system

Abstract

The system according to this embodiment aims to provide feedback and training programs tailored to the individual needs of project managers and product managers. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a feedback provision unit, and a simulation generation unit. The data collection unit collects PM's behavior and performance data. The analysis unit analyzes the data collected by the data collection unit. The feedback provision unit provides individual feedback and specific improvement suggestions based on the analysis results obtained by the analysis unit. The simulation generation unit generates simulations and scenarios based on the feedback provided by the feedback provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide feedback and training programs according to individual needs in the cultivation of project managers and product managers.

[0005] The system according to the embodiment aims to provide feedback and training programs according to the individual needs of project managers and product managers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a feedback provision unit, and a simulation generation unit. The data collection unit collects PM's behavior and performance data. The analysis unit analyzes the data collected by the data collection unit. The feedback provision unit provides individual feedback and specific improvement suggestions based on the analysis results obtained by the analysis unit. The simulation generation unit generates simulations and scenarios based on the feedback provided by the feedback provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide feedback and training programs tailored to the individual needs of project managers and product managers. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The PM coaching assistant system according to an embodiment of the present invention is a system in which AI supports the training and growth of project managers and product managers. This PM coaching assistant system provides customizable training programs and feedback tailored to individual needs, promoting the improvement of leadership and decision-making skills. First, the PM coaching assistant system's AI agent collects and analyzes the PM's behavior and performance data. Next, it provides individual feedback and specific improvement suggestions based on the analysis results. Furthermore, the AI ​​agent is available 24 hours a day, 365 days a year, allowing PMs to access and receive support whenever they need it. This promotes continuous learning and growth. The AI ​​agent can also generate complex simulations and scenarios, enabling practice of decision-making and problem-solving in environments close to actual work. Finally, the AI ​​agent continuously learns new data and applies that knowledge to its coaching actions. This ensures that PMs are always provided with the latest best practices, promoting their growth. For example, the PM coaching assistant system collects data on the PM's behavior and performance. For example, the PM coaching assistant system collects data such as work hours, task completion status, and communication frequency. Next, the PM coaching assistant system analyzes the collected data. For example, the PM Coaching Assistant System analyzes data trends and patterns to identify the PM's strengths and areas for improvement. Next, based on the analysis, the PM Coaching Assistant System provides personalized feedback and specific improvement suggestions. For instance, it offers specific advice and suggestions for improvement regarding the PM's actions. Furthermore, the PM Coaching Assistant System generates complex simulations and scenarios. For example, it provides practice in decision-making and problem-solving in environments closely resembling actual work. Finally, the PM Coaching Assistant System continuously learns from new data and applies that knowledge to its coaching actions.For example, the PM Coaching Assistant System learns the latest best practices and provides them to PMs. This allows the PM Coaching Assistant System to effectively support the training and growth of project managers and product managers.

[0029] The PM coaching assistant system according to this embodiment comprises a data collection unit, an analysis unit, a feedback provision unit, and a simulation generation unit. The data collection unit collects data on the PM's actions and performance. The data collection unit collects data such as work time, task completion status, and communication frequency. The data collection unit can collect data using sensors or tools, for example. The data collection unit can set the frequency of data collection and collect data regularly, for example. The data collection unit can adjust the timing of data collection to collect data at the optimal time, for example. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes data trends and patterns, for example, to identify the PM's strengths and areas for improvement. The analysis unit can set the analysis algorithm to be used and analyze the data, for example. The analysis unit can apply different analysis algorithms depending on the purpose of the analysis, for example. The analysis unit can adjust the level of detail of the analysis based on the importance of the data, for example. The feedback provision unit provides individual feedback and specific improvement suggestions based on the analysis results obtained by the analysis unit. The feedback provision unit provides specific advice and areas for improvement regarding the PM's actions, for example. The feedback provider can, for example, set the format of the feedback and provide it in the form of text or audio. The feedback provider can, for example, set the timing of the feedback and provide it in real time. The feedback provider can, for example, customize the content of the feedback and provide feedback tailored to the PM's needs. The simulation generation unit generates simulations and scenarios based on the feedback provided by the feedback provider unit. The simulation generation unit can, for example, provide practice in decision-making and problem-solving in an environment close to actual work. The simulation generation unit can, for example, generate different scenarios depending on the purpose of the simulation. The simulation generation unit can, for example, customize the content of the simulation and provide simulations tailored to the PM's needs.The simulation generation unit can, for example, evaluate the simulation results and provide feedback. This enables the PM coaching assistant system according to the embodiment to collect, analyze, provide feedback on, and generate simulations based on PM behavior and performance data.

[0030] The data collection unit collects PM (Project Manager) behavior and performance data. Specifically, it collects data such as work hours, task completion status, and communication frequency. This data is important for evaluating the PM's work efficiency and communication skills. The data collection unit can collect data using sensors and tools. For example, work hours are recorded using computer logs or time tracking tools. Task completion status is obtained from project management tools, recording task progress and completion date and time. Communication frequency is obtained from email and chat tool logs, allowing for an understanding of how often the PM communicates with team members. The data collection unit can set the frequency of data collection and collect data regularly. For example, it can be set to collect data daily, weekly, or for specific project phases. The data collection unit can adjust the timing of data collection to collect data at the optimal time. For example, collecting data before and after important project milestones or immediately after the completion of specific tasks allows for a more accurate performance assessment. This enables the data collection unit to efficiently collect detailed data on the PM's behavior and performance and provide it to the analysis and feedback departments.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, the analysis department analyzes data trends and patterns to identify the PM's strengths and areas for improvement. For example, by analyzing work time data, it is possible to evaluate which tasks the PM spends the most time on and how efficiently they manage their time. By analyzing task completion data, it is possible to understand whether the PM is completing tasks on time and which tasks are delayed. By analyzing communication frequency data, it is possible to evaluate how often the PM communicates with team members and how high the quality of that communication is. The analysis department can set the analytical algorithms to be used and analyze the data. For example, machine learning algorithms can be used to extract patterns from the data and identify characteristics of the PM's behavior and performance. The analysis department can apply different analytical algorithms depending on the purpose of the analysis. For example, simple statistical analysis can be used for short-term performance evaluation, and time series analysis can be used for long-term trend analysis. The analysis department can adjust the level of detail of the analysis based on the importance of the data. For example, by performing detailed analysis on important data and simpler analysis on less important data, the data can be analyzed efficiently. This allows the analysis department to quickly and accurately analyze the collected data and identify the project manager's strengths and areas for improvement.

[0032] The Feedback Department provides individual feedback and specific improvement suggestions based on the analysis results obtained by the Analysis Department. Specifically, the Feedback Department provides specific advice and areas for improvement regarding the PM's actions. For example, based on work time data, it provides advice on how to improve time management and how to proceed with tasks efficiently. Based on task completion data, it suggests areas for improvement in task prioritization and schedule management. Based on communication frequency data, it provides specific advice on how to communicate with team members and how to provide feedback. The Feedback Department can set the format of the feedback and provide it in various forms such as text and audio. For example, text-based feedback can be provided via email or chat tools, and audio-based feedback can be provided via voice messages or phone calls. The Feedback Department can set the timing of feedback and provide it in real time. For example, by providing feedback immediately after the completion of a specific task or before and after important project milestones, the PM can immediately grasp areas for improvement and reflect them in their next actions. The Feedback Department can customize the content of the feedback and provide feedback tailored to the PM's needs. For example, by adjusting specific advice and areas for improvement according to the PM's experience and skill level, more effective feedback can be provided. This allows the feedback department to support the improvement of PMs' behavior and performance, thereby promoting their growth.

[0033] The simulation generation unit generates simulations and scenarios based on feedback provided by the feedback provision unit. Specifically, the simulation generation unit provides practice in decision-making and problem-solving in an environment close to actual work. For example, it can simulate challenges and problems that a PM may face and practice how the PM would deal with those situations. The simulation generation unit can generate different scenarios depending on the purpose of the simulation. For example, it can generate scenarios that correspond to the progress of a project or scenarios that strengthen specific skills. The simulation generation unit can customize the content of the simulation and provide simulations tailored to the PM's needs. For example, by adjusting the difficulty and content of the simulation according to the PM's experience and skill level, it can provide more effective practice. The simulation generation unit can evaluate the results of the simulation and provide feedback. For example, based on the simulation results, it can evaluate the PM's decision-making and problem-solving skills and provide specific areas for improvement and advice. This allows the simulation generation unit to improve the PM's skills in dealing with challenges and problems that they may face in actual work. Furthermore, the simulation generation unit can store the simulation results in a database and continuously monitor the PM's growth and progress. This allows the simulation generation unit to support the PM's skill improvement and promote their growth.

