system

Abstract

We provide the system. [Solution] A means for acquiring characteristic information of each work device, A means for automatically assigning tasks based on the load status of each work device, A means of monitoring the progress of the production process in real time and displaying the progress status, A means for generating an evaluation report based on collected performance information, A means of issuing notifications when abnormal events or delays are detected, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 project management, it has become a problem that management staff and team leaders spend a lot of time on task assignment and progress management, which may delay the progress of the project. Also, it is difficult to appropriately allocate roles for each team member, and efficient task assignment based on skill sets may not be carried out. Furthermore, there is also a problem that performance evaluation after project completion is often conducted subjectively, and the fairness of the evaluation is not guaranteed.

Means for Solving the Problems

[0005] This invention provides a system that can acquire the skill information and workload status of each member in real time and automatically assign tasks based on this information. Furthermore, it provides a structure that enables managers and team leaders to efficiently manage projects by monitoring project progress in real time and visualizing the progress. At the end of the project, it enables fair and transparent personnel evaluations by generating an objective evaluation report based on the collected performance information.

[0006] A "task" refers to a specific unit of work that needs to be performed within a project or task.

[0007] A "management system" refers to an information processing device that has the function of supervising the progress of a project or business and operating it efficiently.

[0008] "Members" refer to individuals who belong to a project or organization and are responsible for specific roles or tasks.

[0009] "Skills information" refers to data on the skills and expertise possessed by each member.

[0010] "Load status" refers to information indicating the degree of tasks and workload currently assigned to each member.

[0011] "Automatic assignment" refers to a function where the system assigns tasks to executors without requiring any action from the user, based on predefined criteria.

[0012] "Progress" refers to an indicator or state that shows how far a project or task has progressed.

[0013] "Monitoring in real time" means instantly understanding the progress of work or processes and updating it as needed.

[0014] "Means of displaying progress" refers to interfaces or tools that visually show the progress of a project or task.

[0015] "Performance information" refers to data related to the work efficiency of each team member and the quality of the deliverables.

[0016] "Evaluation report" refers to a document that analyzes the performance of each team member based on performance information and summarizes the results.

Brief Description of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 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.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] The 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.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] The system for carrying out the present invention has the function of acquiring skill information of each member, optimally assigning tasks based on this information, and monitoring project progress in real time. The embodiments thereof are described in detail below.

[0039] First, as soon as the project starts, the server accesses the database to retrieve the skills information of each member. This includes past work history, acquired skills, languages, etc. This information can be entered in advance by each member using a terminal.

[0040] Next, the server monitors the workload of each member. This workload is calculated based on factors such as the number of tasks already assigned to each member, their progress, and the remaining time. Based on this, the server automatically assigns appropriate tasks to each member. For example, a member skilled in programming will be given priority in coding-related tasks.

[0041] Furthermore, progress data entered via the terminal is transmitted to the server in real time. The server analyzes this data and updates a dashboard that visually represents the overall project progress. This dashboard can be viewed by the user at any time via the terminal, making it easy to understand the current status of the project. If any delays or anomalies are detected, the server is configured to immediately notify the user.

[0042] Furthermore, upon project completion, the server compiles all performance information and generates an objective evaluation report. This report quantitatively assesses the work performance of each team member and is used as a reference for user performance evaluations.

[0043] Thus, the present invention streamlines project management and optimizes the use of each team member's skills and workload, thereby improving the project's success rate.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server connects to the database and retrieves skill information for all members. This includes data on each member's expertise, such as their proficiency in programming languages ​​and their experience in past projects.

[0047] Step 2:

[0048] The server calculates the current load on each member. This information is obtained by analyzing the number of tasks currently assigned, their progress, deadlines, and so on.

[0049] Step 3:

[0050] The server automatically assigns tasks. Specifically, it considers each member's skill level and workload to select the most suitable task and notifies each member of the assignment.

[0051] Step 4:

[0052] The terminal receives task progress information from each member and sends it to the server. Progress data is updated regularly to reflect the progress of the work in real time.

[0053] Step 5:

[0054] The server analyzes the received progress data and updates the project progress on the dashboard to the latest status. This dashboard visually displays the overall project progress and the status of each task.

[0055] Step 6:

[0056] The server monitors progress data and immediately generates an alert and notifies the user if any anomalies or delays are detected. This enables a quick response.

[0057] Step 7:

[0058] Upon project completion, the server generates an evaluation report based on the collected performance data. This report objectively assesses the contributions and efficiency of each team member and is presented to the user.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In modern project management, accurately assigning tasks that take into account the skills and workload of team members is crucial. However, managing this manually is time-consuming, labor-intensive, and inefficient. Furthermore, there is a need for a system that tracks project progress in real time and responds quickly to anomalies and delays. Currently, real-time progress management and anomaly response are often not possible, hindering project progress.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for acquiring information on each member, means for calculating the workload status of each member, and means for automatically assigning tasks using a generative AI model. This enables optimal task distribution based on the team's skills and workload status. Furthermore, project progress can be managed in real time through progress analysis and warning notifications in case of anomalies. This has the effect of improving the success rate of projects.

[0064] "Means for obtaining information on each member" refers to a function for collecting data such as the skills and experience of members participating in the project from information sources such as databases.

[0065] "Means for calculating the workload of team members" refers to a system for quantitatively understanding the current workload of each member based on the number and progress of tasks assigned to them.

[0066] "A method for automatically assigning tasks using a generative AI model" refers to a process that utilizes artificial intelligence technology to select and assign tasks that are best suited to each member's skills and workload.

[0067] "A means of analyzing project progress based on progress information received from terminals" refers to a method of understanding the overall progress of a project by receiving project progress data entered by members and analyzing its contents.

[0068] "Means of visually displaying project progress" refers to systems that clearly show the progress of a project to users through graphs, dashboards, and other means.

[0069] "Means for notifying warnings when abnormal events are detected" refers to a notification function that immediately informs relevant users when unexpected delays or problems occur during project progress.

[0070] "Means for generating evaluation reports based on collected information" refers to a function that analyzes the work performance of each member after the completion of a project and creates a report for objective evaluation.

[0071] Specific embodiments for carrying out the present invention will now be described. This system aims to streamline project management and optimally utilize the skills and workload of team members. The details of the system using each device and software are described below.

[0072] The server centrally manages information on each member. A database system is used to store member skill information and past work history. This information is based on data initially entered by members using their terminals. Specifically, an input form accessible from terminals via a web application is provided.

[0073] Next, the server calculates the workload of each member in real time. For this purpose, scheduling software running on the server is used. Task progress and the workload of each member are calculated and updated, for example, every 5 minutes. Furthermore, optimal task assignment is automated through a generative AI model. The AI ​​model utilizes historical task performance data to determine task priorities and assigns them to members based on their skills.

[0074] The terminals are used to send progress reports from team members to the server. Progress information entered through the terminal application is retrieved in real time via the server's API. This progress data is analyzed on the server and used to update a dashboard showing the overall project progress.

[0075] This dashboard allows users to constantly monitor the overall project picture. For example, by accessing it via a web browser, they can visually grasp the progress of each task and future plans. Furthermore, if an anomaly occurs, the server will immediately notify the user. For this purpose, it is possible for the server to integrate with external communication tools, such as notifications via Slack or email.

[0076] Finally, upon project completion, the server generates a report that objectively evaluates the performance of each member. This report provides valuable data for users to conduct performance evaluations.

[0077] As a concrete example, an example of a prompt message generated using the AI ​​model is, "Please assign the UI design task to a member with extensive design experience in Project A." In this way, this system automates and streamlines each process in project management.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server stores the skill information of each member, sent from their terminal, in a database. When a member logs in, they input their skill information using an input form. This information includes their programming language proficiency level and past project experience. The server receives this information and stores it in the database.

[0081] Step 2:

[0082] The server runs scheduling software to calculate the workload of its members. The server retrieves the current number and progress of tasks for each member from the database and calculates the workload for each member based on this data. The input here is task progress data, and the output is the workload value for each member.

[0083] Step 3:

[0084] The server uses a generative AI model to perform optimal task assignments. Based on the skill information obtained in step 1 and the load status in step 2, the AI ​​model selects appropriate members for each task. The model is given the prompt "Assign task B of the new project to members with skill set A." The output is a list of assigned tasks.

[0085] Step 4:

[0086] The terminal provides an interface for members to input progress. Once a member submits a progress report, this information is immediately sent to the server via an API. The input is progress report data for ongoing tasks, and the server output is updated dashboard data.

[0087] Step 5:

[0088] The server analyzes the project's progress and displays a visual progress report to the user. Based on this analysis, the dashboard is updated in real time. Users can access this dashboard through a web browser to view the project overview and issues.

[0089] Step 6:

[0090] The server notifies the user when it detects anomalies in the progress. It monitors progress data, and if problems such as delays occur, the server immediately sends a warning message to the user. This includes using external communication tools.

