system

Abstract

The system according to this embodiment aims to improve data accuracy and achieve smooth construction management and appropriate budget-versus-actual management. [Solution] The system according to the embodiment comprises a registration unit, a navigation unit, a generation unit, and a Q&A unit. The registration unit automatically registers construction projects and FCTs based on past performance data. The navigation unit guides the user's operations based on the data registered by the registration unit. The generation unit generates process plans and budget allocation plans proposed by the navigation unit. The Q&A unit provides an interactive Q&A function based on the plans generated by the generation unit.

Description

Technical Field

[0005]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the accuracy of data in construction projects and the registration and management of FCT is low, and the difference between planned and actual results has become the norm, which may cause trouble to the order recipients.

[0005] The system according to the embodiment aims to improve the accuracy of data and realize smooth construction management and appropriate planned-actual management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a registration unit, a navigation unit, a generation unit, and a Q&A unit. The registration unit automatically registers construction projects and FCTs based on past performance data. The navigation unit guides the user's operations based on the data registered by the registration unit. The generation unit generates process plans and budget allocation plans proposed by the navigation unit. The Q&A unit provides an interactive Q&A function based on the plans generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve data accuracy and realize smooth construction management and appropriate budget-versus-actual management. [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 including 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[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 new system "ARQ" according to an embodiment of the present invention is a system that improves the data accuracy of the construction management system "Spica" and enhances the reliability of suppliers by utilizing generation AI. This system has a function in which generation AI automatically registers construction projects and FCT (construction plan and budget forecast) based on past performance data. As a result, data accuracy is improved and budget variances are reduced. For example, by analyzing past construction data and automatically registering similar construction projects, manual input errors can be prevented. It also provides a user navigation function, in which the generation AI guides the user's operations and proposes the optimal process plan and budget allocation plan. For example, when a user inputs a construction plan, the generation AI proposes the optimal process plan and automatically calculates the budget allocation. Furthermore, it has an interactive Q&A function, in which when a user inputs a question, the generation AI immediately answers by referring to past data and a knowledge base. For example, when a user asks, "What is the progress of this month's construction?", the generation AI provides the latest construction progress data. The generation AI also optimizes the timing and quantity of construction orders and cable material orders. This reduces inconvenience to telecommunications companies and cable manufacturers that receive orders and improves the reliability of suppliers. For example, the generating AI analyzes past order data and proposes the optimal ordering timing and quantity. This new system, "ARQ," improves data accuracy and increases supplier reliability, enabling smooth GC construction and appropriate budget management, thereby contributing to improved work quality. In this way, the new system "ARQ" can improve data accuracy and increase supplier reliability.

[0029] The new system "ARQ" according to this embodiment comprises a registration unit, a navigation unit, a generation unit, and a Q&A unit. The registration unit automatically registers construction projects and FCTs based on past performance data. The registration unit, for example, analyzes past construction data and automatically registers similar construction projects. The registration unit can use generation AI to analyze past construction data and automatically register similar construction projects. The navigation unit guides the user's operations based on the data registered by the registration unit. For example, when the user inputs a construction plan, the generation AI proposes the optimal process plan and budget allocation plan. The navigation unit can use generation AI to guide the user's operations and propose the optimal process plan and budget allocation plan. The generation unit generates the process plan and budget allocation plan proposed by the navigation unit. For example, the generation AI generates the optimal process plan and budget allocation plan. The generation unit can use generation AI to generate the optimal process plan and budget allocation plan. The Q&A unit provides an interactive Q&A function based on the plan generated by the generation unit. The Q&A section, for example, allows a user to input a question, and the generating AI immediately provides an answer by referring to past data and a knowledge base. The Q&A section can use the generating AI to immediately answer user questions by referring to past data and a knowledge base. As a result, the new system "ARQ" according to this embodiment can improve data accuracy and increase the reliability of suppliers.

[0030] The registration unit automatically registers construction projects and FCTs based on past performance data. Specifically, the registration unit collects and analyzes past construction data to automatically register similar construction projects. For example, it collects detailed data such as the type, scale, duration, materials used, and problems encountered in past construction projects, and the generation AI analyzes this data. The generation AI uses natural language processing technology and machine learning algorithms to extract patterns and trends from past data and automatically registers new construction projects based on this. For example, it can analyze data from past road construction projects and automatically register new road construction projects to be carried out under similar conditions. In addition, for FCTs (Fast Close Time), the optimal period is calculated based on past construction data and automatically registered. As a result, the registration unit can efficiently and accurately register construction projects and FCTs, significantly reducing the effort required from users. Furthermore, the registration unit centrally manages the registered data and can link with other departments and systems as needed. For example, registered construction project data is stored on a cloud server so that the navigation unit and generation unit can access it. This allows the registration unit to maintain data integrity and consistency throughout the entire system.

[0031] The navigation unit guides user operations based on data registered by the registration unit. Specifically, when a user inputs a construction plan, the generating AI proposes the optimal process plan and budget allocation plan. The generating AI analyzes the user's input based on the data provided by the registration unit and generates the optimal plan. For example, when a user registers a new construction project, they input basic information such as the type of construction, scale, and budget. The generating AI then refers to data from similar past projects and proposes the optimal process plan and budget allocation plan. The generating AI uses machine learning algorithms to leverage patterns and trends learned from past data to generate a plan that best suits the user's needs. The navigation unit also has a function to guide user operations in real time. For example, when a user is proceeding with a construction plan, it displays the next steps and input content on the screen to support the user in proceeding without getting lost. Furthermore, the navigation unit records the user's operation history and learns the user's operation patterns based on this, enabling it to provide more accurate guidance. As a result, the navigation unit efficiently and accurately supports user operations, allowing for smooth progress in the planning of construction projects.

