system

Abstract

The system according to this embodiment aims to streamline and facilitate project management. [Solution] The system according to the embodiment comprises an assignment unit, a scheduling unit, a task distribution unit, and an issue extraction unit. The assignment unit performs smart personnel assignment according to the suitability and working status of employees. The scheduling unit automatically creates a master schedule that provides an overview of the overall flow based on the personnel assigned by the assignment unit. The task distribution unit optimally distributes individual tasks based on the schedule created by the scheduling unit. The issue extraction unit monitors the progress of tasks distributed by the task distribution unit and extracts potential issues.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to make adjustments for smoothly advancing a project.

[0005] The system according to the embodiment aims to improve the efficiency of project management and advance it smoothly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an assignment unit, a scheduling unit, a task distribution unit, and an issue extraction unit. The assignment unit performs smart personnel assignments according to the suitability and working status of employees. The scheduling unit automatically creates a master schedule that provides an overview of the overall flow based on the personnel assigned by the assignment unit. The task distribution unit optimally distributes individual tasks based on the schedule created by the scheduling unit. The issue extraction unit monitors the progress of tasks distributed by the task distribution unit and extracts potential issues. [Effects of the Invention]

[0007] The system according to this embodiment can streamline and smoothly carry out project management. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 project management system according to an embodiment of the present invention is a system that evolves project management by utilizing generative AI. This project management system performs smart personnel assignment according to the suitability and work status of employees. The generative AI analyzes each employee's skill set, past project experience, and current work status, and assigns the most suitable personnel to the project. This improves project efficiency and ensures that the right people are in the right places. Next, it automatically creates a master schedule that provides an overview of the entire flow. The generative AI grasps the overall picture of the project and generates an optimal schedule considering the dependencies and priorities of each task. This makes it possible to grasp the progress of the project at a glance and makes schedule management easier. Furthermore, it optimally distributes individual tasks. The generative AI analyzes the content, difficulty, and skill set of the person in charge of each task and performs optimal task distribution. This eliminates task duplication and imbalance, and enables efficient task management. In addition, the generative AI instantly extracts potential issues. The generative AI monitors the progress of the project and the progress of each task in real time, and detects potential issues and risks early. This allows for proactive measures to be taken before problems escalate, ensuring smooth project progress. Furthermore, it instantly generates internal reports. The generation AI automatically summarizes project progress and results, generating reports. This eliminates the effort required to create reports and enables rapid information sharing. It also supports development environment selection and software development. The generation AI proposes the optimal development environment according to project requirements and goals, supporting each phase of software development. This improves development efficiency and enables the rapid development of high-quality software. Finally, it organizes various internal control procedures. The generation AI automates and efficiently manages internal control procedures associated with project progress. This reduces the effort required for internal controls and significantly improves the efficiency of project management. In this way, by utilizing generation AI, project management evolves, enabling smoother internal projects and subscription-based services to business partners. As a result, the project management system can achieve project efficiency and smooth operation.

[0029] The project management system according to this embodiment comprises an assignment unit, a scheduling unit, a task allocation unit, and an issue extraction unit. The assignment unit performs smart personnel assignments according to the suitability and availability of employees. For example, the assignment unit analyzes each employee's skill set, past project experience, and current availability to assign the most suitable personnel to the project. For example, the assignment unit can use generational AI to analyze employees' skill sets and assign the most suitable personnel. The assignment unit can also place the right people in the right positions based on past project experience. Furthermore, the assignment unit can perform efficient personnel assignments considering the current availability. The scheduling unit automatically creates a master schedule that provides an overview of the entire project flow based on the personnel assigned by the assignment unit. For example, the scheduling unit uses generational AI to grasp the overall picture of the project and generates an optimal schedule considering the dependencies and priorities of each task. For example, the scheduling unit can grasp the overall picture of the project and generate an optimal schedule considering the dependencies of each task. Furthermore, the scheduling unit can create an efficient schedule considering priorities. Furthermore, the scheduling unit can update the schedule in real time using generative AI. The task allocation unit optimally allocates individual tasks based on the schedule created by the scheduling unit. The task allocation unit can, for example, use generative AI to analyze the content, difficulty level, and skill set of each task to make the optimal task allocation. The task allocation unit can, for example, analyze the content of each task and make the optimal task allocation. The task allocation unit can also make efficient task allocation by considering the difficulty level. Furthermore, the task allocation unit can make appropriate task allocation based on the skill set of the person in charge. The issue extraction unit monitors the progress of tasks allocated by the task allocation unit and extracts potential issues. The issue extraction unit can, for example, use generative AI to monitor the project progress and the progress of each task in real time and detect potential issues and risks early.The issue identification unit can, for example, monitor the progress of a project and identify potential issues. It can also monitor the progress of each task in real time and detect risks early. Furthermore, the issue identification unit can use generative AI to take countermeasures before problems escalate. As a result, the project management system according to this embodiment can achieve increased efficiency and smoother project execution.

[0030] The Assignment Department performs smart personnel assignments based on employee suitability and workload. Specifically, the Assignment Department analyzes each employee's skill set, past project experience, and current workload in detail to assign the most suitable personnel to projects. For example, by using a generation AI to analyze employees' skill sets, it is possible to accurately understand what skills and knowledge each employee possesses. The generation AI uses employee resumes, past project reports, and internal evaluation data as input data to analyze each employee's strengths and weaknesses. This makes it possible to select the most suitable personnel for project requirements. The Assignment Department can also place the right people in the right positions based on past project experience. For example, by reassigning employees who have successfully completed similar projects in the past, the probability of project success can be increased. Furthermore, the Assignment Department performs efficient personnel assignments considering current workload. Employees' current workload is obtained from internal timesheets and project management tools, and the generation AI analyzes this data to allocate personnel without overloading them. This reduces the burden on employees and allows projects to progress smoothly. The assignment department centrally manages and updates this data in real time, enabling them to always assign personnel based on the latest information. This leads to increased efficiency and smoother project execution, and allows for optimal personnel allocation.

[0031] The scheduling department automatically creates a master schedule that provides an overview of the entire project flow based on the personnel assigned by the assignment department. Specifically, it uses a generation AI to grasp the overall picture of the project and generates an optimal schedule considering the dependencies and priorities of each task. The generation AI uses data such as the project start and end dates, the duration of each task, and resource utilization as input to calculate the optimal schedule. For example, to grasp the overall picture of the project, the generation AI analyzes the project's goals, milestones, and the detailed content of each task. This clarifies the dependencies of each task and allows it to determine which tasks need to be completed first. The scheduling department also creates an efficient schedule considering priorities. The generation AI evaluates the importance and urgency of each task and incorporates high-priority tasks into the schedule first. Furthermore, the scheduling department can also update the schedule in real time using the generation AI. For example, if the project progress or resource utilization changes, the generation AI immediately incorporates the new data and recalculates the schedule. This ensures that the scheduling department always provides a schedule based on the latest information, allowing for smooth project progress. Through these functions, the scheduling department can achieve project efficiency and smooth operation, and provide an optimal schedule.

[0032] The task allocation unit optimally distributes individual tasks based on the schedule created by the scheduling unit. Specifically, it uses a generation AI to analyze the content, difficulty level, and assigned skill set of each task to make the optimal task allocation. The generation AI uses data such as the detailed content, required skills, and time required for each task as input to select the most suitable person to handle it. For example, to analyze the content of each task, the generation AI analyzes the task description, requirements definition document, and data from similar past tasks. This allows for an accurate understanding of the task's difficulty level and required skills. The task allocation unit also considers difficulty level to make efficient task allocations. The generation AI evaluates the difficulty level of each task and assigns experienced employees to difficult tasks and new employees to easy tasks. Furthermore, the task allocation unit makes appropriate task allocations based on the assigned person's skill set. The generation AI analyzes the employee's skill set and past project experience to select the most suitable person for each task. This allows the task allocation unit to distribute tasks efficiently and effectively, ensuring smooth project progress. The task allocation unit centrally manages and updates this data in real time, enabling task allocation based on the latest information at all times. This improves project efficiency and smoothness, and allows for optimal task allocation.

[0033] The Issue Identification Unit monitors the progress of tasks assigned by the Task Allocation Unit and identifies potential issues. Specifically, it uses a generative AI to monitor the project's progress and the progress of each task in real time, enabling early detection of potential issues and risks. The generative AI uses data obtained from project management tools and task management systems as input to analyze the progress of each task. For example, to monitor the project's progress, the generative AI analyzes the completion rate of each task, the difference between planned and actual progress, and resource utilization. This allows for the identification of tasks that are behind schedule or areas where resources are insufficient. The Issue Identification Unit also monitors the progress of each task in real time to detect risks early. The generative AI analyzes the progress data of each task to identify tasks that are not progressing as planned or those with increasing risks. Furthermore, the Issue Identification Unit can use the generative AI to take countermeasures before problems escalate. Based on past data and statistical information, the generative AI predicts the probability of potential issues and risks occurring and proposes countermeasures early. This allows the Issue Identification Unit to ensure smooth project progress and minimize risks. The issue identification unit centrally manages this data and updates it in real time, enabling issue identification based on the latest information at all times. This improves the efficiency and smoothness of projects, allowing for the early detection of potential issues and the implementation of countermeasures.