[0091] Step 7:

[0092] Upon project completion, the server generates an evaluation report based on all collected performance data. It analyzes the final deliverables and work efficiency of each team member, performs quantitative evaluations, and then provides the evaluation report to the user. This report is used by the user as a metric for performance evaluations.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] In factory production processes, it is difficult to assign tasks optimally, taking into account the characteristics and load conditions of each piece of equipment. Furthermore, without real-time progress monitoring and immediate detection / notification of anomalies, production efficiency may decline and quality problems may occur. Under these circumstances, there is a need to achieve efficient and stable production management.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes means for acquiring characteristic information of each work device, means for automatically assigning tasks based on the load status of each work device, and means for monitoring the progress of the production process in real time and displaying the progress status. This makes it possible to maximize the capabilities of each work device and improve the overall efficiency of the production process.

[0098] "Work equipment" is a general term for machines and devices designed to perform specific tasks within a factory.

[0099] "Characteristic information" refers to information that indicates the unique capabilities and special skills of each work device, and is used to improve work efficiency and optimize operations.

[0100] "Load status" refers to the current load level of each work device and serves as an indicator for efficient work allocation.

[0101] "Automatically assigning tasks" means distributing tasks to appropriate work devices based on pre-set algorithms or conditions.

[0102] "Progress in the production process" refers to the status of a series of tasks and processes being carried out within a factory, and is an important indicator for confirming whether things are progressing according to plan.

[0103] "Real-time monitoring" means being able to immediately check the current situation and take swift action as needed.

[0104] "Displaying progress" means visually showing the current status of the production process so that stakeholders can easily understand the current situation.

[0105] The system implementing this invention mainly consists of server, terminal, and user components. The server is responsible for retrieving characteristic information of each work device from a database and automatically optimizing and assigning tasks based on that information. This uses programming languages ​​and frameworks such as Python and Django. PostgreSQL is used as the database to manage characteristic information and load status in detail.

[0106] Terminals are used for real-time progress monitoring of the work equipment. These terminals provide information to administrators via a web interface, allowing them to visualize the progress of the production process. A dynamic dashboard is built using the Django framework to display the progress, making it viewable through a browser.

[0107] Furthermore, users can receive immediate notifications when anomalies or delays occur. When the server detects an anomaly, it will alert the user using email or mobile app notification features.

[0108] As a concrete example, in a factory, work machines specializing in welding and those specializing in painting would each handle tasks according to their respective strengths. Managers could monitor the progress of the production line in real time through smart glasses and grasp the overall status at any time.

[0109] An example of a prompt statement would be: "We want to design a system that can optimally assign tasks in real time based on the characteristics and load status of each robot in the factory. A function is needed to immediately notify in case of abnormalities or delays. We want to implement this system as a web application."

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server retrieves characteristic information and load status for each work device from the database. This input information includes work history and current task load. The server analyzes this data to understand the capabilities and load status of each work device. The output is a characteristic and load profile used for work assignment.

[0113] Step 2:

[0114] The server automatically assigns the most suitable task to each work device based on acquired characteristic information and load conditions. Inputs are characteristic information and load profiles, and output is the generation of specific task assignment instructions for each work device. In this process, the server maximizes the capabilities of each device and improves work efficiency.

[0115] Step 3:

[0116] The terminal receives task assignment instructions sent from the server and monitors the progress of the production process in real time. Inputs include task assignment information from the server and progress data from each work device. The terminal processes this data and dynamically displays the progress status, visually informing the user. Output is a progress status dashboard displayed in a browser.

[0117] Step 4:

[0118] Users monitor the progress dashboard from their terminals and respond immediately if an anomaly occurs. By directly observing the progress, users can always understand the status of the entire production process. The input is the progress dashboard, and the output is the decision on the corrective action.

[0119] Step 5:

[0120] The server sends a notification to the user if it detects an anomaly or delay. The input is progress data received from the terminal. The server analyzes this progress data and, if an anomaly is detected, sends an alert to the user via email or notification. The output is a notification message regarding the anomaly.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention provides a task management system that incorporates a new emotion engine. This allows for understanding the emotional state of each team member, thereby improving the efficiency and accuracy of project management.

[0123] First, the server retrieves existing skill information, load status, and newly added emotional information from the database. Emotional information is collected in real time from facial expression analysis of members and biometric sensors, and stored as data.

[0124] Next, the server analyzes the acquired emotional information and evaluates each member's current mental state. For example, if it determines that a member is experiencing stress, the server adjusts how tasks are assigned based on that information.

[0125] Furthermore, the server incorporates emotional information into project progress management. Based on emotional data, it automatically configures an interface appropriate to the project's progress, providing a comfortable working environment for each team member.

[0126] The emotion engine also helps in how to notify users of abnormal events or delays. For example, it can be configured to send appropriate messages to members whose emotional state is more receptive to encouragement than specific criticism.

[0127] For example, if emotional data is collected indicating that a user is feeling tired, the server will reassign that member's tasks to other members, thereby reducing their workload. This allows for maintaining the productivity of individual members while balancing the overall project.

[0128] Thus, by considering the emotional state of team members at each stage of project management, this invention enables more adaptive and flexible task management. By using the emotion engine in conjunction with this, it is expected that not only will the success rate of projects be improved, but the satisfaction and well-being of team members will also be maintained.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The terminal collects real-time emotional data from its members. This includes facial recognition technology using cameras and biometric data obtained from wearable devices.

[0132] Step 2:

[0133] The server analyzes the collected emotional data to determine the emotional state of each member. This includes stress levels, concentration levels, and happiness ratings based on feedback.

[0134] Step 3:

[0135] The server adjusts the optimal task assignment for each member based on their determined emotional state. For example, it assigns less demanding tasks to members who are experiencing high stress levels.

[0136] Step 4:

[0137] The server displays progress along with sentiment information on a dashboard, allowing users to gain a comprehensive understanding of the project's status. Sentiment data is also visualized, making it possible to instantly check the current status of each team member.

[0138] Step 5:

[0139] During project execution, the server adjusts its notification methods for abnormal events based on emotional information. For example, it takes a situation-sensitive approach, such as sending encouraging messages to members who are feeling down.

[0140] Step 6:

[0141] Upon project completion, the server generates and provides a performance report, including sentiment data, to the user. This allows for objective identification of areas for improvement and contributions from team members, which can then be used to determine roles and responsibilities in future projects.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] Modern project management requires considering and integrating not only the skills and workload of team members, but also their mental and emotional states. However, existing systems struggle to adequately grasp and provide feedback on emotions and mental states, leading to stress and decreased productivity. To address this, flexible task management and interface optimization utilizing emotional data are necessary.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes means for acquiring skill information and physiological status of each member, means for analyzing emotional information and evaluating the mental state of the members, and means for dynamically assigning and adjusting tasks based on the mental state of each member. This enables adaptive project management that takes into account the emotional state of the members and provides an interface that reduces stress and increases productivity.

[0147] "Each member" refers to all members participating in the project or task, and their skills information and physiological status are subject to management.

[0148] "Skills information" refers to data on the professional abilities and proficiency levels of team members, and is used for task assignment and coordination.

[0149] "Physiological state" refers to data indicating the physiological state of a member's body, such as heart rate and skin electrical response.

[0150] "Emotional information" refers to data that indicates the psychological and emotional state of the members, and is collected through facial expression analysis and sensors.

[0151] "Mental state" refers to the results of an assessment of the psychological stress and emotional state of the team members, and is used for task adjustment and feedback.

[0152] "Dynamic task assignment" refers to a process of flexibly rearranging tasks according to the current skill levels and mental state of team members.

[0153] "Project progress" refers to information indicating how well the entire project is progressing according to plan, and management that takes emotional information into account is required.

[0154] An "adaptive interface" refers to a user interface that automatically adjusts according to the emotional state of its members, enabling them to work in the optimal environment.

[0155] A "notification message" refers to information that a system sends to its members, including content that may include encouragement or warnings that correspond to their emotional state.

[0156] This invention is a task management system incorporating an emotion engine, and consists of a server, terminals, and users to achieve efficient project management.

[0157] The server is the core of the program, collecting skill information and physiological status data from each member. Hardware such as cameras and sensors connected to terminals are used for data collection. The camera captures facial expressions in real time using commonly used video processing libraries and analyzes emotional information. Physiological sensors measure heart rate and skin electrical responses and transmit this data to the server. The server integrates this information and utilizes generative AI models to evaluate the mental state of each member.

[0158] The terminal functions as a user interface used by members, and the server provides an optimized interface based on collected data. For example, the color scheme and layout dynamically change according to the member's emotional state, designed to improve work efficiency. The terminal also has the functionality to display emotional information and visually inform users of project progress.

[0159] Through this system, users can check the progress of their tasks and projects. The system displays feedback to help users understand their current emotional state and workload. Notification messages also include encouragement and suggestions for rest, thereby reducing user stress and maintaining their health.

[0160] For example, if a user is feeling fatigued, the generative AI model analyzes this information and distributes tasks to other team members. In this case, the user will see a notification on their device such as, "Please get enough rest."

[0161] An example of a prompt message might be, "If a team member's stress level is high, what actions will you take to adjust the task?" In this way, the task management system can take into account the emotional state of its members and optimally adjust the project's progress.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The server acquires skill information and physiological status of each member. It receives facial expression image data and physiological information sensor data acquired from terminals as input. The server stores this data in a database and applies a facial expression analysis algorithm to extract emotional information from facial expressions. This results in the output of the member's latest emotional information.

