system

Abstract

The system according to this embodiment aims to support the smooth operation of a project by identifying risks in the project's planning and design and proposing appropriate countermeasures. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, an automation unit, a management unit, and a review unit. The analysis unit analyzes the project plan and design in detail and identifies potential risks. The proposal unit proposes appropriate countermeasures based on the risks identified by the analysis unit. The automation unit automates routine tasks and standard operations in the upstream processes. The management unit manages the progress of the project and creates progress reports. The review unit reviews the project deliverables and provides feedback on areas that need correction.

Description

Technical Field

[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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, there is a problem that project delays and cost overruns are likely to occur, and the risks of planning and design cannot be sufficiently eliminated.

[0005] The system according to the embodiment aims to support the smooth operation of a project by identifying the risks of project planning and design and proposing appropriate countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, an automation unit, a management unit, and a review unit. The analysis unit analyzes the project plan and design in detail and identifies potential risks. The proposal unit proposes appropriate countermeasures based on the risks identified by the analysis unit. The automation unit automates routine tasks and standard operations in the upstream processes. The management unit manages the project progress and creates progress reports. The review unit reviews the project deliverables and provides feedback on areas that need correction. [Effects of the Invention]

[0007] The system according to this embodiment can support the smooth operation of a project by identifying risks in the project's planning and design and proposing appropriate countermeasures. [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 controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a 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 project management system according to an embodiment of the present invention is a mechanism that significantly improves project delays and cost overruns in the system development field using an AI agent. This project management system supports the smooth operation of projects by eliminating risks such as insufficient project planning and design through the AI ​​agent. For example, the project management system analyzes project plans and designs in detail and identifies potential risks. The AI ​​agent detects risks such as insufficient consideration during planning, specification discrepancies, and complexity beyond the design, and proposes appropriate countermeasures. This significantly reduces the risk of project failure. Next, the project management system thoroughly entrusts tasks in areas where AI excels to AI. For example, the project management system automates routine tasks and standard operations in the upstream processes using the AI ​​agent, reducing the burden on the project manager. The project management system analyzes the schedules and career plans of project members and recommends the most suitable members. The project management system also manages project progress and generates progress reports. This allows project managers to focus on more creative and important decision-making. Furthermore, the project management system reviews project deliverables and provides feedback on areas that need correction. For example, a project management system uses AI agents to review program and document deliverables and provide instant feedback on necessary corrections. This improves project quality. This mechanism leads to improved project quality and a better system development environment. For instance, it increases project success rates and improves adherence to deadlines and budgets. It is also expected to accelerate the development of AI technology and the information revolution. For example, a project management system can significantly reduce development man-hours by having AI agents support the upstream processes of a project. This speeds up system development and is expected to contribute to the information revolution. In this way, a project management system can prevent project delays and cost overruns, and improve project quality.

[0029] The project management system according to this embodiment comprises an analysis unit, a proposal unit, an automation unit, a management unit, and a review unit. The analysis unit analyzes the project plan and design in detail and identifies potential risks. The analysis unit detects risks such as insufficient consideration during project planning, specification discrepancies, and complexity beyond the design. The analysis unit can use AI to analyze the project plan and design in detail and identify potential risks. The proposal unit proposes appropriate countermeasures based on the risks identified by the analysis unit. The proposal unit proposes countermeasures such as risk avoidance measures, risk mitigation measures, and risk transfer measures. The proposal unit can use AI to propose appropriate countermeasures based on the identified risks. The automation unit automates routine tasks and standard operations in the upstream process. The automation unit automates routine tasks and standard operations such as data entry, report creation, and email sending. The automation unit can use AI to automate routine tasks and standard operations in the upstream process. The management unit manages the progress of the project and creates progress reports. The management department, for example, analyzes the schedules and career plans of project members and recommends the most suitable members. The management department can use AI to manage project progress and generate progress reports. The review department reviews project deliverables and provides feedback on areas for correction. The review department reviews deliverables such as programs and documents and provides feedback on areas for correction. The review department can use AI to review project deliverables and provide feedback on areas for correction. As a result, the project management system according to this embodiment can prevent project delays and cost overruns and improve project quality.

[0030] The analysis department meticulously analyzes project plans and designs to identify potential risks. Specifically, it scrutinizes plans and design documents from the early stages of the project to uncover risk factors such as insufficient considerations, specification discrepancies, and design complexity. For example, it checks whether the project schedule is realistic, whether resource allocation is appropriate, and whether technical challenges are being underestimated. By using AI, potential risks can be automatically detected by comparing them with past project data and industry standards. The AI ​​analyzes documents using natural language processing technology and extracts risk factors. Furthermore, it uses machine learning algorithms to learn from the success and failure factors of past projects and applies them to the current project to support early risk detection. This allows the analysis department to identify risks in advance during the project planning and design phases, increasing the project's success rate. In addition, the analysis department evaluates the impact and probability of occurrence of risks and creates a risk matrix, helping project managers prioritize risk responses. This ensures thorough risk management from the project planning stage, preventing project delays and cost overruns.

[0031] The proposal department proposes appropriate countermeasures based on the risks identified by the analysis department. Specifically, it considers and proposes measures such as risk avoidance, risk mitigation, and risk transfer to the project manager. For example, risk avoidance measures could include excluding high-risk tasks from the schedule or considering alternatives. Risk mitigation measures could include allocating additional resources to minimize the impact of risks or strengthening technical support. Risk transfer measures could include taking out insurance to transfer risks to a third party or contracting with an external partner. The proposal department can use AI to automatically propose optimal risk countermeasures by referring to past project data and industry best practices. The AI ​​generates multiple countermeasures depending on the characteristics of the risk and the project situation, and selects the best one from among them. The proposal department also monitors the implementation status of risk countermeasures and makes revisions or additional proposals as needed. In this way, the proposal department can continuously support project risk management and increase the project's success rate. Furthermore, the proposal department evaluates the effectiveness of risk countermeasures and provides feedback to the project manager to improve risk management. This allows the proposal department to comprehensively support project risk management and improve project quality.

[0032] The automation department automates routine tasks and standard operations in the upstream processes. Specifically, it automates routine tasks and standard operations such as data entry, report creation, and email sending. For example, when creating reports to report on project progress, it automatically retrieves data from project management tools and generates reports according to a standard format. It can also automate the sending of periodic progress check emails to project members. By using AI, these tasks can be processed efficiently, and human errors can be reduced. The AI ​​uses natural language generation technology to automatically generate the content of reports and emails, and selects appropriate expressions according to the project situation. In addition, it uses machine learning algorithms to learn the optimal task scheduling from past data and can automatically adjust tasks in line with the progress of the project. As a result, the automation department can reduce the burden on project managers and members and improve project efficiency. Furthermore, the automation department can monitor the progress of tasks in real time and immediately issue alerts if an anomaly occurs. As a result, the automation department can ensure smooth project progress and support the early detection and response to problems.

