system

Abstract

The system according to this embodiment aims to enable project managers to focus on strategic tasks. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a dashboard unit. The data collection unit collects data on project progress, resource allocation, and risk factors. The analysis unit analyzes the data collected by the data collection unit and evaluates the project progress and risk factors. The proposal unit proposes resource allocation and task automation based on the analysis results obtained by the analysis unit. The dashboard unit visualizes the content proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that project managers were occupied with task management and could not concentrate on strategic operations.

[0005] The system according to the embodiment aims to enable project managers to concentrate on strategic operations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a dashboard unit. The data collection unit collects data on project progress, resource allocation, and risk factors. The analysis unit analyzes the data collected by the data collection unit and evaluates the project progress and risk factors. The proposal unit proposes resource allocation and task automation based on the analysis results obtained by the analysis unit. The dashboard unit visualizes the content proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable project managers to focus on strategic tasks. [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, etc. The communication I / F manages 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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) An AI project navigator according to an embodiment of the present invention is a system that automates project progress management and risk assessment using generative AI. The AI ​​project navigator collects data such as project progress, resource allocation, and risk factors, and performs pattern recognition and predictive analysis. This frees project managers from task management, allowing them to focus on more strategic and high-value tasks such as project progress and interdepartmental coordination. For example, the AI ​​project navigator monitors project progress in real time and immediately notifies if delays occur. It also proposes measures to mitigate risks when risk factors are detected. This ensures smooth project progress and reduces wasted costs and time. Furthermore, the generative AI provides a dashboard for visualizing project progress and risk factors. This dashboard is designed so that project managers can grasp the project status at a glance. For example, progress, risk factors, and resource allocation are displayed in graphs and charts. In this way, the AI ​​project navigator becomes a powerful tool for project managers to exercise leadership and lead projects to success. As a result, the AI ​​project navigator automates project progress management and risk assessment, allowing project managers to focus on strategic tasks.

[0029] The AI ​​project navigator according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a dashboard unit. The data collection unit collects data such as project progress, resource allocation, and risk factors. The data collection unit collects data from sources such as project management tools, sensors, and log data. For example, the data collection unit obtains task progress from project management tools. The data collection unit can also monitor resource usage using sensors. Furthermore, the data collection unit can analyze log data to identify risk factors. The analysis unit analyzes the data collected by the data collection unit and evaluates project progress and risk factors. For example, the analysis unit analyzes the data using machine learning algorithms. Furthermore, the analysis unit can predict future risks based on past data. Furthermore, the analysis unit can recognize data patterns to identify critical paths. The proposal unit proposes optimal resource allocation and task automation based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes allocating additional resources to delayed tasks. Furthermore, the proposal unit can propose measures to mitigate risks for high-risk tasks. Furthermore, the proposal unit can propose the optimal allocation of resources. The dashboard section visualizes the content proposed by the proposal section. For example, the dashboard section displays project progress, risk factors, and resource allocation status in graphs and charts. The dashboard section can also update data in real time, allowing project managers to grasp the situation at a glance. Furthermore, the dashboard section can provide a customizable interface, enabling project managers to quickly obtain the information they need. As a result, the AI ​​project navigator according to this embodiment automates project progress management and risk assessment, allowing project managers to focus on strategic tasks.

[0030] The data collection unit collects data such as project progress, resource allocation, and risk factors. Specifically, it can automatically retrieve data using APIs to obtain task progress from project management tools. For example, it collects detailed information such as task start date, end date, assigned person, and progress rate. It can also monitor resource usage using sensors. For example, it can monitor server CPU usage, memory usage, and network traffic in real time to detect resource overload or shortage. Furthermore, the data collection unit can identify risk factors by analyzing log data. For example, it can analyze error logs and warning logs to detect frequent problems and potential risks early. In this way, the data collection unit can comprehensively understand project progress, resource usage, and risk factors, providing foundational data to maintain project health. In addition, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes data collected by the data collection unit to evaluate project progress and risk factors. Specifically, it uses machine learning algorithms to analyze data and evaluate project progress in real time. For example, it analyzes task progress data to identify causes of delays and bottlenecks. It can also predict future risks based on past data. For example, by training the system with past project data, it can predict the probability of risk occurrence under specific conditions and take countermeasures early. Furthermore, the analysis unit can recognize data patterns and identify the critical path. The critical path is a chain of important tasks that determines the shortest project completion time, and identifying it supports the efficient progress of the project. Based on these analysis results, the analysis unit visualizes project progress and risk factors and provides them to the project manager. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The proposal department proposes optimal resource allocation and task automation based on the analysis results obtained by the analysis department. Specifically, it proposes allocating additional resources to delayed tasks. For example, if a particular task is delayed, it will resolve the delay by allocating additional personnel or time to that task. It can also propose measures to mitigate risks for high-risk tasks. For example, it will prevent risks from occurring by implementing risk avoidance measures in advance for high-risk tasks. Furthermore, the proposal department can propose the optimal allocation of resources. For example, it will maximize project efficiency by analyzing the resource usage of the entire project and adjusting resource surpluses and shortages. The proposal department provides these proposals to project managers to support project progress. In addition, the proposal department uses AI to automatically generate proposals, enabling project managers to make quick decisions. For example, it develops algorithms that propose optimal resource allocation and task automation based on project progress and risk factors, and automatically generates proposals. This allows the proposal department to help project managers focus on strategic tasks and improve the project success rate.

[0033] The dashboard visualizes the proposals submitted by the proposal team. Specifically, it displays project progress, risk factors, and resource allocation using graphs and charts. For example, it uses a Gantt chart to visually display task progress, allowing project managers to grasp the situation at a glance. It also displays risk factors using a heatmap, enabling intuitive understanding of high-risk areas. Furthermore, it displays resource allocation using pie charts and bar charts, allowing for visual confirmation of resource usage. The dashboard updates data in real time, ensuring project managers are always aware of the latest situation. For example, it reflects data collected from project management tools and sensors in real time and displays it on the dashboard. The dashboard also provides a customizable interface, allowing project managers to quickly obtain the information they need. For example, project managers can select data and metrics of interest and display them on the dashboard. In this way, the dashboard provides a powerful tool for efficiently managing project progress and assessing risks, supporting project managers in making strategic decisions.

[0034] The data collection unit can collect data from project management tools, sensors, and log data. For example, the data collection unit can obtain task progress from project management tools. For example, the data collection unit can obtain task progress in real time using the API of a project management tool. The data collection unit can also monitor resource usage using sensors. For example, the data collection unit can monitor the temperature of a server room using a temperature sensor and issue an alert if an anomaly occurs. Furthermore, the data collection unit can analyze log data to identify risk factors. For example, the data collection unit can analyze system logs to check the frequency of error messages and identify risk factors. This allows for an accurate understanding of project progress and risk factors by collecting data from diverse data sources. Project management tools include, but are not limited to, JIRA and Trello. Sensors include, but are not limited to, temperature sensors and location sensors. Log data includes, but are not limited to, system logs and user logs. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data acquired from a project management tool into a generation AI, and have the generation AI perform data analysis.

[0035] The analysis unit can analyze the collected data and evaluate the project's progress and risk factors. For example, the analysis unit can analyze the data using machine learning algorithms. For example, it can predict project progress using regression analysis. The analysis unit can also predict future risks based on past data. For example, it can calculate the probability of risk factors occurring using past project data. Furthermore, the analysis unit can recognize data patterns and identify critical paths. For example, it can identify the project's critical path using network analysis. This allows for the evaluation of project progress and risk factors by analyzing the collected data. Progress includes, but is not limited to, task completion rates and schedule adherence. Risk factors include, but are not limited to, technical risks, schedule risks, and cost risks. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0036] The proposal unit can propose resource allocation and task automation based on the analysis results. For example, the proposal unit can propose allocating additional resources to delayed tasks. For example, the proposal unit can propose allocating additional human resources to delayed tasks. The proposal unit can also propose measures to mitigate risks for high-risk tasks. For example, the proposal unit can propose risk avoidance measures for high-risk tasks. Furthermore, the proposal unit can propose the optimal allocation of resources. For example, the proposal unit can propose the reallocation of resources. This improves project efficiency by proposing the optimal resource allocation and task automation based on the analysis results. Resource allocation includes, but is not limited to, the allocation of human resources, physical resources, and time. Task automation includes, but is not limited to, the scheduling of tasks and the allocation of resources. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input the analysis results into a generating AI and have the generating AI execute proposals for resource allocation and task automation.

[0037] The dashboard section can visualize project progress, risk factors, and resource allocation. For example, the dashboard section can display project progress, risk factors, and resource allocation using graphs and charts. For instance, the dashboard section can visually display project progress using a Gantt chart. It can also evaluate and visually display risk factors using a risk matrix. Furthermore, the dashboard section can display resource allocation using pie charts or bar graphs. This allows project managers to grasp the situation at a glance by visualizing project progress, risk factors, and resource allocation. Visualization includes, but is not limited to, line graphs and heatmaps. Some or all of the above processing in the dashboard section may be performed using, for example, AI, or not. For example, the dashboard section can input project progress, risk factors, and resource allocation data into a generating AI, and have the generating AI generate graphs and charts for visualization.