[0034] The feedback provision unit includes a real-time feedback unit that provides feedback in real time. The real-time feedback unit provides, for example, immediate feedback on the PM's actions. The real-time feedback unit can, for example, monitor the PM's actions in real time and provide immediate feedback. The real-time feedback unit can, for example, analyze the PM's behavioral data in real time and provide immediate feedback. The real-time feedback unit can, for example, dynamically adjust the content of the feedback in response to changes in the PM's actions. This enables immediate improvement by providing feedback in real time.

[0035] The feedback provision unit includes a continuous learning support unit to support ongoing learning. The continuous learning support unit, for example, monitors the PM's learning progress and provides learning support at appropriate times. The continuous learning support unit can, for example, analyze the PM's learning data to understand their learning progress. The continuous learning support unit can, for example, customize learning content according to the PM's learning needs. The continuous learning support unit can, for example, adjust the difficulty level of learning according to the PM's learning progress. This promotes the PM's growth by supporting continuous learning.

[0036] The simulation generation unit includes a business simulation unit that provides an environment close to actual work. The business simulation unit simulates, for example, challenges and problems that project managers (PMs) face in their actual work. The business simulation unit can provide scenarios for PMs to practice decision-making and problem-solving. The business simulation unit can provide tools and systems for PMs to practice in an environment close to actual work. The business simulation unit allows PMs to evaluate the simulation results and receive feedback. This enables practice in an environment close to actual work.

[0037] The simulation generation unit includes a best practice provision unit that provides the latest best practices. The best practice provision unit provides, for example, information for PMs to learn and implement the latest best practices. The best practice provision unit can provide, for example, educational materials and tools for PMs to learn the latest best practices. The best practice provision unit can provide, for example, simulations and scenarios for PMs to implement the latest best practices. The best practice provision unit can provide, for example, feedback for PMs to learn and implement the latest best practices. In this way, by providing the latest best practices, it supports the skill improvement of PMs.

[0038] The data collection unit analyzes the PM's past behavioral history and selects the optimal data collection method. For example, the data collection unit can collect data from tools and applications that the PM has frequently used in the past. For example, the data collection unit can analyze the PM's past behavioral patterns and determine the most efficient timing for data collection. For example, the data collection unit can analyze the success factors of the PM's past projects and focus data collection based on that. This allows the optimal data collection method to be selected by analyzing past behavioral history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the PM's past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit filters the data based on the PM's current project status and areas of interest during data collection. For example, the data collection unit collects only data related to the project the PM is currently working on. For example, the data collection unit can prioritize the collection of highly relevant data based on the PM's areas of interest. For example, the data collection unit can dynamically filter the necessary data according to the PM's project progress. This allows for the collection of highly relevant data by filtering the data based on the current project status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the PM's project status data into a generating AI and have the generating AI perform the data filtering.

[0040] The data collection unit prioritizes collecting highly relevant data, taking into account the PM's geographical location information during data collection. For example, if the PM is in a specific region, the data collection unit prioritizes collecting data related to that region. For example, if the PM is on a business trip, the data collection unit can collect data related to the business trip destination. For example, if the PM is working remotely, the data collection unit can collect data around their home. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the PM's geographical location information into a generating AI and have the generating AI perform the data collection.

[0041] The data collection unit analyzes the PM's social media activity and collects relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the PM on social media. For example, the data collection unit can collect data based on the PM's social media followers and topics of interest. For example, the data collection unit can collect data related to groups and events the PM participates in on social media. This allows for the collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the PM's social media data into a generating AI and have the generating AI perform the data collection.

[0042] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on high-importance data. For example, the analysis unit can perform a simplified analysis on low-importance data. The analysis unit can dynamically adjust the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specific financial analysis algorithm to financial data. For example, the analysis unit can apply an analysis algorithm specialized in progress management to project progress data. For example, the analysis unit can apply an analysis algorithm specialized in risk assessment to risk data. By applying the appropriate analysis algorithm according to the data category, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit determines the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit prioritizes the analysis of the most recent data. For example, the analysis unit can analyze current data while referring to past data. For example, the analysis unit can dynamically adjust the analysis priority according to the data collection timing. This allows for the prioritization of the analysis of the most recent data by determining the analysis priority based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0045] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0046] The feedback provision unit provides optimal feedback by referring to the PM's past performance data when providing feedback. For example, the feedback provision unit can provide positive feedback based on the PM's past successes. For example, the feedback provision unit can propose specific areas for improvement based on the PM's past failures. For example, the feedback provision unit can provide optimal feedback by analyzing the PM's past performance data. In this way, optimal feedback can be provided by referring to past performance data. Some or all of the above processing in the feedback provision unit may be performed using AI, for example, or without using AI. For example, the feedback provision unit can input the PM's past performance data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0047] The feedback provider customizes the content of the feedback based on the PM's current project status when providing feedback. For example, the feedback provider provides feedback related to the project the PM is currently working on. For example, the feedback provider can provide feedback at an appropriate time depending on the PM's project progress. For example, the feedback provider can provide feedback that includes specific solutions to the PM's project challenges. This allows for the provision of more appropriate feedback by customizing the content based on the current project status. Some or all of the above processing in the feedback provider may be performed using AI, for example, or not using AI. For example, the feedback provider can input the PM's project status data into a generating AI and have the generating AI perform the customization of the feedback content.

[0048] The feedback provision unit provides optimal feedback by considering the PM's geographical location information when providing feedback. For example, if the PM is in a specific region, the feedback provision unit can provide feedback relevant to that region. For example, if the PM is on a business trip, the feedback provision unit can provide feedback relevant to the business trip destination. For example, if the PM is working remotely, the feedback provision unit can provide feedback based on data around their home. In this way, optimal feedback can be provided by considering geographical location information. Some or all of the above processing in the feedback provision unit may be performed using AI, for example, or without AI. For example, the feedback provision unit can input the PM's geographical location information into a generating AI and have the generating AI perform the feedback provision.

[0049] The feedback provider analyzes the PM's social media activity and adjusts the content of the feedback when providing it. For example, the feedback provider provides relevant feedback based on information shared by the PM on social media. For example, the feedback provider can provide feedback based on the PM's social media followers and topics of interest. For example, the feedback provider can provide feedback related to groups and events the PM participates in on social media. In this way, relevant feedback can be provided by analyzing social media activity. Some or all of the above processing in the feedback provider may be performed using AI, for example, or without AI. For example, the feedback provider can input the PM's social media data into a generating AI and have the generating AI adjust the content of the feedback.

[0050] The simulation generation unit generates the optimal simulation by referring to the PM's past behavioral data during simulation generation. For example, the simulation generation unit can simulate a success scenario based on the PM's past success cases. For example, the simulation generation unit can simulate a failure scenario based on the PM's past failure cases. For example, the simulation generation unit can analyze the PM's past behavioral data and generate the most effective simulation. In this way, the optimal simulation can be generated by referring to past behavioral data. Some or all of the above processing in the simulation generation unit may be performed using AI, for example, or without AI. For example, the simulation generation unit can input the PM's past behavioral data into a generation AI and have the generation AI execute the generation of the optimal simulation.

[0051] The simulation generation unit customizes the simulation scenario based on the PM's current project status when generating the simulation. For example, the simulation generation unit generates a simulation related to the project the PM is currently working on. For example, the simulation generation unit can generate an appropriate scenario depending on the PM's project progress. For example, the simulation generation unit can generate a simulation that includes specific solutions to the PM's project challenges. This allows for the provision of more appropriate simulations by customizing the simulation scenario based on the current project status. Some or all of the above processing in the simulation generation unit may be performed using AI, for example, or without AI. For example, the simulation generation unit can input the PM's project status data into a generation AI and have the generation AI perform the customization of the simulation scenario.

[0052] The simulation generation unit generates the optimal simulation by considering the PM's geographical location information during simulation generation. For example, if the PM is in a specific region, the simulation generation unit can generate a simulation related to that region. For example, if the PM is on a business trip, the simulation generation unit can generate a simulation related to the business trip destination. For example, if the PM is working remotely, the simulation generation unit can generate a simulation based on data around their home. In this way, the optimal simulation can be generated by considering geographical location information. Some or all of the above processing in the simulation generation unit may be performed using AI, for example, or without AI. For example, the simulation generation unit can input the PM's geographical location information into a generation AI and have the generation AI execute the simulation generation.

[0053] The simulation generation unit analyzes the PM's social media activities and adjusts the simulation content during simulation generation. For example, the simulation generation unit generates relevant simulations based on information shared by the PM on social media. For example, the simulation generation unit can generate simulations based on the PM's social media followers and topics of interest. For example, the simulation generation unit can generate simulations related to groups and events the PM participates in on social media. In this way, relevant simulations can be provided by analyzing social media activities. Some or all of the above processing in the simulation generation unit may be performed using AI, for example, or without AI. For example, the simulation generation unit can input the PM's social media data into a generation AI and have the generation AI perform adjustments to the simulation content.

