reflecting it in the system.

[0543] A "learning update method" is a means of incorporating feedback collected from users into the learning model.

[0544] The system that realizes this invention functions by combining a server, terminal, cloud infrastructure, machine learning algorithm, and user interface. The server often runs on a cloud service such as AWS, and it is here that the information analysis means, selection means, and learning update means operate. Specifically, the server receives construction project data, refers to a past database to extract similar project data, and then uses a machine learning library such as TensorFlow to select the optimal elements (such as components) and sends the selection results to the terminal.

[0545] The device is either worn by the user or carried as a smartphone. An application developed using React Native runs on it, providing a list of elements via a user interface. Furthermore, it quickly receives input from field workers and sends it to the cloud for feedback collection. This allows users to receive optimal information in real time and react immediately.

[0546] For example, at a construction site around a new city, a user can simply wear smart glasses and operate them to obtain a list of appropriate structural components. This list also includes supply information, significantly streamlining the project's progress.

[0547] By utilizing a generative AI model, it is possible to analyze information through prompt statements and quickly and appropriately select elements. An example of a prompt statement is as follows:

[0548] "We need to select components for a new construction project. The project is located in an urban area. Please select the optimal antenna type and cable specifications based on data from similar past projects."

[0549] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0550] Step 1:

[0551] The terminal provides an interface for the user to input details of a construction project. This step involves entering information such as the project location, the frequency band to be used, and the scale of the construction. The entered data is formatted and prepared for transmission to the server.

[0552] Step 2:

[0553] The terminal sends formatted construction project data to the server via the cloud. This data is used in subsequent processing steps.

[0554] Step 3:

[0555] The server analyzes the received project data and extracts similar past project data from the database. During this process, the server compares past and current projects and performs an analysis to calculate their similarity.

[0556] Step 4:

[0557] The server uses a machine learning algorithm to generate an optimal list of elements based on the extracted similar project data. This algorithm utilizes a generative AI model to process data based on prompt statements and create the list of elements.

[0558] Step 5:

[0559] The server sends the generated element list back to the terminal, making it available to the user. This element list also includes supplier information.

[0560] Step 6:

[0561] The terminal displays the received list of elements to the user, allowing them to use it in their on-site work. The user can input feedback in real time as needed.

[0562] Step 7:

[0563] The server receives the feedback collected from users again and stores it in the database. This feedback is used to update the learning model and contribute to improving the accuracy of future projects.

[0564] 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.

[0565] This invention streamlines the component selection process in base station construction and provides a more user-friendly system by taking into account the user's emotional state. This system mainly consists of server, terminal, and user interaction, with an emotion engine playing a central role.

[0566] The user uses a terminal to input details of the base station construction project. After input, the data is automatically formatted and sent to the server. The terminal is equipped with an emotion engine that recognizes the user's emotions from their voice and facial expressions, and determines the user's emotional state during input. This emotion information is also sent to the server.

[0567] The server extracts similar past data from the database based on the received project data and uses sentiment data provided by the sentiment engine to consider the user's state of mind when selecting components. Then, it uses a machine learning algorithm to generate an optimal component list. This component selection reflects not only the standard technical specifications of the components but also the user's preferences and tendencies based on their emotions.

[0568] The generated parts list includes details such as "part number," "specifications," "quantity," and "supplier information," and is sent back to the terminal for the user to review. When the user reviews the list, the user interface adjusts its design and response based on the user's emotional state to provide a less stressful operating environment.

[0569] For example, if a user feels anxiety or dissatisfaction with a particular piece of equipment, the emotion engine detects this state, and the server selects components that reflect that emotion. Furthermore, if a user expresses positive emotions towards a component list, that feedback is stored in the server and used in future selection processes.

[0570] This system allows users to select components more comfortably and efficiently, and to receive appropriate support that takes their feelings into consideration.

[0571] The following describes the processing flow.

[0572] Step 1:

[0573] The user inputs detailed information about the base station construction project via a terminal. This information includes the installation location, the frequency to be used, and the construction period. The terminal formats this data into a predetermined format.

[0574] Step 2:

[0575] The device collects emotional data from the user's facial expressions and voice, along with the input information, using its built-in emotion engine. The emotion engine then analyzes the recognized emotions to send them to the server.

[0576] Step 3:

[0577] The device sends formatted project data and emotional data to the server. This enables personalization based on the user's emotional state.

[0578] Step 4:

[0579] The server analyzes the received project data and extracts data from similar past projects from the database. It also considers the received sentiment data to understand the user's current state.

[0580] Step 5:

[0581] The server uses machine learning algorithms to generate an optimal list of components based on project requirements. This list is then refined using sentiment data to reflect the user's interests and concerns.

[0582] Step 6:

[0583] The server adds detailed information such as "model number," "specifications," and "supplier information" to the generated parts list and sends it back to the terminal.

[0584] Step 7:

[0585] The terminal displays data from the server to the user and dynamically adjusts the user interface according to the user's emotions. For example, if the user expresses dissatisfaction, the color scheme and animations are changed to improve visibility.