[0032] The generation unit generates the project schedule and budget allocation plan proposed by the navigation unit. Specifically, the generation AI generates the optimal project schedule and budget allocation plan. The generation AI generates the optimal plan based on user input data and past construction data provided by the navigation unit. For example, the generation AI analyzes information such as the type, scale, and budget of the construction entered by the user, and generates the optimal project schedule plan by referring to data from similar past projects. The generation AI uses machine learning algorithms to utilize patterns and trends learned from past data to generate a plan that best suits the user's needs. The generation unit also has the function of providing the generated plan to the user. For example, it displays the generated project schedule and budget allocation plan on the screen so that the user can check it. Furthermore, the generation unit can centrally manage the generated plan and link it with other departments and systems as needed. For example, the generated plan is stored on a cloud server so that the Q&A unit can access it. This allows the generation unit to maintain the integrity and consistency of data throughout the entire system.

[0033] The Q&A section provides interactive Q&A functionality based on the plans generated by the generation section. Specifically, when a user inputs a question, the generation AI immediately provides an answer by referring to past data and a knowledge base. The generation AI uses natural language processing technology to analyze the user's question and generate the optimal answer. For example, if a user inputs a question about a generated process plan, the generation AI refers to data and a knowledge base from similar past cases and generates the optimal answer. The generation AI uses machine learning algorithms to leverage patterns and trends learned from past data to generate the best answer to the user's question. Furthermore, the Q&A section records the user's question history and learns the user's question patterns based on this, enabling it to provide more accurate answers. In addition, the Q&A section also has a function to provide the generated answers to the user. For example, it can display the generated answers on the screen for the user to review. This allows the Q&A section to answer user questions quickly and accurately, resolving user doubts.

[0034] The registration unit can analyze past construction data and automatically register similar construction projects. For example, the registration unit can analyze past construction data and automatically register similar construction projects. The registration unit can use generation AI to analyze past construction data and automatically register similar construction projects. This prevents errors in manual input by automatically registering similar projects. Past construction data includes, but is not limited to, the type of construction, duration, and cost of the project. Similar construction projects include, but are not limited to, the type of construction, scale, and location of the project.

[0035] The navigation unit can guide the user's operations and propose optimal project plans and budget allocation plans. For example, when the user inputs a construction plan, the generating AI in the navigation unit proposes optimal project plans and budget allocation plans. The navigation unit can use the generating AI to guide the user's operations and propose optimal project plans and budget allocation plans. In this way, by guiding the user's operations, it can propose optimal project plans and budget allocation plans. Optimal project plans and budget allocation plans include, but are not limited to, cost efficiency and time efficiency.

[0036] The generation unit can generate optimal process plans and budget allocation plans using generative AI. For example, the generation unit uses generative AI to generate optimal process plans and budget allocation plans. The generation unit can generate optimal process plans and budget allocation plans using generative AI. Thus, by using generative AI, optimal process plans and budget allocation plans can be generated. Generative AI includes, but is not limited to, the algorithms used and training data.

[0037] The Q&A section can instantly answer user questions by referencing past data and knowledge bases using generative AI. For example, when a user enters a question, the generative AI instantly answers it by referencing past data and knowledge bases. The Q&A section can instantly answer user questions by referencing past data and knowledge bases using generative AI. This means that by using generative AI, user questions can be answered instantly. Past data and knowledge bases include, but are not limited to, past Q&A history and expert knowledge databases.

[0038] The registration unit can analyze past construction data and automatically update the registration details according to the progress of the construction. For example, if the construction is behind schedule, the registration unit can automatically correct the registration details to reflect the latest progress. If the construction is ahead of schedule, the registration unit can update the registration details to expedite the next step. If the progress of the construction is unknown, the registration unit can make a prediction based on past data and provisionally update the registration details. This allows the system to automatically update the registration details according to the progress of the construction, thereby reflecting the latest progress. Past construction data includes, but is not limited to, the type of construction, duration, and cost. Construction progress includes, but is not limited to, the phase of the construction and completion rate.

[0039] The registration unit can provide feedback to prevent input errors when registering construction projects by referring to the user's past input history. For example, if the registration unit does not match data previously entered by the user, it can display a warning and prompt confirmation. The registration unit can prevent input errors by automatically completing data that the user has frequently entered in the past. The registration unit can check the consistency of the input content based on data previously entered by the user and provide feedback. This prevents input errors by referring to the user's past input history. The user's past input history includes, but is not limited to, past input content and frequency.

[0040] The registration unit can prioritize the registration of construction projects that are highly relevant, taking into account the user's geographical location information. For example, the registration unit can prioritize the registration of construction projects that are close to the user's current location. The registration unit can prioritize the registration of construction projects related to places that the user frequently visits. The registration unit can analyze the user's travel patterns and prioritize the registration of highly relevant construction projects. In this way, by considering the user's geographical location information, it is possible to prioritize the registration of highly relevant projects. The user's geographical location information includes, but is not limited to, GPS data and address information.

[0041] The registration unit can analyze a user's social media activity when registering a construction project and register related projects. For example, the registration unit can register construction projects related to locations mentioned by the user on social media. The registration unit can analyze the content of a user's social media posts and suggest related construction projects. The registration unit can register construction projects related to locations that the user's social media followers are interested in. In this way, related projects can be registered by analyzing the user's social media activity. A user's social media activity includes, but is not limited to, posts and followed accounts.

[0042] The navigation unit can analyze the user's operation history and propose the optimal operating procedure. For example, the navigation unit proposes the optimal procedure based on the user's past operations. The navigation unit can propose an efficient operating procedure from the user's operation history. The navigation unit can analyze the user's operation patterns and propose the optimal operating procedure. In this way, by analyzing the user's operation history, the optimal operating procedure can be proposed. The user's operation history includes, but is not limited to, past operations such as content and frequency.

[0043] The navigation unit can update the operation guide in real time according to the progress of the construction. For example, if the construction is behind schedule, the navigation unit will update the operation guide and suggest the next step. If the construction is ahead of schedule, the navigation unit can update the operation guide and expedite the next step. If the progress of the construction is unknown, the navigation unit can provisionally update the operation guide based on past data. This allows the system to provide the latest information by updating the operation guide in real time according to the progress of the construction. The progress of the construction includes, but is not limited to, the phase of the construction and the completion rate. Updating the operation guide in real time includes, but is not limited to, the timing of the update and the method of obtaining information.