[0054] The real-time feedback unit provides optimal feedback by referring to the PM's current behavioral data when providing real-time feedback. The real-time feedback unit provides immediate feedback based on the PM's current behavior, for example. The real-time feedback unit can analyze the PM's behavioral data in real time and provide appropriate feedback, for example. The real-time feedback unit can dynamically adjust the content of the feedback in response to changes in the PM's behavior, for example. This allows the unit to provide optimal real-time feedback by referring to the current behavioral data. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the PM's behavioral data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0055] The real-time feedback unit provides optimal feedback by considering the PM's device information when providing real-time feedback. For example, if the PM is using a smartphone, the real-time feedback unit provides feedback that is adapted to the screen size. For example, if the PM is using a tablet, the real-time feedback unit can provide feedback optimized for a larger screen. For example, if the PM is using a smartwatch, the real-time feedback unit can provide concise and highly visible feedback. In this way, optimal real-time feedback can be provided by considering device information. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the PM's device information into a generating AI and have the generating AI perform the task of providing feedback.

[0056] The Continuous Learning Support Unit provides optimal support by referring to the PM's past learning history when providing learning support. For example, the Continuous Learning Support Unit provides optimal learning content based on the PM's past learning history. For example, the Continuous Learning Support Unit can grasp the learning progress from the PM's past learning history and provide appropriate support. For example, the Continuous Learning Support Unit can analyze the PM's past learning history and provide support according to the learning trends. In this way, optimal learning support can be provided by referring to past learning history. Some or all of the above processes in the Continuous Learning Support Unit may be performed using AI, for example, or without using AI. For example, the Continuous Learning Support Unit can input the PM's past learning history into a generating AI and have the generating AI perform the provision of optimal support.

[0057] The Continuous Learning Support Unit provides optimal support by considering the PM's device information when providing learning support. For example, if the PM is using a smartphone, the Continuous Learning Support Unit can provide learning content that is adapted to the screen size. For example, if the PM is using a tablet, the Continuous Learning Support Unit can provide learning content optimized for a larger screen. For example, if the PM is using a smartwatch, the Continuous Learning Support Unit can provide concise and highly visible learning content. In this way, optimal learning support can be provided by considering device information. Some or all of the above processing in the Continuous Learning Support Unit may be performed using AI, for example, or without AI. For example, the Continuous Learning Support Unit can input the PM's device information into a generating AI and have the generating AI perform the provision of support.

[0058] The Business Simulation Department provides optimal simulations by referencing the PM's past business data when providing business simulations. For example, the Business Simulation Department can simulate success scenarios based on the PM's past success cases. For example, the Business Simulation Department can simulate failure scenarios based on the PM's past failure cases. For example, the Business Simulation Department can analyze the PM's past business data and provide the most effective simulation. In this way, the optimal business simulation can be provided by referring to past business data. Some or all of the above processes in the Business Simulation Department may be performed using AI, for example, or without AI. For example, the Business Simulation Department can input the PM's past business data into a generating AI and have the generating AI execute the provision of the optimal simulation.

[0059] The business simulation department provides optimal simulations by taking into account the PM's device information when providing business simulations. For example, if the PM is using a smartphone, the business simulation department can provide a simulation that matches the screen size. For example, if the PM is using a tablet, the business simulation department can provide a simulation optimized for a larger screen. For example, if the PM is using a smartwatch, the business simulation department can provide a concise and highly visible simulation. In this way, by taking device information into account, the optimal business simulation can be provided. Some or all of the above processing in the business simulation department may be performed using AI, for example, or without AI. For example, the business simulation department can input the PM's device information into a generating AI and have the generating AI execute the simulation provision.

[0060] The best practice provisioning unit provides optimal practices by referring to the PM's past behavioral data when providing best practices. For example, the best practice provisioning unit can provide successful practices based on the PM's past successes. For example, the best practice provisioning unit can provide unsuccessful practices based on the PM's past failures. For example, the best practice provisioning unit can analyze the PM's past behavioral data and provide the most effective practices. In this way, the optimal best practices can be provided by referring to past behavioral data. Some or all of the above processing in the best practice provisioning unit may be performed using AI, for example, or without AI. For example, the best practice provisioning unit can input the PM's past behavioral data into a generating AI and have the generating AI execute the provision of optimal practices.

[0061] The best practice provider unit provides optimal best practices by considering the PM's device information when providing best practices. For example, if the PM is using a smartphone, the best practice provider unit can provide practices that are adapted to the screen size. For example, if the PM is using a tablet, the best practice provider unit can provide practices optimized for a larger screen. For example, if the PM is using a smartwatch, the best practice provider unit can provide concise and highly visible practices. In this way, the best practice provider unit can provide optimal best practices by considering device information. Some or all of the above processing in the best practice provider unit may be performed using AI, for example, or without AI. For example, the best practice provider unit can input the PM's device information into a generating AI and have the generating AI perform the provision of practices.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The data collection unit collects PM's behavior and performance data. For example, the data collection unit collects data such as work time, task completion status, and communication frequency. The data collection unit can collect data using sensors or tools, for example. The data collection unit can set the frequency of data collection and collect data regularly, for example. The data collection unit can adjust the timing of data collection to collect data at the optimal time, for example. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes data trends and patterns to identify the PM's strengths and areas for improvement. For example, the analysis unit can set the analysis algorithm to be used and analyze the data. For example, the analysis unit can apply different analysis algorithms depending on the purpose of the analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The feedback unit provides individual feedback and specific improvement suggestions based on the analysis results obtained by the analysis unit. For example, the feedback unit provides specific advice and areas for improvement regarding the PM's behavior. For example, the feedback unit can set the format of the feedback and provide feedback in the form of text or audio. The feedback provider can, for example, set the timing of feedback and provide feedback in real time. The feedback provider can, for example, customize the content of the feedback and provide feedback tailored to the PM's needs. The simulation generation unit generates simulations and scenarios based on the feedback provided by the feedback provider unit. The simulation generation unit can, for example, provide practice in decision-making and problem-solving in an environment close to actual work. The simulation generation unit can, for example, generate different scenarios depending on the purpose of the simulation. The simulation generation unit can, for example, customize the content of the simulation and provide simulations tailored to the PM's needs. The simulation generation unit can, for example, evaluate the results of the simulation and provide feedback.As a result, the PM coaching assistant system according to this embodiment can collect, analyze, provide feedback on, and generate simulations based on PM behavior and performance data.

[0064] The feedback provision unit includes a real-time feedback unit that provides feedback in real time. The real-time feedback unit provides, for example, immediate feedback on the PM's actions. The real-time feedback unit can, for example, monitor the PM's actions in real time and provide immediate feedback. The real-time feedback unit can, for example, analyze the PM's behavioral data in real time and provide immediate feedback. The real-time feedback unit can, for example, dynamically adjust the content of the feedback in response to changes in the PM's actions. This enables immediate improvement by providing feedback in real time.

[0065] The feedback provision unit includes a continuous learning support unit to support ongoing learning. The continuous learning support unit, for example, monitors the PM's learning progress and provides learning support at appropriate times. The continuous learning support unit can, for example, analyze the PM's learning data to understand their learning progress. The continuous learning support unit can, for example, customize learning content according to the PM's learning needs. The continuous learning support unit can, for example, adjust the difficulty level of learning according to the PM's learning progress. This promotes the PM's growth by supporting continuous learning.

[0066] The simulation generation unit includes a business simulation unit that provides an environment close to actual work. The business simulation unit simulates, for example, challenges and problems that project managers (PMs) face in their actual work. The business simulation unit can provide scenarios for PMs to practice decision-making and problem-solving. The business simulation unit can provide tools and systems for PMs to practice in an environment close to actual work. The business simulation unit allows PMs to evaluate the simulation results and receive feedback. This enables practice in an environment close to actual work.

[0067] The simulation generation unit includes a best practice provision unit that provides the latest best practices. The best practice provision unit provides, for example, information for PMs to learn and implement the latest best practices. The best practice provision unit can provide, for example, educational materials and tools for PMs to learn the latest best practices. The best practice provision unit can provide, for example, simulations and scenarios for PMs to implement the latest best practices. The best practice provision unit can provide, for example, feedback for PMs to learn and implement the latest best practices. In this way, by providing the latest best practices, it supports the skill improvement of PMs.

[0068] The data collection unit analyzes the PM's past behavioral history and selects the optimal data collection method. For example, the data collection unit can collect data from tools and applications that the PM has frequently used in the past. For example, the data collection unit can analyze the PM's past behavioral patterns and determine the most efficient timing for data collection. For example, the data collection unit can analyze the success factors of the PM's past projects and focus data collection based on that. This allows the optimal data collection method to be selected by analyzing past behavioral history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the PM's past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0069] The data collection unit filters the data based on the PM's current project status and areas of interest during data collection. For example, the data collection unit collects only data related to the project the PM is currently working on. For example, the data collection unit can prioritize the collection of highly relevant data based on the PM's areas of interest. For example, the data collection unit can dynamically filter the necessary data according to the PM's project progress. This allows for the collection of highly relevant data by filtering the data based on the current project status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the PM's project status data into a generating AI and have the generating AI perform the data filtering.