[0034] The reporting department can generate internal reports. For example, the reporting department can use a generation AI to automatically summarize project progress and results and generate reports. The reporting department can also use a generation AI to automatically summarize results and generate efficient reports. Furthermore, the reporting department can use a generation AI to enable rapid information sharing. This automates the generation of internal reports and enables efficient information sharing. Some or all of the above processes in the reporting department may be performed using AI, or not. For example, the reporting department can generate reports using an AI model that takes project progress and results as input and outputs reports.

[0035] The environment selection unit can support the selection of a development environment. For example, the environment selection unit can use generative AI to propose the optimal development environment according to the project requirements and objectives. For example, the environment selection unit can analyze the project requirements and propose the optimal development environment. The environment selection unit can also propose an efficient development environment based on the objectives. Furthermore, the environment selection unit can use generative AI to support the selection of a development environment. This makes it possible to select the optimal development environment according to the project requirements. Some or all of the above processing in the environment selection unit may be performed using AI, for example, or without AI. For example, the environment selection unit can select a development environment using an AI model that takes project requirements and objectives as input and outputs the optimal development environment.

[0036] The Development Support Department can support software development. For example, it can support each phase of software development using generative AI. For example, it can analyze software development requirements and provide optimal development support. Furthermore, the Development Support Department can support each phase of development, enabling efficient software development. In addition, the Development Support Department can support the development of high-quality software using generative AI. This supports each phase of software development and improves development efficiency. Some or all of the above-described processes in the Development Support Department may be performed using AI, or not. For example, the Development Support Department can provide development support using an AI model that takes software development requirements as input and outputs optimal development support.

[0037] The Control Department can organize various procedures related to internal control. For example, the Control Department can use generative AI to automate and efficiently manage internal control procedures associated with project progress. The Control Department can also automate and efficiently manage internal control procedures. Furthermore, the Control Department can organize procedures associated with project progress to achieve efficient internal control. In addition, the Control Department can use generative AI to reduce the effort required for internal control. This improves the efficiency of project management through the automation of internal control procedures. Some or all of the above processes in the Control Department may be performed using AI, or not. For example, the Control Department can organize internal control using an AI model that takes procedures associated with project progress as input and outputs efficient internal control procedures.

[0038] The assignment department can analyze employees' past project experience and select the optimal assignment method. For example, the assignment department can assign employees to similar projects based on data from past successful projects. For example, the assignment department can assign employees to projects that take a different approach based on data from past unsuccessful projects. The assignment department can also analyze employees' past project experience and assign them to projects that best suit their skill sets. This enables optimal assignments based on employees' past experience. Some or all of the above processes in the assignment department may be performed using AI, for example, or not. For example, the assignment department can input employees' past project data into a generating AI and have the generating AI select the optimal assignment method.

[0039] The assignment department can filter assignments based on employees' current projects and areas of interest. For example, the assignment department can adjust assignments to avoid burdening employees by considering the progress of their current projects. For example, the assignment department can assign employees to projects that are likely to interest them based on their areas of interest. The assignment department can also analyze the relationship between an employee's current project and a new project and make assignments that are expected to have synergistic effects. This makes it possible to make appropriate assignments that match employees' interests and current projects. Some or all of the above processes in the assignment department may be performed using AI, for example, or not. For example, the assignment department can input data on employees' current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The assignment department can prioritize assignments based on employee geographical location information, thereby ensuring that assignments are as relevant as possible. For example, the assignment department can prioritize assigning projects that are close to the employee. The assignment department can also prioritize assignments that are less burdensome, taking into account employee commute times. Furthermore, the assignment department can perform efficient assignments based on employee geographical location information. This enables efficient assignments based on employee geographical location information. Some or all of the above processes in the assignment department may be performed using AI, for example, or without AI. For example, the assignment department can input employee geographical location information into a generation AI and have the generation AI prioritize assignments based on relevance.

[0041] The assignment department can analyze employees' social media activity during the assignment process and make relevant assignments. For example, the assignment department can assign employees to projects related to areas in which they have shown interest on social media. For example, the assignment department can analyze employees' social media activity and assign them to projects that best suit their skill sets. The assignment department can also assign employees to projects that they are likely to be interested in based on their social media activity. This enables appropriate assignments based on employees' social media activity. Some or all of the above processes in the assignment department may be performed using AI, for example, or not. For example, the assignment department can input employee social media activity data into a generating AI and have the generating AI execute relevant assignments.

[0042] The scheduling unit can adjust the level of detail in the schedule based on the importance of the project when creating the schedule. For example, the scheduling unit can provide a detailed schedule for high-importance projects, and a simplified schedule for low-importance projects. The scheduling unit can also adjust the level of detail in the schedule according to the importance of the project. This makes it possible to create an appropriate schedule according to the importance of the project. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input project importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the schedule.

[0043] The scheduling unit can apply different scheduling algorithms depending on the project category when creating a schedule. For example, the scheduling unit can apply an agile scheduling algorithm to a software development project. For example, the scheduling unit can apply the critical path method to a construction project. Furthermore, the scheduling unit can apply a phase-gate process to a research and development project. This enables the creation of an appropriate schedule according to the project category. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input project category data into a generating AI and have the generating AI execute the application of different scheduling algorithms.

[0044] The scheduling unit can determine schedule priorities based on project submission deadlines when creating schedules. For example, it can prioritize creating schedules for projects with upcoming submission deadlines, and postpone creating schedules for projects with later submission deadlines. The scheduling unit can also adjust schedule priorities based on submission deadlines. This enables the creation of appropriate schedules according to project submission deadlines. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input project submission deadline data into a generating AI and have the generating AI determine schedule priorities.

[0045] The scheduling unit can adjust the order of schedules based on the relevance of projects when creating a schedule. For example, the scheduling unit can prioritize scheduling highly relevant projects. For example, it can postpone scheduling less relevant projects. The scheduling unit can also adjust the order of schedules based on the relevance of projects. This makes it possible to create an appropriate schedule according to the relevance of projects. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input project relevance data into a generating AI and have the generating AI perform the adjustment of the schedule order.

[0046] The task allocation unit can improve the accuracy of task allocation by considering the interrelationships between tasks. For example, the task allocation unit can perform efficient task allocation by considering the dependencies between tasks. For example, the task allocation unit can prioritize the allocation of important tasks by considering the priority of tasks. Furthermore, the task allocation unit can analyze the interrelationships between tasks and perform optimal task allocation. This enables efficient task allocation that takes into account the interrelationships between tasks. Some or all of the above-described processes in the task allocation unit may be performed using AI, for example, or without AI. For example, the task allocation unit can input task interrelationship data into a generating AI and have the generating AI perform the task allocation accuracy improvement.

[0047] The task allocation unit can allocate tasks while considering the attribute information of the task submitter. For example, the task allocation unit can allocate tasks optimally by considering the skill set of the task submitter. For example, the task allocation unit can allocate tasks efficiently by considering the past performance of the task submitter. Furthermore, the task allocation unit can analyze the attribute information of the task submitter to determine the optimal task allocation. This makes it possible to allocate tasks optimally based on the attribute information of the task submitter. Some or all of the above processes in the task allocation unit may be performed using AI, for example, or without using AI. For example, the task allocation unit can input the attribute information of the task submitter into a generating AI and have the generating AI perform the task allocation.

[0048] The task allocation unit can allocate tasks while considering their geographical distribution. For example, the task allocation unit can perform efficient task allocation by considering the geographical distribution of tasks. For example, the task allocation unit can analyze the geographical distribution of tasks and perform optimal task allocation. Furthermore, the task allocation unit can perform efficient task allocation based on the geographical distribution of tasks. This makes efficient task allocation based on the geographical distribution of tasks possible. Some or all of the above-described processes in the task allocation unit may be performed using AI, for example, or without AI. For example, the task allocation unit can input geographical distribution data of tasks into a generating AI and have the generating AI perform the task allocation.

[0049] The task allocation unit can improve the accuracy of task allocation by referring to relevant literature for each task. For example, the task allocation unit can perform optimal task allocation by referring to relevant literature for each task. For example, the task allocation unit can perform efficient task allocation by analyzing relevant literature for each task. Furthermore, the task allocation unit can perform optimal task allocation based on relevant literature for each task. This makes it possible to perform optimal task allocation based on relevant literature for each task. Some or all of the above processing in the task allocation unit may be performed using AI, for example, or without AI. For example, the task allocation unit can input relevant literature data for each task into a generating AI and have the generating AI perform tasks to improve accuracy.

