system

Abstract

The system according to this embodiment aims to efficiently manage the progress of construction work, as well as medium- to long-term construction plans and budget formulation. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a display unit, a proposal unit, and a risk assessment unit. The reception unit receives information about the construction work. The generation unit analyzes the information received by the reception unit and generates a plan to navigate the progress of the construction work. The display unit checks the progress of the construction work in real time based on the plan generated by the generation unit. The proposal unit proposes an optimal plan for medium- to long-term construction planning and budgeting based on the plan generated by the generation unit. The risk assessment unit performs a risk assessment based on the plan generated by the generation unit.

Description

Technical Field

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[0001] The technology of the present disclosure relates to a system. [[ID=,7]]

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently manage the progress of construction work and the formulation of medium- and long-term construction plans and budgets.

[0005] The system according to the embodiment aims to efficiently manage the progress of construction work and the formulation of medium- and long-term construction plans and budgets.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a display unit, a proposal unit, and a risk assessment unit. The reception unit receives information about the construction work. The generation unit analyzes the information received by the reception unit and generates a plan to guide the progress of the construction work. The display unit checks the progress of the construction work in real time based on the plan generated by the generation unit. The proposal unit proposes an optimal plan for medium- to long-term construction planning and budgeting based on the plan generated by the generation unit. The risk assessment unit performs a risk assessment based on the plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage the progress of construction work, as well as medium- to long-term construction plans and budget formulation. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The construction management system according to an embodiment of the present invention promotes the smooth and speedy completion of ordered construction projects within NTT-GC offices and is a mechanism that utilizes a tool equipped with navigation and planning functions using generation AI as an interface to existing in-house business systems in order to create optimal plans for medium- to long-term construction planning and budgeting. The construction management system allows users to input information about construction projects. For example, information such as the type of construction, start date, end date, and budget is entered. This information is input into the generation AI. Next, the generation AI analyzes the input information and generates a plan to navigate the progress of the construction. The generation AI formulates an optimal construction plan based on past construction data and the current construction status. For example, it allocates resources necessary for the progress of the construction and evaluates the risks associated with the progress of the construction. Based on the generated plan, users can check the progress of the construction in real time. For example, graphs and charts showing the progress of the construction are displayed so that users can grasp the progress of the construction at a glance. Furthermore, the generation AI proposes an optimal plan for medium- to long-term construction planning and budgeting. The AI ​​generates future construction plans and optimizes budgets based on past construction data and current construction status. For example, it predicts costs as construction progresses and proposes ways to optimize budget allocation. This tool ensures that ordered construction projects within NTT-GC are completed smoothly and quickly, and optimizes medium- to long-term construction plans and budgeting. Users can monitor the progress of construction in real time and create optimal construction plans and budgets. As a result, the construction management system can ensure that ordered construction projects within NTT-GC are completed smoothly and quickly.

[0029] The construction management system according to this embodiment comprises a reception unit, a generation unit, a display unit, a proposal unit, and a risk assessment unit. The reception unit inputs information about the construction. This information includes, but is not limited to, the type of construction, start date, end date, and budget. For example, the reception unit allows the user to select the type of construction, input the start and end dates in calendar format, and manually input the budget. The reception unit can also input construction information using voice input. For example, the user inputs construction information by voice, and voice recognition technology is used to convert it into text data. The generation unit uses a generation AI to analyze the information input by the reception unit and generate a plan to navigate the progress of the construction. For example, the generation unit formulates an optimal construction plan based on past construction data and the current construction status. For example, the generation AI analyzes past construction data and allocates the resources necessary for the progress of the construction. The generation unit can also evaluate the risks associated with the progress of the construction. For example, the generation AI evaluates the risks associated with the progress of the construction and predicts the probability of the risks occurring. The display unit monitors the progress of the construction in real time based on the plan generated by the generation unit. The display unit displays graphs or charts showing the progress of the construction, for example. For example, the display unit visually displays the progress of the construction using a Gantt chart. The display unit can also display line graphs or bar graphs showing the progress of the construction. The proposal unit proposes an optimal plan for medium- to long-term construction planning and budgeting based on the plan generated by the generation unit. For example, the proposal unit uses generation AI to formulate future construction plans and optimize the budget. For example, the proposal unit predicts costs associated with the progress of the construction and makes proposals to optimize budget allocation. The risk assessment unit performs a risk assessment based on the plan generated by the generation unit. For example, the risk assessment unit evaluates the risks associated with the progress of the construction and predicts the probability of risk occurrence. For example, the risk assessment unit quantitatively evaluates the risks associated with the progress of the construction and calculates the impact of the risks. As a result, the construction management system according to this embodiment can efficiently input, analyze, display, propose, and assess risks related to construction.

[0030] The reception desk receives information about the construction project. This information includes, but is not limited to, the type of project, start date, end date, and budget. For example, the reception desk allows users to select the type of project, enter the start and end dates in a calendar format, and manually enter the budget. The reception desk also allows users to input project information using voice input. For example, users can input project information by voice, and speech recognition technology can convert it into text data. Furthermore, the reception desk provides an interface for users to input detailed specifications and requirements of the project. For example, users can input details such as the location of the project, materials to be used, necessary equipment, and the number of workers. This allows the reception desk to collect comprehensive information about the project and improve the accuracy and efficiency of the entire system. The reception desk also has a function to automatically verify the information entered by the user and notify them of missing or inaccurate information. For example, if the start and end dates are inconsistent, or if the budget is set within an inappropriate range, it will display a warning to the user and prompt them to make corrections. The reception desk can also refer to past project data and provide recommendations based on similar projects. This allows users to input project information quickly and accurately. Furthermore, the reception area is designed to allow multiple users to input information simultaneously, supporting collaborative teamwork. For example, a project manager can input the overall schedule, while each team member inputs their specific tasks and resources. This enables the reception area to centralize construction information, improving transparency and efficiency across the entire project.

[0031] The generation unit uses a generation AI to analyze information entered by the reception unit and generate a plan to navigate the progress of the construction. For example, the generation unit formulates an optimal construction plan based on past construction data and the current construction status. For example, the generation AI analyzes past construction data and allocates the resources necessary for the progress of the construction. The generation unit can also assess the risks associated with the progress of the construction. For example, the generation AI assesses the risks associated with the progress of the construction and predicts the probability of those risks occurring. The generation AI uses natural language processing technology to understand the construction information entered by the user and generates an appropriate plan. For example, if the user enters "construction of a large building," the generation AI refers to data from similar past projects and automatically calculates the necessary resources and schedule. Furthermore, the generation unit has the function to monitor the progress of the construction in real time and modify the plan as needed. For example, if unexpected events occur, such as changes in weather or shortages of resources, the generation AI quickly generates a new plan to minimize construction delays. The generation unit can also simulate multiple scenarios and select the optimal plan. For example, it can experiment with different resource allocations and schedules to identify the most efficient plan. This allows the generation unit to smoothly navigate the progress of the construction and support the success of the project. Furthermore, the generation unit is designed to allow users to customize the generated plans, enabling flexible responses to specific requirements and constraints.