[0070] The data collection unit prioritizes collecting highly relevant data, taking into account the PM's geographical location information during data collection. For example, if the PM is in a specific region, the data collection unit prioritizes collecting data related to that region. For example, if the PM is on a business trip, the data collection unit can collect data related to the business trip destination. For example, if the PM is working remotely, the data collection unit can collect data around their home. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the PM's geographical location information into a generating AI and have the generating AI perform the data collection.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The data collection unit collects PM's behavior and performance data. For example, it collects data such as work time, task completion status, and communication frequency. The data collection unit uses sensors and tools to collect data and can set the frequency and timing of data collection to collect data at the optimal time. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it analyzes data trends and patterns to identify the PM's strengths and areas for improvement. The analysis unit can set the analysis algorithm to be used and adjust the level of detail of the analysis based on the importance of the data. Step 3: The feedback department provides individual feedback and specific improvement suggestions based on the analysis results obtained by the analysis department. For example, it can provide specific advice and areas for improvement regarding the PM's actions, and can set the format and timing of the feedback to provide it in real time. Step 4: The simulation generation unit generates simulations and scenarios based on the feedback provided by the feedback provision unit. For example, it can provide practice in decision-making and problem-solving in an environment similar to actual work, and generate different scenarios depending on the purpose of the simulation.

[0073] (Example of form 2) The PM coaching assistant system according to an embodiment of the present invention is a system in which AI supports the training and growth of project managers and product managers. This PM coaching assistant system provides customizable training programs and feedback tailored to individual needs, promoting the improvement of leadership and decision-making skills. First, the PM coaching assistant system's AI agent collects and analyzes the PM's behavior and performance data. Next, it provides individual feedback and specific improvement suggestions based on the analysis results. Furthermore, the AI ​​agent is available 24 hours a day, 365 days a year, allowing PMs to access and receive support whenever they need it. This promotes continuous learning and growth. The AI ​​agent can also generate complex simulations and scenarios, enabling practice of decision-making and problem-solving in environments close to actual work. Finally, the AI ​​agent continuously learns new data and applies that knowledge to its coaching actions. This ensures that PMs are always provided with the latest best practices, promoting their growth. For example, the PM coaching assistant system collects data on the PM's behavior and performance. For example, the PM coaching assistant system collects data such as work hours, task completion status, and communication frequency. Next, the PM coaching assistant system analyzes the collected data. For example, the PM Coaching Assistant System analyzes data trends and patterns to identify the PM's strengths and areas for improvement. Next, based on the analysis, the PM Coaching Assistant System provides personalized feedback and specific improvement suggestions. For instance, it offers specific advice and suggestions for improvement regarding the PM's actions. Furthermore, the PM Coaching Assistant System generates complex simulations and scenarios. For example, it provides practice in decision-making and problem-solving in environments closely resembling actual work. Finally, the PM Coaching Assistant System continuously learns from new data and applies that knowledge to its coaching actions.For example, the PM Coaching Assistant System learns the latest best practices and provides them to PMs. This allows the PM Coaching Assistant System to effectively support the training and growth of project managers and product managers.

[0074] The PM coaching assistant system according to this embodiment comprises a data collection unit, an analysis unit, a feedback provision unit, and a simulation generation unit. The data collection unit collects data on the PM's actions and performance. The data collection unit collects data such as work time, task completion status, and communication frequency. The data collection unit can collect data using sensors or tools, for example. The data collection unit can set the frequency of data collection and collect data regularly, for example. The data collection unit can adjust the timing of data collection to collect data at the optimal time, for example. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes data trends and patterns, for example, to identify the PM's strengths and areas for improvement. The analysis unit can set the analysis algorithm to be used and analyze the data, for example. The analysis unit can apply different analysis algorithms depending on the purpose of the analysis, for example. The analysis unit can adjust the level of detail of the analysis based on the importance of the data, for example. The feedback provision unit provides individual feedback and specific improvement suggestions based on the analysis results obtained by the analysis unit. The feedback provision unit provides specific advice and areas for improvement regarding the PM's actions, for example. The feedback provider can, for example, set the format of the feedback and provide it in the form of text or audio. The feedback provider can, for example, set the timing of the feedback and provide it in real time. The feedback provider can, for example, customize the content of the feedback and provide feedback tailored to the PM's needs. The simulation generation unit generates simulations and scenarios based on the feedback provided by the feedback provider unit. The simulation generation unit can, for example, provide practice in decision-making and problem-solving in an environment close to actual work. The simulation generation unit can, for example, generate different scenarios depending on the purpose of the simulation. The simulation generation unit can, for example, customize the content of the simulation and provide simulations tailored to the PM's needs.The simulation generation unit can, for example, evaluate the simulation results and provide feedback. This enables the PM coaching assistant system according to the embodiment to collect, analyze, provide feedback on, and generate simulations based on PM behavior and performance data.

[0075] The data collection unit collects PM (Project Manager) behavior and performance data. Specifically, it collects data such as work hours, task completion status, and communication frequency. This data is important for evaluating the PM's work efficiency and communication skills. The data collection unit can collect data using sensors and tools. For example, work hours are recorded using computer logs or time tracking tools. Task completion status is obtained from project management tools, recording task progress and completion date and time. Communication frequency is obtained from email and chat tool logs, allowing for an understanding of how often the PM communicates with team members. The data collection unit can set the frequency of data collection and collect data regularly. For example, it can be set to collect data daily, weekly, or for specific project phases. The data collection unit can adjust the timing of data collection to collect data at the optimal time. For example, collecting data before and after important project milestones or immediately after the completion of specific tasks allows for a more accurate performance assessment. This enables the data collection unit to efficiently collect detailed data on the PM's behavior and performance and provide it to the analysis and feedback departments.

[0076] The analysis department analyzes the data collected by the data collection department. Specifically, the analysis department analyzes data trends and patterns to identify the PM's strengths and areas for improvement. For example, by analyzing work time data, it is possible to evaluate which tasks the PM spends the most time on and how efficiently they manage their time. By analyzing task completion data, it is possible to understand whether the PM is completing tasks on time and which tasks are delayed. By analyzing communication frequency data, it is possible to evaluate how often the PM communicates with team members and how high the quality of that communication is. The analysis department can set the analytical algorithms to be used and analyze the data. For example, machine learning algorithms can be used to extract patterns from the data and identify characteristics of the PM's behavior and performance. The analysis department can apply different analytical algorithms depending on the purpose of the analysis. For example, simple statistical analysis can be used for short-term performance evaluation, and time series analysis can be used for long-term trend analysis. The analysis department can adjust the level of detail of the analysis based on the importance of the data. For example, by performing detailed analysis on important data and simpler analysis on less important data, the data can be analyzed efficiently. This allows the analysis department to quickly and accurately analyze the collected data and identify the project manager's strengths and areas for improvement.

[0077] The Feedback Department provides individual feedback and specific improvement suggestions based on the analysis results obtained by the Analysis Department. Specifically, the Feedback Department provides specific advice and areas for improvement regarding the PM's actions. For example, based on work time data, it provides advice on how to improve time management and how to proceed with tasks efficiently. Based on task completion data, it suggests areas for improvement in task prioritization and schedule management. Based on communication frequency data, it provides specific advice on how to communicate with team members and how to provide feedback. The Feedback Department can set the format of the feedback and provide it in various forms such as text and audio. For example, text-based feedback can be provided via email or chat tools, and audio-based feedback can be provided via voice messages or phone calls. The Feedback Department can set the timing of feedback and provide it in real time. For example, by providing feedback immediately after the completion of a specific task or before and after important project milestones, the PM can immediately grasp areas for improvement and reflect them in their next actions. The Feedback Department can customize the content of the feedback and provide feedback tailored to the PM's needs. For example, by adjusting specific advice and areas for improvement according to the PM's experience and skill level, more effective feedback can be provided. This allows the feedback department to support the improvement of PMs' behavior and performance, thereby promoting their growth.

[0078] The simulation generation unit generates simulations and scenarios based on feedback provided by the feedback provision unit. Specifically, the simulation generation unit provides practice in decision-making and problem-solving in an environment close to actual work. For example, it can simulate challenges and problems that a PM may face and practice how the PM would deal with those situations. The simulation generation unit can generate different scenarios depending on the purpose of the simulation. For example, it can generate scenarios that correspond to the progress of a project or scenarios that strengthen specific skills. The simulation generation unit can customize the content of the simulation and provide simulations tailored to the PM's needs. For example, by adjusting the difficulty and content of the simulation according to the PM's experience and skill level, it can provide more effective practice. The simulation generation unit can evaluate the results of the simulation and provide feedback. For example, based on the simulation results, it can evaluate the PM's decision-making and problem-solving skills and provide specific areas for improvement and advice. This allows the simulation generation unit to improve the PM's skills in dealing with challenges and problems that they may face in actual work. Furthermore, the simulation generation unit can store the simulation results in a database and continuously monitor the PM's growth and progress. This allows the simulation generation unit to support the PM's skill improvement and promote their growth.