[0033] The management department manages project progress and creates progress reports. Specifically, it analyzes project members' schedules and career plans and recommends the most suitable members. For example, it identifies members with the necessary skill sets for each phase of the project and assigns them at the appropriate time. It also monitors project progress in real time and automatically generates progress reports. By using AI, it is possible to analyze project progress in detail and identify the causes of problems and delays. The AI ​​analyzes data obtained from project management tools and visualizes the progress. Furthermore, it can use machine learning algorithms to learn progress patterns from past project data and predict future progress. This allows the management department to accurately grasp project progress and take appropriate action. In addition, the management department supports member growth by evaluating the performance of project members and providing feedback. In this way, the management department can comprehensively support project progress management and increase the success rate of projects.

[0034] The review department reviews project deliverables and provides feedback on areas that need correction. Specifically, it reviews program and document deliverables and provides feedback on areas that need correction. For example, in program code reviews, it checks the quality of the code and whether there are any bugs, and points out areas for improvement. In document reviews, it checks the accuracy and consistency of the content and points out areas that need correction. By using AI, these review tasks can be performed efficiently. AI analyzes documents using natural language processing technology and checks the accuracy and consistency of the content. It can also use machine learning algorithms to learn from past review results and automatically detect similar problems. This allows the review department to improve the quality of project deliverables. Furthermore, the review department can provide feedback on areas that need correction quickly, which helps to smooth the progress of the project. As a result, the review department can improve the quality of project deliverables and increase the success rate of the project.

[0035] The management department can analyze the schedules and career plans of project members and recommend the most suitable members. For example, the management department can analyze the schedules of project members and recommend the most suitable members. The management department can also analyze the career plans of project members and recommend the most suitable members. By analyzing the schedules and career plans of project members, the management department can recommend the most suitable members and improve project efficiency. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input data on the schedules and career plans of project members into an AI and have the AI ​​recommend the most suitable members.

[0036] The review department can review program and document deliverables and provide feedback on areas for correction. For example, the review department can review the source code of a program and provide feedback on areas for correction. The review department can also review the content of a document and provide feedback on areas for correction. This improves the quality of the project by reviewing program and document deliverables and providing feedback on areas for correction. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input data from the program's source code or document into an AI and have the AI ​​provide feedback on areas for correction.

[0037] The proposal department can detect risks such as insufficient consideration during planning, specification discrepancies, and complexity beyond the design, and propose appropriate countermeasures. For example, the proposal department can detect insufficient consideration during planning and propose appropriate countermeasures. The proposal department can also detect specification discrepancies and propose appropriate countermeasures. The proposal department can also detect complexity beyond the design and propose appropriate countermeasures. In this way, by detecting risks such as insufficient consideration during planning, specification discrepancies, and complexity beyond the design, and proposing appropriate countermeasures, the risk of project failure is reduced. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input planning data and specification data into AI and have the AI ​​perform risk detection and propose countermeasures.

[0038] The automation unit can automate routine tasks and standard operations in the upstream processes. For example, the automation unit can automate data entry. The automation unit can also automate report creation. The automation unit can also automate email sending. This reduces the burden on project managers by automating routine tasks and standard operations in the upstream processes. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input tasks such as data entry, report creation, and email sending into the AI ​​and leave the execution of the automation to the AI.

[0039] The analysis unit can improve the accuracy of its analysis by referring to past project data and learning risk patterns from similar projects. For example, the analysis unit can use AI to analyze past project data and identify common risk patterns. The analysis unit can also compare successful and unsuccessful cases of similar projects to learn the causes of risk. The analysis unit can also analyze the frequency of risk occurrence from past data and apply it to the current project. This improves the accuracy of risk analysis by referring to past data and learning risk patterns from similar projects. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past project data into AI and have the AI ​​perform risk pattern learning.

[0040] The analysis unit can reassess risks in real time according to the project's progress during the analysis. For example, the analysis unit can monitor the project's progress in real time and reassess the occurrence of risks. The analysis unit can also dynamically change the priority of risks according to the project's progress. The analysis unit can also recalculate the probability of risk occurrence based on data collected in real time. This improves the accuracy of risk management by reassessing risks in real time according to the project's progress. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input project progress data into AI and have the AI ​​perform the risk reassessment.

[0041] The analysis department can assess risks by considering the geographical factors of the project during the analysis. For example, the analysis department can assess risks by considering the geographical conditions of the project implementation site. The analysis department can also reflect risks due to geographical factors (natural disasters, traffic conditions, etc.) in the analysis. The analysis department can also recalculate the probability of risk occurrence based on geographical factors. This makes more realistic risk management possible by assessing risks while considering the geographical factors of the project. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input the geographical factor data of the project into AI and have the AI ​​perform the risk assessment.

[0042] The analysis department can improve the accuracy of risk assessments by referring to relevant project literature and industry standards during the analysis process. For example, the analysis department can use AI to analyze project-related literature and incorporate the findings into the risk assessment. The analysis department can also refer to industry standards to set criteria for risk assessment. The analysis department can also recalculate the probability of risk occurrence based on relevant literature and industry standards. This allows for more accurate risk management by improving the accuracy of risk assessments by referring to relevant project literature and industry standards. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input data from relevant literature and industry standards into AI and have the AI ​​perform the task of improving the accuracy of risk assessments.

[0043] The proposal department can adjust the level of detail of a proposal based on the severity of the risk. For example, it may propose detailed countermeasures for significant risks, or concise countermeasures for minor risks. The proposal department can also prioritize proposals according to the severity of the risk. This allows for the proposal of more appropriate countermeasures by adjusting the level of detail based on the severity of the risk. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input risk severity data into an AI and have the AI ​​adjust the level of detail of the proposal.

[0044] The proposal unit can apply different proposal algorithms depending on the project category when making a proposal. For example, the proposal unit may apply a specific risk management algorithm to a software development project. It may also apply a different risk management algorithm to a hardware development project. The proposal unit can also select the most suitable proposal algorithm depending on the project category. This allows for more appropriate proposals to be made by applying different proposal algorithms depending on the project category. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input project category data into an AI and have the AI ​​perform the application of the proposal algorithm.

[0045] The proposal department can adjust the timing of proposals based on the project's progress. For example, the proposal department can monitor the project's progress in real time and make proposals at the appropriate time. The proposal department can also dynamically change the timing of proposals according to the project's progress. The proposal department can also optimize the timing of proposals based on data collected in real time. This ensures that proposals are made at the appropriate time by adjusting the timing based on the project's progress. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input project progress data into AI and have the AI ​​adjust the timing of proposals.

[0046] The proposal department can improve the accuracy of its proposals by referring to relevant market data for the project. For example, the proposal department can use AI to analyze market data relevant to the project and incorporate it into the proposal. The proposal department can also adjust the content of the proposal based on the market data. The proposal department can also improve the accuracy of its proposals by referring to relevant market data. As a result, more accurate proposals are made by improving the accuracy of proposals by referring to relevant market data for the project. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input relevant market data into AI and have the AI ​​perform the task of improving the accuracy of the proposals.