[0032] The display unit monitors the progress of construction in real time based on the plan generated by the generation unit. The display unit can, for example, display graphs and charts showing the progress of construction. For instance, it can visually display the progress of construction using a Gantt chart. It can also display line graphs and bar graphs showing the progress of construction. Furthermore, the display unit updates the progress of construction in real time, ensuring users always have access to the latest information. For example, if changes or delays occur during construction, the display unit immediately reflects this information and notifies the user. The display unit also features interactive functions for detailed viewing of the progress of construction. For example, clicking on a specific task displays detailed information and its progress. This allows users to gain a detailed understanding of the progress of construction and take necessary actions quickly. Additionally, the display unit provides a dashboard function for managing multiple projects simultaneously. For example, users can efficiently manage overall progress by using a dashboard that allows them to see the progress of multiple construction projects at a glance. The display unit also provides a customizable layout, allowing users to select a display format that suits their specific needs and preferences. As a result, the display unit is designed to allow users to intuitively and efficiently check the progress of the construction work.

[0033] The proposal unit proposes optimal plans for medium- to long-term construction planning and budgeting based on plans generated by the generation unit. For example, the proposal unit uses generational AI to formulate future construction plans and optimize budgets. For example, the proposal unit predicts costs as construction progresses and makes suggestions to optimize budget allocation. The proposal unit predicts the resources and costs required for future construction based on past construction data and current market trends. For example, it considers fluctuations in material costs and labor costs and proposes the optimal budget allocation. The proposal unit can also simulate multiple scenarios and select the most efficient construction plan. For example, it tries different resource allocations and schedules to identify the most cost-effective plan. Furthermore, the proposal unit is designed to allow users to customize the proposed plans, enabling flexible responses to specific requirements and constraints. For example, if a user inputs specific budget constraints or schedule requirements, the proposal unit recalculates the optimal plan accordingly. The proposal unit also provides an interface for users to evaluate the proposed plans and provide feedback. In this way, the proposal unit can support optimal construction planning and budgeting tailored to the user's needs. Furthermore, the proposal department can also propose construction plans from a long-term perspective, taking into account future market trends and technological innovations. For example, they may consider introducing new construction technologies and materials to reduce future costs and improve efficiency. In this way, the proposal department helps users develop optimal construction plans from a medium- to long-term perspective and effectively manage their budgets.

[0034] The Risk Assessment Unit performs risk assessments based on the plans generated by the Generation Unit. For example, the Risk Assessment Unit evaluates risks associated with the progress of construction and predicts the probability of those risks occurring. For example, the Risk Assessment Unit quantitatively evaluates risks associated with the progress of construction and calculates the impact of those risks. The Risk Assessment Unit builds models to predict the probability of risks occurring based on past construction data and the current situation. For example, it considers various risk factors such as weather changes, resource shortages, and technical problems, and evaluates the probability of each risk occurring and its impact. The Risk Assessment Unit also has an alert function to detect risks early and take appropriate countermeasures. For example, if a particular risk exceeds a certain threshold, it notifies the user and prompts a quick response. Furthermore, the Risk Assessment Unit can also propose measures to minimize the impact of risks. For example, it proposes specific measures such as reallocating resources, adjusting schedules, and implementing additional safety measures to mitigate the impact of risks. The Risk Assessment Unit also visually displays the results of the risk assessment so that users can intuitively understand the risk situation. For example, it uses risk matrices and heatmaps to visually display the probability of risk occurrence and its impact. This allows users to grasp the overall picture of risks and take appropriate measures. Furthermore, the risk assessment department can regularly update the results of the risk assessment to respond to the latest situation. For example, it can update the results of the risk assessment in real time according to the progress of construction and changes in the external environment, providing users with the latest information. In this way, the risk assessment department can effectively manage the risks associated with the progress of construction and support the success of the project.

[0035] The reception desk allows users to input information such as the type of construction, start date, end date, and budget. For example, the reception desk allows users to select the type of construction, input the start and end dates in a calendar format, and manually enter the budget. The reception desk can also input construction information using voice input. For example, users can input construction information by voice, and speech recognition technology can convert it into text data. Furthermore, the reception desk can refer to past construction information to assist with input. For example, it can automatically complete the information entered by the user based on past construction information. As a result, by inputting detailed information about the construction, the generating AI can produce a more accurate plan.

[0036] The generation unit can formulate an optimal construction plan based on past construction data and the current construction status. For example, the generation unit can analyze past construction data and allocate the resources necessary for the progress of the construction. For example, the generation unit can also evaluate the risks associated with the progress of the construction based on past construction data. For example, the generation unit can predict the probability of risks occurring as the construction progresses based on past construction data. For example, the generation unit can monitor the current construction status in real time and evaluate the risks associated with the progress of the construction. By considering past data and the current situation, an optimal construction plan can be formulated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input past construction data and the current construction status into a generation AI and have the generation AI formulate an optimal construction plan.

[0037] The display unit can display graphs and charts showing the progress of the construction work. For example, the display unit can display a Gantt chart showing the progress of the construction work. The display unit can also display line graphs and bar graphs showing the progress of the construction work. The display unit can also display pie charts showing the progress of the construction work. This allows for a visual understanding of the progress of the construction work. Some or all of the above-described processes in the display unit may be performed using AI, or they may not be performed using AI. For example, the display unit can input construction progress data into a generating AI and have the generating AI generate graphs and charts.

[0038] The proposal department can formulate future construction plans and optimize budgets. For example, the proposal department can formulate future construction plans using generative AI. For example, the proposal department can optimize budgets using generative AI. For example, the proposal department can predict costs associated with the progress of construction and make proposals to optimize budget allocation. For example, the proposal department can also propose the optimal allocation of resources as construction progresses. This makes it possible to optimize future construction plans and budgets. Some or all of the above processes in the proposal department may be performed using generative AI or not. For example, the proposal department can input future construction plan data into generative AI and have the generative AI perform budget optimization.

[0039] The risk assessment unit can evaluate the risks associated with the progress of construction work. For example, the risk assessment unit can quantitatively evaluate the risks associated with the progress of construction work. For example, the risk assessment unit can predict the probability of risks occurring as construction work progresses. For example, the risk assessment unit can also calculate the impact of risks associated with the progress of construction work. For example, the risk assessment unit can qualitatively evaluate the risks associated with the progress of construction work. This makes risk management possible by evaluating the risks associated with the progress of construction work. Some or all of the above processes in the risk assessment unit may be performed using AI or not. For example, the risk assessment unit can input construction progress data into a generating AI and have the generating AI perform the risk assessment.

[0040] The reception desk can analyze the user's past construction information input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can analyze patterns in construction information entered by the user in the past and suggest the optimal input method. In this way, the optimal input method can be suggested by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0041] The reception unit can filter construction information based on the user's current projects and areas of interest when it is entered. For example, the reception unit can prioritize inputting information related to projects the user is currently working on. For example, the reception unit can filter and input relevant construction information based on the user's areas of interest. For example, the reception unit can prioritize inputting information related to projects the user has shown interest in in the past. In this way, by filtering information based on the user's areas of interest, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's current project and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0042] The reception desk can prioritize inputting highly relevant information when entering construction information, taking into account the user's geographical location. For example, the reception desk can prioritize inputting construction information that is close to the user's current location. For example, the reception desk can prioritize inputting construction information related to places the user has visited in the past. For example, the reception desk can prioritize inputting construction information related to places the user plans to visit in the future. In this way, highly relevant information can be prioritized by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0043] The reception unit can analyze the user's social media activity and input relevant information when inputting construction information. For example, the reception unit can automatically input construction information that the user has shared on social media. For example, the reception unit can prioritize inputting construction information that the user has shown interest in on social media. For example, the reception unit can filter and input relevant construction information from the user's social media activity. This allows for efficient input of relevant information by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant information.