[0044] The navigation unit can select the optimal display method when providing operation guides, taking into account the user's device information. For example, if the user is using a smartphone, the navigation unit can provide a display method that matches the screen size. If the user is using a tablet, the navigation unit can provide a display method optimized for a larger screen. If the user is using a smartwatch, the navigation unit can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information. User device information includes, but is not limited to, the device type, screen size, and OS.

[0045] The navigation unit can provide the most suitable guide by referring to the user's past operation history when providing an operation guide. For example, the navigation unit can provide the most suitable guide based on the operation procedures the user has performed in the past. The navigation unit can provide an efficient operation guide from the user's operation history. The navigation unit can analyze the user's operation patterns and provide the most suitable operation guide. In this way, the navigation unit can provide the most suitable guide by referring to the user's past operation history. The user's past operation history includes, but is not limited to, past operation content and frequency.

[0046] The generation unit can update the generated plan in real time according to the progress of the construction. For example, if the construction is behind schedule, the generation unit can automatically revise the plan to reflect the latest progress. If the construction is ahead of schedule, the generation unit can update the plan to expedite the next step. If the progress of the construction is unknown, the generation unit can make a prediction based on past data and provisionally update the plan. This allows the plan to be updated in real time according to the progress of the construction, reflecting the latest progress. The progress of the construction includes, but is not limited to, the phase of the construction and the completion rate. Updating the plan in real time includes, but is not limited to, the timing of the update and the method of acquiring information.

[0047] The generation unit can generate the optimal plan by referring to the user's past plan history. For example, the generation unit can generate the optimal plan based on plans the user has created in the past. The generation unit can generate an efficient plan from the user's past plan history. The generation unit can analyze the user's plan history and generate the most effective plan. This allows the optimal plan to be generated by referring to the user's past plan history. The user's past plan history includes, but is not limited to, past plan content and frequency.

[0048] The generation unit can generate an optimal plan by considering the user's geographical location information during plan generation. For example, the generation unit can prioritize incorporating construction projects close to the user's current location into the plan. The generation unit can incorporate construction projects related to places the user frequently visits into the plan. The generation unit can analyze the user's travel patterns and incorporate highly relevant construction projects into the plan. In this way, an optimal plan can be generated by considering the user's geographical location information. The user's geographical location information includes, but is not limited to, GPS data and address information.

[0049] The generation unit can analyze a user's social media activity and generate relevant plans when generating plans. For example, the generation unit can incorporate construction projects related to places mentioned by the user on social media into the plans. The generation unit can analyze the content of a user's social media posts and incorporate relevant construction projects into the plans. The generation unit can incorporate construction projects related to places that the user's social media followers are interested in into the plans. In this way, relevant plans can be generated by analyzing the user's social media activity. A user's social media activity includes, but is not limited to, posts and followed accounts.

[0050] The Q&A department can analyze a user's question history and provide the most appropriate answer. For example, the Q&A department can provide the most appropriate answer based on questions the user has asked in the past. The Q&A department can provide answers that include relevant information from the user's question history. The Q&A department can analyze the user's question patterns and provide the most appropriate answer. In this way, the Q&A department can provide the most appropriate answer by analyzing the user's question history. The user's question history includes, but is not limited to, past question content and frequency.

[0051] The Q&A department can update its answers in real time according to the progress of the construction. For example, if the construction is behind schedule, the Q&A department can provide answers that reflect the latest progress. If the construction is ahead of schedule, the Q&A department can provide answers that reflect the latest progress. If the progress of the construction is unknown, the Q&A department can provide predicted answers based on past data. This allows the department to provide the latest information by updating the answers in real time according to the progress of the construction. The progress of the construction includes, but is not limited to, the phase of the construction and the completion rate. Updating answers in real time includes, but is not limited to, the timing of updates and the method of obtaining information.

[0052] The Q&A section can select the optimal display method when answering questions, taking into account the user's device information. For example, if the user is using a smartphone, the Q&A section can provide a display method that matches the screen size. If the user is using a tablet, the Q&A section can provide a display method optimized for a larger screen. If the user is using a smartwatch, the Q&A section can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information. User device information includes, but is not limited to, the device type, screen size, and OS.

[0053] The Q&A section can provide the most appropriate answer to a question by referring to the user's past question history. For example, the Q&A section can provide the most appropriate answer based on questions the user has asked in the past. The Q&A section can provide answers that include relevant information from the user's question history. The Q&A section can analyze the user's question patterns and provide the most appropriate answer. This allows the Q&A section to provide the most appropriate answer by referring to the user's past question history. The user's past question history includes, but is not limited to, past question content and frequency.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The registration section can provide feedback to prevent input errors when registering construction projects by referring to the user's past input history. For example, if the data does not match the data the user has entered in the past, it can display a warning and prompt the user to check. It can also automatically complete data that the user has frequently entered in the past, preventing input errors. It can check the consistency of the input content based on the data the user has entered in the past and provide feedback. In this way, input errors can be prevented by referring to the user's past input history.

[0056] The navigation unit can select the optimal display method when providing operation guides, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. If the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking the user's device information into consideration.

[0057] The generation unit can create an optimal plan by considering the user's geographical location information during plan generation. For example, it can prioritize incorporating construction projects close to the user's current location into the plan. It can also incorporate construction projects related to places the user frequently visits. By analyzing the user's travel patterns, it can incorporate highly relevant construction projects into the plan. In this way, by considering the user's geographical location information, the optimal plan can be generated.

[0058] The Q&A section can analyze a user's question history and provide the most appropriate answer. For example, it can provide the best answer based on questions the user has asked in the past. It can provide answers that include relevant information from the user's question history. It can analyze the user's question patterns and provide the most appropriate answer. In this way, by analyzing the user's question history, it can provide the best possible answer.