[0047] The automation unit can dynamically change automation tasks according to the project's progress during automation. For example, the automation unit can monitor the project's progress in real time and dynamically change automation tasks. The automation unit can also change the priority of automation tasks according to the project's progress. The automation unit can also optimize automation tasks based on data collected in real time. This allows for more flexible automation by dynamically changing automation tasks according to the project's progress. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input project progress data into the AI ​​and have the AI ​​perform the dynamic changes to automation tasks.

[0048] The automation unit can apply different automation algorithms depending on the characteristics of the project during automation. For example, the automation unit may apply a specific automation algorithm to a software development project. It may also apply a different automation algorithm to a hardware development project. The automation unit can also select the optimal automation algorithm depending on the characteristics of the project. This allows for more appropriate automation by applying different automation algorithms depending on the characteristics of the project. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input project characteristic data into the AI ​​and have the AI ​​execute the application of the automation algorithm.

[0049] The automation unit can adjust automation tasks to take into account the geographical factors of the project during automation. For example, the automation unit can adjust automation tasks considering the geographical conditions of the project's implementation location. The automation unit can also reflect geographical risks (natural disasters, traffic conditions, etc.) in the automation tasks. The automation unit can also reprioritize automation tasks based on geographical factors. This allows for more realistic automation by adjusting automation tasks to take into account the geographical factors of the project. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input project geographical data into AI and have AI perform the adjustment of automation tasks.

[0050] The automation unit can improve the accuracy of automation by referring to relevant project literature and industry standards during the automation process. For example, the automation unit can use AI to analyze project-related literature and reflect it in the automation tasks. The automation unit can also refer to industry standards to set criteria for automation tasks. The automation unit can also improve the accuracy of automation tasks based on relevant literature and industry standards. This allows for more accurate automation by improving the accuracy of automation by referring to relevant project literature and industry standards. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input data from relevant literature and industry standards into the AI ​​and have the AI ​​perform the automation accuracy improvement.

[0051] The management department can improve the accuracy of progress management by referring to past project progress data during the management process. For example, the management department can use AI to analyze past project data and reflect it in progress management. The management department can also set progress management standards based on progress data from similar projects. The management department can also identify factors causing delays in progress from past data and apply them to the current project. This allows for more accurate progress management by improving the accuracy of progress management by referring to past project progress data. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input past progress data into AI and have the AI ​​perform the task of improving the accuracy of progress management.

[0052] The management department can re-evaluate project progress in real time according to the project's progress. For example, the management department can monitor project progress in real time and re-evaluate it. The management department can also dynamically change the priority of progress management according to project progress. The management department can also optimize progress evaluation based on data collected in real time. This improves the accuracy of progress management by re-evaluating progress in real time according to project progress. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input project progress data into AI and have the AI ​​perform the progress re-evaluation.

[0053] The management department can evaluate the progress of a project while considering its geographical factors. For example, the management department can evaluate progress while considering the geographical conditions of the project's implementation location. The management department can also reflect the risk of delays due to geographical factors in the evaluation. The management department can also readjust the progress evaluation criteria based on geographical factors. This allows for more realistic progress management by evaluating progress while considering the geographical factors of the project. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the project's geographical factor data into an AI and have the AI ​​perform the progress evaluation.

[0054] The management department can improve the accuracy of progress management by referring to relevant project literature and industry standards during the management process. For example, the management department can use AI to analyze project-related literature and incorporate it into progress management. The management department can also refer to industry standards to set progress management criteria. The management department can also improve the accuracy of progress management based on relevant literature and industry standards. This allows for more accurate progress management by improving the accuracy of progress management by referring to relevant project literature and industry standards. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input data from relevant literature and industry standards into AI and have the AI ​​perform the improvement of progress management accuracy.

[0055] The review unit can improve the accuracy of its reviews by referring to past project deliverables during the review process. For example, the review unit can use AI to analyze past project deliverables and incorporate the findings into the review. The review unit can also set review criteria based on deliverables from similar projects. The review unit can also identify patterns of corrections from past deliverables and apply them to the current project. This allows for more accurate reviews by improving the accuracy of the review process by referring to past project deliverables. Some or all of the above processes in the review unit may be performed using AI or not. For example, the review unit can input past deliverable data into AI and have the AI ​​perform the review accuracy improvement.

[0056] The review unit can re-evaluate reviews in real time according to the project's progress. For example, the review unit can monitor the project's progress in real time and re-evaluate the reviews. The review unit can also dynamically change the priority of reviews according to the project's progress. The review unit can also optimize the evaluation of reviews based on data collected in real time. This improves the accuracy of reviews by re-evaluating them in real time according to the project's progress. Some or all of the above processes in the review unit may be performed using AI or not. For example, the review unit can input project progress data into the AI ​​and have the AI ​​perform the re-evaluation of the reviews.

[0057] The review department can conduct reviews while considering the geographical factors of the project. For example, the review department can consider the geographical conditions of the project's implementation location when conducting the review. The review department can also reflect risks due to geographical factors (natural disasters, traffic conditions, etc.) in the review. The review department can also readjust the evaluation criteria for the review based on geographical factors. This makes it possible to conduct a more realistic review by considering the geographical factors of the project. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input the project's geographical factor data into AI and leave the execution of the review to the AI.

[0058] The review department can improve the accuracy of its reviews by referring to relevant project literature and industry standards during the review process. For example, the review department can use AI to analyze project-related literature and incorporate the findings into the review. The review department can also refer to industry standards to set review criteria. The review department can also improve the accuracy of its reviews based on relevant literature and industry standards. This allows for more accurate reviews by improving the accuracy of the reviews by referring to relevant project literature and industry standards. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input data on relevant literature and industry standards into AI and have the AI ​​perform the review accuracy improvement.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The analysis department can analyze project plans and designs in detail and identify potential risks. For example, it can detect risks such as insufficient consideration during project planning, specification discrepancies, and complexity beyond the design. Furthermore, the analysis department can re-evaluate risks in real time as the project progresses. For example, it can monitor project progress in real time and re-evaluate the occurrence of risks. This allows for dynamic changes in risk prioritization as the project progresses. The analysis department can also recalculate the probability of risk occurrence based on data collected in real time. This improves the accuracy of risk management by re-evaluating risks in real time as the project progresses.

[0061] The proposal department can detect risks such as insufficient consideration during planning, specification discrepancies, and complexity beyond the design, and propose appropriate countermeasures. For example, it can detect insufficient consideration during planning and propose appropriate countermeasures. Furthermore, the proposal department can adjust the level of detail of the proposal based on the severity of the risk. For example, it can propose detailed countermeasures for significant risks and concise countermeasures for minor risks. By adjusting the level of detail of the proposal based on the severity of the risk, more appropriate countermeasures can be proposed.