[0044] The generation unit can adjust the level of detail in a plan based on the importance of the construction project. For example, the generation unit generates a detailed plan for high-importance projects. For example, it generates a concise plan for low-importance projects. The generation unit also adjusts the allocation of necessary resources according to the importance of the project. This allows for efficient plan generation by adjusting the level of detail according to the importance of the project. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input project importance data into the generation AI and have the generation AI perform the adjustment of the level of detail in the plan.

[0045] The generation unit can apply different generation algorithms depending on the category of the construction work when generating a plan. For example, the generation unit applies a generation algorithm specifically for building construction for building construction, for example for electrical construction, and for civil engineering construction for civil engineering construction. By applying a generation algorithm appropriate to the category of the construction work, the optimal plan can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the category data of the construction work into the generation AI and have the generation AI execute the application of the generation algorithm.

[0046] The generation unit can determine the priority of plans based on the start date of construction work when generating plans. For example, the generation unit will prioritize generating plans for construction work that starts early. For example, the generation unit will postpone generating plans for construction work that starts later. For example, the generation unit will adjust the allocation of necessary resources according to the start date of construction work. This enables efficient plan generation by determining the priority of plans based on the start date of construction work. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input construction start date data into a generation AI and have the generation AI perform the determination of plan priority.

[0047] The generation unit can adjust the order of plans based on the relationships between construction projects during plan generation. For example, the generation unit prioritizes generating plans for highly related projects. For example, it postpones generating plans for less related projects. The generation unit adjusts the allocation of necessary resources according to the relationships between projects. This allows for efficient plan generation by adjusting the order of plans based on the relationships between projects. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input project relationship data into a generation AI and have the generation AI perform the adjustment of the plan order.

[0048] The display unit can adjust the level of detail displayed based on the progress of the construction work. For example, if the construction is progressing smoothly, the display unit will provide a concise display. If the construction is behind schedule, the display unit will provide a detailed display. The display unit will also adjust the allocation of necessary resources according to the progress of the construction work. This allows for efficient information display by adjusting the level of detail based on the progress of the construction work. Some or all of the above processing in the display unit may be performed using AI, or it may be performed without AI. For example, the display unit can input construction progress data into a generating AI and have the generating AI perform the adjustment of the level of detail of the display.

[0049] The display unit can apply different display formats depending on the category of the construction work. For example, the display unit applies a display format specifically for building construction for building construction work, a display format specifically for electrical construction work for electrical construction work, and a display format specifically for civil engineering work for civil engineering work. By applying a display format appropriate to the category of the construction work, optimal information display becomes possible. Some or all of the above processing in the display unit may be performed using AI, or it may be performed without AI. For example, the display unit can input construction category data into a generating AI and have the generating AI perform the application of the display format.

[0050] The display unit can display information while considering the geographical distribution of construction projects. For example, the display unit can prioritize the display of relevant information based on the geographical distribution of construction projects. For example, the display unit can adjust the allocation of necessary resources according to the geographical distribution of construction projects. For example, the display unit can select the optimal display format based on the geographical distribution of construction projects. This allows for the priority display of highly relevant information by considering geographical distribution. Some or all of the above-described processes in the display unit may be performed using AI or not. For example, the display unit can input geographical distribution data of construction projects into a generating AI and have the generating AI perform the display adjustments.

[0051] The display unit can improve the accuracy of its display by referring to relevant construction documents during the display process. For example, the display unit displays detailed information based on relevant construction documents. For example, the display unit improves the accuracy of its display by referring to relevant construction documents. For example, the display unit selects the optimal display format based on relevant construction documents. This improves the accuracy of the display by referring to relevant documents. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input data on relevant construction documents into a generating AI and have the generating AI perform the display accuracy improvement.

[0052] The proposal unit can adjust the level of detail in its proposals based on the importance of the project. For example, the proposal unit will provide detailed proposals for high-priority projects, and concise proposals for low-priority projects. The proposal unit will also adjust the allocation of necessary resources according to the importance of the project. This allows for more efficient proposals by adjusting the level of detail according to the importance of the project. Some or all of the above processing in the proposal unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal unit can input project importance data into a generation AI and have the generation AI adjust the level of detail in the proposals.

[0053] The proposal unit can apply different proposal algorithms depending on the category of the construction work when making a proposal. For example, the proposal unit applies a proposal algorithm specifically for building construction to building construction work. For example, the proposal unit applies a proposal algorithm specifically for electrical construction work to electrical construction work to electrical construction work to electrical construction work to electrical construction work to civil engineering

[0054] The proposal department can determine the priority of proposals based on the start date of the construction work. For example, the proposal department will prioritize proposals for projects that start early. For example, the proposal department will postpone proposals for projects that start later. For example, the proposal department will adjust the allocation of necessary resources according to the start date of the construction work. This enables efficient proposals by determining the priority of proposals based on the start date of the construction work. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can input construction start date data into a generation AI and have the generation AI perform the determination of proposal priorities.

[0055] The proposal department can adjust the order of proposals based on the relevance of the projects. For example, the proposal department can prioritize proposals for highly relevant projects. For example, it can postpone proposals for less relevant projects. For example, the proposal department can adjust the allocation of necessary resources according to the relevance of the projects. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the projects. Some or all of the above processing in the proposal department may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal department can input project relevance data into a generative AI and have the generative AI perform the adjustment of the order of proposals.

[0056] The risk assessment unit can predict current risks by referring to past risk data during risk assessment. For example, the risk assessment unit predicts current risks based on past risk data. For example, the risk assessment unit predicts current risks by extracting similar risk patterns from past risk data. For example, the risk assessment unit predicts current risks by analyzing past risk data. In this way, current risks can be accurately predicted by referring to past risk data. Some or all of the above processes in the risk assessment unit may be performed using AI or not. For example, the risk assessment unit can input past risk data into a generating AI and have the generating AI perform a prediction of current risks.

[0057] The risk assessment unit can apply different risk assessment methods to each construction category during risk assessment. For example, the risk assessment unit applies a risk assessment method specifically for building construction to building construction, for example, a risk assessment method specifically for electrical construction to electrical construction, and for example, a risk assessment method specifically for civil engineering construction to civil engineering construction. This allows for optimal risk assessment by applying a risk assessment method appropriate to the construction category. Some or all of the above processing in the risk assessment unit may be performed using AI, or not. For example, the risk assessment unit can input construction category data into a generating AI and have the generating AI execute the application of risk assessment methods.

[0058] The risk assessment unit can analyze changes in risk based on the start date of construction during the risk assessment. For example, the risk assessment unit predicts changes in risk based on the start date of construction. For example, the risk assessment unit analyzes changes in risk according to the start date of construction. For example, the risk assessment unit adjusts the allocation of necessary resources based on the start date of construction. This makes risk management more efficient by analyzing changes in risk based on the start date of construction. Some or all of the above processes in the risk assessment unit may be performed using AI or not. For example, the risk assessment unit can input construction start date data into a generating AI and have the generating AI perform an analysis of changes in risk.