[0038] The proposal department can allocate additional resources to delayed tasks. For example, the proposal department may propose allocating additional human resources to delayed tasks. For example, the proposal department may allocate additional engineers to delayed tasks. The proposal department may also propose allocating additional physical resources to delayed tasks. For example, the proposal department may provide additional equipment or facilities to delayed tasks. Furthermore, the proposal department may propose allocating additional time to delayed tasks. For example, the proposal department may secure additional working time for delayed tasks. This ensures that the project progresses smoothly by allocating additional resources to delayed tasks. Delayed tasks include, but are not limited to, schedule delays or resource shortages. Additional resources include, but are not limited to, human resources, physical resources, and time. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input delayed task data into a generating AI and have the generating AI perform the allocation of additional resources.

[0039] The proposal department can propose measures to mitigate risks for high-risk tasks. For example, the proposal department can propose risk avoidance measures for high-risk tasks. For example, the proposal department can propose alternatives for high-risk tasks. The proposal department can also propose risk mitigation measures for high-risk tasks. For example, the proposal department can propose additional verification steps for high-risk tasks. Furthermore, the proposal department can propose risk transfer measures for high-risk tasks. For example, the proposal department can propose utilizing external experts for high-risk tasks. This reduces project risk by proposing measures to mitigate risks for high-risk tasks. High-risk tasks include, but are not limited to, technical risks, schedule risks, and cost risks. Measures to mitigate risks include, but are not limited to, risk avoidance measures, risk mitigation measures, and risk transfer measures. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input data on high-risk tasks into a generating AI and have the generating AI generate proposals for risk mitigation measures.

[0040] The data collection unit can change the type of data it collects according to the progress of the project. For example, in the early stages of a project, the data collection unit can focus on collecting planning data and resource allocation data. For example, the data collection unit can obtain project planning data from a project management tool and resource allocation data from sensors. In the middle stages of a project, the data collection unit can also focus on collecting progress data and risk factor data. For example, the data collection unit can obtain project progress data from a project management tool and risk factor data from log data. Furthermore, in the final stages of a project, the data collection unit can focus on collecting deliverable quality data and final evaluation data. For example, the data collection unit can collect deliverable quality data from sensors and final evaluation data from a project management tool. This allows for efficient collection of necessary data by changing the type of data collected according to the progress of the project. The types of data collected include, but are not limited to, planning data, resource allocation data, progress data, risk factor data, quality data, and evaluation data. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input project progress data into the generation AI and have the generation AI change the type of data to be collected.

[0041] The data collection unit can apply the appropriate data collection method for each phase of the project. For example, in the early stages of a project, the data collection unit can collect data using interviews and questionnaires. For instance, the data collection unit can interview stakeholders during the project planning phase to collect necessary data. In the middle stages of a project, the data collection unit can also automatically collect data using sensors and log data. For example, the data collection unit can monitor resource usage using sensors during project progress and analyze log data to understand the progress. Furthermore, in the final stages of a project, the data collection unit can collect data through review meetings and feedback sessions. For example, the data collection unit can collect feedback from stakeholders after project completion to obtain final evaluation data. This enables appropriate data collection by applying different data collection methods for each phase of the project. Project phases include, but are not limited to, the planning, middle, and final stages. Data collection methods include, but are not limited to, interviews, questionnaires, sensors, log data, review meetings, and feedback sessions. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input project phase data into the generation AI and have the generation AI execute the application of the data collection method.

[0042] The data collection unit can prioritize the collection of highly relevant data, taking into account the geographical distribution of the project. For example, if the project spans multiple regions, the data collection unit can prioritize the collection of data appropriate to the characteristics of each region. For example, the data collection unit can apply different data collection methods to each region, taking into account the characteristics of each region. The data collection unit can also prioritize the collection of data from geographically important locations. For example, the data collection unit can prioritize the collection of data from geographically important locations to understand the progress of the project. Furthermore, the data collection unit can prioritize the collection of data from high-risk areas based on geographical distribution. For example, the data collection unit can prioritize the collection of data from high-risk areas to identify risk factors. This enables efficient data collection by prioritizing the collection of highly relevant data, taking into account the geographical distribution of the project. The geographical distribution of the project includes, but is not limited to, regional characteristics and risk factors. Highly relevant data includes, but is not limited to, regional progress data and risk data. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input geographical distribution data of the project into the generating AI, allowing the AI ​​to prioritize the collection of highly relevant data.

[0043] The data collection unit can optimize its data collection methods by referring to past success stories of the project. For example, the data collection unit can extract and apply effective data collection methods from past success stories. For example, the data collection unit can analyze past project data to identify effective data collection methods and apply them to the current project. The data collection unit can also optimize the type and amount of data to be collected based on past success stories. For example, the data collection unit can adjust the type and amount of data to be collected by referring to past success stories. Furthermore, the data collection unit can adjust the timing and frequency of data collection by referring to past success stories. For example, the data collection unit can optimize the timing and frequency of data collection based on past success stories. This enables effective data collection by optimizing the data collection method by referring to past success stories of the project. Past success stories include, but are not limited to, data and evaluation results from successful projects. Data collection methods include, but are not limited to, the means, timing, and frequency of data collection. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input past success stories into a generating AI and have the AI ​​optimize the data collection method.

[0044] The analysis unit can apply different analysis methods depending on the project's progress during the analysis. For example, in the initial stages of the project, the analysis unit can perform predictive analysis based on planning data. For example, the analysis unit can use the project's planning data to predict future progress. In the middle stages of the project, the analysis unit can also perform performance analysis based on progress data. For example, the analysis unit can use the project's progress data to evaluate the current progress. Furthermore, in the final stages of the project, the analysis unit can perform evaluation analysis based on deliverable quality data. For example, the analysis unit can use the project's deliverable quality data to perform a final evaluation. This allows for appropriate analysis by applying different analysis methods depending on the project's progress. Progress includes, but is not limited to, task completion rates and schedule adherence. Analysis methods include, but are not limited to, predictive analysis, performance analysis, and evaluation analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input project progress data into the generating AI and have the generating AI execute the application of analysis methods.

[0045] The analysis unit can classify project risk factors in detail during analysis and propose different countermeasures for each type of risk. For example, the analysis unit can classify risk factors into technical risks, human risks, environmental risks, etc., and propose countermeasures for each. For example, the analysis unit can propose technical countermeasures for technical risks and human countermeasures for human risks. The analysis unit can also propose prioritized countermeasures based on the probability of risk occurrence and its impact. For example, the analysis unit can propose countermeasures preferentially for risks that have a high probability of occurrence and a high impact. Furthermore, the analysis unit can propose countermeasures for each type of risk based on past successful cases. For example, the analysis unit can propose effective countermeasures for each type of risk based on past successful cases. In this way, by classifying project risk factors in detail and proposing different countermeasures for each type of risk, risk management becomes more efficient. Risk factors include, but are not limited to, technical risks, human risks, and environmental risks. Countermeasures include, but are not limited to, risk avoidance measures, risk mitigation measures, and risk transfer measures. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input risk factor data into a generating AI and have the generating AI execute a proposal for risk countermeasures.

[0046] The analysis unit can improve the accuracy of its analysis by referring to past project data during the analysis process. For example, the analysis unit can improve accuracy by training an analysis model based on past project data. For example, the analysis unit can improve the accuracy of its analysis by training a machine learning model using past project data. The analysis unit can also correct the analysis results by referring to past data to compare the current project progress with the current project progress. For example, the analysis unit can use past project data to evaluate the current progress and correct the analysis results. Furthermore, the analysis unit can use past data to predict the probability of risk factors occurring and reflect this in the analysis results. For example, the analysis unit can use past project data to calculate the probability of risk factors occurring and reflect this in the analysis results. This allows for more accurate analysis by improving the accuracy of the analysis by referring to past project data. Past data includes, but is not limited to, past project progress data and risk data. Improving the accuracy of the analysis includes, but is not limited to, training machine learning models, correcting analysis results, and predicting the probability of risk factors occurring. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past project data into a generating AI and have the generating AI perform improvements to the accuracy of the analysis.

[0047] The analysis unit can optimize its analysis methods by referring to relevant project literature during the analysis. For example, the analysis unit can update its analysis methods by referring to the latest research papers. For example, the analysis unit can adjust the parameters of its analysis methods based on the latest research papers. Furthermore, the analysis unit can also adjust the parameters of its analysis methods based on relevant literature. For example, the analysis unit can optimize the parameters of its analysis methods by referring to relevant literature. In addition, the analysis unit can introduce new analysis methods by referring to relevant literature. For example, the analysis unit can improve the accuracy of the analysis by introducing new analysis methods based on relevant literature. This allows the application of the latest analysis methods by optimizing the analysis methods by referring to relevant project literature. Relevant literature includes, but is not limited to, the latest research papers and technical reports. Optimization of the analysis method includes, but is not limited to, adjusting the parameters of the analysis method or introducing new analysis methods. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the optimization of the analysis method.