[0050] The problem extraction unit can predict current problems by referring to past problem data during problem extraction. For example, the problem extraction unit predicts current problems based on problem data from similar past projects. For example, the problem extraction unit can analyze past problem data and apply it to the current project. The problem extraction unit can also predict potential problems by referring to past problem data. This makes it possible to predict current problems based on past problem data. Some or all of the above processing in the problem extraction unit may be performed using AI, for example, or without using AI. For example, the problem extraction unit can input past problem data into a generating AI and have the generating AI perform a prediction of current problems.

[0051] The problem extraction unit can apply different problem analysis methods to each task category during problem extraction. For example, the problem extraction unit can apply a bug tracking method to software development tasks. For example, it can apply a risk assessment method to construction tasks. Furthermore, the problem extraction unit can apply a phase-gate analysis method to research and development tasks. This enables appropriate problem analysis according to the task category. Some or all of the above processing in the problem extraction unit may be performed using AI, for example, or without AI. For example, the problem extraction unit can input task category data into a generating AI and have the generating AI execute the application of different problem analysis methods.

[0052] The task extraction unit can analyze changes in tasks based on their submission dates during task extraction. For example, the task extraction unit can prioritize extracting tasks with upcoming submission dates. For example, it can postpone extracting tasks with later submission dates. The task extraction unit can also analyze changes in tasks based on their submission dates. This enables appropriate analysis of task changes based on task submission dates. Some or all of the above processing in the task extraction unit may be performed using AI, for example, or without AI. For example, the task extraction unit can input task submission date data into a generation AI and have the generation AI perform the analysis of changes in tasks.

[0053] The problem extraction unit can analyze problems by referring to relevant market data for the task during problem extraction. For example, the problem extraction unit analyzes the problems of a task based on market data. For example, the problem extraction unit can predict potential problems by referring to market data. The problem extraction unit can also analyze changes in problems based on market data. This enables appropriate problem analysis based on relevant market data for the task. Some or all of the above processing in the problem extraction unit may be performed using AI, for example, or without AI. For example, the problem extraction unit can input relevant market data for the task into a generating AI and have the generating AI perform the problem analysis.

[0054] The reporting unit can adjust the level of detail in reports based on the importance of the project when generating them. For example, the reporting unit can provide detailed reports for high-importance projects and simplified reports for low-importance projects. The reporting unit can also adjust the level of detail in reports according to the importance of the project. This makes it possible to generate appropriate reports according to the importance of the project. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input project importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the reports.

[0055] The reporting department can determine the priority of reports based on the project submission deadlines when generating reports. For example, the reporting department can prioritize creating reports for projects with upcoming submission deadlines, and postpone creating reports for projects with later submission deadlines. The reporting department can also adjust the priority of reports based on the submission deadlines. This enables the generation of appropriate reports according to the project submission deadlines. Some or all of the above processing in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input project submission deadline data into a generation AI and have the generation AI determine the priority of reports.

[0056] The environment selection unit can propose the optimal environment based on project requirements when selecting a development environment. For example, the environment selection unit can propose the optimal development environment based on project requirements. For example, the environment selection unit can propose the optimal development environment based on project goals. Furthermore, the environment selection unit can propose the optimal development environment based on both project requirements and goals. This makes it possible to propose the optimal development environment according to project requirements. Some or all of the above processing in the environment selection unit may be performed using AI, for example, or without AI. For example, the environment selection unit can input project requirements data into a generating AI and have the generating AI execute a proposal for the optimal development environment.

[0057] The environment selection unit can select a development environment based on the project submission date. For example, the environment selection unit can prioritize selecting a development environment for projects with an approaching submission date. For example, it can postpone selecting a development environment for projects with a distant submission date. The environment selection unit can also adjust the selection of the development environment based on the submission date. This makes it possible to select an appropriate development environment according to the project submission date. Some or all of the above processing in the environment selection unit may be performed using AI, for example, or without AI. For example, the environment selection unit can input project submission date data into a generating AI and have the generating AI perform the environment selection.

[0058] The Development Support Department can propose the optimal support method based on project requirements during development support. For example, the Development Support Department can propose the optimal development support method based on project requirements. For example, the Development Support Department can propose the optimal development support method based on project goals. Furthermore, the Development Support Department can propose the optimal development support method based on both project requirements and goals. This makes it possible to propose the optimal development support method according to project requirements. Some or all of the above processes in the Development Support Department may be performed using AI, for example, or without AI. For example, the Development Support Department can input project requirements data into a generating AI and have the generating AI execute a proposal for the optimal development support method.

[0059] The Development Support Department can adjust its support methods based on the project submission deadline. For example, it can prioritize providing support to projects with an approaching submission deadline, and postpone providing support to projects with a later submission deadline. The Development Support Department can also adjust its support methods based on the submission deadline. This enables appropriate development support according to the project's submission timing. Some or all of the above processes in the Development Support Department may be performed using AI, for example, or without AI. For example, the Development Support Department can input project submission deadline data into a generating AI and have the generating AI adjust the support methods.

[0060] The control department can propose the optimal internal control procedures based on project requirements during internal control procedures. For example, the control department can propose the optimal internal control procedures based on project requirements. For example, the control department can propose the optimal internal control procedures based on project objectives. Furthermore, the control department can propose the optimal internal control procedures based on both project requirements and objectives. This enables optimal internal control procedures tailored to project requirements. Some or all of the above processes in the control department may be performed using AI, for example, or without AI. For example, the control department can input project requirements data into a generating AI and have the generating AI propose the optimal internal control procedures.

[0061] The control department can adjust internal control procedures based on the project submission date. For example, the control department can prioritize providing internal control procedures to projects with upcoming submission dates, and postpone providing them to projects with later submission dates. The control department can also adjust the priority of internal control procedures based on the submission date. This enables appropriate internal control procedures according to the project submission date. Some or all of the above processing in the control department may be performed using AI, for example, or not. For example, the control department can input project submission date data into a generating AI and have the generating AI perform the procedure adjustments.

[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 assignment department can monitor employees' health and adjust assignments based on their health status. For example, if an employee is unwell, their assignments can be reduced to prioritize their recovery. Conversely, if an employee is in good health, they can be assigned to important projects. Furthermore, the department can regularly check employees' health status and conduct long-term health management. This enables appropriate assignments tailored to each employee's health condition.

[0064] The reporting department can provide a dashboard that visualizes project progress. For example, it can display project progress using graphs and charts to make it easier to understand visually. It can also update the progress of each task and the status of those in charge in real time, providing the latest information. Furthermore, it can highlight project risks and issues to encourage prompt action. This allows for a quick overview of project progress and enables efficient management.

[0065] The Environment Selection Department can evaluate the cost-effectiveness of development environments when selecting one. For example, it can compare the implementation and operating costs of various development environments and propose the most cost-effective one. It can also consider the maintenance costs and scalability of the environment, selecting the optimal environment from a long-term perspective. Furthermore, it can evaluate the risks associated with implementing the environment and prioritize the selection of environments with low risk. This makes it possible to select a development environment with excellent cost performance.

[0066] The Development Support Department can assist with the automation of the development process. For example, it can improve development efficiency by automating code generation and testing. It can also automate deployment to enable rapid releases. Furthermore, it can reduce the burden on developers by providing automation tools for each phase of the development process. This enables efficient development through the automation of the development process.

[0067] The control department can provide audit functions to improve the transparency of internal control procedures. For example, it can record each step of the internal control procedure and generate audit logs. It can also analyze these audit logs to detect fraud or anomalies early. Furthermore, it can compile the audit results into a report and submit it to management. This improves the transparency of internal control procedures and enables more reliable project management.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The assignment department performs smart personnel assignments based on employee suitability and workload. Specifically, it analyzes each employee's skill set, past project experience, and current workload to assign the most suitable personnel to projects. Using generational AI, it is possible to analyze employees' skill sets and assign the most suitable personnel. It is also possible to place the right person in the right position based on past project experience. Furthermore, it is possible to perform efficient personnel assignments by considering the current workload. Step 2: The scheduling department automatically creates a master schedule that provides an overview of the entire project flow based on the personnel assigned by the assignment department. Using generation AI, it grasps the overall picture of the project and generates the optimal schedule by considering the dependencies and priorities of each task. The schedule can also be updated in real time. Step 3: The task allocation unit optimally allocates individual tasks based on the schedule created by the scheduling unit. Using a generation AI, it analyzes the content, difficulty level, and the skill set of the person in charge of each task to make the optimal task allocation. Step 4: The Issue Identification Unit monitors the progress of tasks assigned by the Task Allocation Unit and identifies potential issues. Using generation AI, it monitors the project progress and the progress of each task in real time to detect potential issues and risks early.