[0059] The risk assessment unit can analyze risks by referring to relevant market data for the construction project during the risk assessment process. For example, the risk assessment unit analyzes risks based on relevant market data for the construction project. For example, the risk assessment unit predicts changes in risks by referring to relevant market data for the construction project. For example, the risk assessment unit selects the optimal risk assessment method based on relevant market data for the construction project. This allows for accurate prediction of changes in risks by referring to relevant market data. Some or all of the above processes in the risk assessment unit may be performed using AI or not. For example, the risk assessment unit can input relevant market data for the construction project into a generating AI and have the generating AI perform the risk analysis.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can analyze the user's past construction information input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can analyze patterns in construction information entered by the user in the past and suggest the optimal input method. In this way, the optimal input method can be suggested by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0062] The generation unit can adjust the level of detail in a plan based on the importance of the construction project. For example, the generation unit generates a detailed plan for high-importance projects. For example, it generates a concise plan for low-importance projects. The generation unit also adjusts the allocation of necessary resources according to the importance of the project. This allows for efficient plan generation by adjusting the level of detail according to the importance of the project. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input project importance data into the generation AI and have the generation AI perform the adjustment of the level of detail in the plan.

[0063] The generation unit can apply different generation algorithms depending on the category of the construction work when generating a plan. For example, the generation unit applies a generation algorithm specifically for building construction for building construction, for example for electrical construction, and for civil engineering construction for civil engineering construction. By applying a generation algorithm appropriate to the category of the construction work, the optimal plan can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the category data of the construction work into the generation AI and have the generation AI execute the application of the generation algorithm.

[0064] The generation unit can determine the priority of plans based on the start date of construction work when generating plans. For example, the generation unit will prioritize generating plans for construction work that starts early. For example, the generation unit will postpone generating plans for construction work that starts later. For example, the generation unit will adjust the allocation of necessary resources according to the start date of construction work. This enables efficient plan generation by determining the priority of plans based on the start date of construction work. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input construction start date data into a generation AI and have the generation AI perform the determination of plan priority.

[0065] The display unit can adjust the level of detail displayed based on the progress of the construction work. For example, if the construction is progressing smoothly, the display unit will provide a concise display. If the construction is behind schedule, the display unit will provide a detailed display. The display unit will also adjust the allocation of necessary resources according to the progress of the construction work. This allows for efficient information display by adjusting the level of detail based on the progress of the construction work. Some or all of the above processing in the display unit may be performed using AI, or it may be performed without AI. For example, the display unit can input construction progress data into a generating AI and have the generating AI perform the adjustment of the level of detail of the display.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk inputs information about the construction project. This information includes the type of project, start date, end date, and budget. Users can select the type of project, input the start and end dates in a calendar format, and manually enter the budget. They can also input project information using voice input. For example, the user can input project information by voice, and speech recognition technology can be used to convert it into text data. Step 2: The generation unit analyzes the information entered by the reception unit and generates a plan to guide the progress of the construction. The generation unit formulates the optimal construction plan based on past construction data and the current construction status. The generation AI analyzes past construction data and allocates the resources necessary for the progress of the construction. It also assesses the risks associated with the progress of the construction. Step 3: The display unit monitors the progress of the construction in real time based on the plan generated by the generation unit. The display unit displays graphs and charts that show the progress of the construction. For example, it visually displays the progress of the construction using Gantt charts, line graphs, and bar graphs. Step 4: The proposal department proposes an optimal plan for medium- to long-term construction planning and budgeting based on the plan generated by the generation department. The proposal department uses generation AI to formulate future construction plans and optimize the budget. It forecasts costs as construction progresses and makes proposals to optimize budget allocation. Step 5: The risk assessment unit performs a risk assessment based on the plan generated by the generation unit. The risk assessment unit evaluates the risks associated with the progress of the construction and predicts the probability of the risks occurring. It quantitatively evaluates the risks associated with the progress of the construction and calculates the impact of the risks.

[0068] (Example of form 2) The construction management system according to an embodiment of the present invention promotes the smooth and speedy completion of ordered construction projects within NTT-GC offices and is a mechanism that utilizes a tool equipped with navigation and planning functions using generation AI as an interface to existing in-house business systems in order to create optimal plans for medium- to long-term construction planning and budgeting. The construction management system allows users to input information about construction projects. For example, information such as the type of construction, start date, end date, and budget is entered. This information is input into the generation AI. Next, the generation AI analyzes the input information and generates a plan to navigate the progress of the construction. The generation AI formulates an optimal construction plan based on past construction data and the current construction status. For example, it allocates resources necessary for the progress of the construction and evaluates the risks associated with the progress of the construction. Based on the generated plan, users can check the progress of the construction in real time. For example, graphs and charts showing the progress of the construction are displayed so that users can grasp the progress of the construction at a glance. Furthermore, the generation AI proposes an optimal plan for medium- to long-term construction planning and budgeting. The AI ​​generates future construction plans and optimizes budgets based on past construction data and current construction status. For example, it predicts costs as construction progresses and proposes ways to optimize budget allocation. This tool ensures that ordered construction projects within NTT-GC are completed smoothly and quickly, and optimizes medium- to long-term construction plans and budgeting. Users can monitor the progress of construction in real time and create optimal construction plans and budgets. As a result, the construction management system can ensure that ordered construction projects within NTT-GC are completed smoothly and quickly.

[0069] The construction management system according to this embodiment comprises a reception unit, a generation unit, a display unit, a proposal unit, and a risk assessment unit. The reception unit inputs information about the construction. This information includes, but is not limited to, the type of construction, start date, end date, and budget. For example, the reception unit allows the user to select the type of construction, input the start and end dates in calendar format, and manually input the budget. The reception unit can also input construction information using voice input. For example, the user inputs construction information by voice, and voice recognition technology is used to convert it into text data. The generation unit uses a generation AI to analyze the information input by the reception unit and generate a plan to navigate the progress of the construction. For example, the generation unit formulates an optimal construction plan based on past construction data and the current construction status. For example, the generation AI analyzes past construction data and allocates the resources necessary for the progress of the construction. The generation unit can also evaluate the risks associated with the progress of the construction. For example, the generation AI evaluates the risks associated with the progress of the construction and predicts the probability of the risks occurring. The display unit monitors the progress of the construction in real time based on the plan generated by the generation unit. The display unit displays graphs or charts showing the progress of the construction, for example. For example, the display unit visually displays the progress of the construction using a Gantt chart. The display unit can also display line graphs or bar graphs showing the progress of the construction. The proposal unit proposes an optimal plan for medium- to long-term construction planning and budgeting based on the plan generated by the generation unit. For example, the proposal unit uses generation AI to formulate future construction plans and optimize the budget. For example, the proposal unit predicts costs associated with the progress of the construction and makes proposals to optimize budget allocation. The risk assessment unit performs a risk assessment based on the plan generated by the generation unit. For example, the risk assessment unit evaluates the risks associated with the progress of the construction and predicts the probability of risk occurrence. For example, the risk assessment unit quantitatively evaluates the risks associated with the progress of the construction and calculates the impact of the risks. As a result, the construction management system according to this embodiment can efficiently input, analyze, display, propose, and assess risks related to construction.

[0070] The reception desk receives information about the construction project. This information includes, but is not limited to, the type of project, start date, end date, and budget. For example, the reception desk allows users to select the type of project, enter the start and end dates in a calendar format, and manually enter the budget. The reception desk also allows users to input project information using voice input. For example, users can input project information by voice, and speech recognition technology can convert it into text data. Furthermore, the reception desk provides an interface for users to input detailed specifications and requirements of the project. For example, users can input details such as the location of the project, materials to be used, necessary equipment, and the number of workers. This allows the reception desk to collect comprehensive information about the project and improve the accuracy and efficiency of the entire system. The reception desk also has a function to automatically verify the information entered by the user and notify them of missing or inaccurate information. For example, if the start and end dates are inconsistent, or if the budget is set within an inappropriate range, it will display a warning to the user and prompt them to make corrections. The reception desk can also refer to past project data and provide recommendations based on similar projects. This allows users to input project information quickly and accurately. Furthermore, the reception area is designed to allow multiple users to input information simultaneously, supporting collaborative teamwork. For example, a project manager can input the overall schedule, while each team member inputs their specific tasks and resources. This enables the reception area to centralize construction information, improving transparency and efficiency across the entire project.