[0079] The feedback provision unit includes a real-time feedback unit that provides feedback in real time. The real-time feedback unit provides, for example, immediate feedback on the PM's actions. The real-time feedback unit can, for example, monitor the PM's actions in real time and provide immediate feedback. The real-time feedback unit can, for example, analyze the PM's behavioral data in real time and provide immediate feedback. The real-time feedback unit can, for example, dynamically adjust the content of the feedback in response to changes in the PM's actions. This enables immediate improvement by providing feedback in real time.

[0080] The feedback provision unit includes a continuous learning support unit to support ongoing learning. The continuous learning support unit, for example, monitors the PM's learning progress and provides learning support at appropriate times. The continuous learning support unit can, for example, analyze the PM's learning data to understand their learning progress. The continuous learning support unit can, for example, customize learning content according to the PM's learning needs. The continuous learning support unit can, for example, adjust the difficulty level of learning according to the PM's learning progress. This promotes the PM's growth by supporting continuous learning.

[0081] The simulation generation unit includes a business simulation unit that provides an environment close to actual work. The business simulation unit simulates, for example, challenges and problems that project managers (PMs) face in their actual work. The business simulation unit can provide scenarios for PMs to practice decision-making and problem-solving. The business simulation unit can provide tools and systems for PMs to practice in an environment close to actual work. The business simulation unit allows PMs to evaluate the simulation results and receive feedback. This enables practice in an environment close to actual work.

[0082] The simulation generation unit includes a best practice provision unit that provides the latest best practices. The best practice provision unit provides, for example, information for PMs to learn and implement the latest best practices. The best practice provision unit can provide, for example, educational materials and tools for PMs to learn the latest best practices. The best practice provision unit can provide, for example, simulations and scenarios for PMs to implement the latest best practices. The best practice provision unit can provide, for example, feedback for PMs to learn and implement the latest best practices. In this way, by providing the latest best practices, it supports the skill improvement of PMs.

[0083] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of data collection and collects data when the user is relaxed. For example, if the user is focused, the data collection unit can collect detailed data at that time. For example, if the user is tired, the data collection unit can temporarily stop data collection and resume it after rest. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0084] The data collection unit analyzes the PM's past behavioral history and selects the optimal data collection method. For example, the data collection unit can collect data from tools and applications that the PM has frequently used in the past. For example, the data collection unit can analyze the PM's past behavioral patterns and determine the most efficient timing for data collection. For example, the data collection unit can analyze the success factors of the PM's past projects and focus data collection based on that. This allows the optimal data collection method to be selected by analyzing past behavioral history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the PM's past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0085] The data collection unit filters the data based on the PM's current project status and areas of interest during data collection. For example, the data collection unit collects only data related to the project the PM is currently working on. For example, the data collection unit can prioritize the collection of highly relevant data based on the PM's areas of interest. For example, the data collection unit can dynamically filter the necessary data according to the PM's project progress. This allows for the collection of highly relevant data by filtering the data based on the current project status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the PM's project status data into a generating AI and have the generating AI perform the data filtering.

[0086] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. For example, if the user is relaxed, the data collection unit may prioritize the collection of detailed data. For example, if the user is focused, the data collection unit may prioritize the collection of important data. This ensures that important data is collected preferentially by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0087] The data collection unit prioritizes collecting highly relevant data, taking into account the PM's geographical location information during data collection. For example, if the PM is in a specific region, the data collection unit prioritizes collecting data related to that region. For example, if the PM is on a business trip, the data collection unit can collect data related to the business trip destination. For example, if the PM is working remotely, the data collection unit can collect data around their home. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the PM's geographical location information into a generating AI and have the generating AI perform the data collection.

[0088] The data collection unit analyzes the PM's social media activity and collects relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the PM on social media. For example, the data collection unit can collect data based on the PM's social media followers and topics of interest. For example, the data collection unit can collect data related to groups and events the PM participates in on social media. This allows for the collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the PM's social media data into a generating AI and have the generating AI perform the data collection.

[0089] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides simple and visually easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is focused, the analysis unit can provide complex analysis results. By adjusting the presentation of the analysis according to the user's emotions, more easily understandable analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0090] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on high-importance data. For example, the analysis unit can perform a simplified analysis on low-importance data. The analysis unit can dynamically adjust the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0091] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specific financial analysis algorithm to financial data. For example, the analysis unit can apply an analysis algorithm specialized in progress management to project progress data. For example, the analysis unit can apply an analysis algorithm specialized in risk assessment to risk data. By applying the appropriate analysis algorithm according to the data category, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0092] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. For example, if the user is focused, the analysis unit can provide a long, detailed analysis. By adjusting the length of the analysis according to the user's emotions, an analysis of appropriate length can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0093] The analysis unit determines the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit prioritizes the analysis of the most recent data. For example, the analysis unit can analyze current data while referring to past data. For example, the analysis unit can dynamically adjust the analysis priority according to the data collection timing. This allows for the prioritization of the analysis of the most recent data by determining the analysis priority based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0094] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0095] The feedback unit estimates the user's emotions and adjusts the way feedback is presented based on the estimated emotions. For example, if the user is stressed, the feedback unit prioritizes positive feedback. For example, if the user is relaxed, the feedback unit can provide detailed feedback. For example, if the user is focused, the feedback unit can provide feedback that includes specific areas for improvement. This allows for more effective feedback by adjusting the way feedback is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust the way feedback is presented.

[0096] The feedback provision unit provides optimal feedback by referring to the PM's past performance data when providing feedback. For example, the feedback provision unit can provide positive feedback based on the PM's past successes. For example, the feedback provision unit can propose specific areas for improvement based on the PM's past failures. For example, the feedback provision unit can provide optimal feedback by analyzing the PM's past performance data. In this way, optimal feedback can be provided by referring to past performance data. Some or all of the above processing in the feedback provision unit may be performed using AI, for example, or without using AI. For example, the feedback provision unit can input the PM's past performance data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0097] The feedback provider customizes the content of the feedback based on the PM's current project status when providing feedback. For example, the feedback provider provides feedback related to the project the PM is currently working on. For example, the feedback provider can provide feedback at an appropriate time depending on the PM's project progress. For example, the feedback provider can provide feedback that includes specific solutions to the PM's project challenges. This allows for the provision of more appropriate feedback by customizing the content based on the current project status. Some or all of the above processing in the feedback provider may be performed using AI, for example, or not using AI. For example, the feedback provider can input the PM's project status data into a generating AI and have the generating AI perform the customization of the feedback content.

[0098] The feedback unit estimates the user's emotions and determines the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit may postpone less important feedback. For example, if the user is relaxed, the feedback unit may prioritize detailed feedback. For example, if the user is focused, the feedback unit may prioritize important feedback. This ensures that important feedback is prioritized by determining the priority of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit may input user emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0099] The feedback provision unit provides optimal feedback by considering the PM's geographical location information when providing feedback. For example, if the PM is in a specific region, the feedback provision unit can provide feedback relevant to that region. For example, if the PM is on a business trip, the feedback provision unit can provide feedback relevant to the business trip destination. For example, if the PM is working remotely, the feedback provision unit can provide feedback based on data around their home. In this way, optimal feedback can be provided by considering geographical location information. Some or all of the above processing in the feedback provision unit may be performed using AI, for example, or without AI. For example, the feedback provision unit can input the PM's geographical location information into a generating AI and have the generating AI perform the feedback provision.

[0100] The feedback provider analyzes the PM's social media activity and adjusts the content of the feedback when providing it. For example, the feedback provider provides relevant feedback based on information shared by the PM on social media. For example, the feedback provider can provide feedback based on the PM's social media followers and topics of interest. For example, the feedback provider can provide feedback related to groups and events the PM participates in on social media. In this way, relevant feedback can be provided by analyzing social media activity. Some or all of the above processing in the feedback provider may be performed using AI, for example, or without AI. For example, the feedback provider can input the PM's social media data into a generating AI and have the generating AI adjust the content of the feedback.

[0101] The simulation generation unit estimates the user's emotions and adjusts the simulation content based on the estimated emotions. For example, if the user is stressed, the simulation generation unit can provide a simple simulation. For example, if the user is relaxed, the simulation generation unit can provide a detailed simulation. For example, if the user is focused, the simulation generation unit can provide a complex simulation. This allows for the provision of more appropriate simulations by adjusting the simulation content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the simulation generation unit may be performed using AI, or not using AI. For example, the simulation generation unit can input user emotion data into the generative AI and have the generative AI adjust the simulation content.