[0062] The automation unit can automate routine tasks and standard operations in upstream processes. For example, it can automate data entry. Furthermore, the automation unit can dynamically change automated tasks according to the project's progress. For example, it can monitor the project's progress in real time and dynamically change automated tasks. This allows for changing the priority of automated tasks according to the project's progress. It can also optimize automated tasks based on data collected in real time. This enables more flexible automation by dynamically changing automated tasks according to the project's progress.

[0063] The management department can analyze project members' schedules and career plans and recommend the most suitable members. For example, it can analyze project members' schedules and recommend the most suitable members. Furthermore, the management department can re-evaluate progress in real time according to the project's progress. For example, it can monitor the project's progress in real time and re-evaluate it. This allows for dynamic changes in the priority of progress management according to the project's progress. It can also optimize progress evaluation based on data collected in real time. This improves the accuracy of progress management by re-evaluating progress in real time according to the project's progress.

[0064] The review team can review program and document deliverables and provide feedback on areas for correction. For example, they can review program source code and provide feedback on areas for correction. Furthermore, the review team can re-evaluate reviews in real time according to the project's progress. For example, they can monitor the project's progress in real time and re-evaluate reviews. This allows for dynamic changes in review priorities according to the project's progress. They can also optimize review evaluations based on data collected in real time. This improves the accuracy of reviews by re-evaluating them in real time according to the project's progress.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The analysis department conducts a detailed analysis of the project plan and design to identify potential risks. For example, it detects risks such as insufficient consideration during project planning, specification discrepancies, and complexity beyond the design. AI can be used to conduct a detailed analysis of the project plan and design and identify potential risks. Step 2: The proposal department proposes appropriate countermeasures based on the risks identified by the analysis department. For example, it proposes countermeasures such as risk avoidance measures, risk mitigation measures, and risk transfer measures. AI can be used to propose appropriate countermeasures based on the identified risks. Step 3: The automation unit automates routine tasks and standard operations in the upstream processes. For example, it automates routine tasks and standard operations such as data entry, report creation, and email sending. AI can be used to automate routine tasks and standard operations in the upstream processes. Step 4: The management department manages project progress and creates progress reports. For example, they analyze project members' schedules and career plans and recommend the most suitable members. AI can be used to manage project progress and create progress reports. Step 5: The review team reviews the project deliverables and provides feedback on areas for correction. For example, they review program and document deliverables and provide feedback on areas for correction. AI can be used to review project deliverables and provide feedback on areas for correction.

[0067] (Example of form 2) The project management system according to an embodiment of the present invention is a mechanism that significantly improves project delays and cost overruns in the system development field using an AI agent. This project management system supports the smooth operation of projects by eliminating risks such as insufficient project planning and design through the AI ​​agent. For example, the project management system analyzes project plans and designs in detail and identifies potential risks. The AI ​​agent detects risks such as insufficient consideration during planning, specification discrepancies, and complexity beyond the design, and proposes appropriate countermeasures. This significantly reduces the risk of project failure. Next, the project management system thoroughly entrusts tasks in areas where AI excels to AI. For example, the project management system automates routine tasks and standard operations in the upstream processes using the AI ​​agent, reducing the burden on the project manager. The project management system analyzes the schedules and career plans of project members and recommends the most suitable members. The project management system also manages project progress and generates progress reports. This allows project managers to focus on more creative and important decision-making. Furthermore, the project management system reviews project deliverables and provides feedback on areas that need correction. For example, a project management system uses AI agents to review program and document deliverables and provide instant feedback on necessary corrections. This improves project quality. This mechanism leads to improved project quality and a better system development environment. For instance, it increases project success rates and improves adherence to deadlines and budgets. It is also expected to accelerate the development of AI technology and the information revolution. For example, a project management system can significantly reduce development man-hours by having AI agents support the upstream processes of a project. This speeds up system development and is expected to contribute to the information revolution. In this way, a project management system can prevent project delays and cost overruns, and improve project quality.

[0068] The project management system according to this embodiment comprises an analysis unit, a proposal unit, an automation unit, a management unit, and a review unit. The analysis unit analyzes the project plan and design in detail and identifies potential risks. The analysis unit detects risks such as insufficient consideration during project planning, specification discrepancies, and complexity beyond the design. The analysis unit can use AI to analyze the project plan and design in detail and identify potential risks. The proposal unit proposes appropriate countermeasures based on the risks identified by the analysis unit. The proposal unit proposes countermeasures such as risk avoidance measures, risk mitigation measures, and risk transfer measures. The proposal unit can use AI to propose appropriate countermeasures based on the identified risks. The automation unit automates routine tasks and standard operations in the upstream process. The automation unit automates routine tasks and standard operations such as data entry, report creation, and email sending. The automation unit can use AI to automate routine tasks and standard operations in the upstream process. The management unit manages the progress of the project and creates progress reports. The management department, for example, analyzes the schedules and career plans of project members and recommends the most suitable members. The management department can use AI to manage project progress and generate progress reports. The review department reviews project deliverables and provides feedback on areas for correction. The review department reviews deliverables such as programs and documents and provides feedback on areas for correction. The review department can use AI to review project deliverables and provide feedback on areas for correction. As a result, the project management system according to this embodiment can prevent project delays and cost overruns and improve project quality.

[0069] The analysis department meticulously analyzes project plans and designs to identify potential risks. Specifically, it scrutinizes plans and design documents from the early stages of the project to uncover risk factors such as insufficient considerations, specification discrepancies, and design complexity. For example, it checks whether the project schedule is realistic, whether resource allocation is appropriate, and whether technical challenges are being underestimated. By using AI, potential risks can be automatically detected by comparing them with past project data and industry standards. The AI ​​analyzes documents using natural language processing technology and extracts risk factors. Furthermore, it uses machine learning algorithms to learn from the success and failure factors of past projects and applies them to the current project to support early risk detection. This allows the analysis department to identify risks in advance during the project planning and design phases, increasing the project's success rate. In addition, the analysis department evaluates the impact and probability of occurrence of risks and creates a risk matrix, helping project managers prioritize risk responses. This ensures thorough risk management from the project planning stage, preventing project delays and cost overruns.

[0070] The proposal department proposes appropriate countermeasures based on the risks identified by the analysis department. Specifically, it considers and proposes measures such as risk avoidance, risk mitigation, and risk transfer to the project manager. For example, risk avoidance measures could include excluding high-risk tasks from the schedule or considering alternatives. Risk mitigation measures could include allocating additional resources to minimize the impact of risks or strengthening technical support. Risk transfer measures could include taking out insurance to transfer risks to a third party or contracting with an external partner. The proposal department can use AI to automatically propose optimal risk countermeasures by referring to past project data and industry best practices. The AI ​​generates multiple countermeasures depending on the characteristics of the risk and the project situation, and selects the best one from among them. The proposal department also monitors the implementation status of risk countermeasures and makes revisions or additional proposals as needed. In this way, the proposal department can continuously support project risk management and increase the project's success rate. Furthermore, the proposal department evaluates the effectiveness of risk countermeasures and provides feedback to the project manager to improve risk management. This allows the proposal department to comprehensively support project risk management and improve project quality.