[0048] The proposal department can dynamically change the content of its proposals according to the project's progress. For example, in the early stages of a project, the proposal department can propose revisions to the plan and resource allocation. For example, in the planning stage of a project, the proposal department can propose revisions to the plan and optimization of resource allocation. In the middle stages of a project, the proposal department can also propose measures to address delays and mitigate risks. For example, in the course of a project, the proposal department can propose measures to address delays and mitigate risks. Furthermore, in the final stages of a project, the proposal department can propose improvements to the quality of deliverables and final evaluations. For example, after the project is completed, the proposal department can propose improvements to the quality of deliverables and final evaluations. This allows for appropriate proposals by dynamically changing the content of proposals according to the project's progress. Progress includes, but is not limited to, task completion rates and adherence to schedules. Proposal content includes, but is not limited to, revisions to the plan and optimization of resource allocation, measures to address delays, risk mitigation measures, improvements to the quality of deliverables, and final evaluations. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input project progress data into a generating AI and have the generating AI perform dynamic changes to the proposal content.

[0049] The proposal unit can propose different resource allocations based on the project's risk factors at the time of proposal. For example, the proposal unit can propose allocating additional resources to high-risk tasks. For example, the proposal unit can allocate additional human resources to high-risk tasks. The proposal unit can also propose reducing resources to low-risk tasks. For example, the proposal unit can reduce human resources to low-risk tasks. Furthermore, the proposal unit can propose reallocating resources based on risk factors. For example, the proposal unit can improve project efficiency by proposing reallocating resources based on risk factors. This streamlines risk management by proposing different resource allocations based on the project's risk factors. Risk factors include, but are not limited to, technical risks, schedule risks, and cost risks. Resource allocation includes, but are not limited to, human resources, physical resources, and time allocation. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input risk factor data into a generating AI and have the generating AI execute resource allocation proposals.

[0050] The proposal department can propose resource allocation considering the geographical distribution of the project when making a proposal. For example, the proposal department can propose allocating additional resources to geographically important locations. For example, the proposal department can allocate additional human resources to geographically important locations. The proposal department can also propose resource reallocation based on geographical distribution. For example, the proposal department can improve project efficiency by proposing resource reallocation based on geographical distribution. Furthermore, the proposal department can propose the optimal allocation of resources considering geographical characteristics. For example, the proposal department can support project success by proposing the optimal allocation of resources considering geographical characteristics. This enables efficient resource allocation by proposing the optimal resource allocation considering the geographical distribution of the project. Geographical distribution includes, but is not limited to, regional characteristics and risk factors. Resource allocation includes, but is not limited to, the allocation of human resources, material resources, and time. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal unit can input geographical distribution data into a generation AI and have the generation AI generate resource allocation proposals.

[0051] The proposal department can optimize proposal content by referring to past project success stories. For example, the proposal department can extract and apply effective proposal content based on past success stories. For example, the proposal department can analyze past project data to identify effective proposal content and apply it to the current project. The proposal department can also determine the priority of proposal content based on past success stories. For example, the proposal department can adjust the priority of proposal content by referring to past success stories. Furthermore, the proposal department can introduce new proposal content by referring to past success stories. For example, the proposal department can introduce new proposal content based on past success stories to support the success of the project. In this way, effective proposals become possible by optimizing proposal content by referring to past project success stories. Past success stories include, but are not limited to, data and evaluation results of successful projects. Proposal content includes, but is not limited to, the type, timing, and priority of proposals. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input data from past successful cases into a generation AI and have the AI ​​optimize the proposal content.

[0052] The dashboard section can dynamically change its displayed content according to the project's progress when the dashboard is displayed. For example, in the initial stages of a project, the dashboard section can focus on displaying planning data and resource allocation data. For example, in the planning stage of a project, the dashboard section can display planning data and resource allocation data. Furthermore, in the middle stages of a project, the dashboard section can focus on displaying progress data and risk factor data. For example, in the progress of a project, the dashboard section can display progress data and risk factor data. In addition, in the final stages of a project, the dashboard section can focus on displaying deliverable quality data and final evaluation data. For example, after the completion of a project, the dashboard section can display deliverable quality data and final evaluation data. This allows for the provision of appropriate information by dynamically changing the displayed content according to the project's progress. Progress includes, but is not limited to, task completion rates and schedule adherence. Displayed content includes, but is not limited to, planning data, resource allocation data, progress data, risk factor data, quality data, and evaluation data. Some or all of the above-described processes in the dashboard section may be performed using AI, for example, or without AI. For example, the dashboard section can input project progress data into a generating AI and have the generating AI perform dynamic changes to the displayed content.

[0053] The dashboard section can display project risk factors in detail when the dashboard is displayed, and can propose different countermeasures for each type of risk. For example, the dashboard section can classify risk factors into technical risks, human risks, environmental risks, etc., and display countermeasures for each. For example, the dashboard section can display technical countermeasures for technical risks and human countermeasures for human risks. The dashboard section can also display prioritized countermeasures based on the probability of risk occurrence and impact. For example, the dashboard section can prioritize countermeasures for risks with a high probability of occurrence and a high impact. Furthermore, the dashboard section can display countermeasures based on past success stories for each type of risk. For example, the dashboard section can display effective countermeasures for each type of risk based on past success stories. This streamlines risk management by displaying project risk factors in detail and proposing different countermeasures for each type of risk. Risk factors include, but are not limited to, technical risks, human risks, and environmental risks. Countermeasures include, but are not limited to, risk avoidance measures, risk mitigation measures, and risk transfer measures. Some or all of the processing described above in the dashboard section may be performed using AI, for example, or without AI. For example, the dashboard section can input risk factor data into a generating AI and have the generating AI display risk countermeasures.

[0054] The dashboard section can prioritize the display of highly relevant information when the dashboard is displayed, taking into account the geographical distribution of the project. For example, if the project spans multiple regions, the dashboard section can prioritize the display of information tailored to the characteristics of each region. For example, the dashboard section can display different information for each region, taking into account the characteristics of each region. The dashboard section can also prioritize the display of information for geographically important locations. For example, the dashboard section can prioritize the display of information for geographically important locations, allowing users to understand the progress of the project. Furthermore, the dashboard section can prioritize the display of information for high-risk regions based on geographical distribution. For example, the dashboard section can prioritize the display of information for high-risk regions, allowing users to identify risk factors. This enables efficient information provision by prioritizing the display of highly relevant information, taking into account the geographical distribution of the project. Geographical distribution includes, but is not limited to, regional characteristics and risk factors. Highly relevant information includes, but is not limited to, regional progress data and risk data. Some or all of the above processing in the dashboard section may be performed using, for example, AI, or not using AI. For example, the dashboard can input geographical distribution data into a generating AI and have the AI ​​prioritize the display of highly relevant information.

[0055] The dashboard section can optimize its display content by referencing past project data when displaying the dashboard. For example, the dashboard section can optimize the display content based on past project data. For example, the dashboard section can use past project data to evaluate the current project progress and optimize the display content. The dashboard section can also refer to past data to compare the current project progress and correct the display content. For example, the dashboard section can use past project data to evaluate the current progress and correct the display content. Furthermore, the dashboard section can use past data to predict the probability of risk factors occurring and reflect this in the display content. For example, the dashboard section can use past project data to calculate the probability of risk factors occurring and reflect this in the display content. This enables the provision of appropriate information by optimizing the display content by referencing past project data. Past data includes, but is not limited to, past project progress data and risk data. Optimization of display content includes, but is not limited to, correction of display content and prediction of the probability of risk factors occurring. Some or all of the above processing in the dashboard section may be performed using, for example, AI, or without using AI. For example, the dashboard section can input past project data into a generating AI and have the AI ​​optimize the displayed content.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The AI ​​project navigator can also be equipped with a feedback collection unit. This unit collects feedback from stakeholders after each phase of the project and provides it to the analysis unit. For example, the feedback collection unit can collect feedback on the feasibility of the plan in the early stages of the project. It can also collect feedback on progress and risk management in the middle stages of the project. Furthermore, in the final stages of the project, it can collect feedback on the quality of deliverables and the overall project evaluation. This allows the feedback collection unit to implement project management that reflects the opinions of stakeholders, thereby improving the project's success rate.

[0058] The data collection unit can adjust the accuracy of the data it collects according to the progress of the project. For example, in the early stages of the project, it can collect coarse data to grasp the overall direction. In the middle stages of the project, it can collect detailed data to accurately understand the progress and risk factors. Furthermore, in the final stages of the project, it can collect highly accurate data to confirm the final evaluation and the quality of the deliverables. In this way, the data collection unit can achieve efficient data collection by adjusting the accuracy of the data according to the progress of the project.