[0059] The registration unit can analyze past construction data and automatically update the registration details according to the progress of the construction. For example, if the construction is behind schedule, it can automatically correct the registration details to reflect the latest progress. If the construction is ahead of schedule, it can update the registration details to expedite the next stage. If the progress of the construction is unknown, it can make a prediction based on past data and provisionally update the registration details. This allows the system to automatically update the registration details according to the progress of the construction, reflecting the latest progress.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The registration unit automatically registers construction projects and FCTs based on past performance data. For example, it analyzes past construction data and automatically registers similar construction projects. Using generation AI, it is possible to analyze past construction data and automatically register similar construction projects. Step 2: The navigation unit guides the user's operations based on the data registered by the registration unit. For example, when the user inputs a construction plan, the generating AI proposes an optimal process plan and budget allocation plan. Using the generating AI, it is possible to guide the user's operations and propose an optimal process plan and budget allocation plan. Step 3: The generation unit generates the process plan and budget allocation plan proposed by the navigation unit. For example, the generation AI generates the optimal process plan and budget allocation plan. The optimal process plan and budget allocation plan can be generated using the generation AI. Step 4: The Q&A section provides interactive Q&A functionality based on the plan generated by the generation section. For example, when a user enters a question, the generating AI immediately provides an answer by referring to past data and the knowledge base. The generating AI can be used to immediately answer user questions by referring to past data and the knowledge base.

[0062] (Example of form 2) The new system "ARQ" according to an embodiment of the present invention is a system that improves the data accuracy of the construction management system "Spica" and enhances the reliability of suppliers by utilizing generation AI. This system has a function in which generation AI automatically registers construction projects and FCT (construction plan and budget forecast) based on past performance data. As a result, data accuracy is improved and budget variances are reduced. For example, by analyzing past construction data and automatically registering similar construction projects, manual input errors can be prevented. It also provides a user navigation function, in which the generation AI guides the user's operations and proposes the optimal process plan and budget allocation plan. For example, when a user inputs a construction plan, the generation AI proposes the optimal process plan and automatically calculates the budget allocation. Furthermore, it has an interactive Q&A function, in which when a user inputs a question, the generation AI immediately answers by referring to past data and a knowledge base. For example, when a user asks, "What is the progress of this month's construction?", the generation AI provides the latest construction progress data. The generation AI also optimizes the timing and quantity of construction orders and cable material orders. This reduces inconvenience to telecommunications companies and cable manufacturers that receive orders and improves the reliability of suppliers. For example, the generating AI analyzes past order data and proposes the optimal ordering timing and quantity. This new system, "ARQ," improves data accuracy and increases supplier reliability, enabling smooth GC construction and appropriate budget management, thereby contributing to improved work quality. In this way, the new system "ARQ" can improve data accuracy and increase supplier reliability.

[0063] The new system "ARQ" according to this embodiment comprises a registration unit, a navigation unit, a generation unit, and a Q&A unit. The registration unit automatically registers construction projects and FCTs based on past performance data. The registration unit, for example, analyzes past construction data and automatically registers similar construction projects. The registration unit can use generation AI to analyze past construction data and automatically register similar construction projects. The navigation unit guides the user's operations based on the data registered by the registration unit. For example, when the user inputs a construction plan, the generation AI proposes the optimal process plan and budget allocation plan. The navigation unit can use generation AI to guide the user's operations and propose the optimal process plan and budget allocation plan. The generation unit generates the process plan and budget allocation plan proposed by the navigation unit. For example, the generation AI generates the optimal process plan and budget allocation plan. The generation unit can use generation AI to generate the optimal process plan and budget allocation plan. The Q&A unit provides an interactive Q&A function based on the plan generated by the generation unit. The Q&A section, for example, allows a user to input a question, and the generating AI immediately provides an answer by referring to past data and a knowledge base. The Q&A section can use the generating AI to immediately answer user questions by referring to past data and a knowledge base. As a result, the new system "ARQ" according to this embodiment can improve data accuracy and increase the reliability of suppliers.

[0064] The registration unit automatically registers construction projects and FCTs based on past performance data. Specifically, the registration unit collects and analyzes past construction data to automatically register similar construction projects. For example, it collects detailed data such as the type, scale, duration, materials used, and problems encountered in past construction projects, and the generation AI analyzes this data. The generation AI uses natural language processing technology and machine learning algorithms to extract patterns and trends from past data and automatically registers new construction projects based on this. For example, it can analyze data from past road construction projects and automatically register new road construction projects to be carried out under similar conditions. In addition, for FCTs (Fast Close Time), the optimal period is calculated based on past construction data and automatically registered. As a result, the registration unit can efficiently and accurately register construction projects and FCTs, significantly reducing the effort required from users. Furthermore, the registration unit centrally manages the registered data and can link with other departments and systems as needed. For example, registered construction project data is stored on a cloud server so that the navigation unit and generation unit can access it. This allows the registration unit to maintain data integrity and consistency throughout the entire system.

[0065] The navigation unit guides user operations based on data registered by the registration unit. Specifically, when a user inputs a construction plan, the generating AI proposes the optimal process plan and budget allocation plan. The generating AI analyzes the user's input based on the data provided by the registration unit and generates the optimal plan. For example, when a user registers a new construction project, they input basic information such as the type of construction, scale, and budget. The generating AI then refers to data from similar past projects and proposes the optimal process plan and budget allocation plan. The generating AI uses machine learning algorithms to leverage patterns and trends learned from past data to generate a plan that best suits the user's needs. The navigation unit also has a function to guide user operations in real time. For example, when a user is proceeding with a construction plan, it displays the next steps and input content on the screen to support the user in proceeding without getting lost. Furthermore, the navigation unit records the user's operation history and learns the user's operation patterns based on this, enabling it to provide more accurate guidance. As a result, the navigation unit efficiently and accurately supports user operations, allowing for smooth progress in the planning of construction projects.