[0071] The generation unit uses a generation AI to analyze information entered by the reception unit and generate a plan to navigate the progress of the construction. For example, the generation unit formulates an optimal construction plan based on past construction data and the current construction status. For example, the generation AI analyzes past construction data and allocates the resources necessary for the progress of the construction. The generation unit can also assess the risks associated with the progress of the construction. For example, the generation AI assesses the risks associated with the progress of the construction and predicts the probability of those risks occurring. The generation AI uses natural language processing technology to understand the construction information entered by the user and generates an appropriate plan. For example, if the user enters "construction of a large building," the generation AI refers to data from similar past projects and automatically calculates the necessary resources and schedule. Furthermore, the generation unit has the function to monitor the progress of the construction in real time and modify the plan as needed. For example, if unexpected events occur, such as changes in weather or shortages of resources, the generation AI quickly generates a new plan to minimize construction delays. The generation unit can also simulate multiple scenarios and select the optimal plan. For example, it can experiment with different resource allocations and schedules to identify the most efficient plan. This allows the generation unit to smoothly navigate the progress of the construction and support the success of the project. Furthermore, the generation unit is designed to allow users to customize the generated plans, enabling flexible responses to specific requirements and constraints.

[0072] The display unit monitors the progress of construction in real time based on the plan generated by the generation unit. The display unit can, for example, display graphs and charts showing the progress of construction. For instance, it can visually display the progress of construction using a Gantt chart. It can also display line graphs and bar graphs showing the progress of construction. Furthermore, the display unit updates the progress of construction in real time, ensuring users always have access to the latest information. For example, if changes or delays occur during construction, the display unit immediately reflects this information and notifies the user. The display unit also features interactive functions for detailed viewing of the progress of construction. For example, clicking on a specific task displays detailed information and its progress. This allows users to gain a detailed understanding of the progress of construction and take necessary actions quickly. Additionally, the display unit provides a dashboard function for managing multiple projects simultaneously. For example, users can efficiently manage overall progress by using a dashboard that allows them to see the progress of multiple construction projects at a glance. The display unit also provides a customizable layout, allowing users to select a display format that suits their specific needs and preferences. As a result, the display unit is designed to allow users to intuitively and efficiently check the progress of the construction work.

[0073] The proposal unit proposes optimal plans for medium- to long-term construction planning and budgeting based on plans generated by the generation unit. For example, the proposal unit uses generational AI to formulate future construction plans and optimize budgets. For example, the proposal unit predicts costs as construction progresses and makes suggestions to optimize budget allocation. The proposal unit predicts the resources and costs required for future construction based on past construction data and current market trends. For example, it considers fluctuations in material costs and labor costs and proposes the optimal budget allocation. The proposal unit can also simulate multiple scenarios and select the most efficient construction plan. For example, it tries different resource allocations and schedules to identify the most cost-effective plan. Furthermore, the proposal unit is designed to allow users to customize the proposed plans, enabling flexible responses to specific requirements and constraints. For example, if a user inputs specific budget constraints or schedule requirements, the proposal unit recalculates the optimal plan accordingly. The proposal unit also provides an interface for users to evaluate the proposed plans and provide feedback. In this way, the proposal unit can support optimal construction planning and budgeting tailored to the user's needs. Furthermore, the proposal department can also propose construction plans from a long-term perspective, taking into account future market trends and technological innovations. For example, they may consider introducing new construction technologies and materials to reduce future costs and improve efficiency. In this way, the proposal department helps users develop optimal construction plans from a medium- to long-term perspective and effectively manage their budgets.

[0074] The Risk Assessment Unit performs risk assessments based on the plans generated by the Generation Unit. For example, the Risk Assessment Unit evaluates risks associated with the progress of construction and predicts the probability of those risks occurring. For example, the Risk Assessment Unit quantitatively evaluates risks associated with the progress of construction and calculates the impact of those risks. The Risk Assessment Unit builds models to predict the probability of risks occurring based on past construction data and the current situation. For example, it considers various risk factors such as weather changes, resource shortages, and technical problems, and evaluates the probability of each risk occurring and its impact. The Risk Assessment Unit also has an alert function to detect risks early and take appropriate countermeasures. For example, if a particular risk exceeds a certain threshold, it notifies the user and prompts a quick response. Furthermore, the Risk Assessment Unit can also propose measures to minimize the impact of risks. For example, it proposes specific measures such as reallocating resources, adjusting schedules, and implementing additional safety measures to mitigate the impact of risks. The Risk Assessment Unit also visually displays the results of the risk assessment so that users can intuitively understand the risk situation. For example, it uses risk matrices and heatmaps to visually display the probability of risk occurrence and its impact. This allows users to grasp the overall picture of risks and take appropriate measures. Furthermore, the risk assessment department can regularly update the results of the risk assessment to respond to the latest situation. For example, it can update the results of the risk assessment in real time according to the progress of construction and changes in the external environment, providing users with the latest information. In this way, the risk assessment department can effectively manage the risks associated with the progress of construction and support the success of the project.

[0075] The reception desk allows users to input information such as the type of construction, start date, end date, and budget. For example, the reception desk allows users to select the type of construction, input the start and end dates in a calendar format, and manually enter the budget. The reception desk can also input construction information using voice input. For example, users can input construction information by voice, and speech recognition technology can convert it into text data. Furthermore, the reception desk can refer to past construction information to assist with input. For example, it can automatically complete the information entered by the user based on past construction information. As a result, by inputting detailed information about the construction, the generating AI can produce a more accurate plan.

[0076] The generation unit can formulate an optimal construction plan based on past construction data and the current construction status. For example, the generation unit can analyze past construction data and allocate the resources necessary for the progress of the construction. For example, the generation unit can also evaluate the risks associated with the progress of the construction based on past construction data. For example, the generation unit can predict the probability of risks occurring as the construction progresses based on past construction data. For example, the generation unit can monitor the current construction status in real time and evaluate the risks associated with the progress of the construction. By considering past data and the current situation, an optimal construction plan can be formulated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input past construction data and the current construction status into a generation AI and have the generation AI formulate an optimal construction plan.

[0077] The display unit can display graphs and charts showing the progress of the construction work. For example, the display unit can display a Gantt chart showing the progress of the construction work. The display unit can also display line graphs and bar graphs showing the progress of the construction work. The display unit can also display pie charts showing the progress of the construction work. This allows for a visual understanding of the progress of the construction work. Some or all of the above-described processes in the display unit may be performed using AI, or they may not be performed using AI. For example, the display unit can input construction progress data into a generating AI and have the generating AI generate graphs and charts.

[0078] The proposal department can formulate future construction plans and optimize budgets. For example, the proposal department can formulate future construction plans using generative AI. For example, the proposal department can optimize budgets using generative AI. For example, the proposal department can predict costs associated with the progress of construction and make proposals to optimize budget allocation. For example, the proposal department can also propose the optimal allocation of resources as construction progresses. This makes it possible to optimize future construction plans and budgets. Some or all of the above processes in the proposal department may be performed using generative AI or not. For example, the proposal department can input future construction plan data into generative AI and have the generative AI perform budget optimization.