[0586] Step 8:

[0587] The user reviews the parts list via their device and provides additional feedback if necessary. This feedback is then sent back from the device to the server and incorporated into the system's learning model.

[0588] This process allows the system to select components with greater accuracy while taking user emotions into consideration.

[0589] (Example 2)

[0590] 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."

[0591] In large-scale and complex projects such as base station construction, the material selection process is typically based solely on technical requirements, neglecting user emotions and ease of use. This can make the selection process stressful and inefficient for users. Furthermore, the inability to effectively utilize emotional factors and past feedback can lead to insufficient project optimization.

[0592] 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.

[0593] In this invention, the server includes information analysis means, selection means, user interface means, and model update means. This makes it possible to integrate user input data and emotional data to select the optimal materials and further improve the user experience.

[0594] "Information analysis means" refers to devices or processes that receive information related to construction activities and extract similar past data from a database.

[0595] "Selection method" refers to a device or process for integrating user emotional data with information related to construction activities to select the optimal material.

[0596] A "user interface means" is an interface system that provides selected information to a user device and adjusts the response according to the user's emotional state.

[0597] A "model update means" is a device or process for collecting user responses and integrating those collected responses into a learning model.

[0598] "Device function" refers to the function of a device that automatically formats and transmits data provided by the user.

[0599] "Information addition means" refers to devices or processes for adding source information to selected material information.

[0600] This invention is a system that streamlines the material selection process in base station construction projects and provides a more user-friendly experience by taking user emotions into consideration. The embodiments for carrying out this invention are described in detail below.

[0601] The user uses a terminal to input detailed information about the base station construction project. The terminal is equipped with an emotion engine that analyzes voice and facial expressions to determine the user's emotional state in real time. This emotional information and project data are formatted using an automated formatting tool and then securely transmitted to the server.

[0602] The server extracts data from a database of similar past projects based on the received data. Database management systems and SQL queries are used for information analysis. Furthermore, the server uses the received sentiment data to create a list of materials optimized for the user. Here, machine learning algorithms, including sentiment data, are applied to select materials that reflect the user's emotions in addition to technical aspects.

[0603] The generated material list is sent back to the terminal for user review. The terminal adjusts the user interface based on the user's emotional state to provide a more comfortable operating environment. For example, if the user expresses concern about a particular material, a safety-oriented alternative is provided that addresses the reason for the concern.

[0604] For example, if a user feels uneasy about a particular piece of equipment, the emotion engine detects that emotional state, and the server selects materials that reflect that emotion. This allows the user to feel more confident in the material list. Furthermore, if the user provides positive feedback on the material list, this is saved on the server and used in future selection processes.

[0605] An example of a prompt message is: "Consider the detailed information of the base station construction project and user sentiment data to plan the optimal list of materials."

[0606] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0607] Step 1:

[0608] The user enters detailed information about the base station construction project into the terminal. Based on the data entered by the user, the terminal uses a dedicated formatting tool to format the data. This input data includes the scale of the project, required materials, and deadlines. The formatted data is then processed into a format that ensures consistency necessary for subsequent processing.

[0609] Step 2:

[0610] The device analyzes the user's emotional state using an emotion engine. Input includes user voice data and facial expression data, which the device processes in real time. The emotional data generated through analysis is classified as categories such as "anxiety" and "satisfaction," and output as metadata representing the user's emotional state.

[0611] Step 3:

[0612] The terminal sends pre-formatted project data and generated sentiment data as a set to the server. Data security is ensured during transmission through encryption technology. The input data received by the server includes pre-formatted project information and user sentiment metadata.

[0613] Step 4:

[0614] The server references the database and extracts historical similar project data using SQL queries. The input here is data that reflects the characteristics and requirements of the project. Based on this, the server selects historical data that meets the criteria and outputs it as a similar dataset.

[0615] Step 5:

[0616] The server generates an optimal list of materials using a machine learning algorithm that takes user sentiment data into account. This process uses similar datasets and sentiment data as input. Based on this data, the algorithm selects the materials best suited to the user's preferences and actual technical requirements, and outputs a list of materials as a result.

[0617] Step 6:

[0618] The generated material list is sent back from the server to the terminal, where the user reviews its contents. During this process, the screen design and operational feedback are adjusted, taking into account the impact the terminal receives from the user's emotional state. Specifically, the color scheme and layout of the displayed content are adjusted to provide the user with a sense of security.

[0619] Step 7:

[0620] Users input feedback on the material list, and this data is sent back to the server via their terminal. The server receives this feedback as new input data and integrates it into a learning model. As a result, the feedback is accumulated as valuable historical data that will be reflected in future material selection processes.

[0621] (Application Example 2)

[0622] 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."

[0623] In modern factories, parts selection is a complex process for many workers, often leading to stress and anxiety. Such emotional burdens can result in decreased work efficiency and selection errors, highlighting the need for a flexible system that considers emotional well-being. Furthermore, user feedback and emotional states must be taken into account to create a more user-friendly and optimal parts selection process.