[0070] (Example of form 2) The project management system according to an embodiment of the present invention is a system that evolves project management by utilizing generative AI. This project management system performs smart personnel assignment according to the suitability and work status of employees. The generative AI analyzes each employee's skill set, past project experience, and current work status, and assigns the most suitable personnel to the project. This improves project efficiency and ensures that the right people are in the right places. Next, it automatically creates a master schedule that provides an overview of the entire flow. The generative AI grasps the overall picture of the project and generates an optimal schedule considering the dependencies and priorities of each task. This makes it possible to grasp the progress of the project at a glance and makes schedule management easier. Furthermore, it optimally distributes individual tasks. The generative AI analyzes the content, difficulty, and skill set of the person in charge of each task and performs optimal task distribution. This eliminates task duplication and imbalance, and enables efficient task management. In addition, the generative AI instantly extracts potential issues. The generative AI monitors the progress of the project and the progress of each task in real time, and detects potential issues and risks early. This allows for proactive measures to be taken before problems escalate, ensuring smooth project progress. Furthermore, it instantly generates internal reports. The generation AI automatically summarizes project progress and results, generating reports. This eliminates the effort required to create reports and enables rapid information sharing. It also supports development environment selection and software development. The generation AI proposes the optimal development environment according to project requirements and goals, supporting each phase of software development. This improves development efficiency and enables the rapid development of high-quality software. Finally, it organizes various internal control procedures. The generation AI automates and efficiently manages internal control procedures associated with project progress. This reduces the effort required for internal controls and significantly improves the efficiency of project management. In this way, by utilizing generation AI, project management evolves, enabling smoother internal projects and subscription-based services to business partners. As a result, the project management system can achieve project efficiency and smooth operation.

[0071] The project management system according to this embodiment comprises an assignment unit, a scheduling unit, a task allocation unit, and an issue extraction unit. The assignment unit performs smart personnel assignments according to the suitability and availability of employees. For example, the assignment unit analyzes each employee's skill set, past project experience, and current availability to assign the most suitable personnel to the project. For example, the assignment unit can use generational AI to analyze employees' skill sets and assign the most suitable personnel. The assignment unit can also place the right people in the right positions based on past project experience. Furthermore, the assignment unit can perform efficient personnel assignments considering the current availability. The scheduling unit automatically creates a master schedule that provides an overview of the entire project flow based on the personnel assigned by the assignment unit. For example, the scheduling unit uses generational AI to grasp the overall picture of the project and generates an optimal schedule considering the dependencies and priorities of each task. For example, the scheduling unit can grasp the overall picture of the project and generate an optimal schedule considering the dependencies of each task. Furthermore, the scheduling unit can create an efficient schedule considering priorities. Furthermore, the scheduling unit can update the schedule in real time using generative AI. The task allocation unit optimally allocates individual tasks based on the schedule created by the scheduling unit. The task allocation unit can, for example, use generative AI to analyze the content, difficulty level, and skill set of each task to make the optimal task allocation. The task allocation unit can, for example, analyze the content of each task and make the optimal task allocation. The task allocation unit can also make efficient task allocation by considering the difficulty level. Furthermore, the task allocation unit can make appropriate task allocation based on the skill set of the person in charge. The issue extraction unit monitors the progress of tasks allocated by the task allocation unit and extracts potential issues. The issue extraction unit can, for example, use generative AI to monitor the project progress and the progress of each task in real time and detect potential issues and risks early.The issue identification unit can, for example, monitor the progress of a project and identify potential issues. It can also monitor the progress of each task in real time and detect risks early. Furthermore, the issue identification unit can use generative AI to take countermeasures before problems escalate. As a result, the project management system according to this embodiment can achieve increased efficiency and smoother project execution.

[0072] The Assignment Department performs smart personnel assignments based on employee suitability and workload. Specifically, the Assignment Department analyzes each employee's skill set, past project experience, and current workload in detail to assign the most suitable personnel to projects. For example, by using a generation AI to analyze employees' skill sets, it is possible to accurately understand what skills and knowledge each employee possesses. The generation AI uses employee resumes, past project reports, and internal evaluation data as input data to analyze each employee's strengths and weaknesses. This makes it possible to select the most suitable personnel for project requirements. The Assignment Department can also place the right people in the right positions based on past project experience. For example, by reassigning employees who have successfully completed similar projects in the past, the probability of project success can be increased. Furthermore, the Assignment Department performs efficient personnel assignments considering current workload. Employees' current workload is obtained from internal timesheets and project management tools, and the generation AI analyzes this data to allocate personnel without overloading them. This reduces the burden on employees and allows projects to progress smoothly. The assignment department centrally manages and updates this data in real time, enabling them to always assign personnel based on the latest information. This leads to increased efficiency and smoother project execution, and allows for optimal personnel allocation.

[0073] The scheduling department automatically creates a master schedule that provides an overview of the entire project flow based on the personnel assigned by the assignment department. Specifically, it uses a generation AI to grasp the overall picture of the project and generates an optimal schedule considering the dependencies and priorities of each task. The generation AI uses data such as the project start and end dates, the duration of each task, and resource utilization as input to calculate the optimal schedule. For example, to grasp the overall picture of the project, the generation AI analyzes the project's goals, milestones, and the detailed content of each task. This clarifies the dependencies of each task and allows it to determine which tasks need to be completed first. The scheduling department also creates an efficient schedule considering priorities. The generation AI evaluates the importance and urgency of each task and incorporates high-priority tasks into the schedule first. Furthermore, the scheduling department can also update the schedule in real time using the generation AI. For example, if the project progress or resource utilization changes, the generation AI immediately incorporates the new data and recalculates the schedule. This ensures that the scheduling department always provides a schedule based on the latest information, allowing for smooth project progress. Through these functions, the scheduling department can achieve project efficiency and smooth operation, and provide an optimal schedule.

[0074] The task allocation unit optimally distributes individual tasks based on the schedule created by the scheduling unit. Specifically, it uses a generation AI to analyze the content, difficulty level, and assigned skill set of each task to make the optimal task allocation. The generation AI uses data such as the detailed content, required skills, and time required for each task as input to select the most suitable person to handle it. For example, to analyze the content of each task, the generation AI analyzes the task description, requirements definition document, and data from similar past tasks. This allows for an accurate understanding of the task's difficulty level and required skills. The task allocation unit also considers difficulty level to make efficient task allocations. The generation AI evaluates the difficulty level of each task and assigns experienced employees to difficult tasks and new employees to easy tasks. Furthermore, the task allocation unit makes appropriate task allocations based on the assigned person's skill set. The generation AI analyzes the employee's skill set and past project experience to select the most suitable person for each task. This allows the task allocation unit to distribute tasks efficiently and effectively, ensuring smooth project progress. The task allocation unit centrally manages and updates this data in real time, enabling task allocation based on the latest information at all times. This improves project efficiency and smoothness, and allows for optimal task allocation.

[0075] The Issue Identification Unit monitors the progress of tasks assigned by the Task Allocation Unit and identifies potential issues. Specifically, it uses a generative AI to monitor the project's progress and the progress of each task in real time, enabling early detection of potential issues and risks. The generative AI uses data obtained from project management tools and task management systems as input to analyze the progress of each task. For example, to monitor the project's progress, the generative AI analyzes the completion rate of each task, the difference between planned and actual progress, and resource utilization. This allows for the identification of tasks that are behind schedule or areas where resources are insufficient. The Issue Identification Unit also monitors the progress of each task in real time to detect risks early. The generative AI analyzes the progress data of each task to identify tasks that are not progressing as planned or those with increasing risks. Furthermore, the Issue Identification Unit can use the generative AI to take countermeasures before problems escalate. Based on past data and statistical information, the generative AI predicts the probability of potential issues and risks occurring and proposes countermeasures early. This allows the Issue Identification Unit to ensure smooth project progress and minimize risks. The issue identification unit centrally manages this data and updates it in real time, enabling issue identification based on the latest information at all times. This improves the efficiency and smoothness of projects, allowing for the early detection of potential issues and the implementation of countermeasures.

[0076] The reporting department can generate internal reports. For example, the reporting department can use a generation AI to automatically summarize project progress and results and generate reports. The reporting department can also use a generation AI to automatically summarize results and generate efficient reports. Furthermore, the reporting department can use a generation AI to enable rapid information sharing. This automates the generation of internal reports and enables efficient information sharing. Some or all of the above processes in the reporting department may be performed using AI, or not. For example, the reporting department can generate reports using an AI model that takes project progress and results as input and outputs reports.

[0077] The environment selection unit can support the selection of a development environment. For example, the environment selection unit can use generative AI to propose the optimal development environment according to the project requirements and objectives. For example, the environment selection unit can analyze the project requirements and propose the optimal development environment. The environment selection unit can also propose an efficient development environment based on the objectives. Furthermore, the environment selection unit can use generative AI to support the selection of a development environment. This makes it possible to select the optimal development environment according to the project requirements. Some or all of the above processing in the environment selection unit may be performed using AI, for example, or without AI. For example, the environment selection unit can select a development environment using an AI model that takes project requirements and objectives as input and outputs the optimal development environment.