[0079] The risk assessment unit can evaluate the risks associated with the progress of construction work. For example, the risk assessment unit can quantitatively evaluate the risks associated with the progress of construction work. For example, the risk assessment unit can predict the probability of risks occurring as construction work progresses. For example, the risk assessment unit can also calculate the impact of risks associated with the progress of construction work. For example, the risk assessment unit can qualitatively evaluate the risks associated with the progress of construction work. This makes risk management possible by evaluating the risks associated with the progress of construction work. Some or all of the above processes in the risk assessment unit may be performed using AI or not. For example, the risk assessment unit can input construction progress data into a generating AI and have the generating AI perform the risk assessment.

[0080] The reception desk can estimate the user's emotions and adjust the timing of inputting construction information based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can delay the input timing to give the user time to relax. For example, if the user is relaxed, the reception desk can prompt for immediate input to efficiently collect information. For example, if the user is in a hurry, the reception desk can speed up the input timing to quickly collect information. This allows for efficient information collection by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The reception desk can analyze the user's past construction information input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can analyze patterns in construction information entered by the user in the past and suggest the optimal input method. In this way, the optimal input method can be suggested by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0082] The reception unit can filter construction information based on the user's current projects and areas of interest when it is entered. For example, the reception unit can prioritize inputting information related to projects the user is currently working on. For example, the reception unit can filter and input relevant construction information based on the user's areas of interest. For example, the reception unit can prioritize inputting information related to projects the user has shown interest in in the past. In this way, by filtering information based on the user's areas of interest, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's current project and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0083] The reception desk can estimate the user's emotions and determine the priority of construction information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk will postpone less important information. For example, if the user is relaxed, the reception desk will prioritize the input of highly important information. For example, if the user is in a hurry, the reception desk will input the most important information first. This enables efficient information input by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The reception desk can prioritize inputting highly relevant information when entering construction information, taking into account the user's geographical location. For example, the reception desk can prioritize inputting construction information that is close to the user's current location. For example, the reception desk can prioritize inputting construction information related to places the user has visited in the past. For example, the reception desk can prioritize inputting construction information related to places the user plans to visit in the future. In this way, highly relevant information can be prioritized by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0085] The reception unit can analyze the user's social media activity and input relevant information when inputting construction information. For example, the reception unit can automatically input construction information that the user has shared on social media. For example, the reception unit can prioritize inputting construction information that the user has shown interest in on social media. For example, the reception unit can filter and input relevant construction information from the user's social media activity. This allows for efficient input of relevant information by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant information.

[0086] The generation unit can estimate the user's emotions and adjust the way the generated plan is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a plan with detailed explanations. If the user is in a hurry, the generation unit will generate a concise and to-the-point plan. If the user is excited, the generation unit will generate a plan with visually stimulating effects. By adjusting the way the plan is presented according to the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the plan is presented.

[0087] The generation unit can adjust the level of detail in a plan based on the importance of the construction project. For example, the generation unit generates a detailed plan for high-importance projects. For example, it generates a concise plan for low-importance projects. The generation unit also adjusts the allocation of necessary resources according to the importance of the project. This allows for efficient plan generation by adjusting the level of detail according to the importance of the project. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input project importance data into the generation AI and have the generation AI perform the adjustment of the level of detail in the plan.

[0088] The generation unit can apply different generation algorithms depending on the category of the construction work when generating a plan. For example, the generation unit applies a generation algorithm specifically for building construction for building construction, for example for electrical construction, and for civil engineering construction for civil engineering construction. By applying a generation algorithm appropriate to the category of the construction work, the optimal plan can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the category data of the construction work into the generation AI and have the generation AI execute the application of the generation algorithm.

[0089] The generation unit can estimate the user's emotions and adjust the length of the plan it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise plan. If the user is relaxed, the generation unit will generate a longer plan with detailed explanations. If the user is excited, the generation unit will generate a plan with visually stimulating effects. By adjusting the length of the plan according to the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the length of the plan.

[0090] The generation unit can determine the priority of plans based on the start date of construction work when generating plans. For example, the generation unit will prioritize generating plans for construction work that starts early. For example, the generation unit will postpone generating plans for construction work that starts later. For example, the generation unit will adjust the allocation of necessary resources according to the start date of construction work. This enables efficient plan generation by determining the priority of plans based on the start date of construction work. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input construction start date data into a generation AI and have the generation AI perform the determination of plan priority.

[0091] The generation unit can adjust the order of plans based on the relationships between construction projects during plan generation. For example, the generation unit prioritizes generating plans for highly related projects. For example, it postpones generating plans for less related projects. The generation unit adjusts the allocation of necessary resources according to the relationships between projects. This allows for efficient plan generation by adjusting the order of plans based on the relationships between projects. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input project relationship data into a generation AI and have the generation AI perform the adjustment of the plan order.

[0092] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is tense, the display unit provides a simple and highly visible display method. For example, if the user is relaxed, the display unit provides a display method that includes detailed information. For example, if the user is in a hurry, the display unit provides a display method that gets straight to the point. By adjusting the display method according to the user's emotions, more appropriate information can be displayed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0093] The display unit can adjust the level of detail displayed based on the progress of the construction work. For example, if the construction is progressing smoothly, the display unit will provide a concise display. If the construction is behind schedule, the display unit will provide a detailed display. The display unit will also adjust the allocation of necessary resources according to the progress of the construction work. This allows for efficient information display by adjusting the level of detail based on the progress of the construction work. Some or all of the above processing in the display unit may be performed using AI, or it may be performed without AI. For example, the display unit can input construction progress data into a generating AI and have the generating AI perform the adjustment of the level of detail of the display.

[0094] The display unit can apply different display formats depending on the category of the construction work. For example, the display unit applies a display format specifically for building construction for building construction work, a display format specifically for electrical construction work for electrical construction work, and a display format specifically for civil engineering work for civil engineering work. By applying a display format appropriate to the category of the construction work, optimal information display becomes possible. Some or all of the above processing in the display unit may be performed using AI, or it may be performed without AI. For example, the display unit can input construction category data into a generating AI and have the generating AI perform the application of the display format.

[0095] The display unit can estimate the user's emotions and determine the priority of information to display based on the estimated emotions. For example, if the user is stressed, the display unit will postpone displaying less important information. For example, if the user is relaxed, the display unit will prioritize displaying more important information. For example, if the user is in a hurry, the display unit will display the most important information first. This enables efficient information display by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0096] The display unit can display information while considering the geographical distribution of construction projects. For example, the display unit can prioritize the display of relevant information based on the geographical distribution of construction projects. For example, the display unit can adjust the allocation of necessary resources according to the geographical distribution of construction projects. For example, the display unit can select the optimal display format based on the geographical distribution of construction projects. This allows for the priority display of highly relevant information by considering geographical distribution. Some or all of the above-described processes in the display unit may be performed using AI or not. For example, the display unit can input geographical distribution data of construction projects into a generating AI and have the generating AI perform the display adjustments.

[0097] The display unit can improve the accuracy of its display by referring to relevant construction documents during the display process. For example, the display unit displays detailed information based on relevant construction documents. For example, the display unit improves the accuracy of its display by referring to relevant construction documents. For example, the display unit selects the optimal display format based on relevant construction documents. This improves the accuracy of the display by referring to relevant documents. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input data on relevant construction documents into a generating AI and have the generating AI perform the display accuracy improvement.