[0624] 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.

[0625] In this invention, the server includes data analysis means, component selection means using machine learning algorithms, user interface means for collecting user feedback, and emotion analysis means for detecting the worker's emotional state in real time. This makes it possible to select components while taking the worker's emotions into consideration.

[0626] "Data analysis means" refers to a device or method that receives construction project data and has the function of extracting similar data from a database of past projects.

[0627] "Component selection means" refers to an apparatus or method that uses extracted data and a machine learning algorithm to create an optimal component list.

[0628] "User interface means" refers to an interface function that provides the generated component list to the user terminal and collects feedback obtained from it.

[0629] A "learning update means" is a device or method that has the function of updating a learning model based on feedback collected from users, thereby improving the accuracy of component selection in subsequent instances.

[0630] "Emotional analysis means" refers to a device or method for detecting the emotional state of an operator in real time and adjusting the operating environment based on that.

[0631] The embodiment for carrying out the invention describes a method for constructing a system that provides appropriate support to workers in the parts selection process in a factory, tailored to their emotional state. This system functions by combining data analysis, material selection, user interface, learning and updating, and sentiment analysis.

[0632] The server receives data related to the factory construction project and uses data analysis techniques to extract similar information from historical databases. MySQL is used as the database management system for data comparison and matching.

[0633] The terminal uses a smartphone or smart glasses equipped with a camera and microphone to analyze the worker's emotions in real time. In this process, Microsoft Azure's Emotion API is utilized as an emotion recognition library to identify emotional states based on voice and facial expression data.

[0634] Machine learning algorithms (e.g., Scikit-learn) generate optimal component lists based on the received data. This enables selections that consider not only the technical specifications of the parts typically required, but also the worker's emotions.

[0635] The user interface presents the worker with a list of materials and collects feedback. The collected feedback is then used to inform future selections through a learning update mechanism.

[0636] For example, if a worker picking parts in a factory feels anxious about the task, the system can detect this and provide selection options and reference information on the screen to reduce stress. Further improvements in prediction accuracy can be achieved by inputting a prompt such as, "We want to create a simple and accurate parts selection list based on the sentiment data of the work line. What is the recommended approach?" into the generating AI model.

[0637] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0638] Step 1:

[0639] The terminal uses the camera and microphone of a smartphone or smart glasses to acquire emotional data when a worker begins a parts selection task. The input consists of the worker's facial expressions and voice, which are analyzed using the Emotion API to detect the worker's emotional state in real time. Emotional state data is generated as output.

[0640] Step 2:

[0641] The user inputs information about the parts they wish to select into the terminal. The terminal formats the input data and transfers it to the server. Here, the input is the specification information of the parts the user needs, and the output is the formatted data.

[0642] Step 3:

[0643] The server uses the received part specification data to extract data from a database (MySQL) of similar past projects. The input is formatted part data, and the output is data from similar projects. Database queries are performed during this process.

[0644] Step 4:

[0645] The server uses extracted data and sentiment data to apply a machine learning algorithm (Scikit-learn) and generate the optimal component list. The input is historical project data and sentiment data, and the output is a component list. Data analysis and the application of a predictive model are performed here.

[0646] Step 5:

[0647] The server sends the generated parts list to the terminal. The user checks the parts list on the terminal and provides feedback. The input is the parts list, and the output is the user's feedback.

[0648] Step 6:

[0649] The device sends feedback to the server. The server uses a learning update mechanism to incorporate the feedback into the machine learning model. The input is the feedback data, and the output is the updated learning model.

[0650] Step 7:

[0651] The user inputs prompt statements into an AI model to obtain approaches and ideas for further improving the parts selection process. For example, input such as "I want to create a simple and accurate parts selection list based on sentiment data from the work line. What is your recommended approach?" will result in suggestions from the AI ​​model as output.

[0652] 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.

[0653] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0654] 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.

[0655] [Fourth Embodiment]

[0656] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0657] 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.

[0658] 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).

[0659] 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.

[0660] 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.

[0661] 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).

[0662] 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.

[0663] 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.

[0664] 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.

[0665] 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.

[0666] 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.

[0667] 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.

[0668] 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".

[0669] This invention is a system for streamlining the material selection process in base station construction work, and its main components include a server, terminals, and user interaction. The system as a whole consistently manages the flow from data input and analysis to material selection, result provision, and feedback.

[0670] Users input details of a new base station construction project via a terminal. This includes information such as the project location, frequency band to be used, scale of construction, and environmental conditions. The input data is formatted and then sent to the server via the cloud.

[0671] The server analyzes construction project data stored in the database based on project data received from the terminal. This extracts the implementation history and material usage examples of similar projects, which are then input into a machine learning algorithm.

[0672] The server uses a machine learning algorithm to select the most suitable components for the input data, and the generated component list includes details such as "part number," "specifications," "quantity," and "supplier information." This component list is sent back to the terminal for the user to review.