[0071] The automation department automates routine tasks and standard operations in the upstream processes. Specifically, it automates routine tasks and standard operations such as data entry, report creation, and email sending. For example, when creating reports to report on project progress, it automatically retrieves data from project management tools and generates reports according to a standard format. It can also automate the sending of periodic progress check emails to project members. By using AI, these tasks can be processed efficiently, and human errors can be reduced. The AI ​​uses natural language generation technology to automatically generate the content of reports and emails, and selects appropriate expressions according to the project situation. In addition, it uses machine learning algorithms to learn the optimal task scheduling from past data and can automatically adjust tasks in line with the progress of the project. As a result, the automation department can reduce the burden on project managers and members and improve project efficiency. Furthermore, the automation department can monitor the progress of tasks in real time and immediately issue alerts if an anomaly occurs. As a result, the automation department can ensure smooth project progress and support the early detection and response to problems.

[0072] The management department manages project progress and creates progress reports. Specifically, it analyzes project members' schedules and career plans and recommends the most suitable members. For example, it identifies members with the necessary skill sets for each phase of the project and assigns them at the appropriate time. It also monitors project progress in real time and automatically generates progress reports. By using AI, it is possible to analyze project progress in detail and identify the causes of problems and delays. The AI ​​analyzes data obtained from project management tools and visualizes the progress. Furthermore, it can use machine learning algorithms to learn progress patterns from past project data and predict future progress. This allows the management department to accurately grasp project progress and take appropriate action. In addition, the management department supports member growth by evaluating the performance of project members and providing feedback. In this way, the management department can comprehensively support project progress management and increase the success rate of projects.

[0073] The review department reviews project deliverables and provides feedback on areas that need correction. Specifically, it reviews program and document deliverables and provides feedback on areas that need correction. For example, in program code reviews, it checks the quality of the code and whether there are any bugs, and points out areas for improvement. In document reviews, it checks the accuracy and consistency of the content and points out areas that need correction. By using AI, these review tasks can be performed efficiently. AI analyzes documents using natural language processing technology and checks the accuracy and consistency of the content. It can also use machine learning algorithms to learn from past review results and automatically detect similar problems. This allows the review department to improve the quality of project deliverables. Furthermore, the review department can provide feedback on areas that need correction quickly, which helps to smooth the progress of the project. As a result, the review department can improve the quality of project deliverables and increase the success rate of the project.

[0074] The management department can analyze the schedules and career plans of project members and recommend the most suitable members. For example, the management department can analyze the schedules of project members and recommend the most suitable members. The management department can also analyze the career plans of project members and recommend the most suitable members. By analyzing the schedules and career plans of project members, the management department can recommend the most suitable members and improve project efficiency. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input data on the schedules and career plans of project members into an AI and have the AI ​​recommend the most suitable members.

[0075] The review department can review program and document deliverables and provide feedback on areas for correction. For example, the review department can review the source code of a program and provide feedback on areas for correction. The review department can also review the content of a document and provide feedback on areas for correction. This improves the quality of the project by reviewing program and document deliverables and providing feedback on areas for correction. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input data from the program's source code or document into an AI and have the AI ​​provide feedback on areas for correction.

[0076] The proposal department can detect risks such as insufficient consideration during planning, specification discrepancies, and complexity beyond the design, and propose appropriate countermeasures. For example, the proposal department can detect insufficient consideration during planning and propose appropriate countermeasures. The proposal department can also detect specification discrepancies and propose appropriate countermeasures. The proposal department can also detect complexity beyond the design and propose appropriate countermeasures. In this way, by detecting risks such as insufficient consideration during planning, specification discrepancies, and complexity beyond the design, and proposing appropriate countermeasures, the risk of project failure is reduced. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input planning data and specification data into AI and have the AI ​​perform risk detection and propose countermeasures.

[0077] The automation unit can automate routine tasks and standard operations in the upstream processes. For example, the automation unit can automate data entry. The automation unit can also automate report creation. The automation unit can also automate email sending. This reduces the burden on project managers by automating routine tasks and standard operations in the upstream processes. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input tasks such as data entry, report creation, and email sending into the AI ​​and leave the execution of the automation to the AI.

[0078] The analysis unit can estimate the user's emotions and adjust the priority of risk analysis based on the estimated emotions. For example, if the user is stressed, the AI ​​can re-evaluate the risk priorities and address the most important risks first. If the user is relaxed, the AI ​​can set the risk priorities as usual and proceed as planned. If the user is in a hurry, the AI ​​can identify risks that need to be addressed quickly and take immediate action. This allows for more appropriate risk management by adjusting the priority of risk analysis based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the priority of risk analysis.

[0079] The analysis unit can improve the accuracy of its analysis by referring to past project data and learning risk patterns from similar projects. For example, the analysis unit can use AI to analyze past project data and identify common risk patterns. The analysis unit can also compare successful and unsuccessful cases of similar projects to learn the causes of risk. The analysis unit can also analyze the frequency of risk occurrence from past data and apply it to the current project. This improves the accuracy of risk analysis by referring to past data and learning risk patterns from similar projects. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past project data into AI and have the AI ​​perform risk pattern learning.

[0080] The analysis unit can reassess risks in real time according to the project's progress during the analysis. For example, the analysis unit can monitor the project's progress in real time and reassess the occurrence of risks. The analysis unit can also dynamically change the priority of risks according to the project's progress. The analysis unit can also recalculate the probability of risk occurrence based on data collected in real time. This improves the accuracy of risk management by reassessing risks in real time according to the project's progress. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input project progress data into AI and have the AI ​​perform the risk reassessment.

[0081] The analysis unit can estimate the user's emotions and adjust how the risk analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display. If the user is relaxed, the analysis unit can also provide a display that includes detailed information. If the user is in a hurry, the analysis unit can also provide a concise display. By adjusting how the risk analysis results are displayed based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. 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. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust how the risk analysis results are displayed.

[0082] The analysis department can assess risks by considering the geographical factors of the project during the analysis. For example, the analysis department can assess risks by considering the geographical conditions of the project implementation site. The analysis department can also reflect risks due to geographical factors (natural disasters, traffic conditions, etc.) in the analysis. The analysis department can also recalculate the probability of risk occurrence based on geographical factors. This makes more realistic risk management possible by assessing risks while considering the geographical factors of the project. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input the geographical factor data of the project into AI and have the AI ​​perform the risk assessment.

[0083] The analysis department can improve the accuracy of risk assessments by referring to relevant project literature and industry standards during the analysis process. For example, the analysis department can use AI to analyze project-related literature and incorporate the findings into the risk assessment. The analysis department can also refer to industry standards to set criteria for risk assessment. The analysis department can also recalculate the probability of risk occurrence based on relevant literature and industry standards. This allows for more accurate risk management by improving the accuracy of risk assessments by referring to relevant project literature and industry standards. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input data from relevant literature and industry standards into AI and have the AI ​​perform the task of improving the accuracy of risk assessments.