[0102] The simulation generation unit generates the optimal simulation by referring to the PM's past behavioral data during simulation generation. For example, the simulation generation unit can simulate a success scenario based on the PM's past success cases. For example, the simulation generation unit can simulate a failure scenario based on the PM's past failure cases. For example, the simulation generation unit can analyze the PM's past behavioral data and generate the most effective simulation. In this way, the optimal simulation can be generated by referring to past behavioral data. Some or all of the above processing in the simulation generation unit may be performed using AI, for example, or without AI. For example, the simulation generation unit can input the PM's past behavioral data into a generation AI and have the generation AI execute the generation of the optimal simulation.

[0103] The simulation generation unit customizes the simulation scenario based on the PM's current project status when generating the simulation. For example, the simulation generation unit generates a simulation related to the project the PM is currently working on. For example, the simulation generation unit can generate an appropriate scenario depending on the PM's project progress. For example, the simulation generation unit can generate a simulation that includes specific solutions to the PM's project challenges. This allows for the provision of more appropriate simulations by customizing the simulation scenario based on the current project status. Some or all of the above processing in the simulation generation unit may be performed using AI, for example, or without AI. For example, the simulation generation unit can input the PM's project status data into a generation AI and have the generation AI perform the customization of the simulation scenario.

[0104] The simulation generation unit estimates the user's emotions and determines the priority of simulations based on the estimated user emotions. For example, if the user is stressed, the simulation generation unit may postpone less important simulations. For example, if the user is relaxed, the simulation generation unit may prioritize detailed simulations. For example, if the user is focused, the simulation generation unit may prioritize providing important simulations. In this way, by determining the priority of simulations according to the user's emotions, important simulations can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a 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 simulation generation unit may be performed using AI, or not using AI. For example, the simulation generation unit can input user emotion data into the generative AI and have the generative AI perform the determination of simulation priorities.

[0105] The simulation generation unit generates the optimal simulation by considering the PM's geographical location information during simulation generation. For example, if the PM is in a specific region, the simulation generation unit can generate a simulation related to that region. For example, if the PM is on a business trip, the simulation generation unit can generate a simulation related to the business trip destination. For example, if the PM is working remotely, the simulation generation unit can generate a simulation based on data around their home. In this way, the optimal simulation can be generated by considering geographical location information. Some or all of the above processing in the simulation generation unit may be performed using AI, for example, or without AI. For example, the simulation generation unit can input the PM's geographical location information into a generation AI and have the generation AI execute the simulation generation.

[0106] The simulation generation unit analyzes the PM's social media activities and adjusts the simulation content during simulation generation. For example, the simulation generation unit generates relevant simulations based on information shared by the PM on social media. For example, the simulation generation unit can generate simulations based on the PM's social media followers and topics of interest. For example, the simulation generation unit can generate simulations related to groups and events the PM participates in on social media. In this way, relevant simulations can be provided by analyzing social media activities. Some or all of the above processing in the simulation generation unit may be performed using AI, for example, or without AI. For example, the simulation generation unit can input the PM's social media data into a generation AI and have the generation AI perform adjustments to the simulation content.

[0107] The real-time feedback unit estimates the user's emotions and adjusts the content of the real-time feedback based on the estimated emotions. For example, if the user is stressed, the real-time feedback unit prioritizes positive feedback. For example, if the user is relaxed, the real-time feedback unit can provide detailed feedback. For example, if the user is focused, the real-time feedback unit can provide feedback that includes specific areas for improvement. This allows for more effective feedback by adjusting the content of the real-time feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 real-time feedback unit may be performed using AI or not. For example, the real-time feedback unit can input user emotion data into the generative AI and have the generative AI adjust the content of the real-time feedback.

[0108] The real-time feedback unit provides optimal feedback by referring to the PM's current behavioral data when providing real-time feedback. The real-time feedback unit provides immediate feedback based on the PM's current behavior, for example. The real-time feedback unit can analyze the PM's behavioral data in real time and provide appropriate feedback, for example. The real-time feedback unit can dynamically adjust the content of the feedback in response to changes in the PM's behavior, for example. This allows the unit to provide optimal real-time feedback by referring to the current behavioral data. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the PM's behavioral data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0109] The real-time feedback unit estimates the user's emotions and determines the priority of real-time feedback based on the estimated emotions. For example, if the user is stressed, the real-time feedback unit may postpone less important feedback. For example, if the user is relaxed, the real-time feedback unit may prioritize detailed feedback. For example, if the user is focused, the real-time feedback unit may prioritize important feedback. In this way, important feedback can be prioritized by determining the priority of real-time feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the real-time feedback unit may be performed using AI or not using AI. For example, the real-time feedback unit can input user emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0110] The real-time feedback unit provides optimal feedback by considering the PM's device information when providing real-time feedback. For example, if the PM is using a smartphone, the real-time feedback unit provides feedback that is adapted to the screen size. For example, if the PM is using a tablet, the real-time feedback unit can provide feedback optimized for a larger screen. For example, if the PM is using a smartwatch, the real-time feedback unit can provide concise and highly visible feedback. In this way, optimal real-time feedback can be provided by considering device information. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the PM's device information into a generating AI and have the generating AI perform the task of providing feedback.

[0111] The continuous learning support unit estimates the user's emotions and adjusts the learning support content based on the estimated emotions. For example, if the user is feeling stressed, the continuous learning support unit can provide simple learning content. For example, if the user is relaxed, the continuous learning support unit can provide detailed learning content. For example, if the user is focused, the continuous learning support unit can provide more difficult learning content. In this way, by adjusting the learning support content according to the user's emotions, more effective learning support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the continuous learning support unit may be performed using AI, for example, or without AI. For example, the continuous learning support unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the learning support content.

[0112] The Continuous Learning Support Unit provides optimal support by referring to the PM's past learning history when providing learning support. For example, the Continuous Learning Support Unit provides optimal learning content based on the PM's past learning history. For example, the Continuous Learning Support Unit can grasp the learning progress from the PM's past learning history and provide appropriate support. For example, the Continuous Learning Support Unit can analyze the PM's past learning history and provide support according to the learning trends. In this way, optimal learning support can be provided by referring to past learning history. Some or all of the above processes in the Continuous Learning Support Unit may be performed using AI, for example, or without using AI. For example, the Continuous Learning Support Unit can input the PM's past learning history into a generating AI and have the generating AI perform the provision of optimal support.

[0113] The continuous learning support unit estimates the user's emotions and determines the priority of learning support based on the estimated emotions. For example, if the user is stressed, the continuous learning support unit may postpone less important learning content. For example, if the user is relaxed, the continuous learning support unit may prioritize detailed learning content. For example, if the user is focused, the continuous learning support unit may prioritize providing important learning content. In this way, by determining the priority of learning support according to the user's emotions, important learning content can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, 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 continuous learning support unit may be performed using AI, for example, or without AI. For example, the continuous learning support unit can input user emotion data into the generative AI and have the generative AI perform the determination of learning support priorities.

[0114] The Continuous Learning Support Unit provides optimal support by considering the PM's device information when providing learning support. For example, if the PM is using a smartphone, the Continuous Learning Support Unit can provide learning content that is adapted to the screen size. For example, if the PM is using a tablet, the Continuous Learning Support Unit can provide learning content optimized for a larger screen. For example, if the PM is using a smartwatch, the Continuous Learning Support Unit can provide concise and highly visible learning content. In this way, optimal learning support can be provided by considering device information. Some or all of the above processing in the Continuous Learning Support Unit may be performed using AI, for example, or without AI. For example, the Continuous Learning Support Unit can input the PM's device information into a generating AI and have the generating AI perform the provision of support.

[0115] The business simulation unit estimates the user's emotions and adjusts the content of the business simulation based on the estimated user emotions. For example, if the user is stressed, the business simulation unit can provide a simple business simulation. For example, if the user is relaxed, the business simulation unit can provide a detailed business simulation. For example, if the user is focused, the business simulation unit can provide a complex business simulation. In this way, by adjusting the content of the business simulation according to the user's emotions, a more appropriate simulation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the business simulation unit may be performed using AI, for example, or without AI. For example, the business simulation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the content of the business simulation.

[0116] The Business Simulation Department provides optimal simulations by referencing the PM's past business data when providing business simulations. For example, the Business Simulation Department can simulate success scenarios based on the PM's past success cases. For example, the Business Simulation Department can simulate failure scenarios based on the PM's past failure cases. For example, the Business Simulation Department can analyze the PM's past business data and provide the most effective simulation. In this way, the optimal business simulation can be provided by referring to past business data. Some or all of the above processes in the Business Simulation Department may be performed using AI, for example, or without AI. For example, the Business Simulation Department can input the PM's past business data into a generating AI and have the generating AI execute the provision of the optimal simulation.