[0673] Users can use their devices to review the proposed list of components and provide feedback and additional information as needed. This feedback is sent back to the server and used as learning material for future projects. For example, when installing a new base station between buildings in an urban area, the server can automatically select the optimal antenna type and cable specifications based on data from similar past projects and notify the user.

[0674] In this way, the present invention significantly improves the efficiency of material selection in base station construction, thereby increasing the success rate of projects. The system enables efficient allocation of resources, creating a situation where engineers can concentrate on more strategic tasks.

[0675] The following describes the processing flow.

[0676] Step 1:

[0677] The user uses a terminal to input information about a new base station construction project. This information includes the planned installation location, the frequency band to be used, the scale of the construction, and environmental conditions. This input data is formatted into a predetermined format on the terminal before transmission.

[0678] Step 2:

[0679] The terminal sends the formatted project data to the server. This process is conducted via the cloud to ensure secure, real-time data communication.

[0680] Step 3:

[0681] The server analyzes the received project data. First, it identifies related similar projects from the past project database and extracts this data.

[0682] Step 4:

[0683] The server executes a machine learning algorithm based on the extracted data. This algorithm uses patterns learned from similar projects to derive the optimal list of components for the current project.

[0684] Step 5:

[0685] The server adds "part number," "specifications," "quantity," and "supplier information" to the generated parts list and sends it back to the terminal. This list is customized to meet project requirements.

[0686] Step 6:

[0687] The terminal displays the parts list received from the server to the user. The user can review the list and add additional comments or modifications as needed.

[0688] Step 7:

[0689] The user sends feedback from their device to the server after reviewing the product. This feedback is treated as important information for improving the accuracy of component selection.

[0690] Step 8:

[0691] The server analyzes user feedback and uses it to update machine learning algorithms. This will allow for more appropriate component selection in future projects.

[0692] (Example 1)

[0693] 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".

[0694] Traditional base station construction component selection processes rely on experience and individual judgment, leading to challenges in efficiency and accuracy. Furthermore, selecting appropriate components for each project is difficult, hindering optimization. This can result in wasted costs and extended construction periods.

[0695] 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.

[0696] In this invention, the server includes a user interface means for inputting project information from a user terminal and transmitting it to the server via data communication; a data analysis means for analyzing the received construction project information and extracting similar project information from a past database; and a configuration selection means for generating an optimal list of components using a machine learning algorithm. This makes it possible to select components in construction projects efficiently and with high accuracy.

[0697] A "user interface means" is an interface that allows a user to input project information and exchange information with a server via data communication.

[0698] A "data analysis tool" is a tool that analyzes received project information and extracts similar project information from past databases.

[0699] A "configuration selection method" is a means of generating an optimal list of components from data analyzed using a machine learning algorithm.

[0700] An "interface means" is a means of providing a configuration list to the user terminal and collecting feedback from the user.

[0701] "Learning improvement methods" refer to means of continuously updating the learning model and improving its accuracy based on user feedback.

[0702] The "terminal function" is a function that is responsible for appropriately formatting project information entered by the user and sending it to the server.

[0703] The "data addition function" is a feature that adds supplier information to the generated configuration list, making the list more detailed and practical.

[0704] This invention is an innovative system that streamlines the component selection process in base station construction. This system primarily operates through the coordinated interaction of servers, terminals, and users.

[0705] Users input details of new construction projects via a terminal. The terminal formats the entered information into the appropriate data format and transmits it to the server while ensuring security. In this process, a standard computer device is used as the terminal and communicates with the server over the internet. The formatted information includes details such as the project location, the frequency band to be used, the scale of the construction, and environmental conditions.

[0706] The server performs data analysis based on the received data, using a database that holds historical project data. This database contains extensive historical data on past project histories and used components. The server extracts similar project data and uses a machine learning model to generate an optimal component list based on the analysis results. Specific machine learning algorithms are employed in this process to improve performance and maximize accuracy.

[0707] The generated component list consists of detailed information including "part number," "specifications," "quantity," and "supplier information." This list is sent to the terminal, allowing the user to review it. The user has the opportunity to review the list on the terminal and provide feedback as needed. The feedback is returned to the server and used as training material for the machine learning model, continuously improving the accuracy and efficiency of the entire system.

[0708] For example, when a user plans to install a new wireless base station in an urban area, the server can select the optimal antennas and cables based on past project data from urban environments and notify the user. This significantly streamlines the project planning process and leads to greater success.

[0709] As an example of a prompt, input such as, "Please propose the optimal configuration for a new 5G base station installation project in an urban environment. The operating frequency will be 3.5GHz, and the construction scale will be medium-sized," is possible. Such a configuration is expected to lead to sustained improvements in project outcomes.

[0710] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0711] Step 1:

[0712] The user enters project details using a terminal.

[0713] Input: Project location, operating frequency, scale of construction, environmental conditions.

[0714] Operation: The terminal formats the entered information based on the form on the screen.

[0715] Output: Saved as formatted project data.

[0716] Step 2:

[0717] The terminal sends the formatted data to the server.