[0165] Step 2:

[0166] The server uses the extracted emotional information to evaluate the mental state of its members using a generative AI model. The input includes the emotional information obtained in step 1. The AI ​​model quantifies each emotional state and determines stress levels and fatigue levels. This evaluation outputs the mental state of each member.

[0167] Step 3:

[0168] The server reallocates tasks based on the outputted mental state data. It uses the current task list and mental state data of each member as input. Based on this information, the server optimizes the workload for each member and reassigns tasks to other members as needed. This results in an updated task assignment status being output.

[0169] Step 4:

[0170] The terminal optimizes the user interface based on updated task information and mental state received from the server. It receives the current mental state and task information of the members as input. The terminal dynamically adjusts the screen's color tone and layout to provide a comfortable working environment for the user. This process results in an optimized user interface.

[0171] Step 5:

[0172] The server generates and sends notification messages based on emotional information to the terminal. It uses updated mental state and task status as input. The server creates messages to alleviate the psychological burden on members and sends notifications such as "Please take a rest" to the terminal. This ensures that appropriate feedback notifications are output.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0175] Traditional task management systems assign tasks based solely on members' skill levels and workload, neglecting their emotional state and potentially leading to decreased productivity and motivation. Furthermore, the lack of real-time notifications and progress tracking based on emotional data can cause stress and unnecessary burdens on members.

[0176] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0177] In this invention, the server includes means for acquiring member skill information, means for automatically reassigning tasks based on member workload and emotional information, and means for monitoring project progress in real time and displaying progress that takes emotional status into account. This enables flexible task management that takes member emotional status into account.

[0178] A "task management system" is a framework that provides means and processes for efficiently and effectively carrying out the tasks assigned to its members.

[0179] "Skills information" refers to data related to the abilities of team members in performing their duties, such as their specialized knowledge, skills, and experience.

[0180] "Workload status" refers to information indicating the amount of work currently assigned to each member and the mental and physical impact that work has on them.

[0181] "Emotional information" refers to data about the emotional state of a member at a given time, obtained through their facial expressions and biological responses.

[0182] "Automatic reassignment" refers to a function in which the system operates based on pre-set conditions and reassigns tasks according to the status and circumstances of the members.

[0183] "Means of monitoring progress in real time" refers to methods and functions for immediately grasping the progress of ongoing tasks and visualizing it.

[0184] "Displaying progress while considering emotional state" refers to a method of visually showing information indicating the progress of work while taking into account the emotional data of the team members.

[0185] "Notifications based on emotional data" are messages created based on data about the emotional state of members, and are designed to convey appropriate information according to the situation.

[0186] The system that implements this application consists of a server and terminals. The server acquires the skill information, workload status, and emotional information of its members and reassigns tasks based on this information. Emotional information is collected using biometric sensors and facial recognition cameras and analyzed in real time. Specifically, a facial analysis program using Python and OpenCV processes the emotional data, and it is stored in a database using Firebase.

[0187] The devices are primarily smartphones, each provided to a member to receive notifications from the server. These notifications, based on emotional information, include encouragement and suggestions tailored to the member's current emotional state. For example, if stress levels are high, a rest is suggested; if motivation is high, work is encouraged to leverage that state.

[0188] For example, if the system determines that a member is feeling fatigued, the server will send a notification to that member suggesting that their workload be reduced. An example of a prompt message from the generated AI model might be: "Signs of fatigue have been detected in member A's emotional data. How should the tasks be adjusted?"

[0189] This system enables flexible task management that takes into account the emotional state of each member, and creates a more comfortable work environment.

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The server retrieves each member's skill information and workload status from a database. It also collects emotional information from members in real time using biometric sensors and facial recognition cameras. This provides skill information, workload status, and emotional information as input data. In particular, emotional information is analyzed using OpenCV with heart rate data from sensors and camera images, and output as an emotional state.

[0193] Step 2:

[0194] The server analyzes the emotional information obtained in Step 1 to evaluate the emotional state of each member. The server filters the input emotional data to identify emotional states such as stress and fatigue. As a result of the evaluation, a report on the emotional state of each member is generated, and task reassignment is considered based on this report.

[0195] Step 3:

[0196] The server re-evaluates the tasks assigned to each member based on their emotional state and reassigns them as needed. This reassignment is performed using an optimization algorithm based on a generative AI model. It processes the input emotional state and workload to output a new task assignment plan. For example, if fatigue is detected, specific mitigation measures are proposed.

[0197] Step 4:

[0198] The server sends notifications to each member's terminal based on the task reassignment results. The notification content is customized and communicated based on the emotional state. Prompt messages are generated according to the situation and designed to encourage appropriate action from the member. The outputted notification content provides the user with guidance on how to receive and respond.

[0199] Step 5:

[0200] Users perform tasks according to the reassigned tasks and notifications displayed on their devices. This system allows users to quickly understand and act upon tasks optimized for their emotional state. This enables the implementation of task assignments and corresponding emotional care.

[0201] 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.

[0202] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0203] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0208] 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.

[0209] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0210] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0211] 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.

[0212] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0213] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0214] The 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.

[0215] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0216] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0217] The system for carrying out the present invention has the function of acquiring skill information of each member, optimally assigning tasks based on this information, and monitoring project progress in real time. The embodiments thereof are described in detail below.

[0218] First, as soon as the project starts, the server accesses the database to retrieve the skills information of each member. This includes past work history, acquired skills, languages, etc. This information can be entered in advance by each member using a terminal.

[0219] Next, the server monitors the workload of each member. This workload is calculated based on factors such as the number of tasks already assigned to each member, their progress, and the remaining time. Based on this, the server automatically assigns appropriate tasks to each member. For example, a member skilled in programming will be given priority in coding-related tasks.

[0220] Furthermore, progress data entered via the terminal is transmitted to the server in real time. The server analyzes this data and updates a dashboard that visually represents the overall project progress. This dashboard can be viewed by the user at any time via the terminal, making it easy to understand the current status of the project. If any delays or anomalies are detected, the server is configured to immediately notify the user.

[0221] Furthermore, upon project completion, the server compiles all performance information and generates an objective evaluation report. This report quantitatively assesses the work performance of each team member and is used as a reference for user performance evaluations.

[0222] Thus, the present invention streamlines project management and optimizes the use of each team member's skills and workload, thereby improving the project's success rate.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The server connects to the database and retrieves skill information for all members. This includes data on each member's expertise, such as their proficiency in programming languages ​​and their experience in past projects.

[0226] Step 2:

[0227] The server calculates the current load on each member. This information is obtained by analyzing the number of tasks currently assigned, their progress, deadlines, and so on.

[0228] Step 3:

[0229] The server automatically assigns tasks. Specifically, it considers each member's skill level and workload to select the most suitable task and notifies each member of the assignment.

[0230] Step 4:

[0231] The terminal receives task progress information from each member and sends it to the server. Progress data is updated regularly to reflect the progress of the work in real time.

[0232] Step 5:

[0233] The server analyzes the received progress data and updates the project progress on the dashboard to the latest status. This dashboard visually displays the overall project progress and the status of each task.

[0234] Step 6:

[0235] The server monitors progress data and immediately generates an alert and notifies the user if any anomalies or delays are detected. This enables a quick response.

[0236] Step 7:

[0237] Upon project completion, the server generates an evaluation report based on the collected performance data. This report objectively assesses the contributions and efficiency of each team member and is presented to the user.

[0238] (Example 1)

[0239] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0240] In modern project management, accurately assigning tasks that take into account the skills and workload of team members is crucial. However, managing this manually is time-consuming, labor-intensive, and inefficient. Furthermore, there is a need for a system that tracks project progress in real time and responds quickly to anomalies and delays. Currently, real-time progress management and anomaly response are often not possible, hindering project progress.

[0241] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0242] In this invention, the server includes means for acquiring information on each member, means for calculating the workload status of each member, and means for automatically assigning tasks using a generative AI model. This enables optimal task distribution based on the team's skills and workload status. Furthermore, project progress can be managed in real time through progress analysis and warning notifications in case of anomalies. This has the effect of improving the success rate of projects.

[0243] "Means for obtaining information on each member" refers to a function for collecting data such as the skills and experience of members participating in the project from information sources such as databases.

[0244] "Means for calculating the workload of team members" refers to a system for quantitatively understanding the current workload of each member based on the number and progress of tasks assigned to them.

[0245] "A method for automatically assigning tasks using a generative AI model" refers to a process that utilizes artificial intelligence technology to select and assign tasks that are best suited to each member's skills and workload.

[0246] "A means of analyzing project progress based on progress information received from terminals" refers to a method of understanding the overall progress of a project by receiving project progress data entered by members and analyzing its contents.

[0247] "Means of visually displaying project progress" refers to systems that clearly show the progress of a project to users through graphs, dashboards, and other means.

[0248] "Means for notifying warnings when abnormal events are detected" refers to a notification function that immediately informs relevant users when unexpected delays or problems occur during project progress.