[0059] The proposal department can adjust the timing of proposals according to the project's progress. For example, in the early stages of a project, they can propose revisions to the plan and resource allocation early on. In the middle stages of the project, they can quickly propose countermeasures for delays and risk factors. Furthermore, in the final stages of the project, they can propose improvements to the quality of deliverables and final evaluations in a timely manner. In this way, the proposal department can make appropriate proposals by adjusting the timing of proposals according to the project's progress.

[0060] The data collection unit can dynamically change the types of data it collects according to the project's progress. For example, in the initial stages of a project, it can focus on collecting planning data and resource allocation data. In the middle stages of the project, it can focus on collecting progress data and risk factor data. Furthermore, in the final stages of the project, it can focus on collecting deliverable quality data and final evaluation data. This allows the data collection unit to efficiently collect the necessary data by dynamically changing the types of data it collects according to the project's progress.

[0061] The analysis unit can adjust the frequency of analysis according to the project's progress. For example, in the initial stages of a project, the analysis frequency can be set low to grasp the overall direction. In the middle stages of the project, the analysis frequency can be set high to accurately grasp the progress and risk factors. Furthermore, in the final stages of the project, analysis can be performed at a very high frequency to confirm the final evaluation and the quality of deliverables. In this way, the analysis unit can achieve efficient analysis by adjusting the analysis frequency according to the project's progress.

[0062] The dashboard section can dynamically change its layout according to the project's progress. For example, in the initial stages of a project, it can provide a layout that primarily displays planning data and resource allocation data. In the middle stages of the project, it can provide a layout that primarily displays progress data and risk factor data. Furthermore, in the final stages of the project, it can provide a layout that primarily displays deliverable quality data and final evaluation data. In this way, the dashboard section can dynamically change its layout according to the project's progress, enabling the provision of appropriate information.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects data on project progress, resource allocation, and risk factors. The data collection unit collects data from project management tools, sensors, log data, etc. For example, it obtains task progress from project management tools, monitors resource usage using sensors, and identifies risk factors by analyzing log data. Step 2: The analysis unit analyzes the data collected by the data collection unit to evaluate the project's progress and risk factors. The analysis unit uses machine learning algorithms to analyze the data, predict future risks based on past data, recognize data patterns, and identify critical paths. Step 3: The proposal unit proposes resource allocation and task automation based on the analysis results obtained by the analysis unit. The proposal unit proposes allocating additional resources to delayed tasks, proposes measures to mitigate risks for high-risk tasks, and proposes the optimal allocation of resources. Step 4: The dashboard section visualizes the proposals made by the proposal section. The dashboard section displays project progress, risk factors, and resource allocation in graphs and charts, updating data in real time so that project managers can grasp the situation at a glance. Furthermore, it provides a customizable interface so that project managers can quickly obtain the information they need.

[0065] (Example of form 2) An AI project navigator according to an embodiment of the present invention is a system that automates project progress management and risk assessment using generative AI. The AI ​​project navigator collects data such as project progress, resource allocation, and risk factors, and performs pattern recognition and predictive analysis. This frees project managers from task management, allowing them to focus on more strategic and high-value tasks such as project progress and interdepartmental coordination. For example, the AI ​​project navigator monitors project progress in real time and immediately notifies if delays occur. It also proposes measures to mitigate risks when risk factors are detected. This ensures smooth project progress and reduces wasted costs and time. Furthermore, the generative AI provides a dashboard for visualizing project progress and risk factors. This dashboard is designed so that project managers can grasp the project status at a glance. For example, progress, risk factors, and resource allocation are displayed in graphs and charts. In this way, the AI ​​project navigator becomes a powerful tool for project managers to exercise leadership and lead projects to success. As a result, the AI ​​project navigator automates project progress management and risk assessment, allowing project managers to focus on strategic tasks.

[0066] The AI ​​project navigator according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a dashboard unit. The data collection unit collects data such as project progress, resource allocation, and risk factors. The data collection unit collects data from sources such as project management tools, sensors, and log data. For example, the data collection unit obtains task progress from project management tools. The data collection unit can also monitor resource usage using sensors. Furthermore, the data collection unit can analyze log data to identify risk factors. The analysis unit analyzes the data collected by the data collection unit and evaluates project progress and risk factors. For example, the analysis unit analyzes the data using machine learning algorithms. Furthermore, the analysis unit can predict future risks based on past data. Furthermore, the analysis unit can recognize data patterns to identify critical paths. The proposal unit proposes optimal resource allocation and task automation based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes allocating additional resources to delayed tasks. Furthermore, the proposal unit can propose measures to mitigate risks for high-risk tasks. Furthermore, the proposal unit can propose the optimal allocation of resources. The dashboard section visualizes the content proposed by the proposal section. For example, the dashboard section displays project progress, risk factors, and resource allocation status in graphs and charts. The dashboard section can also update data in real time, allowing project managers to grasp the situation at a glance. Furthermore, the dashboard section can provide a customizable interface, enabling project managers to quickly obtain the information they need. As a result, the AI ​​project navigator according to this embodiment automates project progress management and risk assessment, allowing project managers to focus on strategic tasks.

[0067] The data collection unit collects data such as project progress, resource allocation, and risk factors. Specifically, it can automatically retrieve data using APIs to obtain task progress from project management tools. For example, it collects detailed information such as task start date, end date, assigned person, and progress rate. It can also monitor resource usage using sensors. For example, it can monitor server CPU usage, memory usage, and network traffic in real time to detect resource overload or shortage. Furthermore, the data collection unit can identify risk factors by analyzing log data. For example, it can analyze error logs and warning logs to detect frequent problems and potential risks early. In this way, the data collection unit can comprehensively understand project progress, resource usage, and risk factors, providing foundational data to maintain project health. In addition, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0068] The analysis unit analyzes data collected by the data collection unit to evaluate project progress and risk factors. Specifically, it uses machine learning algorithms to analyze data and evaluate project progress in real time. For example, it analyzes task progress data to identify causes of delays and bottlenecks. It can also predict future risks based on past data. For example, by training the system with past project data, it can predict the probability of risk occurrence under specific conditions and take countermeasures early. Furthermore, the analysis unit can recognize data patterns and identify the critical path. The critical path is a chain of important tasks that determines the shortest project completion time, and identifying it supports the efficient progress of the project. Based on these analysis results, the analysis unit visualizes project progress and risk factors and provides them to the project manager. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0069] The proposal department proposes optimal resource allocation and task automation based on the analysis results obtained by the analysis department. Specifically, it proposes allocating additional resources to delayed tasks. For example, if a particular task is delayed, it will resolve the delay by allocating additional personnel or time to that task. It can also propose measures to mitigate risks for high-risk tasks. For example, it will prevent risks from occurring by implementing risk avoidance measures in advance for high-risk tasks. Furthermore, the proposal department can propose the optimal allocation of resources. For example, it will maximize project efficiency by analyzing the resource usage of the entire project and adjusting resource surpluses and shortages. The proposal department provides these proposals to project managers to support project progress. In addition, the proposal department uses AI to automatically generate proposals, enabling project managers to make quick decisions. For example, it develops algorithms that propose optimal resource allocation and task automation based on project progress and risk factors, and automatically generates proposals. This allows the proposal department to help project managers focus on strategic tasks and improve the project success rate.

[0070] The dashboard visualizes the proposals submitted by the proposal team. Specifically, it displays project progress, risk factors, and resource allocation using graphs and charts. For example, it uses a Gantt chart to visually display task progress, allowing project managers to grasp the situation at a glance. It also displays risk factors using a heatmap, enabling intuitive understanding of high-risk areas. Furthermore, it displays resource allocation using pie charts and bar charts, allowing for visual confirmation of resource usage. The dashboard updates data in real time, ensuring project managers are always aware of the latest situation. For example, it reflects data collected from project management tools and sensors in real time and displays it on the dashboard. The dashboard also provides a customizable interface, allowing project managers to quickly obtain the information they need. For example, project managers can select data and metrics of interest and display them on the dashboard. In this way, the dashboard provides a powerful tool for efficiently managing project progress and assessing risks, supporting project managers in making strategic decisions.

[0071] The data collection unit can collect data from project management tools, sensors, and log data. For example, the data collection unit can obtain task progress from project management tools. For example, the data collection unit can obtain task progress in real time using the API of a project management tool. The data collection unit can also monitor resource usage using sensors. For example, the data collection unit can monitor the temperature of a server room using a temperature sensor and issue an alert if an anomaly occurs. Furthermore, the data collection unit can analyze log data to identify risk factors. For example, the data collection unit can analyze system logs to check the frequency of error messages and identify risk factors. This allows for an accurate understanding of project progress and risk factors by collecting data from diverse data sources. Project management tools include, but are not limited to, JIRA and Trello. Sensors include, but are not limited to, temperature sensors and location sensors. Log data includes, but are not limited to, system logs and user logs. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data acquired from a project management tool into a generation AI, and have the generation AI perform data analysis.