[0718] Input: Formatted project data.

[0719] Operation: Transfers data to the server according to the data communication protocol.

[0720] Output: The server receives confirmation that the transmission is complete.

[0721] Step 3:

[0722] The server searches the database based on the received data.

[0723] Input: Received project data.

[0724] Operation: Query the past project database and extract similar projects.

[0725] Output: Historical data of similar projects.

[0726] Step 4:

[0727] The server uses machine learning algorithms to generate the optimal components.

[0728] Input: Historical data of similar projects.

[0729] Operation: Input data into the generation AI model and generate the optimal list of components.

[0730] Output: Optimized list of components.

[0731] Step 5:

[0732] The server sends the generated list of components to the terminal.

[0733] Input: Optimized component list.

[0734] Action: Transfers the list to the user's device so they can view it.

[0735] Output: A list of components displayed on the terminal.

[0736] Step 6:

[0737] The user reviews the component list on their device and provides feedback.

[0738] Input: List of components.

[0739] Action: Review the details and write down suggestions for improvement or feedback as needed.

[0740] Output: Provided feedback.

[0741] Step 7:

[0742] The server updates the learned model using the feedback it receives.

[0743] Input: Provided feedback.

[0744] Operation: Improves machine learning models and enhances algorithm accuracy based on feedback.

[0745] Output: Updated machine learning model.

[0746] (Application Example 1)

[0747] 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".

[0748] To improve on-site work efficiency, it is necessary to provide real-time information on the optimal material selection. However, currently, there is no centralized system for this information, making it difficult to immediately reflect on-site feedback. Furthermore, the inability to effectively incorporate user feedback is causing a decline in project efficiency.

[0749] 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.

[0750] In this invention, the server includes information analysis means, selection means, interface means, on-site input processing means, and learning update means. This improves on-site work efficiency and allows for real-time incorporation of user feedback.

[0751] "Information analysis means" refers to a method for receiving construction project data and extracting and analyzing similar information from past project databases.

[0752] "Selection method" refers to a means of generating the optimal list of elements using a machine learning algorithm based on the extracted data.

[0753] An "interface means" is a means for providing the generated list of elements to the user's terminal and for collecting feedback from the user.

[0754] "On-site input processing methods" refer to methods for receiving information entered at a construction site in real time and reflecting it in the system.

[0755] A "learning update method" is a means of incorporating feedback collected from users into the learning model.

[0756] The system that realizes this invention functions by combining a server, terminal, cloud infrastructure, machine learning algorithm, and user interface. The server often runs on a cloud service such as AWS, and it is here that the information analysis means, selection means, and learning update means operate. Specifically, the server receives construction project data, refers to a past database to extract similar project data, and then uses a machine learning library such as TensorFlow to select the optimal elements (such as components) and sends the selection results to the terminal.

[0757] The device is either worn by the user or carried as a smartphone. An application developed using React Native runs on it, providing a list of elements via a user interface. Furthermore, it quickly receives input from field workers and sends it to the cloud for feedback collection. This allows users to receive optimal information in real time and react immediately.

[0758] For example, at a construction site around a new city, a user can simply wear smart glasses and operate them to obtain a list of appropriate structural components. This list also includes supply information, significantly streamlining the project's progress.

[0759] By utilizing a generative AI model, it is possible to analyze information through prompt statements and quickly and appropriately select elements. An example of a prompt statement is as follows:

[0760] "We need to select components for a new construction project. The project is located in an urban area. Please select the optimal antenna type and cable specifications based on data from similar past projects."

[0761] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0762] Step 1:

[0763] The terminal provides an interface for the user to input details of a construction project. This step involves entering information such as the project location, the frequency band to be used, and the scale of the construction. The entered data is formatted and prepared for transmission to the server.

[0764] Step 2:

[0765] The terminal sends formatted construction project data to the server via the cloud. This data is used in subsequent processing steps.

[0766] Step 3:

[0767] The server analyzes the received project data and extracts similar past project data from the database. During this process, the server compares past and current projects and performs an analysis to calculate their similarity.

[0768] Step 4:

[0769] The server uses a machine learning algorithm to generate an optimal list of elements based on the extracted similar project data. This algorithm utilizes a generative AI model to process data based on prompt statements and create the list of elements.

[0770] Step 5:

[0771] The server sends the generated element list back to the terminal, making it available to the user. This element list also includes supplier information.

[0772] Step 6:

[0773] The terminal displays the received list of elements to the user, allowing them to use it in their on-site work. The user can input feedback in real time as needed.

[0774] Step 7:

[0775] The server receives the feedback collected from users again and stores it in the database. This feedback is used to update the learning model and contribute to improving the accuracy of future projects.

[0776] 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.

[0777] This invention streamlines the component selection process in base station construction and provides a more user-friendly system by taking into account the user's emotional state. This system mainly consists of server, terminal, and user interaction, with an emotion engine playing a central role.