[0249] "Means for generating evaluation reports based on collected information" refers to a function that analyzes the work performance of each member after the completion of a project and creates a report for objective evaluation.

[0250] Specific embodiments for carrying out the present invention will now be described. This system aims to streamline project management and optimally utilize the skills and workload of team members. The details of the system using each device and software are described below.

[0251] The server centrally manages information on each member. A database system is used to store member skill information and past work history. This information is based on data initially entered by members using their terminals. Specifically, an input form accessible from terminals via a web application is provided.

[0252] Next, the server calculates the workload of each member in real time. For this purpose, scheduling software running on the server is used. Task progress and the workload of each member are calculated and updated, for example, every 5 minutes. Furthermore, optimal task assignment is automated through a generative AI model. The AI ​​model utilizes historical task performance data to determine task priorities and assigns them to members based on their skills.

[0253] The terminals are used to send progress reports from team members to the server. Progress information entered through the terminal application is retrieved in real time via the server's API. This progress data is analyzed on the server and used to update a dashboard showing the overall project progress.

[0254] This dashboard allows users to constantly monitor the overall project picture. For example, by accessing it via a web browser, they can visually grasp the progress of each task and future plans. Furthermore, if an anomaly occurs, the server will immediately notify the user. For this purpose, it is possible for the server to integrate with external communication tools, such as notifications via Slack or email.

[0255] Finally, upon project completion, the server generates a report that objectively evaluates the performance of each member. This report provides valuable data for users to conduct performance evaluations.

[0256] As a concrete example, an example of a prompt message generated using the AI ​​model is, "Please assign the UI design task to a member with extensive design experience in Project A." In this way, this system automates and streamlines each process in project management.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The server stores the skill information of each member, sent from their terminal, in a database. When a member logs in, they input their skill information using an input form. This information includes their programming language proficiency level and past project experience. The server receives this information and stores it in the database.

[0260] Step 2:

[0261] The server runs scheduling software to calculate the workload of its members. The server retrieves the current number and progress of tasks for each member from the database and calculates the workload for each member based on this data. The input here is task progress data, and the output is the workload value for each member.

[0262] Step 3:

[0263] The server uses a generative AI model to perform optimal task assignments. Based on the skill information obtained in step 1 and the load status in step 2, the AI ​​model selects appropriate members for each task. The model is given the prompt "Assign task B of the new project to members with skill set A." The output is a list of assigned tasks.

[0264] Step 4:

[0265] The terminal provides an interface for members to input progress. Once a member submits a progress report, this information is immediately sent to the server via an API. The input is progress report data for ongoing tasks, and the server output is updated dashboard data.

[0266] Step 5:

[0267] The server analyzes the project's progress and displays a visual progress report to the user. Based on this analysis, the dashboard is updated in real time. Users can access this dashboard through a web browser to view the project overview and issues.

[0268] Step 6:

[0269] The server notifies the user when it detects anomalies in the progress. It monitors progress data, and if problems such as delays occur, the server immediately sends a warning message to the user. This includes using external communication tools.

[0270] Step 7:

[0271] Upon project completion, the server generates an evaluation report based on all collected performance data. It analyzes the final deliverables and work efficiency of each team member, performs quantitative evaluations, and then provides the evaluation report to the user. This report is used by the user as a metric for performance evaluations.

[0272] (Application Example 1)

[0273] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0274] In factory production processes, it is difficult to assign tasks optimally, taking into account the characteristics and load conditions of each piece of equipment. Furthermore, without real-time progress monitoring and immediate detection / notification of anomalies, production efficiency may decline and quality problems may occur. Under these circumstances, there is a need to achieve efficient and stable production management.

[0275] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0276] In this invention, the server includes means for acquiring characteristic information of each work device, means for automatically assigning tasks based on the load status of each work device, and means for monitoring the progress of the production process in real time and displaying the progress status. This makes it possible to maximize the capabilities of each work device and improve the overall efficiency of the production process.

[0277] "Work equipment" is a general term for machines and devices designed to perform specific tasks within a factory.

[0278] "Characteristic information" refers to information that indicates the unique capabilities and special skills of each work device, and is used to improve work efficiency and optimize operations.

[0279] "Load status" refers to the current load level of each work device and serves as an indicator for efficient work allocation.

[0280] "Automatically assigning tasks" means distributing tasks to appropriate work devices based on pre-set algorithms or conditions.

[0281] "Progress in the production process" refers to the status of a series of tasks and processes being carried out within a factory, and is an important indicator for confirming whether things are progressing according to plan.

[0282] "Real-time monitoring" means being able to immediately check the current situation and take swift action as needed.

[0283] "Displaying progress" means visually showing the current status of the production process so that stakeholders can easily understand the current situation.

[0284] The system for implementing this invention mainly consists of components such as a server, a terminal, and a user. The server is responsible for retrieving the characteristic information of each working device from the database and automatically optimizing and allocating tasks based on it. For this, programming languages and frameworks such as Python and Django are used. Also, PostgreSQL is used for the database to manage the characteristic information and load status in detail.

[0285] A terminal is used for real-time progress monitoring of the working device. The terminal provides information for the administrator to visualize the progress of the production process via a web interface. To display the progress status, a dynamic dashboard is constructed using the Django framework and made viewable via a browser.

[0286] Furthermore, the user can receive immediate notifications when abnormalities or delays occur. When the server detects an abnormality, it issues a warning to the user using the notification functions of email or a mobile app.

[0287] As a specific example, in a factory, a working device good at welding work and a device good at painting work are each responsible for tasks according to their respective specialties. The administrator can monitor the progress of the production line in real time through smart glasses and grasp the overall state at any timing.

[0288] Examples of prompt sentences include the sentence "I want to design a system that can optimally allocate tasks in real time based on the characteristics and load status of each robot in the factory. A function to immediately notify when abnormalities or delays occur is required. I want to implement this system as a web application."

[0289] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0290] Step 1:

[0291] The server retrieves characteristic information and load status for each work device from the database. This input information includes work history and current task load. The server analyzes this data to understand the capabilities and load status of each work device. The output is a characteristic and load profile used for work assignment.

[0292] Step 2:

[0293] The server automatically assigns the most suitable task to each work device based on acquired characteristic information and load conditions. Inputs are characteristic information and load profiles, and output is the generation of specific task assignment instructions for each work device. In this process, the server maximizes the capabilities of each device and improves work efficiency.

[0294] Step 3:

[0295] The terminal receives task assignment instructions sent from the server and monitors the progress of the production process in real time. Inputs include task assignment information from the server and progress data from each work device. The terminal processes this data and dynamically displays the progress status, visually informing the user. Output is a progress status dashboard displayed in a browser.

[0296] Step 4:

[0297] Users monitor the progress dashboard from their terminals and respond immediately if an anomaly occurs. By directly observing the progress, users can always understand the status of the entire production process. The input is the progress dashboard, and the output is the decision on the corrective action.

[0298] Step 5:

[0299] The server sends a notification to the user if it detects an anomaly or delay. The input is progress data received from the terminal. The server analyzes this progress data and, if an anomaly is detected, sends an alert to the user via email or notification. The output is a notification message regarding the anomaly.

[0300] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0301] This invention provides a task management system that incorporates a new emotion engine. This allows for understanding the emotional state of each team member, thereby improving the efficiency and accuracy of project management.

[0302] First, the server retrieves existing skill information, load status, and newly added emotional information from the database. Emotional information is collected in real time from facial expression analysis of members and biometric sensors, and stored as data.

[0303] Next, the server analyzes the acquired emotional information and evaluates each member's current mental state. For example, if it determines that a member is experiencing stress, the server adjusts how tasks are assigned based on that information.

[0304] Furthermore, the server incorporates emotional information into project progress management. Based on emotional data, it automatically configures an interface appropriate to the project's progress, providing a comfortable working environment for each team member.

[0305] The emotion engine also helps in how to notify users of abnormal events or delays. For example, it can be configured to send appropriate messages to members whose emotional state is more receptive to encouragement than specific criticism.

[0306] For example, when emotion data indicating that a user feels tired is collected, the server allocates the tasks of that team member to other members to reduce the burden on the team members. Thereby, while maintaining the productivity of the team members, it is possible to balance the entire project.

[0307] As described above, the present invention enables more adaptable and flexible task management by considering the emotional states of team members at each stage of project management. By using an emotion engine in combination, not only can the success rate of the project be improved, but it is also expected to have the effect of maintaining the satisfaction and health of team members.

[0308] The following describes the processing flow.

[0309] Step 1:

[0310] The terminal collects the real-time emotion data of team members. This includes facial recognition technology using a camera and biometric information data obtained from wearable devices.

[0311] Step 2:

[0312] The server analyzes the collected emotion data and determines the emotional state of each team member. This includes stress level, concentration, happiness evaluation based on feedback, etc.

[0313] Step 3:

[0314] Based on the determined emotional state, the server adjusts the optimal task assignment for each team member. For example, a task with less burden is assigned to a team member with high stress.

[0315] Step 4:

[0316] The server displays the progress status on the dashboard together with emotion information so that the user can comprehensively grasp the status of the project. The emotion data is also visualized, and the current situation of each team member can be immediately confirmed.