[0072] The analysis unit can analyze the collected data and evaluate the project's progress and risk factors. For example, the analysis unit can analyze the data using machine learning algorithms. For example, it can predict project progress using regression analysis. The analysis unit can also predict future risks based on past data. For example, it can calculate the probability of risk factors occurring using past project data. Furthermore, the analysis unit can recognize data patterns and identify critical paths. For example, it can identify the project's critical path using network analysis. This allows for the evaluation of project progress and risk factors by analyzing the collected data. Progress includes, but is not limited to, task completion rates and schedule adherence. Risk factors include, but are not limited to, technical risks, schedule risks, and cost risks. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0073] The proposal unit can propose resource allocation and task automation based on the analysis results. For example, the proposal unit can propose allocating additional resources to delayed tasks. For example, the proposal unit can propose allocating additional human resources to delayed tasks. The proposal unit can also propose measures to mitigate risks for high-risk tasks. For example, the proposal unit can propose risk avoidance measures for high-risk tasks. Furthermore, the proposal unit can propose the optimal allocation of resources. For example, the proposal unit can propose the reallocation of resources. This improves project efficiency by proposing the optimal resource allocation and task automation based on the analysis results. Resource allocation includes, but is not limited to, the allocation of human resources, physical resources, and time. Task automation includes, but is not limited to, the scheduling of tasks and the allocation of resources. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input the analysis results into a generating AI and have the generating AI execute proposals for resource allocation and task automation.

[0074] The dashboard section can visualize project progress, risk factors, and resource allocation. For example, the dashboard section can display project progress, risk factors, and resource allocation using graphs and charts. For instance, the dashboard section can visually display project progress using a Gantt chart. It can also evaluate and visually display risk factors using a risk matrix. Furthermore, the dashboard section can display resource allocation using pie charts or bar graphs. This allows project managers to grasp the situation at a glance by visualizing project progress, risk factors, and resource allocation. Visualization includes, but is not limited to, line graphs and heatmaps. Some or all of the above processing in the dashboard section may be performed using, for example, AI, or not. For example, the dashboard section can input project progress, risk factors, and resource allocation data into a generating AI, and have the generating AI generate graphs and charts for visualization.

[0075] The proposal department can allocate additional resources to delayed tasks. For example, the proposal department may propose allocating additional human resources to delayed tasks. For example, the proposal department may allocate additional engineers to delayed tasks. The proposal department may also propose allocating additional physical resources to delayed tasks. For example, the proposal department may provide additional equipment or facilities to delayed tasks. Furthermore, the proposal department may propose allocating additional time to delayed tasks. For example, the proposal department may secure additional working time for delayed tasks. This ensures that the project progresses smoothly by allocating additional resources to delayed tasks. Delayed tasks include, but are not limited to, schedule delays or resource shortages. Additional resources include, but are not limited to, human resources, physical resources, and time. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input delayed task data into a generating AI and have the generating AI perform the allocation of additional resources.

[0076] The proposal department can propose measures to mitigate risks for high-risk tasks. For example, the proposal department can propose risk avoidance measures for high-risk tasks. For example, the proposal department can propose alternatives for high-risk tasks. The proposal department can also propose risk mitigation measures for high-risk tasks. For example, the proposal department can propose additional verification steps for high-risk tasks. Furthermore, the proposal department can propose risk transfer measures for high-risk tasks. For example, the proposal department can propose utilizing external experts for high-risk tasks. This reduces project risk by proposing measures to mitigate risks for high-risk tasks. High-risk tasks include, but are not limited to, technical risks, schedule risks, and cost risks. Measures to mitigate risks include, but are not limited to, risk avoidance measures, risk mitigation measures, and risk transfer measures. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input data on high-risk tasks into a generating AI and have the generating AI generate proposals for risk mitigation measures.

[0077] The data collection unit can analyze the user's emotions and adjust the timing of data collection based on the analyzed emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, the data collection unit can estimate the user's stress level using an emotion analysis algorithm and adjust the frequency of data collection. The data collection unit can also increase the frequency of data collection and collect more detailed data if the user is relaxed. For example, the data collection unit can estimate the user's level of relaxation using an emotion analysis algorithm and adjust the frequency of data collection. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting only important data and process it quickly. For example, the data collection unit can estimate the user's hurried state using an emotion analysis algorithm and prioritize collecting important data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. User emotions include, but are not limited to, stress, relaxation, and hurried states. Data collection timing includes, but is not limited to, collection frequency and collection trigger conditions. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. The 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 collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0078] The data collection unit can change the type of data it collects according to the progress of the project. For example, in the early stages of a project, the data collection unit can focus on collecting planning data and resource allocation data. For example, the data collection unit can obtain project planning data from a project management tool and resource allocation data from sensors. In the middle stages of a project, the data collection unit can also focus on collecting progress data and risk factor data. For example, the data collection unit can obtain project progress data from a project management tool and risk factor data from log data. Furthermore, in the final stages of a project, the data collection unit can focus on collecting deliverable quality data and final evaluation data. For example, the data collection unit can collect deliverable quality data from sensors and final evaluation data from a project management tool. This allows for efficient collection of necessary data by changing the type of data collected according to the progress of the project. The types of data collected include, but are not limited to, planning data, resource allocation data, progress data, risk factor data, quality data, and evaluation data. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input project progress data into the generation AI and have the generation AI change the type of data to be collected.

[0079] The data collection unit can apply the appropriate data collection method for each phase of the project. For example, in the early stages of a project, the data collection unit can collect data using interviews and questionnaires. For instance, the data collection unit can interview stakeholders during the project planning phase to collect necessary data. In the middle stages of a project, the data collection unit can also automatically collect data using sensors and log data. For example, the data collection unit can monitor resource usage using sensors during project progress and analyze log data to understand the progress. Furthermore, in the final stages of a project, the data collection unit can collect data through review meetings and feedback sessions. For example, the data collection unit can collect feedback from stakeholders after project completion to obtain final evaluation data. This enables appropriate data collection by applying different data collection methods for each phase of the project. Project phases include, but are not limited to, the planning, middle, and final stages. Data collection methods include, but are not limited to, interviews, questionnaires, sensors, log data, review meetings, and feedback sessions. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input project phase data into the generation AI and have the generation AI execute the application of the data collection method.

[0080] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only high-importance data. For example, the data collection unit can estimate the user's stress level using an emotion analysis algorithm and prioritize collecting high-importance data. Also, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, the data collection unit can estimate the user's level of relaxation using an emotion analysis algorithm and prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. For example, the data collection unit can estimate the user's state of urgency using an emotion analysis algorithm and prioritize collecting data that can be collected quickly. In this way, by determining the priority of data to collect according to the user's emotions, important data can be collected preferentially. User emotions include, but are not limited to, stress, relaxation, and urgency. Data prioritization includes, but is not limited to, criteria for evaluating priority and methods for changing priority. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The 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 collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0081] The data collection unit can prioritize the collection of highly relevant data, taking into account the geographical distribution of the project. For example, if the project spans multiple regions, the data collection unit can prioritize the collection of data appropriate to the characteristics of each region. For example, the data collection unit can apply different data collection methods to each region, taking into account the characteristics of each region. The data collection unit can also prioritize the collection of data from geographically important locations. For example, the data collection unit can prioritize the collection of data from geographically important locations to understand the progress of the project. Furthermore, the data collection unit can prioritize the collection of data from high-risk areas based on geographical distribution. For example, the data collection unit can prioritize the collection of data from high-risk areas to identify risk factors. This enables efficient data collection by prioritizing the collection of highly relevant data, taking into account the geographical distribution of the project. The geographical distribution of the project includes, but is not limited to, regional characteristics and risk factors. Highly relevant data includes, but is not limited to, regional progress data and risk data. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input geographical distribution data of the project into the generating AI, allowing the AI ​​to prioritize the collection of highly relevant data.

[0082] The data collection unit can optimize its data collection methods by referring to past success stories of the project. For example, the data collection unit can extract and apply effective data collection methods from past success stories. For example, the data collection unit can analyze past project data to identify effective data collection methods and apply them to the current project. The data collection unit can also optimize the type and amount of data to be collected based on past success stories. For example, the data collection unit can adjust the type and amount of data to be collected by referring to past success stories. Furthermore, the data collection unit can adjust the timing and frequency of data collection by referring to past success stories. For example, the data collection unit can optimize the timing and frequency of data collection based on past success stories. This enables effective data collection by optimizing the data collection method by referring to past success stories of the project. Past success stories include, but are not limited to, data and evaluation results from successful projects. Data collection methods include, but are not limited to, the means, timing, and frequency of data collection. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input past success stories into a generating AI and have the AI ​​optimize the data collection method.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a rapid analysis using a simplified algorithm. For example, the analysis unit can estimate the user's stress level using an emotion analysis algorithm and apply the simplified algorithm. The analysis unit can also use a complex algorithm for detailed analysis if the user is relaxed. For example, the analysis unit can estimate the user's level of relaxation using an emotion analysis algorithm and apply an algorithm for detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can use an algorithm that analyzes only the important data. For example, the analysis unit can estimate the user's state of being in a hurry using an emotion analysis algorithm and apply an algorithm that analyzes only the important data. This allows for appropriate analysis by adjusting the analysis algorithm according to the user's emotions. User emotions include, but are not limited to, stress, relaxation, and a state of being in a hurry. Analysis algorithms include, but are not limited to, simplified algorithms, complex algorithms, and algorithms that analyze only the important data. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The 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 using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the analysis algorithm.