[0117] The business simulation unit estimates the user's emotions and determines the priority of business simulations based on the estimated user emotions. For example, if the user is stressed, the business simulation unit will postpone less important simulations. For example, if the user is relaxed, the business simulation unit can prioritize detailed simulations. For example, if the user is focused, the business simulation unit can prioritize providing important simulations. In this way, by determining the priority of business simulations according to the user's emotions, important simulations can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the business simulation unit may be performed using AI or not using AI. For example, the business simulation unit can input user emotion data into a generative AI and have the generative AI perform the determination of business simulation priorities.

[0118] The business simulation department provides optimal simulations by taking into account the PM's device information when providing business simulations. For example, if the PM is using a smartphone, the business simulation department can provide a simulation that matches the screen size. For example, if the PM is using a tablet, the business simulation department can provide a simulation optimized for a larger screen. For example, if the PM is using a smartwatch, the business simulation department can provide a concise and highly visible simulation. In this way, by taking device information into account, the optimal business simulation can be provided. Some or all of the above processing in the business simulation department may be performed using AI, for example, or without AI. For example, the business simulation department can input the PM's device information into a generating AI and have the generating AI execute the simulation provision.

[0119] The best practice provider estimates the user's emotions and adjusts the content of the best practices based on the estimated emotions. For example, if the user is stressed, the best practice provider can provide simple best practices. For example, if the user is relaxed, the best practice provider can provide detailed best practices. For example, if the user is focused, the best practice provider can provide complex best practices. This allows for the provision of more appropriate practices by adjusting the content of the best practices according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 best practice provider may be performed using AI or not. For example, the best practice provider can input user emotion data into a generative AI and have the generative AI adjust the content of the best practices.

[0120] The best practice provisioning unit provides optimal practices by referring to the PM's past behavioral data when providing best practices. For example, the best practice provisioning unit can provide successful practices based on the PM's past successes. For example, the best practice provisioning unit can provide unsuccessful practices based on the PM's past failures. For example, the best practice provisioning unit can analyze the PM's past behavioral data and provide the most effective practices. In this way, the optimal best practices can be provided by referring to past behavioral data. Some or all of the above processing in the best practice provisioning unit may be performed using AI, for example, or without AI. For example, the best practice provisioning unit can input the PM's past behavioral data into a generating AI and have the generating AI execute the provision of optimal practices.

[0121] The best practice provider estimates the user's emotions and prioritizes best practices based on the estimated emotions. For example, if the user is stressed, the best practice provider may postpone less important practices. If the user is relaxed, the best practice provider may prioritize detailed practices. If the user is focused, the best practice provider may prioritize important practices. This ensures that important practices are prioritized by determining the priority of best practices according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the best practice provider may be performed using AI or not. For example, the best practice provider can input user emotion data into a generative AI and have the generative AI determine the priority of best practices.

[0122] The best practice provider unit provides optimal best practices by considering the PM's device information when providing best practices. For example, if the PM is using a smartphone, the best practice provider unit can provide practices that are adapted to the screen size. For example, if the PM is using a tablet, the best practice provider unit can provide practices optimized for a larger screen. For example, if the PM is using a smartwatch, the best practice provider unit can provide concise and highly visible practices. In this way, the best practice provider unit can provide optimal best practices by considering device information. Some or all of the above processing in the best practice provider unit may be performed using AI, for example, or without AI. For example, the best practice provider unit can input the PM's device information into a generating AI and have the generating AI perform the provision of practices.

[0123] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0124] The data collection unit collects PM's behavior and performance data. For example, the data collection unit collects data such as work time, task completion status, and communication frequency. The data collection unit can collect data using sensors or tools, for example. The data collection unit can set the frequency of data collection and collect data regularly, for example. The data collection unit can adjust the timing of data collection to collect data at the optimal time, for example. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes data trends and patterns to identify the PM's strengths and areas for improvement. For example, the analysis unit can set the analysis algorithm to be used and analyze the data. For example, the analysis unit can apply different analysis algorithms depending on the purpose of the analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The feedback unit provides individual feedback and specific improvement suggestions based on the analysis results obtained by the analysis unit. For example, the feedback unit provides specific advice and areas for improvement regarding the PM's behavior. For example, the feedback unit can set the format of the feedback and provide feedback in the form of text or audio. The feedback provider can, for example, set the timing of feedback and provide feedback in real time. The feedback provider can, for example, customize the content of the feedback and provide feedback tailored to the PM's needs. The simulation generation unit generates simulations and scenarios based on the feedback provided by the feedback provider unit. The simulation generation unit can, for example, provide practice in decision-making and problem-solving in an environment close to actual work. The simulation generation unit can, for example, generate different scenarios depending on the purpose of the simulation. The simulation generation unit can, for example, customize the content of the simulation and provide simulations tailored to the PM's needs. The simulation generation unit can, for example, evaluate the results of the simulation and provide feedback.As a result, the PM coaching assistant system according to this embodiment can collect, analyze, provide feedback on, and generate simulations based on PM behavior and performance data.

[0125] The feedback provision unit includes a real-time feedback unit that provides feedback in real time. The real-time feedback unit provides, for example, immediate feedback on the PM's actions. The real-time feedback unit can, for example, monitor the PM's actions in real time and provide immediate feedback. The real-time feedback unit can, for example, analyze the PM's behavioral data in real time and provide immediate feedback. The real-time feedback unit can, for example, dynamically adjust the content of the feedback in response to changes in the PM's actions. This enables immediate improvement by providing feedback in real time.

[0126] The feedback provision unit includes a continuous learning support unit to support ongoing learning. The continuous learning support unit, for example, monitors the PM's learning progress and provides learning support at appropriate times. The continuous learning support unit can, for example, analyze the PM's learning data to understand their learning progress. The continuous learning support unit can, for example, customize learning content according to the PM's learning needs. The continuous learning support unit can, for example, adjust the difficulty level of learning according to the PM's learning progress. This promotes the PM's growth by supporting continuous learning.

[0127] The simulation generation unit includes a business simulation unit that provides an environment close to actual work. The business simulation unit simulates, for example, challenges and problems that project managers (PMs) face in their actual work. The business simulation unit can provide scenarios for PMs to practice decision-making and problem-solving. The business simulation unit can provide tools and systems for PMs to practice in an environment close to actual work. The business simulation unit allows PMs to evaluate the simulation results and receive feedback. This enables practice in an environment close to actual work.

[0128] The simulation generation unit includes a best practice provision unit that provides the latest best practices. The best practice provision unit provides, for example, information for PMs to learn and implement the latest best practices. The best practice provision unit can provide, for example, educational materials and tools for PMs to learn the latest best practices. The best practice provision unit can provide, for example, simulations and scenarios for PMs to implement the latest best practices. The best practice provision unit can provide, for example, feedback for PMs to learn and implement the latest best practices. In this way, by providing the latest best practices, it supports the skill improvement of PMs.

[0129] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of data collection and collects data when the user is relaxed. For example, if the user is focused, the data collection unit can collect detailed data at that time. For example, if the user is tired, the data collection unit can temporarily stop data collection and resume it after rest. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0130] The data collection unit analyzes the PM's past behavioral history and selects the optimal data collection method. For example, the data collection unit can collect data from tools and applications that the PM has frequently used in the past. For example, the data collection unit can analyze the PM's past behavioral patterns and determine the most efficient timing for data collection. For example, the data collection unit can analyze the success factors of the PM's past projects and focus data collection based on that. This allows the optimal data collection method to be selected by analyzing past behavioral history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the PM's past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0131] The data collection unit filters the data based on the PM's current project status and areas of interest during data collection. For example, the data collection unit collects only data related to the project the PM is currently working on. For example, the data collection unit can prioritize the collection of highly relevant data based on the PM's areas of interest. For example, the data collection unit can dynamically filter the necessary data according to the PM's project progress. This allows for the collection of highly relevant data by filtering the data based on the current project status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the PM's project status data into a generating AI and have the generating AI perform the data filtering.

[0132] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. For example, if the user is relaxed, the data collection unit may prioritize the collection of detailed data. For example, if the user is focused, the data collection unit may prioritize the collection of important data. This ensures that important data is collected preferentially by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0133] The data collection unit prioritizes collecting highly relevant data, taking into account the PM's geographical location information during data collection. For example, if the PM is in a specific region, the data collection unit prioritizes collecting data related to that region. For example, if the PM is on a business trip, the data collection unit can collect data related to the business trip destination. For example, if the PM is working remotely, the data collection unit can collect data around their home. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the PM's geographical location information into a generating AI and have the generating AI perform the data collection.

[0134] The following briefly describes the processing flow for example form 2.