[0098] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit will provide suggestions with detailed explanations. If the user is in a hurry, the suggestion unit will provide concise and to-the-point suggestions. If the user is excited, the suggestion unit will provide suggestions with visually stimulating effects. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0099] The proposal unit can adjust the level of detail in its proposals based on the importance of the project. For example, the proposal unit will provide detailed proposals for high-priority projects, and concise proposals for low-priority projects. The proposal unit will also adjust the allocation of necessary resources according to the importance of the project. This allows for more efficient proposals by adjusting the level of detail according to the importance of the project. Some or all of the above processing in the proposal unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal unit can input project importance data into a generation AI and have the generation AI adjust the level of detail in the proposals.

[0100] The proposal unit can apply different proposal algorithms depending on the category of the construction work when making a proposal. For example, the proposal unit applies a proposal algorithm specifically for building construction to building construction work. For example, the proposal unit applies a proposal algorithm specifically for electrical construction work to electrical construction work to electrical construction work to electrical construction work to electrical construction work to civil engineering

[0101] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit will provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit will provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0102] The proposal department can determine the priority of proposals based on the start date of the construction work. For example, the proposal department will prioritize proposals for projects that start early. For example, the proposal department will postpone proposals for projects that start later. For example, the proposal department will adjust the allocation of necessary resources according to the start date of the construction work. This enables efficient proposals by determining the priority of proposals based on the start date of the construction work. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can input construction start date data into a generation AI and have the generation AI perform the determination of proposal priorities.

[0103] The proposal department can adjust the order of proposals based on the relevance of the projects. For example, the proposal department can prioritize proposals for highly relevant projects. For example, it can postpone proposals for less relevant projects. For example, the proposal department can adjust the allocation of necessary resources according to the relevance of the projects. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the projects. Some or all of the above processing in the proposal department may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal department can input project relevance data into a generative AI and have the generative AI perform the adjustment of the order of proposals.

[0104] The risk assessment unit can estimate the user's emotions and adjust the risk assessment method based on the estimated user emotions. For example, if the user is nervous, the risk assessment unit provides a simple and highly visible risk assessment method. For example, if the user is relaxed, the risk assessment unit provides a detailed risk assessment method. For example, if the user is in a hurry, the risk assessment unit provides a concise risk assessment method. By adjusting the risk assessment method according to the user's emotions, a more appropriate risk assessment becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the risk assessment unit may be performed using AI or not. For example, the risk assessment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the risk assessment method.

[0105] The risk assessment unit can predict current risks by referring to past risk data during risk assessment. For example, the risk assessment unit predicts current risks based on past risk data. For example, the risk assessment unit predicts current risks by extracting similar risk patterns from past risk data. For example, the risk assessment unit predicts current risks by analyzing past risk data. In this way, current risks can be accurately predicted by referring to past risk data. Some or all of the above processes in the risk assessment unit may be performed using AI or not. For example, the risk assessment unit can input past risk data into a generating AI and have the generating AI perform a prediction of current risks.

[0106] The risk assessment unit can apply different risk assessment methods to each construction category during risk assessment. For example, the risk assessment unit applies a risk assessment method specifically for building construction to building construction, for example, a risk assessment method specifically for electrical construction to electrical construction, and for example, a risk assessment method specifically for civil engineering construction to civil engineering construction. This allows for optimal risk assessment by applying a risk assessment method appropriate to the construction category. Some or all of the above processing in the risk assessment unit may be performed using AI, or not. For example, the risk assessment unit can input construction category data into a generating AI and have the generating AI execute the application of risk assessment methods.

[0107] The risk assessment unit can estimate the user's emotions and adjust the importance of risk assessments based on the estimated user emotions. For example, if the user is stressed, the risk assessment unit will postpone low-importance risks. For example, if the user is relaxed, the risk assessment unit will prioritize evaluating high-importance risks. For example, if the user is in a hurry, the risk assessment unit will evaluate the most important risks first. This allows for more appropriate risk assessment by adjusting the importance of risk assessments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the risk assessment unit may be performed using AI or not. For example, the risk assessment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of risk assessment importance.

[0108] The risk assessment unit can analyze changes in risk based on the start date of construction during the risk assessment. For example, the risk assessment unit predicts changes in risk based on the start date of construction. For example, the risk assessment unit analyzes changes in risk according to the start date of construction. For example, the risk assessment unit adjusts the allocation of necessary resources based on the start date of construction. This makes risk management more efficient by analyzing changes in risk based on the start date of construction. Some or all of the above processes in the risk assessment unit may be performed using AI or not. For example, the risk assessment unit can input construction start date data into a generating AI and have the generating AI perform an analysis of changes in risk.

[0109] The risk assessment unit can analyze risks by referring to relevant market data for the construction project during the risk assessment process. For example, the risk assessment unit analyzes risks based on relevant market data for the construction project. For example, the risk assessment unit predicts changes in risks by referring to relevant market data for the construction project. For example, the risk assessment unit selects the optimal risk assessment method based on relevant market data for the construction project. This allows for accurate prediction of changes in risks by referring to relevant market data. Some or all of the above processes in the risk assessment unit may be performed using AI or not. For example, the risk assessment unit can input relevant market data for the construction project into a generating AI and have the generating AI perform the risk analysis.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception desk can estimate the user's emotions and adjust the timing of inputting construction information based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can delay the input timing to give the user time to relax. For example, if the user is relaxed, the reception desk can prompt for immediate input to efficiently collect information. For example, if the user is in a hurry, the reception desk can speed up the input timing to quickly collect information. This allows for efficient information collection by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0112] The reception desk can analyze the user's past construction information input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can analyze patterns in construction information entered by the user in the past and suggest the optimal input method. In this way, the optimal input method can be suggested by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0113] The generation unit can estimate the user's emotions and adjust the way the generated plan is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a plan with detailed explanations. If the user is in a hurry, the generation unit will generate a concise and to-the-point plan. If the user is excited, the generation unit will generate a plan with visually stimulating effects. By adjusting the way the plan is presented according to the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the plan is presented.

[0114] The generation unit can adjust the level of detail in a plan based on the importance of the construction project. For example, the generation unit generates a detailed plan for high-importance projects. For example, it generates a concise plan for low-importance projects. The generation unit also adjusts the allocation of necessary resources according to the importance of the project. This allows for efficient plan generation by adjusting the level of detail according to the importance of the project. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input project importance data into the generation AI and have the generation AI perform the adjustment of the level of detail in the plan.

[0115] The generation unit can apply different generation algorithms depending on the category of the construction work when generating a plan. For example, the generation unit applies a generation algorithm specifically for building construction for building construction, for example for electrical construction, and for civil engineering construction for civil engineering construction. By applying a generation algorithm appropriate to the category of the construction work, the optimal plan can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the category data of the construction work into the generation AI and have the generation AI execute the application of the generation algorithm.

[0116] The generation unit can estimate the user's emotions and adjust the length of the plan it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise plan. If the user is relaxed, the generation unit will generate a longer plan with detailed explanations. If the user is excited, the generation unit will generate a plan with visually stimulating effects. By adjusting the length of the plan according to the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the length of the plan.

[0117] The generation unit can determine the priority of plans based on the start date of construction work when generating plans. For example, the generation unit will prioritize generating plans for construction work that starts early. For example, the generation unit will postpone generating plans for construction work that starts later. For example, the generation unit will adjust the allocation of necessary resources according to the start date of construction work. This enables efficient plan generation by determining the priority of plans based on the start date of construction work. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input construction start date data into a generation AI and have the generation AI perform the determination of plan priority.