[0084] The analysis unit can apply different analysis methods depending on the project's progress during the analysis. For example, in the initial stages of the project, the analysis unit can perform predictive analysis based on planning data. For example, the analysis unit can use the project's planning data to predict future progress. In the middle stages of the project, the analysis unit can also perform performance analysis based on progress data. For example, the analysis unit can use the project's progress data to evaluate the current progress. Furthermore, in the final stages of the project, the analysis unit can perform evaluation analysis based on deliverable quality data. For example, the analysis unit can use the project's deliverable quality data to perform a final evaluation. This allows for appropriate analysis by applying different analysis methods depending on the project's progress. Progress includes, but is not limited to, task completion rates and schedule adherence. Analysis methods include, but are not limited to, predictive analysis, performance analysis, and evaluation analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input project progress data into the generating AI and have the generating AI execute the application of analysis methods.

[0085] The analysis unit can classify project risk factors in detail during analysis and propose different countermeasures for each type of risk. For example, the analysis unit can classify risk factors into technical risks, human risks, environmental risks, etc., and propose countermeasures for each. For example, the analysis unit can propose technical countermeasures for technical risks and human countermeasures for human risks. The analysis unit can also propose prioritized countermeasures based on the probability of risk occurrence and its impact. For example, the analysis unit can propose countermeasures preferentially for risks that have a high probability of occurrence and a high impact. Furthermore, the analysis unit can propose countermeasures for each type of risk based on past successful cases. For example, the analysis unit can propose effective countermeasures for each type of risk based on past successful cases. In this way, by classifying project risk factors in detail and proposing different countermeasures for each type of risk, risk management becomes more efficient. Risk factors include, but are not limited to, technical risks, human risks, and environmental risks. Countermeasures include, but are not limited to, risk avoidance measures, risk mitigation measures, and risk transfer measures. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input risk factor data into a generating AI and have the generating AI execute a proposal for risk countermeasures.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, the analysis unit can estimate the user's stress level using an emotion analysis algorithm and apply a simple display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, the analysis unit can estimate the user's level of relaxation using an emotion analysis algorithm and apply a detailed display method. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. For example, the analysis unit can estimate the user's state of being in a hurry using an emotion analysis algorithm and apply a concise display method. This allows for the provision of appropriate information by adjusting the display method of the analysis results according to the user's emotions. User emotions include, but are not limited to, stress, relaxation, and being in a hurry. Display methods include, but are not limited to, simple, detailed, and concise display methods. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the display method.

[0087] The analysis unit can improve the accuracy of its analysis by referring to past project data during the analysis process. For example, the analysis unit can improve accuracy by training an analysis model based on past project data. For example, the analysis unit can improve the accuracy of its analysis by training a machine learning model using past project data. The analysis unit can also correct the analysis results by referring to past data to compare the current project progress with the current project progress. For example, the analysis unit can use past project data to evaluate the current progress and correct the analysis results. Furthermore, the analysis unit can use past data to predict the probability of risk factors occurring and reflect this in the analysis results. For example, the analysis unit can use past project data to calculate the probability of risk factors occurring and reflect this in the analysis results. This allows for more accurate analysis by improving the accuracy of the analysis by referring to past project data. Past data includes, but is not limited to, past project progress data and risk data. Improving the accuracy of the analysis includes, but is not limited to, training machine learning models, correcting analysis results, and predicting the probability of risk factors occurring. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past project data into a generating AI and have the generating AI perform improvements to the accuracy of the analysis.

[0088] The analysis unit can optimize its analysis methods by referring to relevant project literature during the analysis. For example, the analysis unit can update its analysis methods by referring to the latest research papers. For example, the analysis unit can adjust the parameters of its analysis methods based on the latest research papers. Furthermore, the analysis unit can also adjust the parameters of its analysis methods based on relevant literature. For example, the analysis unit can optimize the parameters of its analysis methods by referring to relevant literature. In addition, the analysis unit can introduce new analysis methods by referring to relevant literature. For example, the analysis unit can improve the accuracy of the analysis by introducing new analysis methods based on relevant literature. This allows the application of the latest analysis methods by optimizing the analysis methods by referring to relevant project literature. Relevant literature includes, but is not limited to, the latest research papers and technical reports. Optimization of the analysis method includes, but is not limited to, adjusting the parameters of the analysis method or introducing new analysis methods. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the optimization of the analysis method.

[0089] The suggestion function can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is stressed, the suggestion function can provide simple and easy-to-understand suggestions. For instance, it can estimate the user's stress level using an emotion analysis algorithm and apply simple suggestions. Furthermore, if the user is relaxed, the suggestion function can provide suggestions that include detailed information. For example, it can estimate the user's level of relaxation using an emotion analysis algorithm and apply detailed suggestions. Additionally, if the user is in a hurry, the suggestion function can provide concise and quick suggestions. For example, it can estimate the user's state of urgency using an emotion analysis algorithm and apply concise suggestions. This allows for appropriate suggestions by adjusting the presentation of suggestions according to the user's emotions. User emotions include, but are not limited to, stress, relaxation, and urgency. The presentation of suggestions includes, but are not limited to, simple suggestions, detailed suggestions, and concise suggestions. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit can input user emotion data into the generative AI and have the generative AI adjust the way the proposal is expressed.

[0090] The proposal department can dynamically change the content of its proposals according to the project's progress. For example, in the early stages of a project, the proposal department can propose revisions to the plan and resource allocation. For example, in the planning stage of a project, the proposal department can propose revisions to the plan and optimization of resource allocation. In the middle stages of a project, the proposal department can also propose measures to address delays and mitigate risks. For example, in the course of a project, the proposal department can propose measures to address delays and mitigate risks. Furthermore, in the final stages of a project, the proposal department can propose improvements to the quality of deliverables and final evaluations. For example, after the project is completed, the proposal department can propose improvements to the quality of deliverables and final evaluations. This allows for appropriate proposals by dynamically changing the content of proposals according to the project's progress. Progress includes, but is not limited to, task completion rates and adherence to schedules. Proposal content includes, but is not limited to, revisions to the plan and optimization of resource allocation, measures to address delays, risk mitigation measures, improvements to the quality of deliverables, and final evaluations. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input project progress data into a generating AI and have the generating AI perform dynamic changes to the proposal content.

[0091] The proposal unit can propose different resource allocations based on the project's risk factors at the time of proposal. For example, the proposal unit can propose allocating additional resources to high-risk tasks. For example, the proposal unit can allocate additional human resources to high-risk tasks. The proposal unit can also propose reducing resources to low-risk tasks. For example, the proposal unit can reduce human resources to low-risk tasks. Furthermore, the proposal unit can propose reallocating resources based on risk factors. For example, the proposal unit can improve project efficiency by proposing reallocating resources based on risk factors. This streamlines risk management by proposing different resource allocations based on the project's risk factors. Risk factors include, but are not limited to, technical risks, schedule risks, and cost risks. Resource allocation includes, but are not limited to, human resources, physical resources, and time allocation. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input risk factor data into a generating AI and have the generating AI execute resource allocation proposals.

[0092] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion function will prioritize high-importance suggestions. For example, the suggestion function can estimate the user's stress level using an emotion analysis algorithm and prioritize high-importance suggestions. The suggestion function can also prioritize detailed suggestions if the user is relaxed. For example, the suggestion function can estimate the user's level of relaxation using an emotion analysis algorithm and prioritize detailed suggestions. Furthermore, if the user is in a hurry, the suggestion function can prioritize suggestions that can be acted upon quickly. For example, the suggestion function can estimate the user's hurried state using an emotion analysis algorithm and prioritize suggestions that can be acted upon quickly. In this way, by prioritizing suggestions according to the user's emotions, important suggestions can be prioritized. User emotions include, but are not limited to, stress, relaxation, and hurried states. Suggestion prioritization includes, but is not limited to, criteria for evaluating priority and methods for changing priority. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using AI or not using AI. For example, the proposal unit can input user emotion data into a generative AI and have the generative AI determine the priority of proposals.

[0093] The proposal department can propose resource allocation considering the geographical distribution of the project when making a proposal. For example, the proposal department can propose allocating additional resources to geographically important locations. For example, the proposal department can allocate additional human resources to geographically important locations. The proposal department can also propose resource reallocation based on geographical distribution. For example, the proposal department can improve project efficiency by proposing resource reallocation based on geographical distribution. Furthermore, the proposal department can propose the optimal allocation of resources considering geographical characteristics. For example, the proposal department can support project success by proposing the optimal allocation of resources considering geographical characteristics. This enables efficient resource allocation by proposing the optimal resource allocation considering the geographical distribution of the project. Geographical distribution includes, but is not limited to, regional characteristics and risk factors. Resource allocation includes, but is not limited to, the allocation of human resources, material resources, and time. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal unit can input geographical distribution data into a generation AI and have the generation AI generate resource allocation proposals.