[0778] The user uses a terminal to input details of the base station construction project. After input, the data is automatically formatted and sent to the server. The terminal is equipped with an emotion engine that recognizes the user's emotions from their voice and facial expressions, and determines the user's emotional state during input. This emotion information is also sent to the server.

[0779] The server extracts similar past data from the database based on the received project data and uses sentiment data provided by the sentiment engine to consider the user's state of mind when selecting components. Then, it uses a machine learning algorithm to generate an optimal component list. This component selection reflects not only the standard technical specifications of the components but also the user's preferences and tendencies based on their emotions.

[0780] The generated parts list includes details such as "part number," "specifications," "quantity," and "supplier information," and is sent back to the terminal for the user to review. When the user reviews the list, the user interface adjusts its design and response based on the user's emotional state to provide a less stressful operating environment.

[0781] For example, if a user feels anxiety or dissatisfaction with a particular piece of equipment, the emotion engine detects this state, and the server selects components that reflect that emotion. Furthermore, if a user expresses positive emotions towards a component list, that feedback is stored in the server and used in future selection processes.

[0782] This system allows users to select components more comfortably and efficiently, and to receive appropriate support that takes their feelings into consideration.

[0783] The following describes the processing flow.

[0784] Step 1:

[0785] The user inputs detailed information about the base station construction project via a terminal. This information includes the installation location, the frequency to be used, and the construction period. The terminal formats this data into a predetermined format.

[0786] Step 2:

[0787] The device collects emotional data from the user's facial expressions and voice, along with the input information, using its built-in emotion engine. The emotion engine then analyzes the recognized emotions to send them to the server.

[0788] Step 3:

[0789] The device sends formatted project data and emotional data to the server. This enables personalization based on the user's emotional state.

[0790] Step 4:

[0791] The server analyzes the received project data and extracts data from similar past projects from the database. It also considers the received sentiment data to understand the user's current state.

[0792] Step 5:

[0793] The server uses machine learning algorithms to generate an optimal list of components based on project requirements. This list is then refined using sentiment data to reflect the user's interests and concerns.

[0794] Step 6:

[0795] The server adds detailed information such as "model number," "specifications," and "supplier information" to the generated parts list and sends it back to the terminal.

[0796] Step 7:

[0797] The terminal displays data from the server to the user and dynamically adjusts the user interface according to the user's emotions. For example, if the user expresses dissatisfaction, the color scheme and animations are changed to improve visibility.

[0798] Step 8:

[0799] The user reviews the parts list via their device and provides additional feedback if necessary. This feedback is then sent back from the device to the server and incorporated into the system's learning model.

[0800] This process allows the system to select components with greater accuracy while taking user emotions into consideration.

[0801] (Example 2)

[0802] 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".

[0803] In large-scale and complex projects such as base station construction, the material selection process is typically based solely on technical requirements, neglecting user emotions and ease of use. This can make the selection process stressful and inefficient for users. Furthermore, the inability to effectively utilize emotional factors and past feedback can lead to insufficient project optimization.

[0804] 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.

[0805] In this invention, the server includes information analysis means, selection means, user interface means, and model update means. This makes it possible to integrate user input data and emotional data to select the optimal materials and further improve the user experience.

[0806] "Information analysis means" refers to devices or processes that receive information related to construction activities and extract similar past data from a database.

[0807] "Selection method" refers to a device or process for integrating user emotional data with information related to construction activities to select the optimal material.

[0808] A "user interface means" is an interface system that provides selected information to a user device and adjusts the response according to the user's emotional state.

[0809] A "model update means" is a device or process for collecting user responses and integrating those collected responses into a learning model.

[0810] "Device function" refers to the function of a device that automatically formats and transmits data provided by the user.

[0811] "Information addition means" refers to devices or processes for adding source information to selected material information.

[0812] This invention is a system that streamlines the material selection process in base station construction projects and provides a more user-friendly experience by taking user emotions into consideration. The embodiments for carrying out this invention are described in detail below.

[0813] The user uses a terminal to input detailed information about the base station construction project. The terminal is equipped with an emotion engine that analyzes voice and facial expressions to determine the user's emotional state in real time. This emotional information and project data are formatted using an automated formatting tool and then securely transmitted to the server.

[0814] The server extracts data from a database of similar past projects based on the received data. Database management systems and SQL queries are used for information analysis. Furthermore, the server uses the received sentiment data to create a list of materials optimized for the user. Here, machine learning algorithms, including sentiment data, are applied to select materials that reflect the user's emotions in addition to technical aspects.

[0815] The generated material list is sent back to the terminal for user review. The terminal adjusts the user interface based on the user's emotional state to provide a more comfortable operating environment. For example, if the user expresses concern about a particular material, a safety-oriented alternative is provided that addresses the reason for the concern.

[0816] For example, if a user feels uneasy about a particular piece of equipment, the emotion engine detects that emotional state, and the server selects materials that reflect that emotion. This allows the user to feel more confident in the material list. Furthermore, if the user provides positive feedback on the material list, this is saved on the server and used in future selection processes.