[0317] Step 5:

[0318] During project execution, the server adjusts its notification methods for abnormal events based on emotional information. For example, it takes a situation-sensitive approach, such as sending encouraging messages to members who are feeling down.

[0319] Step 6:

[0320] Upon project completion, the server generates and provides a performance report, including sentiment data, to the user. This allows for objective identification of areas for improvement and contributions from team members, which can then be used to determine roles and responsibilities for future projects.

[0321] (Example 2)

[0322] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0323] Modern project management requires considering and integrating not only the skills and workload of team members, but also their mental and emotional states. However, existing systems struggle to adequately grasp and provide feedback on emotions and mental states, leading to stress and decreased productivity. To address this, flexible task management and interface optimization utilizing emotional data are necessary.

[0324] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0325] In this invention, the server includes means for acquiring skill information and physiological status of each member, means for analyzing emotional information and evaluating the mental state of the members, and means for dynamically assigning and adjusting tasks based on the mental state of each member. This enables adaptive project management that takes into account the emotional state of the members and provides an interface that reduces stress and increases productivity.

[0326] "Each member" refers to all members participating in the project or task, and their skills information and physiological status are subject to management.

[0327] "Skills information" refers to data on the professional abilities and proficiency levels of team members, and is used for task assignment and coordination.

[0328] "Physiological state" refers to data indicating the physiological state of a member's body, such as heart rate and skin electrical response.

[0329] "Emotional information" refers to data that indicates the psychological and emotional state of the members, and is collected through facial expression analysis and sensors.

[0330] "Mental state" refers to the results of an assessment of the psychological stress and emotional state of the team members, and is used for task adjustment and feedback.

[0331] "Dynamic task assignment" refers to a process of flexibly rearranging tasks according to the current skill levels and mental state of team members.

[0332] "Project progress" refers to information indicating how well the entire project is progressing according to plan, and management that takes emotional information into account is required.

[0333] An "adaptive interface" refers to a user interface that automatically adjusts according to the emotional state of its members, enabling them to work in the optimal environment.

[0334] A "notification message" refers to information that a system sends to its members, including content that may include encouragement or warnings that correspond to their emotional state.

[0335] This invention is a task management system incorporating an emotion engine, and consists of a server, terminals, and users to achieve efficient project management.

[0336] The server is the core of the program, collecting skill information and physiological status data from each member. Hardware such as cameras and sensors connected to terminals are used for data collection. The camera captures facial expressions in real time using commonly used video processing libraries and analyzes emotional information. Physiological sensors measure heart rate and skin electrical responses and transmit this data to the server. The server integrates this information and utilizes generative AI models to evaluate the mental state of each member.

[0337] The terminal functions as a user interface used by members, and the server provides an optimized interface based on collected data. For example, the color scheme and layout dynamically change according to the member's emotional state, designed to improve work efficiency. The terminal also has the functionality to display emotional information and visually inform users of project progress.

[0338] Through this system, users can check the progress of their tasks and projects. The system displays feedback to help users understand their current emotional state and workload. Notification messages also include encouragement and suggestions for rest, thereby reducing user stress and maintaining their health.

[0339] For example, if a user is feeling fatigued, the generative AI model analyzes this information and distributes tasks to other team members. In this case, the user will see a notification on their device such as, "Please get enough rest."

[0340] An example of a prompt message might be, "If a team member's stress level is high, what actions will you take to adjust the task?" In this way, the task management system can take into account the emotional state of its members and optimally adjust the project's progress.

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] The server acquires skill information and physiological status of each member. It receives facial expression image data and physiological information sensor data acquired from terminals as input. The server stores this data in a database and applies a facial expression analysis algorithm to extract emotional information from facial expressions. This results in the output of the member's latest emotional information.

[0344] Step 2:

[0345] The server uses the extracted emotional information to evaluate the mental state of its members using a generative AI model. The input includes the emotional information obtained in step 1. The AI ​​model quantifies each emotional state and determines stress levels and fatigue levels. This evaluation outputs the mental state of each member.

[0346] Step 3:

[0347] The server reallocates tasks based on the outputted mental state data. It uses the current task list and mental state data of each member as input. Based on this information, the server optimizes the workload for each member and reassigns tasks to other members as needed. This results in an updated task assignment status being output.

[0348] Step 4:

[0349] The terminal optimizes the user interface based on updated task information and mental state received from the server. It receives the current mental state and task information of the members as input. The terminal dynamically adjusts the screen's color tone and layout to provide a comfortable working environment for the user. This process results in an optimized user interface.

[0350] Step 5:

[0351] The server generates and sends notification messages based on emotional information to the terminal. It uses updated mental state and task status as input. The server creates messages to alleviate the psychological burden on members and sends notifications such as "Please take a rest" to the terminal. This ensures that appropriate feedback notifications are output.

[0352] (Application Example 2)

[0353] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0354] Traditional task management systems assign tasks based solely on members' skill levels and workload, neglecting their emotional state and potentially leading to decreased productivity and motivation. Furthermore, the lack of real-time notifications and progress tracking based on emotional data can cause stress and unnecessary burdens on members.

[0355] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0356] In this invention, the server includes means for acquiring member skill information, means for automatically reassigning tasks based on member workload and emotional information, and means for monitoring project progress in real time and displaying progress that takes emotional status into account. This enables flexible task management that takes member emotional status into account.

[0357] A "task management system" is a framework that provides means and processes for efficiently and effectively carrying out the tasks assigned to its members.

[0358] "Skills information" refers to data related to the abilities of team members in performing their duties, such as their specialized knowledge, skills, and experience.

[0359] "Workload status" refers to information indicating the amount of work currently assigned to each member and the mental and physical impact that work has on them.

[0360] "Emotional information" refers to data about the emotional state of a member at a given time, obtained through their facial expressions and biological responses.

[0361] "Automatic reassignment" refers to a function in which the system operates based on pre-set conditions and reassigns tasks according to the status and circumstances of the members.

[0362] "Means of monitoring progress in real time" refers to methods and functions for immediately grasping the progress of ongoing tasks and visualizing it.

[0363] "Displaying progress while considering emotional state" refers to a method of visually showing information indicating the progress of work while taking into account the emotional data of the team members.

[0364] "Notifications based on emotional data" are messages created based on data about the emotional state of members, and are designed to convey appropriate information according to the situation.

[0365] The system that implements this application consists of a server and terminals. The server acquires the skill information, workload status, and emotional information of its members and reassigns tasks based on this information. Emotional information is collected using biometric sensors and facial recognition cameras and analyzed in real time. Specifically, a facial analysis program using Python and OpenCV processes the emotional data, and it is stored in a database using Firebase.

[0366] The devices are primarily smartphones, each provided to a member to receive notifications from the server. These notifications, based on emotional information, include encouragement and suggestions tailored to the member's current emotional state. For example, if stress levels are high, a rest is suggested; if motivation is high, work is encouraged to leverage that state.

[0367] For example, if the system determines that a member is feeling fatigued, the server will send a notification to that member suggesting that their workload be reduced. An example of a prompt message from the generated AI model might be: "Signs of fatigue have been detected in member A's emotional data. How should the tasks be adjusted?"

[0368] This system enables flexible task management that takes into account the emotional state of each member, and creates a more comfortable work environment.

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] The server retrieves each member's skill information and workload status from a database. It also collects emotional information from members in real time using biometric sensors and facial recognition cameras. This provides skill information, workload status, and emotional information as input data. In particular, emotional information is analyzed using OpenCV with heart rate data from sensors and camera images, and output as an emotional state.

[0372] Step 2:

[0373] The server analyzes the emotional information obtained in Step 1 to evaluate the emotional state of each member. The server filters the input emotional data to identify emotional states such as stress and fatigue. As a result of the evaluation, a report on the emotional state of each member is generated, and task reassignment is considered based on this report.

[0374] Step 3:

[0375] The server re-evaluates the tasks assigned to each member based on their emotional state and reassigns them as needed. This reassignment is performed using an optimization algorithm based on a generative AI model. It processes the input emotional state and workload to output a new task assignment plan. For example, if fatigue is detected, specific mitigation measures are proposed.

[0376] Step 4:

[0377] The server sends notifications to each member's terminal based on the task reassignment results. The notification content is customized and communicated based on the emotional state. Prompt messages are generated according to the situation and designed to encourage appropriate action from the member. The outputted notification content provides the user with guidance on how to receive and respond.

[0378] Step 5:

[0379] Users perform tasks according to the reassigned tasks and notifications displayed on their devices. This system allows users to quickly understand and act upon tasks optimized for their emotional state. This enables the implementation of task assignments and corresponding emotional care.

[0380] 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.

[0381] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0382] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0383] [Third Embodiment]

[0384] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0385] 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.

[0386] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0387] 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.

[0388] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0389] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0390] 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.

[0391] 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.

[0392] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0393] The 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.

[0394] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0395] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0396] The system for carrying out the present invention has the function of acquiring skill information of each member, optimally assigning tasks based on this information, and monitoring project progress in real time. The embodiments thereof are described in detail below.