[0078] The Development Support Department can support software development. For example, it can support each phase of software development using generative AI. For example, it can analyze software development requirements and provide optimal development support. Furthermore, the Development Support Department can support each phase of development, enabling efficient software development. In addition, the Development Support Department can support the development of high-quality software using generative AI. This supports each phase of software development and improves development efficiency. Some or all of the above-described processes in the Development Support Department may be performed using AI, or not. For example, the Development Support Department can provide development support using an AI model that takes software development requirements as input and outputs optimal development support.

[0079] The Control Department can organize various procedures related to internal control. For example, the Control Department can use generative AI to automate and efficiently manage internal control procedures associated with project progress. The Control Department can also automate and efficiently manage internal control procedures. Furthermore, the Control Department can organize procedures associated with project progress to achieve efficient internal control. In addition, the Control Department can use generative AI to reduce the effort required for internal control. This improves the efficiency of project management through the automation of internal control procedures. Some or all of the above processes in the Control Department may be performed using AI, or not. For example, the Control Department can organize internal control using an AI model that takes procedures associated with project progress as input and outputs efficient internal control procedures.

[0080] The assignment department can estimate employees' emotions and adjust the timing of assignments based on those emotions. For example, if an employee is stressed, the assignment department can delay the assignment to allow time for relaxation. If an employee is highly motivated, the assignment department can assign them immediately to accelerate project progress. Furthermore, if an employee is tired, the assignment department can assign them after a break to promote efficient work. This enables assignments to be made at the appropriate time according to the employee'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-described processes in the assignment department may be performed using AI or not. For example, the assignment department can input employee emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The assignment department can analyze employees' past project experience and select the optimal assignment method. For example, the assignment department can assign employees to similar projects based on data from past successful projects. For example, the assignment department can assign employees to projects that take a different approach based on data from past unsuccessful projects. The assignment department can also analyze employees' past project experience and assign them to projects that best suit their skill sets. This enables optimal assignments based on employees' past experience. Some or all of the above processes in the assignment department may be performed using AI, for example, or not. For example, the assignment department can input employees' past project data into a generating AI and have the generating AI select the optimal assignment method.

[0082] The assignment department can filter assignments based on employees' current projects and areas of interest. For example, the assignment department can adjust assignments to avoid burdening employees by considering the progress of their current projects. For example, the assignment department can assign employees to projects that are likely to interest them based on their areas of interest. The assignment department can also analyze the relationship between an employee's current project and a new project and make assignments that are expected to have synergistic effects. This makes it possible to make appropriate assignments that match employees' interests and current projects. Some or all of the above processes in the assignment department may be performed using AI, for example, or not. For example, the assignment department can input data on employees' current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0083] The assignment department can estimate employees' emotions and determine assignment priorities based on those estimated emotions. For example, if an employee is stressed, the assignment department can lower the priority to reduce their burden. If an employee is highly motivated, the assignment department can raise the priority to accelerate project progress. The assignment department can also adjust priorities to allow employees to take breaks if they are tired. This enables assignments to be prioritized according to employees' 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 assignment department may be performed using AI or not. For example, the assignment department can input employee emotion data into a generative AI and have the generative AI determine assignment priorities.

[0084] The assignment department can prioritize assignments based on employee geographical location information, thereby ensuring that assignments are as relevant as possible. For example, the assignment department can prioritize assigning projects that are close to the employee. The assignment department can also prioritize assignments that are less burdensome, taking into account employee commute times. Furthermore, the assignment department can perform efficient assignments based on employee geographical location information. This enables efficient assignments based on employee geographical location information. Some or all of the above processes in the assignment department may be performed using AI, for example, or without AI. For example, the assignment department can input employee geographical location information into a generation AI and have the generation AI prioritize assignments based on relevance.

[0085] The assignment department can analyze employees' social media activity during the assignment process and make relevant assignments. For example, the assignment department can assign employees to projects related to areas in which they have shown interest on social media. For example, the assignment department can analyze employees' social media activity and assign them to projects that best suit their skill sets. The assignment department can also assign employees to projects that they are likely to be interested in based on their social media activity. This enables appropriate assignments based on employees' social media activity. Some or all of the above processes in the assignment department may be performed using AI, for example, or not. For example, the assignment department can input employee social media activity data into a generating AI and have the generating AI execute relevant assignments.

[0086] The scheduling unit can estimate employees' emotions and adjust the way the schedule is presented based on those emotions. For example, if an employee is stressed, the scheduling unit can provide a simple schedule display. If an employee is relaxed, the scheduling unit can provide a detailed schedule display. Furthermore, if an employee is in a hurry, the scheduling unit can provide a concise schedule display. This enables appropriate schedule representation according to the employee'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 scheduling unit may be performed using AI, or not. For example, the scheduling unit can input employee emotion data into a generative AI and have the generative AI adjust the way the schedule is presented.

[0087] The scheduling unit can adjust the level of detail in the schedule based on the importance of the project when creating the schedule. For example, the scheduling unit can provide a detailed schedule for high-importance projects, and a simplified schedule for low-importance projects. The scheduling unit can also adjust the level of detail in the schedule according to the importance of the project. This makes it possible to create an appropriate schedule according to the importance of the project. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input project importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the schedule.

[0088] The scheduling unit can apply different scheduling algorithms depending on the project category when creating a schedule. For example, the scheduling unit can apply an agile scheduling algorithm to a software development project. For example, the scheduling unit can apply the critical path method to a construction project. Furthermore, the scheduling unit can apply a phase-gate process to a research and development project. This enables the creation of an appropriate schedule according to the project category. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input project category data into a generating AI and have the generating AI execute the application of different scheduling algorithms.

[0089] The scheduling unit can estimate an employee's emotions and adjust the length of the schedule based on the estimated emotions. For example, if an employee is stressed, the scheduling unit can provide a shorter schedule. If an employee is relaxed, the scheduling unit can provide a longer schedule. Furthermore, if an employee is in a hurry, the scheduling unit can provide a concise, short schedule. This ensures that an appropriate schedule length is provided according to the employee'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 scheduling unit may be performed using AI, or not. For example, the scheduling unit can input employee emotion data into a generative AI and have the generative AI adjust the schedule length.

[0090] The scheduling unit can determine schedule priorities based on project submission deadlines when creating schedules. For example, it can prioritize creating schedules for projects with upcoming submission deadlines, and postpone creating schedules for projects with later submission deadlines. The scheduling unit can also adjust schedule priorities based on submission deadlines. This enables the creation of appropriate schedules according to project submission deadlines. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input project submission deadline data into a generating AI and have the generating AI determine schedule priorities.

[0091] The scheduling unit can adjust the order of schedules based on the relevance of projects when creating a schedule. For example, the scheduling unit can prioritize scheduling highly relevant projects. For example, it can postpone scheduling less relevant projects. The scheduling unit can also adjust the order of schedules based on the relevance of projects. This makes it possible to create an appropriate schedule according to the relevance of projects. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input project relevance data into a generating AI and have the generating AI perform the adjustment of the schedule order.

[0092] The task allocation unit can estimate employees' emotions and adjust task allocation criteria based on the estimated emotions. For example, if an employee is stressed, the task allocation unit can prioritize assigning less burdensome tasks. For example, if an employee is highly motivated, the task allocation unit can prioritize assigning important tasks. Furthermore, if an employee is tired, the task allocation unit can adjust task allocation to include breaks. This enables appropriate task allocation according to employees' 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 task allocation unit may be performed using AI or not. For example, the task allocation unit can input employee emotion data into a generative AI and have the generative AI adjust the task allocation criteria.

[0093] The task allocation unit can improve the accuracy of task allocation by considering the interrelationships between tasks. For example, the task allocation unit can perform efficient task allocation by considering the dependencies between tasks. For example, the task allocation unit can prioritize the allocation of important tasks by considering the priority of tasks. Furthermore, the task allocation unit can analyze the interrelationships between tasks and perform optimal task allocation. This enables efficient task allocation that takes into account the interrelationships between tasks. Some or all of the above-described processes in the task allocation unit may be performed using AI, for example, or without AI. For example, the task allocation unit can input task interrelationship data into a generating AI and have the generating AI perform the task allocation accuracy improvement.

[0094] The task allocation unit can allocate tasks while considering the attribute information of the task submitter. For example, the task allocation unit can allocate tasks optimally by considering the skill set of the task submitter. For example, the task allocation unit can allocate tasks efficiently by considering the past performance of the task submitter. Furthermore, the task allocation unit can analyze the attribute information of the task submitter to determine the optimal task allocation. This makes it possible to allocate tasks optimally based on the attribute information of the task submitter. Some or all of the above processes in the task allocation unit may be performed using AI, for example, or without using AI. For example, the task allocation unit can input the attribute information of the task submitter into a generating AI and have the generating AI perform the task allocation.