[0066] The generation unit generates the project schedule and budget allocation plan proposed by the navigation unit. Specifically, the generation AI generates the optimal project schedule and budget allocation plan. The generation AI generates the optimal plan based on user input data and past construction data provided by the navigation unit. For example, the generation AI analyzes information such as the type, scale, and budget of the construction entered by the user, and generates the optimal project schedule plan by referring to data from similar past projects. The generation AI uses machine learning algorithms to utilize patterns and trends learned from past data to generate a plan that best suits the user's needs. The generation unit also has the function of providing the generated plan to the user. For example, it displays the generated project schedule and budget allocation plan on the screen so that the user can check it. Furthermore, the generation unit can centrally manage the generated plan and link it with other departments and systems as needed. For example, the generated plan is stored on a cloud server so that the Q&A unit can access it. This allows the generation unit to maintain the integrity and consistency of data throughout the entire system.

[0067] The Q&A section provides interactive Q&A functionality based on the plans generated by the generation section. Specifically, when a user inputs a question, the generation AI immediately provides an answer by referring to past data and a knowledge base. The generation AI uses natural language processing technology to analyze the user's question and generate the optimal answer. For example, if a user inputs a question about a generated process plan, the generation AI refers to data and a knowledge base from similar past cases and generates the optimal answer. The generation AI uses machine learning algorithms to leverage patterns and trends learned from past data to generate the best answer to the user's question. Furthermore, the Q&A section records the user's question history and learns the user's question patterns based on this, enabling it to provide more accurate answers. In addition, the Q&A section also has a function to provide the generated answers to the user. For example, it can display the generated answers on the screen for the user to review. This allows the Q&A section to answer user questions quickly and accurately, resolving user doubts.

[0068] The registration unit can analyze past construction data and automatically register similar construction projects. For example, the registration unit can analyze past construction data and automatically register similar construction projects. The registration unit can use generation AI to analyze past construction data and automatically register similar construction projects. This prevents errors in manual input by automatically registering similar projects. Past construction data includes, but is not limited to, the type of construction, duration, and cost of the project. Similar construction projects include, but are not limited to, the type of construction, scale, and location of the project.

[0069] The navigation unit can guide the user's operations and propose optimal project plans and budget allocation plans. For example, when the user inputs a construction plan, the generating AI in the navigation unit proposes optimal project plans and budget allocation plans. The navigation unit can use the generating AI to guide the user's operations and propose optimal project plans and budget allocation plans. In this way, by guiding the user's operations, it can propose optimal project plans and budget allocation plans. Optimal project plans and budget allocation plans include, but are not limited to, cost efficiency and time efficiency.

[0070] The generation unit can generate optimal process plans and budget allocation plans using generative AI. For example, the generation unit uses generative AI to generate optimal process plans and budget allocation plans. The generation unit can generate optimal process plans and budget allocation plans using generative AI. Thus, by using generative AI, optimal process plans and budget allocation plans can be generated. Generative AI includes, but is not limited to, the algorithms used and training data.

[0071] The Q&A section can instantly answer user questions by referencing past data and knowledge bases using generative AI. For example, when a user enters a question, the generative AI instantly answers it by referencing past data and knowledge bases. The Q&A section can instantly answer user questions by referencing past data and knowledge bases using generative AI. This means that by using generative AI, user questions can be answered instantly. Past data and knowledge bases include, but are not limited to, past Q&A history and expert knowledge databases.

[0072] The registration unit can estimate the user's emotions and adjust the registration timing for construction projects and FCTs based on the estimated emotions. For example, if the user is stressed, the registration unit can delay the registration timing to reduce the user's burden. If the user is relaxed, the registration unit can speed up the registration timing to allow for more efficient work. If the user is in a hurry, the registration unit can register immediately to provide a quick response. In this way, the user's burden can be reduced by adjusting the registration timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The registration unit can analyze past construction data and automatically update the registration details according to the progress of the construction. For example, if the construction is behind schedule, the registration unit can automatically correct the registration details to reflect the latest progress. If the construction is ahead of schedule, the registration unit can update the registration details to expedite the next step. If the progress of the construction is unknown, the registration unit can make a prediction based on past data and provisionally update the registration details. This allows the system to automatically update the registration details according to the progress of the construction, thereby reflecting the latest progress. Past construction data includes, but is not limited to, the type of construction, duration, and cost. Construction progress includes, but is not limited to, the phase of the construction and completion rate.

[0074] The registration unit can provide feedback to prevent input errors when registering construction projects by referring to the user's past input history. For example, if the registration unit does not match data previously entered by the user, it can display a warning and prompt confirmation. The registration unit can prevent input errors by automatically completing data that the user has frequently entered in the past. The registration unit can check the consistency of the input content based on data previously entered by the user and provide feedback. This prevents input errors by referring to the user's past input history. The user's past input history includes, but is not limited to, past input content and frequency.

[0075] The registration unit can estimate the user's emotions and determine the priority of construction projects to register based on the estimated emotions. For example, if the user is stressed, the registration unit will postpone low-priority projects. If the user is relaxed, the registration unit can prioritize the registration of high-priority projects. If the user is in a hurry, the registration unit can prioritize the registration of urgent projects. This allows for efficient work by determining the priority of projects according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The priority of construction projects includes, but is not limited to, urgency and importance.

[0076] The registration unit can prioritize the registration of construction projects that are highly relevant, taking into account the user's geographical location information. For example, the registration unit can prioritize the registration of construction projects that are close to the user's current location. The registration unit can prioritize the registration of construction projects related to places that the user frequently visits. The registration unit can analyze the user's travel patterns and prioritize the registration of highly relevant construction projects. In this way, by considering the user's geographical location information, it is possible to prioritize the registration of highly relevant projects. The user's geographical location information includes, but is not limited to, GPS data and address information.

[0077] The registration unit can analyze a user's social media activity when registering a construction project and register related projects. For example, the registration unit can register construction projects related to locations mentioned by the user on social media. The registration unit can analyze the content of a user's social media posts and suggest related construction projects. The registration unit can register construction projects related to locations that the user's social media followers are interested in. In this way, related projects can be registered by analyzing the user's social media activity. A user's social media activity includes, but is not limited to, posts and followed accounts.