[0397] First, as soon as the project starts, the server accesses the database to retrieve the skills information of each member. This includes past work history, acquired skills, languages, etc. This information can be entered in advance by each member using a terminal.

[0398] Next, the server monitors the workload of each member. This workload is calculated based on factors such as the number of tasks already assigned to each member, their progress, and the remaining time. Based on this, the server automatically assigns appropriate tasks to each member. For example, a member skilled in programming will be given priority in coding-related tasks.

[0399] Furthermore, progress data entered via the terminal is transmitted to the server in real time. The server analyzes this data and updates a dashboard that visually represents the overall project progress. This dashboard can be viewed by the user at any time via the terminal, making it easy to understand the current status of the project. If any delays or anomalies are detected, the server is configured to immediately notify the user.

[0400] Furthermore, upon project completion, the server compiles all performance information and generates an objective evaluation report. This report quantitatively assesses the work performance of each team member and is used as a reference for user performance evaluations.

[0401] Thus, the present invention streamlines project management and optimizes the use of each team member's skills and workload, thereby improving the project's success rate.

[0402] The following describes the processing flow.

[0403] Step 1:

[0404] The server connects to the database and retrieves skill information for all members. This includes data on each member's expertise, such as their proficiency in programming languages ​​and their experience in past projects.

[0405] Step 2:

[0406] The server calculates the current load on each member. This information is obtained by analyzing the number of tasks currently assigned, their progress, deadlines, and so on.

[0407] Step 3:

[0408] The server automatically assigns tasks. Specifically, it considers each member's skill level and workload to select the most suitable task and notifies each member of the assignment.

[0409] Step 4:

[0410] The terminal receives task progress information from each member and sends it to the server. Progress data is updated regularly to reflect the progress of the work in real time.

[0411] Step 5:

[0412] The server analyzes the received progress data and updates the project progress on the dashboard to the latest status. This dashboard visually displays the overall project progress and the status of each task.

[0413] Step 6:

[0414] The server monitors progress data and immediately generates an alert and notifies the user if any anomalies or delays are detected. This enables a quick response.

[0415] Step 7:

[0416] Upon project completion, the server generates an evaluation report based on the collected performance data. This report objectively assesses the contributions and efficiency of each team member and is presented to the user.

[0417] (Example 1)

[0418] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0419] In modern project management, accurately assigning tasks that take into account the skills and workload of team members is crucial. However, managing this manually is time-consuming, labor-intensive, and inefficient. Furthermore, there is a need for a system that tracks project progress in real time and responds quickly to anomalies and delays. Currently, real-time progress management and anomaly response are often not possible, hindering project progress.

[0420] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0421] In this invention, the server includes means for acquiring information on each member, means for calculating the workload status of each member, and means for automatically assigning tasks using a generative AI model. This enables optimal task distribution based on the team's skills and workload status. Furthermore, project progress can be managed in real time through progress analysis and warning notifications in case of anomalies. This has the effect of improving the success rate of projects.

[0422] "Means for obtaining information on each member" refers to a function for collecting data such as the skills and experience of members participating in the project from information sources such as databases.

[0423] "Means for calculating the workload of team members" refers to a system for quantitatively understanding the current workload of each member based on the number and progress of tasks assigned to them.

[0424] "A method for automatically assigning tasks using a generative AI model" refers to a process that utilizes artificial intelligence technology to select and assign tasks that are best suited to each member's skills and workload.

[0425] "A means of analyzing project progress based on progress information received from terminals" refers to a method of understanding the overall progress of a project by receiving project progress data entered by members and analyzing its contents.

[0426] "Means of visually displaying project progress" refers to systems that clearly show the progress of a project to users through graphs, dashboards, and other means.

[0427] "Means for notifying warnings when abnormal events are detected" refers to a notification function that immediately informs relevant users when unexpected delays or problems occur during project progress.

[0428] "Means for generating evaluation reports based on collected information" refers to a function that analyzes the work performance of each member after the completion of a project and creates a report for objective evaluation.

[0429] Specific embodiments for carrying out the present invention will now be described. This system aims to streamline project management and optimally utilize the skills and workload of team members. The details of the system using each device and software are described below.

[0430] The server centrally manages information on each member. A database system is used to store member skill information and past work history. This information is based on data initially entered by members using their terminals. Specifically, an input form accessible from terminals via a web application is provided.

[0431] Next, the server calculates the workload of each member in real time. For this purpose, scheduling software running on the server is used. Task progress and the workload of each member are calculated and updated, for example, every 5 minutes. Furthermore, optimal task assignment is automated through a generative AI model. The AI ​​model utilizes historical task performance data to determine task priorities and assigns them to members based on their skills.

[0432] The terminals are used to send progress reports from team members to the server. Progress information entered through the terminal application is retrieved in real time via the server's API. This progress data is analyzed on the server and used to update a dashboard showing the overall project progress.

[0433] This dashboard allows users to constantly monitor the overall project picture. For example, by accessing it via a web browser, they can visually grasp the progress of each task and future plans. Furthermore, if an anomaly occurs, the server will immediately notify the user. For this purpose, it is possible for the server to integrate with external communication tools, such as notifications via Slack or email.

[0434] Finally, upon project completion, the server generates a report that objectively evaluates the performance of each member. This report provides valuable data for users to conduct performance evaluations.

[0435] As a concrete example, an example of a prompt message generated using the AI ​​model is, "Please assign the UI design task to a member with extensive design experience in Project A." In this way, this system automates and streamlines each process in project management.

[0436] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0437] Step 1:

[0438] The server stores the skill information of each member, sent from their terminal, in a database. When a member logs in, they input their skill information using an input form. This information includes their programming language proficiency level and past project experience. The server receives this information and stores it in the database.

[0439] Step 2:

[0440] The server runs scheduling software to calculate the workload of its members. The server retrieves the current number and progress of tasks for each member from the database and calculates the workload for each member based on this data. The input here is task progress data, and the output is the workload value for each member.

[0441] Step 3:

[0442] The server uses a generative AI model to perform optimal task assignments. Based on the skill information obtained in step 1 and the load status in step 2, the AI ​​model selects appropriate members for each task. The model is given the prompt "Assign task B of the new project to members with skill set A." The output is a list of assigned tasks.

[0443] Step 4:

[0444] The terminal provides an interface for members to input progress. Once a member submits a progress report, this information is immediately sent to the server via an API. The input is progress report data for ongoing tasks, and the server output is updated dashboard data.

[0445] Step 5:

[0446] The server analyzes the project's progress and displays a visual progress report to the user. Based on this analysis, the dashboard is updated in real time. Users can access this dashboard through a web browser to view the project overview and issues.

[0447] Step 6:

[0448] The server notifies the user when it detects anomalies in the progress. It monitors progress data, and if problems such as delays occur, the server immediately sends a warning message to the user. This includes using external communication tools.

[0449] Step 7:

[0450] Upon project completion, the server generates an evaluation report based on all collected performance data. It analyzes the final deliverables and work efficiency of each team member, performs quantitative evaluations, and then provides the evaluation report to the user. This report is used by the user as a metric for performance evaluations.

[0451] (Application Example 1)

[0452] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0453] In factory production processes, it is difficult to assign tasks optimally, taking into account the characteristics and load conditions of each piece of equipment. Furthermore, without real-time progress monitoring and immediate detection / notification of anomalies, production efficiency may decline and quality problems may occur. Under these circumstances, there is a need to achieve efficient and stable production management.

[0454] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0455] In this invention, the server includes means for acquiring characteristic information of each work device, means for automatically assigning tasks based on the load status of each work device, and means for monitoring the progress of the production process in real time and displaying the progress status. This makes it possible to maximize the capabilities of each work device and improve the overall efficiency of the production process.

[0456] "Work equipment" is a general term for machines and devices designed to perform specific tasks within a factory.

[0457] "Characteristic information" refers to information that indicates the unique capabilities and special skills of each work device, and is used to improve work efficiency and optimize operations.

[0458] "Load status" refers to the current load level of each work device and serves as an indicator for efficient work allocation.

[0459] "Automatically assigning tasks" means distributing tasks to appropriate work devices based on pre-set algorithms or conditions.

[0460] "Progress in the production process" refers to the status of a series of tasks and processes being carried out within a factory, and is an important indicator for confirming whether things are progressing according to plan.

[0461] "Real-time monitoring" means being able to immediately check the current situation and take swift action as needed.

[0462] "Displaying progress" means visually showing the current status of the production process so that stakeholders can easily understand the current situation.

[0463] The system implementing this invention mainly consists of server, terminal, and user components. The server is responsible for retrieving characteristic information of each work device from a database and automatically optimizing and assigning tasks based on that information. This uses programming languages ​​and frameworks such as Python and Django. PostgreSQL is used as the database to manage characteristic information and load status in detail.

[0464] Terminals are used for real-time progress monitoring of the work equipment. These terminals provide information to administrators via a web interface, allowing them to visualize the progress of the production process. A dynamic dashboard is built using the Django framework to display the progress, making it viewable through a browser.

[0465] Furthermore, users can receive immediate notifications when anomalies or delays occur. When the server detects an anomaly, it will alert the user using email or mobile app notification features.