[0095] The task allocation unit can estimate employees' emotions and adjust the order in which task allocation results are displayed based on the estimated emotions. For example, if an employee is feeling stressed, the task allocation unit can display less burdensome tasks first. For example, if an employee is highly motivated, the task allocation unit can display important tasks first. Furthermore, if an employee is tired, the task allocation unit can display task allocation results with breaks in between. This makes it possible to display task allocation results in an appropriate order according to the employee's emotions. 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 task allocation unit may be performed using AI, or not using AI. For example, the task allocation unit can input employee emotion data into the generative AI and have the generative AI adjust the display order of task allocation results.

[0096] The task allocation unit can allocate tasks while considering their geographical distribution. For example, the task allocation unit can perform efficient task allocation by considering the geographical distribution of tasks. For example, the task allocation unit can analyze the geographical distribution of tasks and perform optimal task allocation. Furthermore, the task allocation unit can perform efficient task allocation based on the geographical distribution of tasks. This makes efficient task allocation based on the geographical distribution of tasks possible. Some or all of the above-described processes in the task allocation unit may be performed using AI, for example, or without AI. For example, the task allocation unit can input geographical distribution data of tasks into a generating AI and have the generating AI perform the task allocation.

[0097] The task allocation unit can improve the accuracy of task allocation by referring to relevant literature for each task. For example, the task allocation unit can perform optimal task allocation by referring to relevant literature for each task. For example, the task allocation unit can perform efficient task allocation by analyzing relevant literature for each task. Furthermore, the task allocation unit can perform optimal task allocation based on relevant literature for each task. This makes it possible to perform optimal task allocation based on relevant literature for each task. Some or all of the above processing in the task allocation unit may be performed using AI, for example, or without AI. For example, the task allocation unit can input relevant literature data for each task into a generating AI and have the generating AI perform tasks to improve accuracy.

[0098] The issue extraction unit can estimate an employee's emotions and adjust how issues are displayed based on the estimated emotions. For example, if an employee is stressed, the issue extraction unit can provide a simple issue display. If an employee is relaxed, the issue extraction unit can provide a detailed issue display. Furthermore, if an employee is in a hurry, the issue extraction unit can provide a concise issue display. This enables appropriate issue display according to the employee'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 processing in the issue extraction unit may be performed using AI, or not using AI. For example, the issue extraction unit can input employee emotion data into the generative AI and have the generative AI adjust how issues are displayed.

[0099] The problem extraction unit can predict current problems by referring to past problem data during problem extraction. For example, the problem extraction unit predicts current problems based on problem data from similar past projects. For example, the problem extraction unit can analyze past problem data and apply it to the current project. The problem extraction unit can also predict potential problems by referring to past problem data. This makes it possible to predict current problems based on past problem data. Some or all of the above processing in the problem extraction unit may be performed using AI, for example, or without using AI. For example, the problem extraction unit can input past problem data into a generating AI and have the generating AI perform a prediction of current problems.

[0100] The problem extraction unit can apply different problem analysis methods to each task category during problem extraction. For example, the problem extraction unit can apply a bug tracking method to software development tasks. For example, it can apply a risk assessment method to construction tasks. Furthermore, the problem extraction unit can apply a phase-gate analysis method to research and development tasks. This enables appropriate problem analysis according to the task category. Some or all of the above processing in the problem extraction unit may be performed using AI, for example, or without AI. For example, the problem extraction unit can input task category data into a generating AI and have the generating AI execute the application of different problem analysis methods.

[0101] The issue extraction unit can estimate employees' emotions and adjust the importance of issues based on those emotions. For example, if an employee is stressed, the issue extraction unit can prioritize displaying low-importance issues. For example, if an employee is highly motivated, the issue extraction unit can prioritize displaying high-importance issues. Furthermore, if an employee is tired, the issue extraction unit can prioritize displaying low-importance issues. This enables appropriate adjustment of issue importance according to employees' 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 processing in the issue extraction unit may be performed using AI, or not. For example, the issue extraction unit can input employee emotion data into a generative AI and have the generative AI perform the adjustment of issue importance.

[0102] The task extraction unit can analyze changes in tasks based on their submission dates during task extraction. For example, the task extraction unit can prioritize extracting tasks with upcoming submission dates. For example, it can postpone extracting tasks with later submission dates. The task extraction unit can also analyze changes in tasks based on their submission dates. This enables appropriate analysis of task changes based on task submission dates. Some or all of the above processing in the task extraction unit may be performed using AI, for example, or without AI. For example, the task extraction unit can input task submission date data into a generation AI and have the generation AI perform the analysis of changes in tasks.

[0103] The problem extraction unit can analyze problems by referring to relevant market data for the task during problem extraction. For example, the problem extraction unit analyzes the problems of a task based on market data. For example, the problem extraction unit can predict potential problems by referring to market data. The problem extraction unit can also analyze changes in problems based on market data. This enables appropriate problem analysis based on relevant market data for the task. Some or all of the above processing in the problem extraction unit may be performed using AI, for example, or without AI. For example, the problem extraction unit can input relevant market data for the task into a generating AI and have the generating AI perform the problem analysis.

[0104] The reporting department can estimate employees' emotions and adjust the presentation of reports based on those estimated emotions. For example, if an employee is stressed, the reporting department can provide a simple report. If an employee is relaxed, the reporting department can provide a detailed report. Furthermore, if an employee is in a hurry, the reporting department can provide a concise report. This allows for appropriate presentation of reports according to the employee'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 reporting department may be performed using AI or not. For example, the reporting department can input employee emotion data into a generative AI and have the generative AI adjust the presentation of the reports.

[0105] The reporting unit can adjust the level of detail in reports based on the importance of the project when generating them. For example, the reporting unit can provide detailed reports for high-importance projects and simplified reports for low-importance projects. The reporting unit can also adjust the level of detail in reports according to the importance of the project. This makes it possible to generate appropriate reports according to the importance of the project. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input project importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the reports.

[0106] The reporting department can estimate an employee's emotions and adjust the length of the report based on the estimated emotions. For example, if an employee is stressed, the reporting department can provide a short report. If an employee is relaxed, the reporting department can provide a long report. Furthermore, if an employee is in a hurry, the reporting department can provide a concise report. This ensures that the report length is appropriate to the employee'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 reporting department may be performed using AI or not. For example, the reporting department can input employee emotion data into a generative AI and have the generative AI adjust the length of the report.

[0107] The reporting department can determine the priority of reports based on the project submission deadlines when generating reports. For example, the reporting department can prioritize creating reports for projects with upcoming submission deadlines, and postpone creating reports for projects with later submission deadlines. The reporting department can also adjust the priority of reports based on the submission deadlines. This enables the generation of appropriate reports according to the project submission deadlines. Some or all of the above processing in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input project submission deadline data into a generation AI and have the generation AI determine the priority of reports.

[0108] The environment selection unit can estimate employees' emotions and adjust the development environment selection method based on the estimated emotions. For example, if an employee is stressed, the environment selection unit can provide a simple development environment. For example, if an employee is relaxed, the environment selection unit can provide a detailed development environment. Furthermore, if an employee is in a hurry, the environment selection unit can provide a concise development environment. This makes it possible to select an appropriate development environment according to the employee'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 environment selection unit may be performed using AI, for example, or not using AI. For example, the environment selection unit can input employee emotion data into a generative AI and have the generative AI adjust the development environment selection method.

[0109] The environment selection unit can propose the optimal environment based on project requirements when selecting a development environment. For example, the environment selection unit can propose the optimal development environment based on project requirements. For example, the environment selection unit can propose the optimal development environment based on project goals. Furthermore, the environment selection unit can propose the optimal development environment based on both project requirements and goals. This makes it possible to propose the optimal development environment according to project requirements. Some or all of the above processing in the environment selection unit may be performed using AI, for example, or without AI. For example, the environment selection unit can input project requirements data into a generating AI and have the generating AI execute a proposal for the optimal development environment.

[0110] The environment selection unit can estimate employees' emotions and determine development environment priorities based on those estimated emotions. For example, if an employee is stressed, the environment selection unit can prioritize providing a simple development environment. If an employee is relaxed, the environment selection unit can prioritize providing a detailed development environment. Furthermore, if an employee is in a hurry, the environment selection unit can prioritize providing a concise development environment. This ensures that appropriate development environment priorities are provided according to the employee'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-described processes in the environment selection unit may be performed using AI or not. For example, the environment selection unit can input employee emotion data into a generative AI and have the generative AI determine the development environment priorities.

[0111] The environment selection unit can select a development environment based on the project submission date. For example, the environment selection unit can prioritize selecting a development environment for projects with an approaching submission date. For example, it can postpone selecting a development environment for projects with a distant submission date. The environment selection unit can also adjust the selection of the development environment based on the submission date. This makes it possible to select an appropriate development environment according to the project submission date. Some or all of the above processing in the environment selection unit may be performed using AI, for example, or without AI. For example, the environment selection unit can input project submission date data into a generating AI and have the generating AI perform the environment selection.