[0084] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit can make simple and clear suggestions. If the user is relaxed, the suggestion unit can also make suggestions that include detailed explanations. If the user is in a hurry, the suggestion unit can also make quick and concise suggestions. By adjusting the way suggestions are presented based on the user's emotions, it becomes possible to make suggestions that are easy for the user to understand. 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. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the way suggestions are presented.

[0085] The proposal department can adjust the level of detail of a proposal based on the severity of the risk. For example, it may propose detailed countermeasures for significant risks, or concise countermeasures for minor risks. The proposal department can also prioritize proposals according to the severity of the risk. This allows for the proposal of more appropriate countermeasures by adjusting the level of detail based on the severity of the risk. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input risk severity data into an AI and have the AI ​​adjust the level of detail of the proposal.

[0086] The proposal unit can apply different proposal algorithms depending on the project category when making a proposal. For example, the proposal unit may apply a specific risk management algorithm to a software development project. It may also apply a different risk management algorithm to a hardware development project. The proposal unit can also select the most suitable proposal algorithm depending on the project category. This allows for more appropriate proposals to be made by applying different proposal algorithms depending on the project category. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input project category data into an AI and have the AI ​​perform the application of the proposal algorithm.

[0087] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will present the most important suggestions first. If the user is relaxed, the suggestion unit can also present suggestions in the usual order of priority. If the user is in a hurry, the suggestion unit can also prioritize suggestions that require immediate attention. This ensures that more important suggestions are prioritized by determining the priority of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​determine the priority of suggestions.

[0088] The proposal department can adjust the timing of proposals based on the project's progress. For example, the proposal department can monitor the project's progress in real time and make proposals at the appropriate time. The proposal department can also dynamically change the timing of proposals according to the project's progress. The proposal department can also optimize the timing of proposals based on data collected in real time. This ensures that proposals are made at the appropriate time by adjusting the timing based on the project's progress. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input project progress data into AI and have the AI ​​adjust the timing of proposals.

[0089] The proposal department can improve the accuracy of its proposals by referring to relevant market data for the project. For example, the proposal department can use AI to analyze market data relevant to the project and incorporate it into the proposal. The proposal department can also adjust the content of the proposal based on the market data. The proposal department can also improve the accuracy of its proposals by referring to relevant market data. As a result, more accurate proposals are made by improving the accuracy of proposals by referring to relevant market data for the project. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input relevant market data into AI and have the AI ​​perform the task of improving the accuracy of the proposals.

[0090] The automation unit can estimate the user's emotions and adjust the scope of automation based on the estimated emotions. For example, if the user is stressed, the automation unit can expand the scope of automation to reduce the user's burden. If the user is relaxed, the automation unit can proceed with the work within the normal scope of automation. If the user is in a hurry, the automation unit can also automate tasks that require quick action by the AI. This reduces the user's burden by adjusting the scope of automation based on 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. Some or all of the above processing in the automation unit may be performed using AI or not. For example, the automation unit can input user emotion data into the AI ​​and have the AI ​​adjust the scope of automation.

[0091] The automation unit can dynamically change automation tasks according to the project's progress during automation. For example, the automation unit can monitor the project's progress in real time and dynamically change automation tasks. The automation unit can also change the priority of automation tasks according to the project's progress. The automation unit can also optimize automation tasks based on data collected in real time. This allows for more flexible automation by dynamically changing automation tasks according to the project's progress. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input project progress data into the AI ​​and have the AI ​​perform the dynamic changes to automation tasks.

[0092] The automation unit can apply different automation algorithms depending on the characteristics of the project during automation. For example, the automation unit may apply a specific automation algorithm to a software development project. It may also apply a different automation algorithm to a hardware development project. The automation unit can also select the optimal automation algorithm depending on the characteristics of the project. This allows for more appropriate automation by applying different automation algorithms depending on the characteristics of the project. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input project characteristic data into the AI ​​and have the AI ​​execute the application of the automation algorithm.

[0093] The automation unit can estimate the user's emotions and determine the priority of automated tasks based on the estimated user emotions. For example, if the user is stressed, the automation unit will automate the most important tasks first. If the user is relaxed, the automation unit can automate tasks in the usual priority order. If the user is in a hurry, the automation unit can prioritize and automate tasks that require immediate attention. This ensures that more important tasks are prioritized by determining the priority of automated tasks based on 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. Some or all of the above processing in the automation unit may be performed using AI or not. For example, the automation unit can input user emotion data into an AI and have the AI ​​determine the priority of automated tasks.

[0094] The automation unit can adjust automation tasks to take into account the geographical factors of the project during automation. For example, the automation unit can adjust automation tasks considering the geographical conditions of the project's implementation location. The automation unit can also reflect geographical risks (natural disasters, traffic conditions, etc.) in the automation tasks. The automation unit can also reprioritize automation tasks based on geographical factors. This allows for more realistic automation by adjusting automation tasks to take into account the geographical factors of the project. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input project geographical data into AI and have AI perform the adjustment of automation tasks.

[0095] The automation unit can improve the accuracy of automation by referring to relevant project literature and industry standards during the automation process. For example, the automation unit can use AI to analyze project-related literature and reflect it in the automation tasks. The automation unit can also refer to industry standards to set criteria for automation tasks. The automation unit can also improve the accuracy of automation tasks based on relevant literature and industry standards. This allows for more accurate automation by improving the accuracy of automation by referring to relevant project literature and industry standards. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input data from relevant literature and industry standards into the AI ​​and have the AI ​​perform the automation accuracy improvement.

[0096] The management department can estimate the user's emotions and adjust the progress management method based on the estimated emotions. For example, if the user is stressed, the management department can provide a simple and clear progress management method. If the user is relaxed, the management department can also provide a progress management method that includes detailed information. If the user is in a hurry, the management department can also provide a quick and concise progress management method. By adjusting the progress management method based on the user's emotions, progress management that is easy for the user to understand becomes possible. 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. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input user emotion data into AI and have the AI ​​perform the adjustment of the progress management method.

[0097] The management department can improve the accuracy of progress management by referring to past project progress data during the management process. For example, the management department can use AI to analyze past project data and reflect it in progress management. The management department can also set progress management standards based on progress data from similar projects. The management department can also identify factors causing delays in progress from past data and apply them to the current project. This allows for more accurate progress management by improving the accuracy of progress management by referring to past project progress data. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input past progress data into AI and have the AI ​​perform the task of improving the accuracy of progress management.

[0098] The management department can re-evaluate project progress in real time according to the project's progress. For example, the management department can monitor project progress in real time and re-evaluate it. The management department can also dynamically change the priority of progress management according to project progress. The management department can also optimize progress evaluation based on data collected in real time. This improves the accuracy of progress management by re-evaluating progress in real time according to project progress. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input project progress data into AI and have the AI ​​perform the progress re-evaluation.