[0078] The navigation unit can estimate the user's emotions and adjust the presentation of the operation guide based on the estimated emotions. For example, if the user is nervous, the navigation unit can provide a simple and highly visible operation guide. If the user is relaxed, the navigation unit can provide an operation guide that includes detailed information. If the user is in a hurry, the navigation unit can provide an operation guide that gets straight to the point. In this way, the burden on the user can be reduced by adjusting the presentation of the operation guide according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The presentation of the operation guide is, but is not limited to, text, audio, and visuals.

[0079] The navigation unit can analyze the user's operation history and propose the optimal operating procedure. For example, the navigation unit proposes the optimal procedure based on the user's past operations. The navigation unit can propose an efficient operating procedure from the user's operation history. The navigation unit can analyze the user's operation patterns and propose the optimal operating procedure. In this way, by analyzing the user's operation history, the optimal operating procedure can be proposed. The user's operation history includes, but is not limited to, past operations such as content and frequency.

[0080] The navigation unit can update the operation guide in real time according to the progress of the construction. For example, if the construction is behind schedule, the navigation unit will update the operation guide and suggest the next step. If the construction is ahead of schedule, the navigation unit can update the operation guide and expedite the next step. If the progress of the construction is unknown, the navigation unit can provisionally update the operation guide based on past data. This allows the system to provide the latest information by updating the operation guide in real time according to the progress of the construction. The progress of the construction includes, but is not limited to, the phase of the construction and the completion rate. Updating the operation guide in real time includes, but is not limited to, the timing of the update and the method of obtaining information.

[0081] The navigation unit can estimate the user's emotions and prioritize the operation guides based on those emotions. For example, if the user is stressed, the navigation unit will postpone less important operation guides. If the user is relaxed, the navigation unit can prioritize providing more important operation guides. If the user is in a hurry, the navigation unit can prioritize providing most urgent operation guides. This allows for more efficient work by prioritizing operation guides 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Prioritization of operation guides includes, but is not limited to, importance and urgency.

[0082] The navigation unit can select the optimal display method when providing operation guides, taking into account the user's device information. For example, if the user is using a smartphone, the navigation unit can provide a display method that matches the screen size. If the user is using a tablet, the navigation unit can provide a display method optimized for a larger screen. If the user is using a smartwatch, the navigation unit can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information. User device information includes, but is not limited to, the device type, screen size, and OS.

[0083] The navigation unit can provide the most suitable guide by referring to the user's past operation history when providing an operation guide. For example, the navigation unit can provide the most suitable guide based on the operation procedures the user has performed in the past. The navigation unit can provide an efficient operation guide from the user's operation history. The navigation unit can analyze the user's operation patterns and provide the most suitable operation guide. In this way, the navigation unit can provide the most suitable guide by referring to the user's past operation history. The user's past operation history includes, but is not limited to, past operation content and frequency.

[0084] The generation unit can estimate the user's emotions and adjust the presentation of the generated plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate a plan that emphasizes the shortest route. If the user is excited, the generation unit can generate a plan with visually stimulating effects. This reduces the user's burden by adjusting the presentation of the plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The presentation of the plan is, but is not limited to, text, graphs, charts, etc.

[0085] The generation unit can update the generated plan in real time according to the progress of the construction. For example, if the construction is behind schedule, the generation unit can automatically revise the plan to reflect the latest progress. If the construction is ahead of schedule, the generation unit can update the plan to expedite the next step. If the progress of the construction is unknown, the generation unit can make a prediction based on past data and provisionally update the plan. This allows the plan to be updated in real time according to the progress of the construction, reflecting the latest progress. The progress of the construction includes, but is not limited to, the phase of the construction and the completion rate. Updating the plan in real time includes, but is not limited to, the timing of the update and the method of acquiring information.

[0086] The generation unit can generate the optimal plan by referring to the user's past plan history. For example, the generation unit can generate the optimal plan based on plans the user has created in the past. The generation unit can generate an efficient plan from the user's past plan history. The generation unit can analyze the user's plan history and generate the most effective plan. This allows the optimal plan to be generated by referring to the user's past plan history. The user's past plan history includes, but is not limited to, past plan content and frequency.

[0087] The generation unit can estimate the user's emotions and determine the priority of the plans to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will postpone less important plans. If the user is relaxed, the generation unit can prioritize generating more important plans. If the user is in a hurry, the generation unit can prioritize generating the most urgent plans. This allows for more efficient work by prioritizing plans according to the user's emotions. 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. Plan priorities include, but are not limited to, importance and urgency.

[0088] The generation unit can generate an optimal plan by considering the user's geographical location information during plan generation. For example, the generation unit can prioritize incorporating construction projects close to the user's current location into the plan. The generation unit can incorporate construction projects related to places the user frequently visits into the plan. The generation unit can analyze the user's travel patterns and incorporate highly relevant construction projects into the plan. In this way, an optimal plan can be generated by considering the user's geographical location information. The user's geographical location information includes, but is not limited to, GPS data and address information.

[0089] The generation unit can analyze a user's social media activity and generate relevant plans when generating plans. For example, the generation unit can incorporate construction projects related to places mentioned by the user on social media into the plans. The generation unit can analyze the content of a user's social media posts and incorporate relevant construction projects into the plans. The generation unit can incorporate construction projects related to places that the user's social media followers are interested in into the plans. In this way, relevant plans can be generated by analyzing the user's social media activity. A user's social media activity includes, but is not limited to, posts and followed accounts.

[0090] The Q&A section can estimate the user's emotions and adjust the way it presents its answers based on those emotions. For example, if the user is nervous, the Q&A section can provide simple and easily understandable answers. If the user is relaxed, the Q&A section can provide answers that include detailed information. If the user is in a hurry, the Q&A section can provide concise answers. This reduces the user's burden by adjusting the way the answers are presented according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The way answers are presented includes, but is not limited to, text, audio, and visuals.