[0094] The proposal department can optimize proposal content by referring to past project success stories. For example, the proposal department can extract and apply effective proposal content based on past success stories. For example, the proposal department can analyze past project data to identify effective proposal content and apply it to the current project. The proposal department can also determine the priority of proposal content based on past success stories. For example, the proposal department can adjust the priority of proposal content by referring to past success stories. Furthermore, the proposal department can introduce new proposal content by referring to past success stories. For example, the proposal department can introduce new proposal content based on past success stories to support the success of the project. In this way, effective proposals become possible by optimizing proposal content by referring to past project success stories. Past success stories include, but are not limited to, data and evaluation results of successful projects. Proposal content includes, but is not limited to, the type, timing, and priority of proposals. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input data from past successful cases into a generation AI and have the AI ​​optimize the proposal content.

[0095] The dashboard can estimate the user's emotions and adjust the dashboard display method based on the estimated emotions. For example, if the user is stressed, the dashboard can provide a simple and highly visible display method. For example, the dashboard can estimate the user's stress level using an emotion analysis algorithm and apply a simple display method. The dashboard can also provide a display method that includes detailed information if the user is relaxed. For example, the dashboard can estimate the user's level of relaxation using an emotion analysis algorithm and apply a detailed display method. Furthermore, if the user is in a hurry, the dashboard can provide a concise display method. For example, the dashboard can estimate the user's state of being in a hurry using an emotion analysis algorithm and apply a concise display method. This allows for the provision of appropriate information by adjusting the dashboard display method according to the user's emotions. User emotions include, but are not limited to, stress, relaxation, and being in a hurry. Display methods include, but are not limited to, simple, detailed, and concise displays. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the dashboard section may be performed using AI, or not using AI. For example, the dashboard section can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0096] The dashboard section can dynamically change its displayed content according to the project's progress when the dashboard is displayed. For example, in the initial stages of a project, the dashboard section can focus on displaying planning data and resource allocation data. For example, in the planning stage of a project, the dashboard section can display planning data and resource allocation data. Furthermore, in the middle stages of a project, the dashboard section can focus on displaying progress data and risk factor data. For example, in the progress of a project, the dashboard section can display progress data and risk factor data. In addition, in the final stages of a project, the dashboard section can focus on displaying deliverable quality data and final evaluation data. For example, after the completion of a project, the dashboard section can display deliverable quality data and final evaluation data. This allows for the provision of appropriate information by dynamically changing the displayed content according to the project's progress. Progress includes, but is not limited to, task completion rates and schedule adherence. Displayed content includes, but is not limited to, planning data, resource allocation data, progress data, risk factor data, quality data, and evaluation data. Some or all of the above-described processes in the dashboard section may be performed using AI, for example, or without AI. For example, the dashboard section can input project progress data into a generating AI and have the generating AI perform dynamic changes to the displayed content.

[0097] The dashboard section can display project risk factors in detail when the dashboard is displayed, and can propose different countermeasures for each type of risk. For example, the dashboard section can classify risk factors into technical risks, human risks, environmental risks, etc., and display countermeasures for each. For example, the dashboard section can display technical countermeasures for technical risks and human countermeasures for human risks. The dashboard section can also display prioritized countermeasures based on the probability of risk occurrence and impact. For example, the dashboard section can prioritize countermeasures for risks with a high probability of occurrence and a high impact. Furthermore, the dashboard section can display countermeasures based on past success stories for each type of risk. For example, the dashboard section can display effective countermeasures for each type of risk based on past success stories. This streamlines risk management by displaying project risk factors in detail and proposing different countermeasures for each type of risk. Risk factors include, but are not limited to, technical risks, human risks, and environmental risks. Countermeasures include, but are not limited to, risk avoidance measures, risk mitigation measures, and risk transfer measures. Some or all of the processing described above in the dashboard section may be performed using AI, for example, or without AI. For example, the dashboard section can input risk factor data into a generating AI and have the generating AI display risk countermeasures.

[0098] The dashboard can estimate the user's emotions and determine the priority items to display on the dashboard based on the estimated emotions. For example, if the user is stressed, the dashboard can prioritize displaying items of high importance. For example, the dashboard can estimate the user's stress level using an emotion analysis algorithm and prioritize displaying items of high importance. Also, if the user is relaxed, the dashboard can prioritize displaying detailed information. For example, the dashboard can estimate the user's level of relaxation using an emotion analysis algorithm and prioritize displaying detailed information. Furthermore, if the user is in a hurry, the dashboard can prioritize displaying items that can be quickly checked. For example, the dashboard can estimate the user's state of urgency using an emotion analysis algorithm and prioritize displaying items that can be quickly checked. In this way, by determining the priority items to display on the dashboard according to the user's emotions, important information can be prioritized. User emotions include, but are not limited to, stress, relaxation, and urgency. Prioritized display items include, but are not limited to, items of high importance, detailed information, and items that can be quickly checked. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dashboard section may be performed using AI, or not using AI. For example, the dashboard section can input user emotion data into a generative AI and have the generative AI determine the priority display items.

[0099] The dashboard section can prioritize the display of highly relevant information when the dashboard is displayed, taking into account the geographical distribution of the project. For example, if the project spans multiple regions, the dashboard section can prioritize the display of information tailored to the characteristics of each region. For example, the dashboard section can display different information for each region, taking into account the characteristics of each region. The dashboard section can also prioritize the display of information for geographically important locations. For example, the dashboard section can prioritize the display of information for geographically important locations, allowing users to understand the progress of the project. Furthermore, the dashboard section can prioritize the display of information for high-risk regions based on geographical distribution. For example, the dashboard section can prioritize the display of information for high-risk regions, allowing users to identify risk factors. This enables efficient information provision by prioritizing the display of highly relevant information, taking into account the geographical distribution of the project. Geographical distribution includes, but is not limited to, regional characteristics and risk factors. Highly relevant information includes, but is not limited to, regional progress data and risk data. Some or all of the above processing in the dashboard section may be performed using, for example, AI, or not using AI. For example, the dashboard can input geographical distribution data into a generating AI and have the AI ​​prioritize the display of highly relevant information.

[0100] The dashboard section can optimize its display content by referencing past project data when displaying the dashboard. For example, the dashboard section can optimize the display content based on past project data. For example, the dashboard section can use past project data to evaluate the current project progress and optimize the display content. The dashboard section can also refer to past data to compare the current project progress and correct the display content. For example, the dashboard section can use past project data to evaluate the current progress and correct the display content. Furthermore, the dashboard section can use past data to predict the probability of risk factors occurring and reflect this in the display content. For example, the dashboard section can use past project data to calculate the probability of risk factors occurring and reflect this in the display content. This enables the provision of appropriate information by optimizing the display content by referencing past project data. Past data includes, but is not limited to, past project progress data and risk data. Optimization of display content includes, but is not limited to, correction of display content and prediction of the probability of risk factors occurring. Some or all of the above processing in the dashboard section may be performed using, for example, AI, or without using AI. For example, the dashboard section can input past project data into a generating AI and have the AI ​​optimize the displayed content.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The AI ​​project navigator can also be equipped with a feedback collection unit. This unit collects feedback from stakeholders after each phase of the project and provides it to the analysis unit. For example, the feedback collection unit can collect feedback on the feasibility of the plan in the early stages of the project. It can also collect feedback on progress and risk management in the middle stages of the project. Furthermore, in the final stages of the project, it can collect feedback on the quality of deliverables and the overall project evaluation. This allows the feedback collection unit to implement project management that reflects the opinions of stakeholders, thereby improving the project's success rate.

[0103] The data collection unit can adjust the accuracy of the data it collects according to the progress of the project. For example, in the early stages of the project, it can collect coarse data to grasp the overall direction. In the middle stages of the project, it can collect detailed data to accurately understand the progress and risk factors. Furthermore, in the final stages of the project, it can collect highly accurate data to confirm the final evaluation and the quality of the deliverables. In this way, the data collection unit can achieve efficient data collection by adjusting the accuracy of the data according to the progress of the project.

[0104] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, it can provide a concise and to-the-point notification. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can quickly notify only the important information. In this way, the analysis unit can provide appropriate information by adjusting the notification method of the analysis results according to the user's emotions.

[0105] The proposal department can adjust the timing of proposals according to the project's progress. For example, in the early stages of a project, they can propose revisions to the plan and resource allocation early on. In the middle stages of the project, they can quickly propose countermeasures for delays and risk factors. Furthermore, in the final stages of the project, they can propose improvements to the quality of deliverables and final evaluations in a timely manner. In this way, the proposal department can make appropriate proposals by adjusting the timing of proposals according to the project's progress.

[0106] The dashboard section can estimate the user's emotions and customize the dashboard based on those emotions. For example, if the user is stressed, a simple and highly visual dashboard can be provided. If the user is relaxed, a dashboard with more detailed information can be provided. Furthermore, if the user is in a hurry, a dashboard that gets straight to the point can be provided. In this way, the dashboard section can provide appropriate information by customizing the dashboard according to the user's emotions.