[0466] As a concrete example, in a factory, work machines specializing in welding and those specializing in painting would each handle tasks according to their respective strengths. Managers could monitor the progress of the production line in real time through smart glasses and grasp the overall status at any time.

[0467] An example of a prompt statement would be: "We want to design a system that can optimally assign tasks in real time based on the characteristics and load status of each robot in the factory. A function is needed to immediately notify in case of abnormalities or delays. We want to implement this system as a web application."

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0469] Step 1:

[0470] The server retrieves characteristic information and load status for each work device from the database. This input information includes work history and current task load. The server analyzes this data to understand the capabilities and load status of each work device. The output is a characteristic and load profile used for work assignment.

[0471] Step 2:

[0472] The server automatically assigns the most suitable task to each work device based on acquired characteristic information and load conditions. Inputs are characteristic information and load profiles, and output is the generation of specific task assignment instructions for each work device. In this process, the server maximizes the capabilities of each device and improves work efficiency.

[0473] Step 3:

[0474] The terminal receives task assignment instructions sent from the server and monitors the progress of the production process in real time. Inputs include task assignment information from the server and progress data from each work device. The terminal processes this data and dynamically displays the progress status, visually informing the user. Output is a progress status dashboard displayed in a browser.

[0475] Step 4:

[0476] Users monitor the progress dashboard from their terminals and respond immediately if an anomaly occurs. By directly observing the progress, users can always understand the status of the entire production process. The input is the progress dashboard, and the output is the decision on the corrective action.

[0477] Step 5:

[0478] The server sends a notification to the user if it detects an anomaly or delay. The input is progress data received from the terminal. The server analyzes this progress data and, if an anomaly is detected, sends an alert to the user via email or notification. The output is a notification message regarding the anomaly.

[0479] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0480] This invention provides a task management system that incorporates a new emotion engine. This allows for understanding the emotional state of each team member, thereby improving the efficiency and accuracy of project management.

[0481] First, the server retrieves existing skill information, load status, and newly added emotional information from the database. Emotional information is collected in real time from facial expression analysis of members and biometric sensors, and stored as data.

[0482] Next, the server analyzes the acquired emotional information and evaluates each member's current mental state. For example, if it determines that a member is experiencing stress, the server adjusts how tasks are assigned based on that information.

[0483] Furthermore, the server incorporates emotional information into project progress management. Based on emotional data, it automatically configures an interface appropriate to the project's progress, providing a comfortable working environment for each team member.

[0484] The emotion engine also helps in how to notify users of abnormal events or delays. For example, it can be configured to send appropriate messages to members whose emotional state is more receptive to encouragement than specific criticism.

[0485] For example, if emotional data is collected indicating that a user is feeling tired, the server will reassign that member's tasks to other members, thereby reducing their workload. This allows for maintaining the productivity of individual members while balancing the overall project.

[0486] Thus, by considering the emotional state of team members at each stage of project management, this invention enables more adaptive and flexible task management. By using the emotion engine in conjunction with this, it is expected that not only will the success rate of projects be improved, but the satisfaction and well-being of team members will also be maintained.

[0487] The following describes the processing flow.

[0488] Step 1:

[0489] The terminal collects real-time emotional data from its members. This includes facial recognition technology using cameras and biometric data obtained from wearable devices.

[0490] Step 2:

[0491] The server analyzes the collected emotional data to determine the emotional state of each member. This includes stress levels, concentration levels, and happiness ratings based on feedback.

[0492] Step 3:

[0493] The server adjusts the optimal task assignment for each member based on their determined emotional state. For example, it assigns less demanding tasks to members who are experiencing high stress levels.

[0494] Step 4:

[0495] The server displays progress along with sentiment information on a dashboard, allowing users to gain a comprehensive understanding of the project's status. Sentiment data is also visualized, making it possible to instantly check the current status of each team member.

[0496] Step 5:

[0497] During project execution, the server adjusts its notification methods for abnormal events based on emotional information. For example, it takes a situation-sensitive approach, such as sending encouraging messages to members who are feeling down.

[0498] Step 6:

[0499] Upon project completion, the server generates and provides a performance report, including sentiment data, to the user. This allows for objective identification of areas for improvement and contributions from team members, which can then be used to determine roles and responsibilities in future projects.

[0500] (Example 2)

[0501] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0502] Modern project management requires considering and integrating not only the skills and workload of team members, but also their mental and emotional states. However, existing systems struggle to adequately grasp and provide feedback on emotions and mental states, leading to stress and decreased productivity. To address this, flexible task management and interface optimization utilizing emotional data are necessary.

[0503] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0504] In this invention, the server includes means for acquiring skill information and physiological status of each member, means for analyzing emotional information and evaluating the mental state of the members, and means for dynamically assigning and adjusting tasks based on the mental state of each member. This enables adaptive project management that takes into account the emotional state of the members and provides an interface that reduces stress and increases productivity.

[0505] "Each member" refers to all members participating in the project or task, and their skills information and physiological status are subject to management.

[0506] "Skills information" refers to data on the professional abilities and proficiency levels of team members, and is used for task assignment and coordination.

[0507] "Physiological state" refers to data indicating the physiological state of a member's body, such as heart rate and skin electrical response.

[0508] "Emotional information" refers to data that indicates the psychological and emotional state of the members, and is collected through facial expression analysis and sensors.

[0509] "Mental state" refers to the results of an assessment of the psychological stress and emotional state of the team members, and is used for task adjustment and feedback.

[0510] "Dynamic task assignment" refers to a process of flexibly rearranging tasks according to the current skill levels and mental state of team members.

[0511] "Project progress" refers to information indicating how well the entire project is progressing according to plan, and management that takes emotional information into account is required.

[0512] An "adaptive interface" refers to a user interface that automatically adjusts according to the emotional state of its members, enabling them to work in the optimal environment.

[0513] A "notification message" refers to information that a system sends to its members, including content that may include encouragement or warnings that correspond to their emotional state.

[0514] This invention is a task management system incorporating an emotion engine, and consists of a server, terminals, and users to achieve efficient project management.

[0515] The server is the core of the program, collecting skill information and physiological status data from each member. Hardware such as cameras and sensors connected to terminals are used for data collection. The camera captures facial expressions in real time using commonly used video processing libraries and analyzes emotional information. Physiological sensors measure heart rate and skin electrical responses and transmit this data to the server. The server integrates this information and utilizes generative AI models to evaluate the mental state of each member.

[0516] The terminal functions as a user interface used by members, and the server provides an optimized interface based on collected data. For example, the color scheme and layout dynamically change according to the member's emotional state, designed to improve work efficiency. The terminal also has the functionality to display emotional information and visually inform users of project progress.

[0517] Through this system, users can check the progress of their tasks and projects. The system displays feedback to help users understand their current emotional state and workload. Notification messages also include encouragement and suggestions for rest, thereby reducing user stress and maintaining their health.

[0518] For example, if a user is feeling fatigued, the generative AI model analyzes this information and distributes tasks to other team members. In this case, the user will see a notification on their device such as, "Please get enough rest."

[0519] An example of a prompt message might be, "If a team member's stress level is high, what actions will you take to adjust the task?" In this way, the task management system can take into account the emotional state of its members and optimally adjust the project's progress.

[0520] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0521] Step 1:

[0522] The server acquires skill information and physiological status of each member. It receives facial expression image data and physiological information sensor data acquired from terminals as input. The server stores this data in a database and applies a facial expression analysis algorithm to extract emotional information from facial expressions. This results in the output of the member's latest emotional information.

[0523] Step 2:

[0524] The server uses the extracted emotional information to evaluate the mental state of its members using a generative AI model. The input includes the emotional information obtained in step 1. The AI ​​model quantifies each emotional state and determines stress levels and fatigue levels. This evaluation outputs the mental state of each member.

[0525] Step 3:

[0526] The server reallocates tasks based on the outputted mental state data. It uses the current task list and mental state data of each member as input. Based on this information, the server optimizes the workload for each member and reassigns tasks to other members as needed. This results in an updated task assignment status being output.

[0527] Step 4:

[0528] The terminal optimizes the user interface based on updated task information and mental state received from the server. It receives the current mental state and task information of the members as input. The terminal dynamically adjusts the screen's color tone and layout to provide a comfortable working environment for the user. This process results in an optimized user interface.

[0529] Step 5:

[0530] The server generates and sends notification messages based on emotional information to the terminal. It uses updated mental state and task status as input. The server creates messages to alleviate the psychological burden on members and sends notifications such as "Please take a rest" to the terminal. This ensures that appropriate feedback notifications are output.

[0531] (Application Example 2)

[0532] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0533] Traditional task management systems assign tasks based solely on members' skill levels and workload, neglecting their emotional state and potentially leading to decreased productivity and motivation. Furthermore, the lack of real-time notifications and progress tracking based on emotional data can cause stress and unnecessary burdens on members.

[0534] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0535] In this invention, the server includes means for acquiring member skill information, means for automatically reassigning tasks based on member workload and emotional information, and means for monitoring project progress in real time and displaying progress that takes emotional status into account. This enables flexible task management that takes member emotional status into account.