[0099] The management department can estimate the user's emotions and adjust the display method of progress reports based on the estimated emotions. For example, if the user is stressed, the management department can provide a simple and highly visible display method. If the user is relaxed, the management department can also provide a display method that includes detailed information. If the user is in a hurry, the management department can also provide a display method that gets straight to the point. By adjusting the display method of progress reports based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. 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. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input user emotion data into AI and have the AI ​​adjust the display method of progress reports.

[0100] The management department can evaluate the progress of a project while considering its geographical factors. For example, the management department can evaluate progress while considering the geographical conditions of the project's implementation location. The management department can also reflect the risk of delays due to geographical factors in the evaluation. The management department can also readjust the progress evaluation criteria based on geographical factors. This allows for more realistic progress management by evaluating progress while considering the geographical factors of the project. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the project's geographical factor data into an AI and have the AI ​​perform the progress evaluation.

[0101] The management department can improve the accuracy of progress management by referring to relevant project literature and industry standards during the management process. For example, the management department can use AI to analyze project-related literature and incorporate it into progress management. The management department can also refer to industry standards to set progress management criteria. The management department can also improve the accuracy of progress management based on relevant literature and industry standards. This allows for more accurate progress management by improving the accuracy of progress management by referring to relevant project literature and industry standards. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input data from relevant literature and industry standards into AI and have the AI ​​perform the improvement of progress management accuracy.

[0102] The review unit can estimate the user's emotions and adjust the review method based on the estimated emotions. For example, if the user is stressed, the review unit can provide a simple and clear review method. If the user is relaxed, the review unit can also provide a review method that includes detailed information. If the user is in a hurry, the review unit can also provide a quick and concise review method. By adjusting the review method based on the user's emotions, it becomes possible to provide reviews that are easy for the user to understand. 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. Some or all of the above processing in the review unit may be performed using AI or not. For example, the review unit can input user emotion data into AI and have the AI ​​perform the adjustment of the review method.

[0103] The review unit can improve the accuracy of its reviews by referring to past project deliverables during the review process. For example, the review unit can use AI to analyze past project deliverables and incorporate the findings into the review. The review unit can also set review criteria based on deliverables from similar projects. The review unit can also identify patterns of corrections from past deliverables and apply them to the current project. This allows for more accurate reviews by improving the accuracy of the review process by referring to past project deliverables. Some or all of the above processes in the review unit may be performed using AI or not. For example, the review unit can input past deliverable data into AI and have the AI ​​perform the review accuracy improvement.

[0104] The review unit can re-evaluate reviews in real time according to the project's progress. For example, the review unit can monitor the project's progress in real time and re-evaluate the reviews. The review unit can also dynamically change the priority of reviews according to the project's progress. The review unit can also optimize the evaluation of reviews based on data collected in real time. This improves the accuracy of reviews by re-evaluating them in real time according to the project's progress. Some or all of the above processes in the review unit may be performed using AI or not. For example, the review unit can input project progress data into the AI ​​and have the AI ​​perform the re-evaluation of the reviews.

[0105] The review unit can estimate the user's emotions and adjust how the review results are displayed based on the estimated emotions. For example, if the user is nervous, the review unit can provide a simple and highly visible display. If the user is relaxed, the review unit can also provide a display that includes detailed information. If the user is in a hurry, the review unit can also provide a display that gets straight to the point. By adjusting how the review results are displayed based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. 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. Some or all of the above processing in the review unit may be performed using AI or not. For example, the review unit can input user emotion data into AI and have the AI ​​adjust how the review results are displayed.

[0106] The review department can conduct reviews while considering the geographical factors of the project. For example, the review department can consider the geographical conditions of the project's implementation location when conducting the review. The review department can also reflect risks due to geographical factors (natural disasters, traffic conditions, etc.) in the review. The review department can also readjust the evaluation criteria for the review based on geographical factors. This makes it possible to conduct a more realistic review by considering the geographical factors of the project. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input the project's geographical factor data into AI and leave the execution of the review to the AI.

[0107] The review department can improve the accuracy of its reviews by referring to relevant project literature and industry standards during the review process. For example, the review department can use AI to analyze project-related literature and incorporate the findings into the review. The review department can also refer to industry standards to set review criteria. The review department can also improve the accuracy of its reviews based on relevant literature and industry standards. This allows for more accurate reviews by improving the accuracy of the reviews by referring to relevant project literature and industry standards. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input data on relevant literature and industry standards into AI and have the AI ​​perform the review accuracy improvement.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The analysis department can analyze project plans and designs in detail and identify potential risks. For example, it can detect risks such as insufficient consideration during project planning, specification discrepancies, and complexity beyond the design. Furthermore, the analysis department can re-evaluate risks in real time as the project progresses. For example, it can monitor project progress in real time and re-evaluate the occurrence of risks. This allows for dynamic changes in risk prioritization as the project progresses. The analysis department can also recalculate the probability of risk occurrence based on data collected in real time. This improves the accuracy of risk management by re-evaluating risks in real time as the project progresses.

[0110] The proposal department can detect risks such as insufficient consideration during planning, specification discrepancies, and complexity beyond the design, and propose appropriate countermeasures. For example, it can detect insufficient consideration during planning and propose appropriate countermeasures. Furthermore, the proposal department can adjust the level of detail of the proposal based on the severity of the risk. For example, it can propose detailed countermeasures for significant risks and concise countermeasures for minor risks. By adjusting the level of detail of the proposal based on the severity of the risk, more appropriate countermeasures can be proposed.

[0111] The automation unit can automate routine tasks and standard operations in upstream processes. For example, it can automate data entry. Furthermore, the automation unit can dynamically change automated tasks according to the project's progress. For example, it can monitor the project's progress in real time and dynamically change automated tasks. This allows for changing the priority of automated tasks according to the project's progress. It can also optimize automated tasks based on data collected in real time. This enables more flexible automation by dynamically changing automated tasks according to the project's progress.

[0112] The management department can analyze project members' schedules and career plans and recommend the most suitable members. For example, it can analyze project members' schedules and recommend the most suitable members. Furthermore, the management department can re-evaluate progress in real time according to the project's progress. For example, it can monitor the project's progress in real time and re-evaluate it. This allows for dynamic changes in the priority of progress management according to the project's progress. It can also optimize progress evaluation based on data collected in real time. This improves the accuracy of progress management by re-evaluating progress in real time according to the project's progress.

[0113] The review team can review program and document deliverables and provide feedback on areas for correction. For example, they can review program source code and provide feedback on areas for correction. Furthermore, the review team can re-evaluate reviews in real time according to the project's progress. For example, they can monitor the project's progress in real time and re-evaluate reviews. This allows for dynamic changes in review priorities according to the project's progress. They can also optimize review evaluations based on data collected in real time. This improves the accuracy of reviews by re-evaluating them in real time according to the project's progress.