[0135] Step 1: The data collection unit collects PM's behavior and performance data. For example, it collects data such as work time, task completion status, and communication frequency. The data collection unit uses sensors and tools to collect data and can set the frequency and timing of data collection to collect data at the optimal time. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it analyzes data trends and patterns to identify the PM's strengths and areas for improvement. The analysis unit can set the analysis algorithm to be used and adjust the level of detail of the analysis based on the importance of the data. Step 3: The feedback department provides individual feedback and specific improvement suggestions based on the analysis results obtained by the analysis department. For example, it can provide specific advice and areas for improvement regarding the PM's actions, and can set the format and timing of the feedback to provide it in real time. Step 4: The simulation generation unit generates simulations and scenarios based on the feedback provided by the feedback provision unit. For example, it can provide practice in decision-making and problem-solving in an environment similar to actual work, and generate different scenarios depending on the purpose of the simulation.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the data collection unit, analysis unit, feedback provision unit, simulation generation unit, real-time feedback unit, continuous learning support unit, business simulation unit, and best practice provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the sensors and tools of the smart device 14 and analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The feedback provision unit provides feedback based on the analysis results by the specific processing unit 290 of the data processing unit 12 and provides real-time feedback by the control unit 46A of the smart device 14. The simulation generation unit generates a simulation by the specific processing unit 290 of the data processing unit 12 and provides it by the control unit 46A of the smart device 14. The continuous learning support unit provides learning support by the specific processing unit 290 of the data processing unit 12 and provides the learning content by the control unit 46A of the smart device 14. The business simulation unit generates a business simulation by the specific processing unit 290 of the data processing unit 12 and provides it by the control unit 46A of the smart device 14. The best practice provision unit provides best practices through the specific processing unit 290 of the data processing unit 12, and is provided by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0140] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the data collection unit, analysis unit, feedback provision unit, simulation generation unit, real-time feedback unit, continuous learning support unit, business simulation unit, and best practice provision unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the sensors and tools of the smart glasses 214 and analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The feedback provision unit provides feedback based on the analysis results by the specific processing unit 290 of the data processing unit 12 and provides real-time feedback by the control unit 46A of the smart glasses 214. The simulation generation unit generates a simulation by the specific processing unit 290 of the data processing unit 12 and provides it by the control unit 46A of the smart glasses 214. The continuous learning support unit provides learning support by the specific processing unit 290 of the data processing unit 12 and provides the learning content by the control unit 46A of the smart glasses 214. The business simulation unit generates a business simulation by the specific processing unit 290 of the data processing unit 12 and provides it by the control unit 46A of the smart glasses 214. The best practice provision unit provides best practices through the specific processing unit 290 of the data processing unit 12 and through the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the data collection unit, analysis unit, feedback provision unit, simulation generation unit, real-time feedback unit, continuous learning support unit, business simulation unit, and best practice provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the sensors and tools of the headset terminal 314 and analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The feedback provision unit provides feedback based on the analysis results by the specific processing unit 290 of the data processing unit 12 and provides real-time feedback by the control unit 46A of the headset terminal 314. The simulation generation unit generates a simulation by the specific processing unit 290 of the data processing unit 12 and provides it by the control unit 46A of the headset terminal 314. The continuous learning support unit provides learning support by the specific processing unit 290 of the data processing unit 12 and provides learning content by the control unit 46A of the headset terminal 314. The business simulation unit generates a business simulation by the specific processing unit 290 of the data processing unit 12 and provides it by the control unit 46A of the headset terminal 314. The best practice provision unit provides best practices through the specific processing unit 290 of the data processing device 12, and through the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0172] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Each of the multiple elements described above, including the data collection unit, analysis unit, feedback provision unit, simulation generation unit, real-time feedback unit, continuous learning support unit, business simulation unit, and best practice provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the sensors and tools of the robot 414 and analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The feedback provision unit provides feedback based on the analysis results by the specific processing unit 290 of the data processing unit 12 and provides real-time feedback by the control unit 46A of the robot 414. The simulation generation unit generates a simulation by the specific processing unit 290 of the data processing unit 12 and provides it by the control unit 46A of the robot 414. The continuous learning support unit provides learning support by the specific processing unit 290 of the data processing unit 12 and provides the learning content by the control unit 46A of the robot 414. The business simulation unit generates a business simulation by the specific processing unit 290 of the data processing unit 12 and provides it by the control unit 46A of the robot 414. The best practice provision unit provides best practices through the specific processing unit 290 of the data processing unit 12, and is provided by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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."

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] (Note 1) The collection unit collects PM's behavior and performance data, An analysis unit analyzes the data collected by the aforementioned collection unit, A feedback provision unit provides individual feedback and specific improvement suggestions based on the analysis results obtained by the aforementioned analysis unit. The system includes a simulation generation unit that generates simulations and scenarios based on the feedback provided by the aforementioned feedback provision unit. A system characterized by the following features. (Note 2) The aforementioned feedback provision unit, It features a real-time feedback unit that provides feedback in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned feedback provision unit, It features a continuous learning support unit to assist with ongoing learning. The system described in Appendix 1, characterized by the features described herein. (Note 4) The simulation generation unit, It has a business simulation department that provides an environment close to actual work. The system described in Appendix 1, characterized by the features described herein. (Note 5) The simulation generation unit, We have a best practice provision department that provides the latest best practices. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the PM's past behavioral history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the PM's current project status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is When collecting data, the geographical location information of PMs is taken into consideration to prioritize the collection of highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, analyze the PM's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned feedback provision unit, It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback provision unit, When providing feedback, we refer to the PM's past performance data to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback provision unit, When providing feedback, customize the content of the feedback based on the PM's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback provision unit, It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback provision unit, When providing feedback, we will consider the PM's geographical location to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback provision unit, When providing feedback, we analyze the PM's social media activity and adjust the content of the feedback accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 24) The simulation generation unit, It estimates the user's emotions and adjusts the simulation content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The simulation generation unit, When generating simulations, the PM's past behavioral data is referenced to generate the optimal simulation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The simulation generation unit, When generating simulations, customize the simulation scenario based on the PM's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The simulation generation unit, It estimates the user's emotions and determines the priority of simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The simulation generation unit, When generating simulations, the optimal simulation is generated by considering the geographical location information of the PM. The system described in Appendix 1, characterized by the features described herein. (Note 29) The simulation generation unit, During simulation generation, the PM's social media activity is analyzed and adjusted to optimize the simulation content. The system described in Appendix 1, characterized by the features described herein. (Note 30) The real-time feedback unit described above is: It estimates the user's emotions and adjusts the content of real-time feedback based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The real-time feedback unit described above is: When providing real-time feedback, we refer to the PM's current behavioral data to provide optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 32) The real-time feedback unit described above is: It estimates the user's emotions and prioritizes real-time feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The real-time feedback unit described above is: When providing real-time feedback, we take the PM's device information into consideration to provide optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned continuous learning support unit is: The system estimates the user's emotions and adjusts the learning support content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned continuous learning support unit is: When providing learning support, we refer to the PM's past learning history to provide the most suitable support. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned continuous learning support unit is: It estimates the user's emotions and prioritizes learning support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned continuous learning support unit is: When providing learning support, we take the PM's device information into consideration to provide the most suitable support. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned business simulation unit, The system estimates user emotions and adjusts the content of the business simulation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned business simulation unit, When providing business simulations, we refer to the PM's past business data to provide the optimal simulation. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned business simulation unit, It estimates user emotions and determines the priority of business simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned business simulation unit, When providing business simulations, we take into account the PM's device information to provide the optimal simulation. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned best practice provision department, We estimate user emotions and adjust best practices based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned best practice provision department, When providing best practices, we refer to the PM's past behavioral data to provide the most suitable practices. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned best practice provision department, Estimate user emotions and prioritize best practices based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned best practice provision department, When providing best practices, we will consider the PM's device information to provide the most suitable practices. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0208] 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 collection unit collects PM's behavior and performance data, An analysis unit analyzes the data collected by the aforementioned collection unit, A feedback provision unit provides individual feedback and specific improvement suggestions based on the analysis results obtained by the aforementioned analysis unit. The system includes a simulation generation unit that generates simulations and scenarios based on the feedback provided by the aforementioned feedback provision unit. A system characterized by the following features.

2. The aforementioned feedback provision unit, It features a real-time feedback unit that provides feedback in real time. The system according to feature 1.

3. The aforementioned feedback provision unit, It features a continuous learning support unit to assist with ongoing learning. The system according to feature 1.

4. The simulation generation unit, It has a business simulation department that provides an environment close to actual work. The system according to feature 1.

5. The simulation generation unit, We have a best practice provision department that provides the latest best practices. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the PM's past behavioral history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the PM's current project status and areas of interest. The system according to feature 1.

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