[0817] An example of a prompt message is: "Consider the detailed information of the base station construction project and user sentiment data to plan the optimal list of materials."

[0818] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0819] Step 1:

[0820] The user enters detailed information about the base station construction project into the terminal. Based on the data entered by the user, the terminal uses a dedicated formatting tool to format the data. This input data includes the scale of the project, required materials, and deadlines. The formatted data is then processed into a format that ensures consistency necessary for subsequent processing.

[0821] Step 2:

[0822] The device analyzes the user's emotional state using an emotion engine. Input includes user voice data and facial expression data, which the device processes in real time. The emotional data generated through analysis is classified as categories such as "anxiety" and "satisfaction," and output as metadata representing the user's emotional state.

[0823] Step 3:

[0824] The terminal sends pre-formatted project data and generated sentiment data as a set to the server. Data security is ensured during transmission through encryption technology. The input data received by the server includes pre-formatted project information and user sentiment metadata.

[0825] Step 4:

[0826] The server references the database and extracts historical similar project data using SQL queries. The input here is data that reflects the characteristics and requirements of the project. Based on this, the server selects historical data that meets the criteria and outputs it as a similar dataset.

[0827] Step 5:

[0828] The server generates an optimal list of materials using a machine learning algorithm that takes user sentiment data into account. This process uses similar datasets and sentiment data as input. Based on this data, the algorithm selects the materials best suited to the user's preferences and actual technical requirements, and outputs a list of materials as a result.

[0829] Step 6:

[0830] The generated material list is sent back from the server to the terminal, where the user reviews its contents. During this process, the screen design and operational feedback are adjusted, taking into account the impact the terminal receives from the user's emotional state. Specifically, the color scheme and layout of the displayed content are adjusted to provide the user with a sense of security.

[0831] Step 7:

[0832] Users input feedback on the material list, and this data is sent back to the server via their terminal. The server receives this feedback as new input data and integrates it into a learning model. As a result, the feedback is accumulated as valuable historical data that will be reflected in future material selection processes.

[0833] (Application Example 2)

[0834] 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".

[0835] In modern factories, parts selection is a complex process for many workers, often leading to stress and anxiety. Such emotional burdens can result in decreased work efficiency and selection errors, highlighting the need for a flexible system that considers emotional well-being. Furthermore, user feedback and emotional states must be taken into account to create a more user-friendly and optimal parts selection process.

[0836] 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.

[0837] In this invention, the server includes data analysis means, component selection means using machine learning algorithms, user interface means for collecting user feedback, and emotion analysis means for detecting the worker's emotional state in real time. This makes it possible to select components while taking the worker's emotions into consideration.

[0838] "Data analysis means" refers to a device or method that receives construction project data and has the function of extracting similar data from a database of past projects.

[0839] "Component selection means" refers to an apparatus or method that uses extracted data and a machine learning algorithm to create an optimal component list.

[0840] "User interface means" refers to an interface function that provides the generated component list to the user terminal and collects feedback obtained from it.

[0841] A "learning update means" is a device or method that has the function of updating a learning model based on feedback collected from users, thereby improving the accuracy of component selection in subsequent instances.

[0842] "Emotional analysis means" refers to a device or method for detecting the emotional state of an operator in real time and adjusting the operating environment based on that.

[0843] The embodiment for carrying out the invention describes a method for constructing a system that provides appropriate support to workers in the parts selection process in a factory, tailored to their emotional state. This system functions by combining data analysis, material selection, user interface, learning and updating, and sentiment analysis.

[0844] The server receives data related to the factory construction project and uses data analysis techniques to extract similar information from historical databases. MySQL is used as the database management system for data comparison and matching.

[0845] The terminal uses a smartphone or smart glasses equipped with a camera and microphone to analyze the worker's emotions in real time. In this process, Microsoft Azure's Emotion API is utilized as an emotion recognition library to identify emotional states based on voice and facial expression data.

[0846] Machine learning algorithms (e.g., Scikit-learn) generate optimal component lists based on the received data. This enables selections that consider not only the technical specifications of the parts typically required, but also the worker's emotions.

[0847] The user interface presents the worker with a list of materials and collects feedback. The collected feedback is then used to inform future selections through a learning update mechanism.

[0848] For example, if a worker picking parts in a factory feels anxious about the task, the system can detect this and provide selection options and reference information on the screen to reduce stress. Further improvements in prediction accuracy can be achieved by inputting a prompt such as, "We want to create a simple and accurate parts selection list based on the sentiment data of the work line. What is the recommended approach?" into the generating AI model.

[0849] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0850] Step 1:

[0851] The terminal uses the camera and microphone of a smartphone or smart glasses to acquire emotional data when a worker begins a parts selection task. The input consists of the worker's facial expressions and voice, which are analyzed using the Emotion API to detect the worker's emotional state in real time. Emotional state data is generated as output.

[0852] Step 2:

[0853] The user inputs information about the parts they wish to select into the terminal. The terminal formats the input data and transfers it to the server. Here, the input is the specification information of the parts the user needs, and the output is the formatted data.