[0112] The Development Support Department can estimate employees' emotions and adjust its development support methods based on those estimates. For example, if an employee is stressed, the Development Support Department can provide simple development support. If an employee is relaxed, the Development Support Department can provide detailed development support. Furthermore, if an employee is in a hurry, the Development Support Department can provide concise development support. This enables appropriate development support tailored to the employee'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-described processes in the Development Support Department may be performed using AI or not. For example, the Development Support Department can input employee emotion data into a generative AI and have the generative AI adjust its development support methods.

[0113] The Development Support Department can propose the optimal support method based on project requirements during development support. For example, the Development Support Department can propose the optimal development support method based on project requirements. For example, the Development Support Department can propose the optimal development support method based on project goals. Furthermore, the Development Support Department can propose the optimal development support method based on both project requirements and goals. This makes it possible to propose the optimal development support method according to project requirements. Some or all of the above processes in the Development Support Department may be performed using AI, for example, or without AI. For example, the Development Support Department can input project requirements data into a generating AI and have the generating AI execute a proposal for the optimal development support method.

[0114] The Development Support Department can estimate employees' emotions and determine the priority of development support based on those emotions. For example, if an employee is stressed, the Development Support Department can prioritize providing simple development support. If an employee is relaxed, the Development Support Department can prioritize providing detailed development support. Furthermore, if an employee is in a hurry, the Development Support Department can prioritize providing concise development support. This ensures that development support is prioritized appropriately according to the employee'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 Development Support Department may be performed using AI or not. For example, the Development Support Department can input employee emotion data into a generative AI and have the generative AI determine the priority of development support.

[0115] The Development Support Department can adjust its support methods based on the project submission deadline. For example, it can prioritize providing support to projects with an approaching submission deadline, and postpone providing support to projects with a later submission deadline. The Development Support Department can also adjust its support methods based on the submission deadline. This enables appropriate development support according to the project's submission timing. Some or all of the above processes in the Development Support Department may be performed using AI, for example, or without AI. For example, the Development Support Department can input project submission deadline data into a generating AI and have the generating AI adjust the support methods.

[0116] The control department can estimate employees' emotions and adjust internal control procedures based on those estimated emotions. For example, if an employee is stressed, the control department can provide simple internal control procedures. If an employee is relaxed, the control department can provide detailed internal control procedures. Furthermore, if an employee is in a hurry, the control department can provide concise internal control procedures. This enables appropriate internal control procedures tailored to the employee'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 control department may be performed using AI or not. For example, the control department can input employee emotion data into a generative AI and have the generative AI adjust the internal control procedures.

[0117] The control department can propose the optimal internal control procedures based on project requirements during internal control procedures. For example, the control department can propose the optimal internal control procedures based on project requirements. For example, the control department can propose the optimal internal control procedures based on project objectives. Furthermore, the control department can propose the optimal internal control procedures based on both project requirements and objectives. This enables optimal internal control procedures tailored to project requirements. Some or all of the above processes in the control department may be performed using AI, for example, or without AI. For example, the control department can input project requirements data into a generating AI and have the generating AI propose the optimal internal control procedures.

[0118] The control department can estimate employees' emotions and determine the priority of internal control procedures based on those estimated emotions. For example, if an employee is stressed, the control department can prioritize simple internal control procedures. If an employee is relaxed, the control department can prioritize detailed internal control procedures. Furthermore, if an employee is in a hurry, the control department can prioritize concise internal control procedures. This ensures that appropriate prioritization of internal control procedures is provided according to the employee'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 control department may be performed using AI or not. For example, the control department can input employee emotion data into a generative AI and have the generative AI determine the priority of internal control procedures.

[0119] The control department can adjust internal control procedures based on the project submission date. For example, the control department can prioritize providing internal control procedures to projects with upcoming submission dates, and postpone providing them to projects with later submission dates. The control department can also adjust the priority of internal control procedures based on the submission date. This enables appropriate internal control procedures according to the project submission date. Some or all of the above processing in the control department may be performed using AI, for example, or not. For example, the control department can input project submission date data into a generating AI and have the generating AI perform the procedure adjustments.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The assignment department can monitor employees' health and adjust assignments based on their health status. For example, if an employee is unwell, their assignments can be reduced to prioritize their recovery. Conversely, if an employee is in good health, they can be assigned to important projects. Furthermore, the department can regularly check employees' health status and conduct long-term health management. This enables appropriate assignments tailored to each employee's health condition.

[0122] The reporting department can provide a dashboard that visualizes project progress. For example, it can display project progress using graphs and charts to make it easier to understand visually. It can also update the progress of each task and the status of those in charge in real time, providing the latest information. Furthermore, it can highlight project risks and issues to encourage prompt action. This allows for a quick overview of project progress and enables efficient management.

[0123] The Environment Selection Department can evaluate the cost-effectiveness of development environments when selecting one. For example, it can compare the implementation and operating costs of various development environments and propose the most cost-effective one. It can also consider the maintenance costs and scalability of the environment, selecting the optimal environment from a long-term perspective. Furthermore, it can evaluate the risks associated with implementing the environment and prioritize the selection of environments with low risk. This makes it possible to select a development environment with excellent cost performance.

[0124] The Development Support Department can assist with the automation of the development process. For example, it can improve development efficiency by automating code generation and testing. It can also automate deployment to enable rapid releases. Furthermore, it can reduce the burden on developers by providing automation tools for each phase of the development process. This enables efficient development through the automation of the development process.

[0125] The control department can provide audit functions to improve the transparency of internal control procedures. For example, it can record each step of the internal control procedure and generate audit logs. It can also analyze these audit logs to detect fraud or anomalies early. Furthermore, it can compile the audit results into a report and submit it to management. This improves the transparency of internal control procedures and enables more reliable project management.

[0126] The assignment department can estimate employees' emotions and adjust the timing of assignments based on those estimates. For example, if an employee is feeling stressed, the assignment can be delayed to allow time for relaxation. If an employee is highly motivated, the assignment can be made immediately to accelerate project progress. Furthermore, if an employee is tired, the assignment can be made after a break to promote efficient work. This allows for assignments to be made at the appropriate time, tailored to each employee's emotional state.

[0127] The scheduling function can estimate employees' emotions and adjust the way schedules are presented based on those estimates. For example, if an employee is stressed, a simple schedule display can be provided. If an employee is relaxed, a detailed schedule display can be provided. Furthermore, if an employee is in a hurry, a concise schedule display can be provided. This enables appropriate schedule presentation tailored to each employee's emotions.

[0128] The task allocation unit can estimate employees' emotions and adjust task allocation criteria based on those estimates. For example, if an employee is stressed, it can prioritize assigning less burdensome tasks. If an employee is highly motivated, it can prioritize assigning important tasks. Furthermore, if an employee is tired, it can adjust task allocation to include breaks. This enables appropriate task allocation tailored to employees' emotions.

[0129] The issue extraction unit can estimate employees' emotions and adjust how issues are displayed based on those estimates. For example, if an employee is stressed, a simple issue display can be provided. If an employee is relaxed, a detailed issue display can be provided. Furthermore, if an employee is in a hurry, a concise issue display can be provided. This enables the display of issues appropriately according to the employee's emotions.