[0118] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is tense, the display unit provides a simple and highly visible display method. For example, if the user is relaxed, the display unit provides a display method that includes detailed information. For example, if the user is in a hurry, the display unit provides a display method that gets straight to the point. By adjusting the display method according to the user's emotions, more appropriate information can be displayed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0119] The display unit can adjust the level of detail displayed based on the progress of the construction work. For example, if the construction is progressing smoothly, the display unit will provide a concise display. If the construction is behind schedule, the display unit will provide a detailed display. The display unit will also adjust the allocation of necessary resources according to the progress of the construction work. This allows for efficient information display by adjusting the level of detail based on the progress of the construction work. Some or all of the above processing in the display unit may be performed using AI, or it may be performed without AI. For example, the display unit can input construction progress data into a generating AI and have the generating AI perform the adjustment of the level of detail of the display.

[0120] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit will provide suggestions with detailed explanations. If the user is in a hurry, the suggestion unit will provide concise and to-the-point suggestions. If the user is excited, the suggestion unit will provide suggestions with visually stimulating effects. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk inputs information about the construction project. This information includes the type of project, start date, end date, and budget. Users can select the type of project, input the start and end dates in a calendar format, and manually enter the budget. They can also input project information using voice input. For example, the user can input project information by voice, and speech recognition technology can be used to convert it into text data. Step 2: The generation unit analyzes the information entered by the reception unit and generates a plan to guide the progress of the construction. The generation unit formulates the optimal construction plan based on past construction data and the current construction status. The generation AI analyzes past construction data and allocates the resources necessary for the progress of the construction. It also assesses the risks associated with the progress of the construction. Step 3: The display unit monitors the progress of the construction in real time based on the plan generated by the generation unit. The display unit displays graphs and charts that show the progress of the construction. For example, it visually displays the progress of the construction using Gantt charts, line graphs, and bar graphs. Step 4: The proposal department proposes an optimal plan for medium- to long-term construction planning and budgeting based on the plan generated by the generation department. The proposal department uses generation AI to formulate future construction plans and optimize the budget. It forecasts costs as construction progresses and makes proposals to optimize budget allocation. Step 5: The risk assessment unit performs a risk assessment based on the plan generated by the generation unit. The risk assessment unit evaluates the risks associated with the progress of the construction and predicts the probability of the risks occurring. It quantitatively evaluates the risks associated with the progress of the construction and calculates the impact of the risks.

[0123] 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.

[0124] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, generation unit, display unit, proposal unit, and risk assessment unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing the user to input construction information. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates a plan to navigate the progress of the construction using a generation AI. The display unit is implemented by, for example, the output device 40 of the smart device 14, and displays the progress of the construction in real time. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and proposes an optimal plan for medium- to long-term construction planning and budgeting. The risk assessment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and evaluates the risks associated with the progress of the construction. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0132] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] 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.

[0134] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, generation unit, display unit, proposal unit, and risk assessment unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input construction information by voice. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a plan to navigate the progress of the construction using generation AI. The display unit is implemented, for example, by the speaker 240 of the smart glasses 214, and notifies the user of the progress of the construction in real time by voice. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes an optimal plan for medium- to long-term construction planning and budgeting. The risk assessment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and evaluates the risks associated with the progress of the construction. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0148] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] 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.

[0150] 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.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, generation unit, display unit, proposal unit, and risk assessment unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input construction information by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates a plan to navigate the progress of the construction using a generation AI. The display unit is implemented by, for example, the display 343 of the headset terminal 314, which displays the progress of the construction in real time. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes an optimal plan for medium- to long-term construction planning and budgeting. The risk assessment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates the risks associated with the progress of the construction. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0164] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] 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.

[0166] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] 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.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, generation unit, display unit, proposal unit, and risk assessment unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input construction information by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates a plan to navigate the progress of the construction using a generation AI. The display unit is implemented by, for example, the speaker 240 of the robot 414, which notifies the progress of the construction in real time by voice. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes an optimal plan for medium- to long-term construction planning and budgeting. The risk assessment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates the risks associated with the progress of the construction. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0176] 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.

[0177] Figure 9 shows the 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.

[0178] 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.

[0179] 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.

[0180] 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, and motorcycles, 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 based, for example, 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.

[0181] 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."

[0182] 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.

[0183] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] 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 other things 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.

[0193] 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.

[0194] (Note 1) The reception area for entering information about the construction work, A generation unit analyzes the information entered by the reception unit and generates a plan to guide the progress of the construction work, A display unit that checks the progress of construction in real time based on the plan generated by the generation unit, Based on the plan generated by the generation unit, the proposal unit proposes an optimal plan for medium- to long-term construction planning and budget formulation. The system includes a risk assessment unit that performs a risk assessment based on the plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter information such as the type of construction work, start date, end date, and budget. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is We will develop the optimal construction plan based on past construction data and the current construction status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is Display graphs and charts showing the progress of the construction work. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, To formulate future construction plans and optimize the budget. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned risk assessment unit, We will assess the risks associated with the progress of the construction work. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting construction information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past construction information input history and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering construction information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of construction information to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering construction information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering construction information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts how the generated plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a plan, adjust the level of detail in the plan based on the importance of the construction work. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a plan, different generation algorithms are applied depending on the category of the construction work. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the plan generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a plan, prioritize the plan based on the start date of construction. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a plan, adjust the order of the plans based on the relationships between the construction projects. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is When displaying information, adjust the level of detail based on the progress of the construction work. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying, different display formats are applied depending on the category of the construction work. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is It estimates the user's emotions and determines the priority of the information to display based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is When displaying information, the geographical distribution of construction sites should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When displaying information, we refer to relevant construction-related literature to improve the accuracy of the display. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When submitting a proposal, different proposal algorithms are applied depending on the category of the project. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When submitting proposals, prioritize them based on the start date of construction. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the construction work. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned risk assessment unit, We estimate user sentiment and adjust the risk assessment method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned risk assessment unit, When assessing risk, historical risk data is used to predict current risk. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned risk assessment unit, When conducting risk assessments, different risk assessment methods are applied to each category of construction work. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned risk assessment unit, The system estimates user sentiment and adjusts the importance of risk assessment based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned risk assessment unit, When assessing risk, analyze how risk changes based on the start date of construction. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned risk assessment unit, When assessing risk, we analyze the risks by referring to relevant market data for the construction project. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area for entering information about the construction work, A generation unit analyzes the information entered by the reception unit and generates a plan to guide the progress of the construction work, A display unit that checks the progress of construction in real time based on the plan generated by the generation unit, Based on the plan generated by the generation unit, the proposal unit proposes an optimal plan for medium- to long-term construction planning and budget formulation. The system includes a risk assessment unit that performs a risk assessment based on the plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is Enter information such as the type of construction work, start date, end date, and budget. The system according to feature 1.

3. The generating unit is We will develop the optimal construction plan based on past construction data and the current construction status. The system according to feature 1.

4. The aforementioned display unit is Display graphs and charts showing the progress of the construction work. The system according to feature 1.

5. The aforementioned proposal section is, To formulate future construction plans and optimize the budget. The system according to feature 1.

6. The aforementioned risk assessment unit, We will assess the risks associated with the progress of the construction work. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting construction information based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past construction information input history and selects the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering construction information, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of construction information to be entered based on those estimated emotions. The system according to feature 1.

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