[0854] Step 3:

[0855] The server uses the received part specification data to extract data from a database (MySQL) of similar past projects. The input is formatted part data, and the output is data from similar projects. Database queries are performed during this process.

[0856] Step 4:

[0857] The server uses extracted data and sentiment data to apply a machine learning algorithm (Scikit-learn) and generate the optimal component list. The input is historical project data and sentiment data, and the output is a component list. Data analysis and the application of a predictive model are performed here.

[0858] Step 5:

[0859] The server sends the generated parts list to the terminal. The user checks the parts list on the terminal and provides feedback. The input is the parts list, and the output is the user's feedback.

[0860] Step 6:

[0861] The device sends feedback to the server. The server uses a learning update mechanism to incorporate the feedback into the machine learning model. The input is the feedback data, and the output is the updated learning model.

[0862] Step 7:

[0863] The user inputs prompt statements into an AI model to obtain approaches and ideas for further improving the parts selection process. For example, input such as "I want to create a simple and accurate parts selection list based on sentiment data from the work line. What is your recommended approach?" will result in suggestions from the AI ​​model as output.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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."

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] The following is further disclosed regarding the embodiments described above.

[0886] (Claim 1)

[0887] A data analysis method that receives construction project data and extracts similar data from a past project database,

[0888] A component selection means that generates an optimal component list using a machine learning algorithm with extracted data,

[0889] A user interface means that provides a parts list to the user terminal and collects feedback from the user,

[0890] A learning update method that incorporates collected feedback into the learning model,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, comprising a terminal function for formatting user input data and sending it to a server.

[0894] (Claim 3)

[0895] The system according to claim 1, further comprising a data addition means for adding supplier information to an optimized list of components.

[0896] "Example 1"

[0897] (Claim 1)

[0898] A user interface means for inputting project information from a user terminal and transmitting it to a server via data communication,

[0899] A data analysis method that analyzes received construction project information and extracts similar project information from past databases,

[0900] A configuration selection means that generates an optimal list of components using a machine learning algorithm,

[0901] An interface means for providing a configuration list to the terminal and collecting user feedback,

[0902] A learning improvement method that updates the learning model based on user feedback,

[0903] A system that includes this.

[0904] (Claim 2)

[0905] The system according to claim 1, comprising a terminal function that is responsible for formatting project information from users and sending it to a server.

[0906] (Claim 3)

[0907] The system according to claim 1, further comprising a data addition function for adding supplier information to the generated optimized configuration list.

[0908] "Application Example 1"

[0909] (Claim 1)

[0910] An information analysis tool that receives construction project data and extracts similar data from a past project database,

[0911] A selection method that generates an optimal list of elements using a machine learning algorithm with extracted data,

[0912] An interface means for providing an element list to the user's terminal and collecting feedback from the user,

[0913] An input processing method that receives on-site input in real time,

[0914] A learning update method that incorporates collected feedback into the learning model,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, comprising a communication function for formatting user input data and sending it to a server.

[0918] (Claim 3)

[0919] The system according to claim 1, comprising data addition means for adding supply information to an optimized element list.

[0920] "Example 2 of combining an emotion engine"

[0921] (Claim 1)

[0922] An information analysis means that receives data related to construction activities and extracts similar data from past activity information,

[0923] A selection method that analyzes user emotional data and integrates activity-related information with emotional data to select the optimal substance,

[0924] A user interface means that provides selected information to the user device and adjusts the response according to the emotional state,

[0925] A model update means for collecting user responses and integrating the collected responses into a learning model,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, comprising a device function for automatically formatting and transmitting data provided by the user.

[0929] (Claim 3)

[0930] The system according to claim 1, further comprising information adding means for adding source information to selected substance information.

[0931] "Application example 2 when combining with an emotional engine"

[0932] (Claim 1)

[0933] A data analysis method that receives construction project data and extracts similar data from a past project database,

[0934] A component selection means that generates an optimal component list using a machine learning algorithm with extracted data,

[0935] A user interface means that provides a parts list to the user terminal and collects feedback from the user,

[0936] A learning update method that incorporates collected feedback into the learning model,

[0937] An emotion analysis means that detects the emotional state of an operator in real time and provides an operating environment based on that,

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, comprising a terminal function for formatting user input data and sending it to a server.

[0941] (Claim 3)

[0942] The system according to claim 1, further comprising a data addition means for adding supplier information to an optimized list of components. [Explanation of Symbols]

[0943] 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. An information analysis tool that receives construction project data and extracts similar data from a past project database, A selection method that generates an optimal list of elements using a machine learning algorithm with extracted data, An interface means for providing an element list to the user's terminal and collecting feedback from the user, An input processing method that receives on-site input in real time, A learning update method that incorporates collected feedback into the learning model, A system that includes this.

2. The system according to claim 1, comprising a communication function for formatting user input data and sending it to a server.

3. The system according to claim 1, further comprising data addition means for adding supply information to an optimized element list.

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