[0130] The reporting department can estimate employees' emotions and adjust the presentation of reports based on those estimates. For example, if an employee is stressed, a simple report can be provided. If an employee is relaxed, a detailed report can be provided. Furthermore, if an employee is in a hurry, a concise report can be provided. This allows for the appropriate presentation of reports tailored to each employee's emotional state.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The assignment department performs smart personnel assignments based on employee suitability and workload. Specifically, it analyzes each employee's skill set, past project experience, and current workload to assign the most suitable personnel to projects. Using generational AI, it is possible to analyze employees' skill sets and assign the most suitable personnel. It is also possible to place the right person in the right position based on past project experience. Furthermore, it is possible to perform efficient personnel assignments by considering the current workload. Step 2: The scheduling department automatically creates a master schedule that provides an overview of the entire project flow based on the personnel assigned by the assignment department. Using generation AI, it grasps the overall picture of the project and generates the optimal schedule by considering the dependencies and priorities of each task. The schedule can also be updated in real time. Step 3: The task allocation unit optimally allocates individual tasks based on the schedule created by the scheduling unit. Using a generation AI, it analyzes the content, difficulty level, and the skill set of the person in charge of each task to make the optimal task allocation. Step 4: The Issue Identification Unit monitors the progress of tasks assigned by the Task Allocation Unit and identifies potential issues. Using generation AI, it monitors the project progress and the progress of each task in real time to detect potential issues and risks early.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements mentioned above, including the assignment unit, scheduling unit, task allocation unit, issue extraction unit, reporting unit, environment selection unit, development support unit, and control unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the assignment unit is implemented by the control unit 46A of the smart device 14, which analyzes employees' skill sets and work status to assign the most suitable personnel. The scheduling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which grasps the overall picture of the project and generates a master schedule. The task allocation unit is implemented by, for example, the control unit 46A of the smart device 14, which analyzes the content and difficulty of each task to perform optimal task allocation. The issue extraction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors the progress of the project and extracts potential issues. The reporting unit is implemented by, for example, the control unit 46A of the smart device 14, which summarizes the progress and results of the project and generates a report. The environment selection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes the optimal development environment according to the project requirements. The development support unit is implemented, for example, by the control unit 46A of the smart device 14, and supports each phase of software development. The control unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automates and efficiently manages internal control procedures. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements mentioned above, including the assignment unit, scheduling unit, task allocation unit, issue extraction unit, reporting unit, environment selection unit, development support unit, and control unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the assignment unit is implemented by the control unit 46A of the smart glasses 214, which analyzes employees' skill sets and work status to assign the most suitable personnel. The scheduling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which grasps the overall picture of the project and generates a master schedule. The task allocation unit is implemented by, for example, the control unit 46A of the smart glasses 214, which analyzes the content and difficulty of each task to perform optimal task allocation. The issue extraction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors the progress of the project and extracts potential issues. The reporting unit is implemented by, for example, the control unit 46A of the smart glasses 214, which summarizes the progress and results of the project and generates a report. The environment selection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes the optimal development environment according to the project requirements. The development support unit is implemented, for example, by the control unit 46A of the smart glasses 214, and supports each phase of software development. The control unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automates and efficiently manages internal control procedures. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements mentioned above, including the assignment unit, scheduling unit, task distribution unit, issue extraction unit, reporting unit, environment selection unit, development support unit, and control unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the assignment unit is implemented by the control unit 46A of the headset terminal 314, which analyzes employees' skill sets and work status to assign the most suitable personnel. The scheduling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which grasps the overall picture of the project and generates a master schedule. The task distribution unit is implemented by, for example, the control unit 46A of the headset terminal 314, which analyzes the content and difficulty of each task to perform optimal task distribution. The issue extraction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors the progress of the project and extracts potential issues. The reporting unit is implemented by, for example, the control unit 46A of the headset terminal 314, which summarizes the progress and results of the project and generates a report. The environment selection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes the optimal development environment according to the project requirements. The development support unit is implemented, for example, by the control unit 46A of the headset terminal 314, and supports each phase of software development. The control unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automates and efficiently manages internal control procedures. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements mentioned above, including the assignment unit, scheduling unit, task allocation unit, issue extraction unit, reporting unit, environment selection unit, development support unit, and control unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the assignment unit is implemented by the control unit 46A of the robot 414, which analyzes the skill sets and working conditions of employees to assign the most suitable personnel. The scheduling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which grasps the overall picture of the project and generates a master schedule. The task allocation unit is implemented by, for example, the control unit 46A of the robot 414, which analyzes the content and difficulty level of each task to perform optimal task allocation. The issue extraction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors the progress of the project and extracts potential issues. The reporting unit is implemented by, for example, the control unit 46A of the robot 414, which summarizes the progress and results of the project and generates a report. The environment selection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes the optimal development environment according to the project requirements. The development support unit is implemented, for example, by the control unit 46A of the robot 414, and supports each phase of software development. The control unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automates and efficiently manages internal control procedures. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) The Assignment Department performs smart personnel assignments that are tailored to the aptitudes and workloads of employees, A scheduling unit automatically creates a master schedule that provides an overview of the entire process based on the personnel assigned by the aforementioned assignment unit, A task allocation unit that optimally allocates individual tasks based on the schedule created by the aforementioned scheduling unit, The system includes a task extraction unit that monitors the progress of tasks allocated by the task allocation unit and extracts potential issues. A system characterized by the following features. (Note 2) It has a reporting department that generates internal reports. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an environment selection department to support the selection of development environments. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a development support department to assist with software development. The system described in Appendix 1, characterized by the features described herein. (Note 5) The company has a control department that organizes various procedures related to internal controls. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned assignment unit is, The system estimates employees' emotions and adjusts assignment timing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned assignment unit is, Analyze employees' past project experience to select the optimal assignment method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned assignment unit is, During the assignment process, filtering is performed based on the employee's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned assignment unit is, The system estimates employees' emotions and prioritizes assignments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned assignment unit is, When assigning tasks, we prioritize highly relevant assignments by taking into account employees' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned assignment unit is, During the assignment process, we analyze employees' social media activity and assign relevant tasks accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned scheduling unit is The system estimates employees' emotions and adjusts how schedules are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned scheduling unit is When creating a schedule, adjust the level of detail based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned scheduling unit is When creating a schedule, different scheduling algorithms are applied depending on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned scheduling unit is The system estimates employees' emotions and adjusts the length of schedules based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned scheduling unit is When creating a schedule, prioritize the schedule based on the project submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned scheduling unit is When creating a schedule, adjust the order of the schedule based on the relevance of the projects. The system described in Appendix 1, characterized by the features described herein. (Note 18) The task allocation unit, Estimate employees' emotions and adjust task allocation criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The task allocation unit, When assigning tasks, improve the accuracy of task assignment by considering the interrelationships between tasks. The system described in Appendix 1, characterized by the features described herein. (Note 20) The task allocation unit, When assigning tasks, consider the attribute information of the task submitter. The system described in Appendix 1, characterized by the features described herein. (Note 21) The task allocation unit, The system estimates employees' emotions and adjusts the order in which task assignment results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The task allocation unit, When allocating tasks, consider the geographical distribution of those tasks. The system described in Appendix 1, characterized by the features described herein. (Note 23) The task allocation unit, When assigning tasks, refer to relevant literature to improve the accuracy of task assignment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned problem extraction unit, The system estimates employees' emotions and adjusts how issues are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned problem extraction unit, When identifying issues, we refer to past issue data to predict current issues. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned problem extraction unit, When identifying issues, different problem analysis methods are applied to each task category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned problem extraction unit, The system estimates employees' emotions and adjusts the importance of issues based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned problem extraction unit, When identifying issues, analyze how those issues change based on the timing of task submissions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned problem extraction unit, When identifying problems, analyze them by referring to relevant market data for those tasks. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reporting department, We estimate the emotions of our employees and adjust the way reports are presented based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned reporting department, When generating reports, adjust the level of detail in the reports based on the importance of the project. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned reporting department, Estimate employees' emotions and adjust the length of reports based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned reporting department, When generating reports, prioritize them based on the project's submission deadline. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned environment selection unit, We estimate employees' emotions and adjust the development environment selection method based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned environment selection unit, When selecting a development environment, we propose the optimal environment based on the project requirements. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned environment selection unit, The system estimates employee sentiment and prioritizes the development environment based on that estimation. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned environment selection unit, When selecting a development environment, the environment should be chosen based on the project submission deadline. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned Development Support Department, We estimate employees' emotions and adjust development support methods based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned Development Support Department, When providing development support, we propose the most suitable support method based on the project requirements. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned Development Support Department, The system estimates employees' emotions and prioritizes development support based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned Development Support Department, During development support, we adjust the support method based on the project submission date. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned control unit, Estimate employees' sentiments and adjust internal control procedures based on those estimated sentiments. The system described in Appendix 5, characterized by the features described herein. (Note 43) The aforementioned control unit, When performing internal control procedures, we propose the most suitable procedures based on the project requirements. The system described in Appendix 5, characterized by the features described herein. (Note 44) The aforementioned control unit, Estimate employee sentiment and determine the priority of internal control procedures based on the estimated employee sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 45) The aforementioned control unit, During internal control procedures, adjust the procedures based on the project submission date. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0205] 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 Assignment Department performs smart personnel assignments that are tailored to the aptitudes and workloads of employees, A scheduling unit automatically creates a master schedule that provides an overview of the entire process based on the personnel assigned by the aforementioned assignment unit, A task allocation unit that optimally allocates individual tasks based on the schedule created by the aforementioned scheduling unit, The system includes a task extraction unit that monitors the progress of tasks allocated by the task allocation unit and extracts potential issues. A system characterized by the following features.

2. It has a reporting department that generates internal reports. The system according to feature 1.

3. It includes an environment selection department to support the selection of development environments. The system according to feature 1.

4. It has a development support department to assist with software development. The system according to feature 1.

5. The company has a control department that organizes various procedures related to internal controls. The system according to feature 1.

6. The aforementioned assignment unit is, The system estimates employees' emotions and adjusts assignment timing based on those estimated emotions. The system according to feature 1.

7. The aforementioned assignment unit is, Analyze employees' past project experience to select the optimal assignment method. The system according to feature 1.

8. The aforementioned assignment unit is, During the assignment process, filtering is performed based on the employee's current projects and areas of interest. The system according to feature 1.

9. The aforementioned assignment unit is, The system estimates employees' emotions and prioritizes assignments based on those estimated emotions. The system according to feature 1.

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