[0107] The data collection unit can dynamically change the types of data it collects according to the project's progress. For example, in the initial stages of a project, it can focus on collecting planning data and resource allocation data. In the middle stages of the project, it can focus on collecting progress data and risk factor data. Furthermore, in the final stages of the project, it can focus on collecting deliverable quality data and final evaluation data. This allows the data collection unit to efficiently collect the necessary data by dynamically changing the types of data it collects according to the project's progress.

[0108] The analysis unit can adjust the frequency of analysis according to the project's progress. For example, in the initial stages of a project, the analysis frequency can be set low to grasp the overall direction. In the middle stages of the project, the analysis frequency can be set high to accurately grasp the progress and risk factors. Furthermore, in the final stages of the project, analysis can be performed at a very high frequency to confirm the final evaluation and the quality of deliverables. In this way, the analysis unit can achieve efficient analysis by adjusting the analysis frequency according to the project's progress.

[0109] The suggestion function can estimate the user's emotions and adjust the content of the suggestions based on those emotions. For example, if the user is stressed, it can provide concise and easy-to-implement suggestions. If the user is relaxed, it can provide suggestions that include more detailed information. Furthermore, if the user is in a hurry, it can provide suggestions that can be implemented quickly. In this way, the suggestion function can provide appropriate suggestions by adjusting the content of the suggestions according to the user's emotions.

[0110] The dashboard section can dynamically change its layout according to the project's progress. For example, in the initial stages of a project, it can provide a layout that primarily displays planning data and resource allocation data. In the middle stages of the project, it can provide a layout that primarily displays progress data and risk factor data. Furthermore, in the final stages of the project, it can provide a layout that primarily displays deliverable quality data and final evaluation data. In this way, the dashboard section can dynamically change its layout according to the project's progress, enabling the provision of appropriate information.

[0111] The dashboard can estimate the user's emotions and adjust the notification frequency based on that estimation. For example, if the user is stressed, the notification frequency can be set lower to reduce the user's burden. Conversely, if the user is relaxed, the notification frequency can be set higher to provide more detailed information. Furthermore, if the user is in a hurry, only important information can be quickly notified. In this way, the dashboard can provide appropriate information by adjusting the notification frequency according to the user's emotions.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The data collection unit collects data on project progress, resource allocation, and risk factors. The data collection unit collects data from project management tools, sensors, log data, etc. For example, it obtains task progress from project management tools, monitors resource usage using sensors, and identifies risk factors by analyzing log data. Step 2: The analysis unit analyzes the data collected by the data collection unit to evaluate the project's progress and risk factors. The analysis unit uses machine learning algorithms to analyze the data, predict future risks based on past data, recognize data patterns, and identify critical paths. Step 3: The proposal unit proposes resource allocation and task automation based on the analysis results obtained by the analysis unit. The proposal unit proposes allocating additional resources to delayed tasks, proposes measures to mitigate risks for high-risk tasks, and proposes the optimal allocation of resources. Step 4: The dashboard section visualizes the proposals made by the proposal section. The dashboard section displays project progress, risk factors, and resource allocation in graphs and charts, updating data in real time so that project managers can grasp the situation at a glance. Furthermore, it provides a customizable interface so that project managers can quickly obtain the information they need.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] For example, the data collection unit can collect data such as project progress, resource allocation, and risk factors using the camera 42 and microphone 38B of the smart device 14. The data collection unit can also be implemented by the specific processing unit 290 of the data processing device 12, which can collect data from project management tools, sensors, log data, etc. The analysis unit, implemented by the specific processing unit 290 of the data processing device 12, analyzes the collected data and evaluates the project progress and risk factors. The proposal unit, implemented by the specific processing unit 290 of the data processing device 12, proposes optimal resource allocation and task automation based on the analysis results. The dashboard unit, implemented by the control unit 46A of the smart device 14, visualizes the proposed content and displays the project progress, risk factors, and resource allocation status in graphs and charts. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] For example, the data collection unit can collect data such as project progress, resource allocation, and risk factors using the camera 42 and microphone 238 of the smart glasses 214. The data collection unit can also be implemented by the specific processing unit 290 of the data processing device 12, which can collect data from project management tools, sensors, log data, etc. The analysis unit, implemented by the specific processing unit 290 of the data processing device 12, analyzes the collected data and evaluates project progress and risk factors. The proposal unit, implemented by the specific processing unit 290 of the data processing device 12, proposes optimal resource allocation and task automation based on the analysis results. The dashboard unit, implemented by the control unit 46A of the smart glasses 214, visualizes the proposed content and displays project progress, risk factors, and resource allocation status in graphs and charts. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] For example, the data collection unit can collect data such as project progress, resource allocation, and risk factors using the camera 42 and microphone 238 of the headset terminal 314. The data collection unit can also be implemented by the specific processing unit 290 of the data processing device 12, which can collect data from project management tools, sensors, log data, etc. The analysis unit, implemented by the specific processing unit 290 of the data processing device 12, analyzes the collected data and evaluates project progress and risk factors. The proposal unit, implemented by the specific processing unit 290 of the data processing device 12, proposes optimal resource allocation and task automation based on the analysis results. The dashboard unit, implemented by the control unit 46A of the headset terminal 314, visualizes the proposed content and displays project progress, risk factors, and resource allocation status in graphs and charts. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] For example, the data collection unit can collect data such as project progress, resource allocation, and risk factors using the camera 42 and microphone 238 of the robot 414. The data collection unit can also be implemented by the specific processing unit 290 of the data processing device 12, which can collect data from project management tools, sensors, log data, etc. The analysis unit, implemented by the specific processing unit 290 of the data processing device 12, analyzes the collected data and evaluates the project progress and risk factors. The proposal unit, implemented by the specific processing unit 290 of the data processing device 12, proposes optimal resource allocation and task automation based on the analysis results. The dashboard unit, implemented by the control unit 46A of the robot 414, visualizes the proposed content and displays the project progress, risk factors, and resource allocation status in graphs and charts. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) The data collection unit collects data on project progress, resource allocation, and risk factors. An analysis unit analyzes the data collected by the aforementioned collection unit to evaluate the project's progress and risk factors, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes resource allocation and task automation. The system includes a dashboard that visualizes the content proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data from project management tools, sensors, and log data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data will be analyzed to assess the project's progress and risk factors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we propose resource allocation and task automation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned dashboard section is Visualize project progress, risk factors, and resource allocation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Allocate additional resources to delayed tasks The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, Implement measures to mitigate risks for high-risk tasks. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze user emotions and adjust the timing of data collection based on the analyzed user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The type of data collected will be changed depending on the progress of the project. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, apply the appropriate collection method for each phase of the project. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking into account the geographical distribution of the project. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data, refer to past success stories of the project to optimize the data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, different analysis methods are applied depending on the progress of the project. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, we will classify the project's risk factors in detail and propose different countermeasures for each type of risk. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, historical project data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, we optimize the analysis method by referring to relevant project literature. The system described in Appendix 1, characterized by the features described herein. (Note 20) 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 21) The aforementioned proposal section is, When making a proposal, dynamically change the proposal content according to the progress of the project. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, we suggest different resource allocations based on the project's risk factors. The system described in Appendix 1, characterized by the features described herein. (Note 23) 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 24) The aforementioned proposal section is, When making a proposal, we will suggest resource allocations that take into account the geographical distribution of the project. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, we optimize the proposal by referring to past success stories of the project. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dashboard section is It estimates the user's emotions and adjusts how the dashboard is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dashboard section is When the dashboard is displayed, the content displayed will dynamically change according to the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dashboard section is When the dashboard is displayed, project risk factors are shown in detail, and different countermeasures are suggested for each type of risk. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned dashboard section is It estimates the user's emotions and determines the priority display items on the dashboard based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned dashboard section is When displaying the dashboard, the system prioritizes showing the most relevant information, taking into account the geographical distribution of the projects. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned dashboard section is When displaying the dashboard, the display content is optimized by referencing the project's historical data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 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 data collection unit collects data on project progress, resource allocation, and risk factors. An analysis unit analyzes the data collected by the aforementioned collection unit to evaluate the project's progress and risk factors, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes resource allocation and task automation. The system includes a dashboard that visualizes the content proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data from project management tools, sensors, and log data. The system according to feature 1.

3. The aforementioned analysis unit, The collected data will be analyzed to assess the project's progress and risk factors. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we propose resource allocation and task automation. The system according to feature 1.

5. The aforementioned dashboard section is Visualize project progress, risk factors, and resource allocation. The system according to feature 1.

6. The aforementioned proposal section is, Allocate additional resources to delayed tasks The system according to feature 1.

7. The aforementioned proposal section is, Implement measures to mitigate risks for high-risk tasks. The system according to feature 1.

8. The aforementioned collection unit is We analyze user emotions and adjust the timing of data collection based on the analyzed user emotions. The system according to feature 1.

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