[0091] The Q&A department can analyze a user's question history and provide the most appropriate answer. For example, the Q&A department can provide the most appropriate answer based on questions the user has asked in the past. The Q&A department can provide answers that include relevant information from the user's question history. The Q&A department can analyze the user's question patterns and provide the most appropriate answer. In this way, the Q&A department can provide the most appropriate answer by analyzing the user's question history. The user's question history includes, but is not limited to, past question content and frequency.

[0092] The Q&A department can update its answers in real time according to the progress of the construction. For example, if the construction is behind schedule, the Q&A department can provide answers that reflect the latest progress. If the construction is ahead of schedule, the Q&A department can provide answers that reflect the latest progress. If the progress of the construction is unknown, the Q&A department can provide predicted answers based on past data. This allows the department to provide the latest information by updating the answers in real time according to the progress of the construction. The progress of the construction includes, but is not limited to, the phase of the construction and the completion rate. Updating answers in real time includes, but is not limited to, the timing of updates and the method of obtaining information.

[0093] The Q&A section can estimate the user's emotions and prioritize answers based on those emotions. For example, if the user is stressed, the Q&A section will postpone less important questions. If the user is relaxed, the Q&A section can prioritize answering more important questions. If the user is in a hurry, the Q&A section can prioritize answering urgent questions. This allows for more efficient work by prioritizing answers 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Answer prioritization includes, but is not limited to, importance and urgency.

[0094] The Q&A section can select the optimal display method when answering questions, taking into account the user's device information. For example, if the user is using a smartphone, the Q&A section can provide a display method that matches the screen size. If the user is using a tablet, the Q&A section can provide a display method optimized for a larger screen. If the user is using a smartwatch, the Q&A section can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information. User device information includes, but is not limited to, the device type, screen size, and OS.

[0095] The Q&A section can provide the most appropriate answer to a question by referring to the user's past question history. For example, the Q&A section can provide the most appropriate answer based on questions the user has asked in the past. The Q&A section can provide answers that include relevant information from the user's question history. The Q&A section can analyze the user's question patterns and provide the most appropriate answer. This allows the Q&A section to provide the most appropriate answer by referring to the user's past question history. The user's past question history includes, but is not limited to, past question content and frequency.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The registration unit can estimate the user's emotions and customize the registration content of construction projects based on those emotions. For example, if the user is stressed, the registration content can be simplified and only essential information can be displayed. If the user is relaxed, the registration content can include detailed information. If the user is in a hurry, the most important information can be prioritized, allowing for quick registration completion. This reduces the user's burden by customizing the registration content according to their emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0098] The navigation unit can estimate the user's emotions and adjust the content of the operation guide based on those emotions. For example, if the user is nervous, the operation guide can be simplified and presented in a visually easy-to-understand format. If the user is relaxed, a detailed explanation can be provided. If the user is in a hurry, a concise operation guide focusing on the essentials can be provided. In this way, the user's burden can be reduced by adjusting the content of the operation guide according to their emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0099] The generation unit can estimate the user's emotions and adjust the content of the generated plan based on those emotions. For example, if the user is relaxed, it can generate a detailed process plan. If the user is in a hurry, it can generate a plan that prioritizes the most important steps. If the user is stressed, it can generate a concise and easy-to-understand plan. By adjusting the plan content according to the user's emotions, the burden on the user can be reduced. Emotion estimation is achieved using an emotion engine or generation AI.

[0100] The Q&A section can estimate the user's emotions and adjust the content of the response based on those emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand response. If the user is relaxed, it can provide a response that includes detailed information. If the user is in a hurry, it can provide a concise response. By adjusting the content of the response according to the user's emotions, the burden on the user can be reduced. Emotion estimation is achieved using an emotion engine or generative AI.

[0101] The registration unit can estimate the user's emotions and prioritize construction projects based on those emotions. For example, if the user is stressed, lower-priority projects will be postponed. If the user is relaxed, high-priority projects can be registered first. If the user is in a hurry, urgent projects can be registered first. This allows for more efficient work by prioritizing projects according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0102] The registration section can provide feedback to prevent input errors when registering construction projects by referring to the user's past input history. For example, if the data does not match the data the user has entered in the past, it can display a warning and prompt the user to check. It can also automatically complete data that the user has frequently entered in the past, preventing input errors. It can check the consistency of the input content based on the data the user has entered in the past and provide feedback. In this way, input errors can be prevented by referring to the user's past input history.

[0103] The navigation unit can select the optimal display method when providing operation guides, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. If the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking the user's device information into consideration.

[0104] The generation unit can create an optimal plan by considering the user's geographical location information during plan generation. For example, it can prioritize incorporating construction projects close to the user's current location into the plan. It can also incorporate construction projects related to places the user frequently visits. By analyzing the user's travel patterns, it can incorporate highly relevant construction projects into the plan. In this way, by considering the user's geographical location information, the optimal plan can be generated.

[0105] The Q&A section can analyze a user's question history and provide the most appropriate answer. For example, it can provide the best answer based on questions the user has asked in the past. It can provide answers that include relevant information from the user's question history. It can analyze the user's question patterns and provide the most appropriate answer. In this way, by analyzing the user's question history, it can provide the best possible answer.