[0114] The analytics department can estimate the user's emotions and adjust the priority of risk analysis based on those emotions. For example, if the user is stressed, the AI ​​will re-evaluate the risk priorities and address the most important risks first. Furthermore, if the user is in a hurry, the analytics department can identify risks that require immediate attention and respond immediately. This allows for more effective risk management by adjusting the priority of risk analysis based on the user's emotions.

[0115] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, it will offer simple and clear suggestions. Furthermore, if the user is in a hurry, the suggestion function can offer quick and concise suggestions. By adjusting the presentation of suggestions based on the user's emotions, it becomes possible to create suggestions that are easy for the user to understand.

[0116] The automation unit can estimate the user's emotions and adjust the scope of automation based on those emotions. For example, if the user is stressed, the AI ​​can expand the scope of automation to reduce the user's burden. Furthermore, if the user is in a hurry, the automation unit can also automate tasks that require quick action. This reduces the user's burden by adjusting the scope of automation based on the user's emotions.

[0117] The management department can estimate user emotions and adjust progress management methods based on those estimates. For example, if a user is stressed, they can provide a simple and clear progress management method. Furthermore, if a user is in a hurry, the management department can provide a quick and concise progress management method. By adjusting progress management methods based on user emotions, progress management becomes easier for users to understand.

[0118] The review team can estimate the user's emotions and adjust the review method based on those emotions. For example, if the user is stressed, it can provide a simple and clear review method. Furthermore, if the user is in a hurry, the review team can provide a quick and concise review method. By adjusting the review method based on the user's emotions, it becomes possible to create reviews that are easy for users to understand.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The analysis department conducts a detailed analysis of the project plan and design to identify potential risks. For example, it detects risks such as insufficient consideration during project planning, specification discrepancies, and complexity beyond the design. AI can be used to conduct a detailed analysis of the project plan and design and identify potential risks. Step 2: The proposal department proposes appropriate countermeasures based on the risks identified by the analysis department. For example, it proposes countermeasures such as risk avoidance measures, risk mitigation measures, and risk transfer measures. AI can be used to propose appropriate countermeasures based on the identified risks. Step 3: The automation unit automates routine tasks and standard operations in the upstream processes. For example, it automates routine tasks and standard operations such as data entry, report creation, and email sending. AI can be used to automate routine tasks and standard operations in the upstream processes. Step 4: The management department manages project progress and creates progress reports. For example, they analyze project members' schedules and career plans and recommend the most suitable members. AI can be used to manage project progress and create progress reports. Step 5: The review team reviews the project deliverables and provides feedback on areas for correction. For example, they review program and document deliverables and provide feedback on areas for correction. AI can be used to review project deliverables and provide feedback on areas for correction.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the analysis unit, proposal unit, automation unit, management unit, and review unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12. The automation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The management unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The review unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the analysis unit, proposal unit, automation unit, management unit, and review unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The automation unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The management unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The review unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the analysis unit, proposal unit, automation unit, management unit, and review unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The automation unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The management unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The review unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the analysis unit, proposal unit, automation unit, management unit, and review unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The automation unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The management unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The review unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The analysis department analyzes project plans and designs in detail and identifies potential risks, A proposal unit proposes appropriate countermeasures based on the risks identified by the aforementioned analysis unit, The automation unit automates routine tasks and standard operations in the upstream processes, The management department manages the project's progress and creates progress reports, It includes a review section that reviews project deliverables and provides feedback on areas that need correction. A system characterized by the following features. (Note 2) The aforementioned management department, Analyze project members' schedules and career plans to recommend the most suitable members. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned review section, Review program and documentation deliverables and provide feedback on areas that need correction. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We detect risks such as insufficient consideration during planning, discrepancies in specifications, and complexity beyond the design, and propose appropriate countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned automation unit, Automate routine tasks and standard operations in the upstream processes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is It estimates user sentiment and adjusts the priority of risk analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is By referencing past project data and learning risk patterns from similar projects, we improve the accuracy of our analysis. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is During analysis, risks are reassessed in real time according to the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is Adjust the way we estimate user sentiment and display risk analysis results based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is When conducting the analysis, assess the risks by considering the geographical factors of the project. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During analysis, we improve the accuracy of risk assessments by referring to relevant project literature and industry standards. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the severity of the risks. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When submitting a proposal, different proposal algorithms are applied depending on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, adjust the timing of the proposal based on the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, we refer to relevant market data for the project to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned automation unit, It estimates the user's emotions and adjusts the scope of automation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned automation unit, During automation, dynamically change the automation tasks according to the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned automation unit, When automating a project, different automation algorithms are applied depending on the project's characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned automation unit, It estimates user emotions and prioritizes automated tasks based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned automation unit, When automating, adjust automation tasks to take into account the geographical factors of the project. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned automation unit, When automating processes, we improve the accuracy of the automation by referring to relevant project documentation and industry standards. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, We estimate the user's emotions and adjust the progress management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, During management, refer to past project progress data to improve the accuracy of progress tracking. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, During management, the project's progress is re-evaluated in real time according to its progress status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, The system estimates the user's emotions and adjusts how progress reports are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, During management, assess the project's progress while considering its geographical factors. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, During management, refer to relevant project literature and industry standards to improve the accuracy of progress tracking. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned review section, We estimate user sentiment and adjust the review process based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned review section, During the review process, refer to the project's past deliverables to improve the accuracy of the review. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned review section, During the review process, the review will be re-evaluated in real time based on the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned review section, It estimates the user's sentiment and adjusts how review results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned review section, During the review process, the geographical factors of the project will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned review section, During the review process, we refer to relevant project literature and industry standards to improve the accuracy of the review. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes project plans and designs in detail and identifies potential risks, A proposal unit proposes appropriate countermeasures based on the risks identified by the aforementioned analysis unit, The automation unit automates routine tasks and standard operations in the upstream processes, The management department manages the project's progress and creates progress reports, It includes a review section that reviews project deliverables and provides feedback on areas that need correction. A system characterized by the following features.

2. The aforementioned management department, Analyze project members' schedules and career plans to recommend the most suitable members. The system according to feature 1.

3. The aforementioned review section, Review program and documentation deliverables and provide feedback on areas that need correction. The system according to feature 1.

4. The aforementioned proposal section is, We detect risks such as insufficient consideration during planning, discrepancies in specifications, and complexity beyond the design, and propose appropriate countermeasures. The system according to feature 1.

5. The aforementioned automation unit, Automate routine tasks and standard operations in the upstream processes. The system according to feature 1.

6. The aforementioned analysis unit is It estimates user sentiment and adjusts the priority of risk analysis based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned analysis unit is By referencing past project data and learning risk patterns from similar projects, we improve the accuracy of our analysis. The system according to feature 1.

8. The aforementioned analysis unit is During analysis, risks are reassessed in real time according to the project's progress. The system according to feature 1.

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