[0106] The registration unit can analyze past construction data and automatically update the registration details according to the progress of the construction. For example, if the construction is behind schedule, it can automatically correct the registration details to reflect the latest progress. If the construction is ahead of schedule, it can update the registration details to expedite the next stage. If the progress of the construction is unknown, it can make a prediction based on past data and provisionally update the registration details. This allows the system to automatically update the registration details according to the progress of the construction, reflecting the latest progress.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The registration unit automatically registers construction projects and FCTs based on past performance data. For example, it analyzes past construction data and automatically registers similar construction projects. Using generation AI, it is possible to analyze past construction data and automatically register similar construction projects. Step 2: The navigation unit guides the user's operations based on the data registered by the registration unit. For example, when the user inputs a construction plan, the generating AI proposes an optimal process plan and budget allocation plan. Using the generating AI, it is possible to guide the user's operations and propose an optimal process plan and budget allocation plan. Step 3: The generation unit generates the process plan and budget allocation plan proposed by the navigation unit. For example, the generation AI generates the optimal process plan and budget allocation plan. The optimal process plan and budget allocation plan can be generated using the generation AI. Step 4: The Q&A section provides interactive Q&A functionality based on the plan generated by the generation section. For example, when a user enters a question, the generating AI immediately provides an answer by referring to past data and the knowledge base. The generating AI can be used to immediately answer user questions by referring to past data and the knowledge base.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the registration unit, navigation unit, generation unit, and Q&A unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14, which analyzes past construction data and automatically registers similar construction projects. The navigation unit is implemented by the specific processing unit 290 of the data processing unit 12, which guides the user's operation and proposes the optimal process plan and budget allocation plan. The generation unit is implemented by the control unit 46A of the smart device 14, which generates the optimal process plan and budget allocation plan. The Q&A unit is implemented by the specific processing unit 290 of the data processing unit 12, which immediately answers the user's questions by referring to past data and a knowledge base. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the registration unit, navigation unit, generation unit, and Q&A unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214, which analyzes past construction data and automatically registers similar construction projects. The navigation unit is implemented by the specific processing unit 290 of the data processing unit 12, which guides the user's operation and proposes the optimal process plan and budget allocation plan. The generation unit is implemented by the control unit 46A of the smart glasses 214, which generates the optimal process plan and budget allocation plan. The Q&A unit is implemented by the specific processing unit 290 of the data processing unit 12, which immediately answers the user's questions by referring to past data and a knowledge base. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the registration unit, navigation unit, generation unit, and Q&A unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314, which analyzes past construction data and automatically registers similar construction projects. The navigation unit is implemented by the specific processing unit 290 of the data processing unit 12, which guides the user's operation and proposes the optimal process plan and budget allocation plan. The generation unit is implemented by the control unit 46A of the headset terminal 314, which generates the optimal process plan and budget allocation plan. The Q&A unit is implemented by the specific processing unit 290 of the data processing unit 12, which immediately answers user questions by referring to past data and a knowledge base. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the registration unit, navigation unit, generation unit, and Q&A unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414, which analyzes past construction data and automatically registers similar construction projects. The navigation unit is implemented by the specific processing unit 290 of the data processing unit 12, which guides the user's operation and proposes the optimal process plan and budget allocation plan. The generation unit is implemented by the control unit 46A of the robot 414, which generates the optimal process plan and budget allocation plan. The Q&A unit is implemented by the specific processing unit 290 of the data processing unit 12, which immediately answers user questions by referring to past data and a knowledge base. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A registration unit that automatically registers construction projects and FCTs based on past performance data, A navigation unit that guides the user's operations based on the data registered by the registration unit, A generation unit that generates process plans and budget allocation plans proposed by the aforementioned navigation unit, The system includes a Q&A unit that provides an interactive Q&A function based on the plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned registration unit is By analyzing past construction data, similar construction projects are automatically registered. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned navigation unit is It guides the user through the process and proposes optimal project plans and budget allocation plans. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate optimal process plans and budget allocation plans using generational AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Q&A section is, Using generative AI, the system instantly answers user questions by referencing past data and a knowledge base. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned registration unit is The system estimates user sentiment and adjusts the timing of construction project and FCT registration based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is By analyzing past construction data, the system automatically updates the registered information according to the progress of the construction. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is When registering construction projects, the system provides feedback to prevent input errors by referring to the user's past input history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is The system estimates the user's emotions and determines the priority of construction projects to register based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is When registering construction projects, the system prioritizes registering projects that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is When registering construction projects, the system analyzes the user's social media activity and registers related projects. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned navigation unit is The system estimates the user's emotions and adjusts the way the user guide is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned navigation unit is It analyzes the user's operation history and suggests the optimal operating procedure. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned navigation unit is The operation guide will be updated in real time according to the progress of the construction work. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned navigation unit is The system estimates the user's emotions and prioritizes the user guide based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned navigation unit is When providing the user guide, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned navigation unit is When providing an operation guide, we refer to the user's past operation history to provide the most suitable guide. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The generating unit is The generated plan is updated in real time according to the progress of the construction. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is The system generates the optimal plan by referencing the user's past plan history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and determines the priority of the plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a plan, the system takes the user's geographical location into consideration to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating a plan, the system analyzes the user's social media activity and generates a relevant plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Q&A section is, It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Q&A section is, Analyze the user's question history and provide the best possible answer. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Q&A section is, The response content will be updated in real time according to the progress of the construction. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Q&A section is, The system estimates the user's emotions and prioritizes responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Q&A section is, When answering questions, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Q&A section is, When answering a question, the system provides the most appropriate answer by referring to the user's past question history. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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. A registration unit that automatically registers construction projects and FCTs based on past performance data, A navigation unit that guides the user's operations based on the data registered by the registration unit, A generation unit that generates process plans and budget allocation plans proposed by the aforementioned navigation unit, The system includes a Q&A unit that provides an interactive Q&A function based on the plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned registration unit is By analyzing past construction data, similar construction projects are automatically registered. The system according to feature 1.

3. The aforementioned navigation unit is It guides the user through the process and proposes optimal project plans and budget allocation plans. The system according to feature 1.

4. The generating unit is We use generative AI to generate optimal process plans and budget allocation plans. The system according to feature 1.

5. The aforementioned Q&A section is, Using generative AI, it instantly answers user questions by referencing past data and a knowledge base. The system according to feature 1.

6. The aforementioned registration unit is The system estimates user sentiment and adjusts the timing of construction project and FCT registration based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned registration unit is By analyzing past construction data, the system automatically updates the registered information according to the progress of the construction. The system according to feature 1.

8. The aforementioned registration unit is When registering construction projects, the system provides feedback to prevent input errors by referring to the user's past input history. The system according to feature 1.

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