[0536] A "task management system" is a framework that provides means and processes for efficiently and effectively carrying out the tasks assigned to its members.

[0537] "Skills information" refers to data related to the abilities of team members in performing their duties, such as their specialized knowledge, skills, and experience.

[0538] "Workload status" refers to information indicating the amount of work currently assigned to each member and the mental and physical impact that work has on them.

[0539] "Emotional information" refers to data about the emotional state of a member at a given time, obtained through their facial expressions and biological responses.

[0540] "Automatic reassignment" refers to a function in which the system operates based on pre-set conditions and reassigns tasks according to the status and circumstances of the members.

[0541] "Means of monitoring progress in real time" refers to methods and functions for immediately grasping and visualizing the progress of ongoing tasks.

[0542] "Displaying progress while considering emotional state" refers to a method of visually showing information indicating the progress of work while taking into account the emotional data of the team members.

[0543] "Notifications based on emotional data" are messages created based on data about the emotional state of members, and are designed to convey appropriate information according to the situation.

[0544] The system that implements this application consists of a server and terminals. The server acquires the skill information, workload status, and emotional information of its members and reassigns tasks based on this information. Emotional information is collected using biometric sensors and facial recognition cameras and analyzed in real time. Specifically, a facial analysis program using Python and OpenCV processes the emotional data, and it is stored in a database using Firebase.

[0545] The devices are primarily smartphones, each provided to a member to receive notifications from the server. These notifications, based on emotional information, include encouragement and suggestions tailored to the member's current emotional state. For example, if stress levels are high, a rest is suggested; if motivation is high, work is encouraged to leverage that state.

[0546] For example, if the system determines that a member is feeling fatigued, the server will send a notification to that member suggesting that their workload be reduced. An example of a prompt message from the generated AI model might be: "Signs of fatigue have been detected in member A's emotional data. How should the tasks be adjusted?"

[0547] This system enables flexible task management that takes into account the emotional state of each member, and creates a more comfortable working environment.

[0548] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0549] Step 1:

[0550] The server retrieves each member's skill information and workload status from a database. It also collects emotional information from members in real time using biometric sensors and facial recognition cameras. This provides skill information, workload status, and emotional information as input data. In particular, emotional information is analyzed using OpenCV with heart rate data from sensors and camera images, and output as an emotional state.

[0551] Step 2:

[0552] The server analyzes the emotional information obtained in Step 1 to evaluate the emotional state of each member. The server filters the input emotional data to identify emotional states such as stress and fatigue. As a result of the evaluation, a report on the emotional state of each member is generated, and task reassignment is considered based on this report.

[0553] Step 3:

[0554] The server re-evaluates the tasks assigned to each member based on their emotional state and reassigns them as needed. This reassignment is performed using an optimization algorithm based on a generative AI model. It processes the input emotional state and workload to output a new task assignment plan. For example, if fatigue is detected, specific mitigation measures are proposed.

[0555] Step 4:

[0556] The server sends notifications to each member's terminal based on the task reassignment results. The notification content is customized and communicated based on the emotional state. Prompt messages are generated according to the situation and designed to encourage appropriate action from the member. The outputted notification content provides the user with guidance on how to receive and respond.

[0557] Step 5:

[0558] Users perform tasks according to the reassigned tasks and notifications displayed on their devices. This system allows users to quickly understand and act upon tasks optimized for their emotional state. This enables the implementation of task assignments and corresponding emotional care.

[0559] 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.

[0560] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0561] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0562] [Fourth Embodiment]

[0563] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0564] 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.

[0565] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0566] 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.

[0567] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0568] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0569] 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.

[0570] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0571] 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.

[0572] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0573] The 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.

[0574] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0575] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0576] The system for carrying out the present invention has the function of acquiring skill information of each member, optimally assigning tasks based on this information, and monitoring project progress in real time. The embodiments thereof are described in detail below.

[0577] First, as soon as the project starts, the server accesses the database to retrieve the skills information of each member. This includes past work history, acquired skills, languages, etc. This information can be entered in advance by each member using a terminal.

[0578] Next, the server monitors the workload of each member. This workload is calculated based on factors such as the number of tasks already assigned to each member, their progress, and the remaining time. Based on this, the server automatically assigns appropriate tasks to each member. For example, a member skilled in programming will be given priority in coding-related tasks.

[0579] Furthermore, progress data entered via the terminal is transmitted to the server in real time. The server analyzes this data and updates a dashboard that visually represents the overall project progress. This dashboard can be viewed by the user at any time via the terminal, making it easy to understand the current status of the project. If any delays or anomalies are detected, the server is configured to immediately notify the user.

[0580] Furthermore, upon project completion, the server compiles all performance information and generates an objective evaluation report. This report quantitatively assesses the work performance of each team member and is used as a reference for user performance evaluations.

[0581] Thus, the present invention streamlines project management and optimizes the use of each team member's skills and workload, thereby improving the project's success rate.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] The server connects to the database and retrieves skill information for all members. This includes data on each member's expertise, such as their proficiency in programming languages ​​and their experience in past projects.

[0585] Step 2:

[0586] The server calculates the current load on each member. This information is obtained by analyzing the number of tasks currently assigned, their progress, deadlines, and so on.

[0587] Step 3:

[0588] The server automatically assigns tasks. Specifically, it considers each member's skill level and workload to select the most suitable task and notifies each member of the assignment.

[0589] Step 4:

[0590] The terminal receives task progress information from each member and sends it to the server. Progress data is updated regularly to reflect the progress of the work in real time.

[0591] Step 5:

[0592] The server analyzes the received progress data and updates the project progress on the dashboard to the latest status. This dashboard visually displays the overall project progress and the status of each task.

[0593] Step 6:

[0594] The server monitors progress data and immediately generates an alert and notifies the user if any anomalies or delays are detected. This enables a quick response.

[0595] Step 7:

[0596] Upon project completion, the server generates an evaluation report based on the collected performance data. This report objectively assesses the contributions and efficiency of each team member and is presented to the user.

[0597] (Example 1)

[0598] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0599] In modern project management, accurately assigning tasks that take into account the skills and workload of team members is crucial. However, managing this manually is time-consuming, labor-intensive, and inefficient. Furthermore, there is a need for a system that tracks project progress in real time and responds quickly to anomalies and delays. Currently, real-time progress management and anomaly response are often not possible, hindering project progress.

[0600] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0601] In this invention, the server includes means for acquiring information on each member, means for calculating the workload status of each member, and means for automatically assigning tasks using a generative AI model. This enables optimal task distribution based on the team's skills and workload status. Furthermore, project progress can be managed in real time through progress analysis and warning notifications in case of anomalies. This has the effect of improving the success rate of projects.

[0602] "Means for obtaining information on each member" refers to a function for collecting data such as the skills and experience of members participating in the project from information sources such as databases.

[0603] "Means for calculating the workload of team members" refers to a system for quantitatively understanding the current workload of each member based on the number and progress of tasks assigned to them.

[0604] "A method for automatically assigning tasks using a generative AI model" refers to a process that utilizes artificial intelligence technology to select and assign tasks that are best suited to each member's skills and workload.

[0605] "A means of analyzing project progress based on progress information received from terminals" refers to a method of understanding the overall progress of a project by receiving project progress data entered by members and analyzing its contents.

[0606] "Means of visually displaying project progress" refers to systems that clearly show the progress of a project to users through graphs, dashboards, and other means.

[0607] "Means for notifying warnings when abnormal events are detected" refers to a notification function that immediately informs relevant users when unexpected delays or problems occur during project progress.

[0608] "Means for generating evaluation reports based on collected information" refers to a function that analyzes the work performance of each member after the completion of a project and creates a report for objective evaluation.

[0609] Specific embodiments for carrying out the present invention will now be described. This system aims to streamline project management and optimally utilize the skills and workload of team members. The details of the system using each device and software are described below.

[0610] The server centrally manages information on each member. A database system is used to store member skill information and past work history. This information is based on data initially entered by members using their terminals. Specifically, an input form accessible from terminals via a web application is provided.

[0611] Next, the server calculates the workload of each member in real time. For this purpose, scheduling software running on the server is used. Task progress and the workload of each member are calculated and updated, for example, every 5 minutes. Furthermore, optimal task assignment is automated through a generative AI model. The AI ​​model utilizes historical task performance data to determine task priorities and assigns them to members based on their skills.

[0612] The terminals are used to send progress reports from team members to the server. Progress information entered through the terminal application is retrieved in real time via the server's API. This progress data is analyzed on the server and used to update a dashboard showing the overall project progress.

[0613] This dashboard allows users to constantly monitor the overall project picture. For example, by accessing it via a web browser, they can visually grasp the progress of each task and future plans. Furthermore, if an anomaly occurs, the server will immediately notify the user. For this purpose, it is possible for the server to integrate with external communication tools, such as notifications via Slack or email.

[0614] Finally, upon project completion, the server generates a report that objectively evaluates the performance of each member. This report provides valuable data for users to conduct performance evaluations.

[0615] As a concrete example, an example of a prompt message generated using the AI ​​model is, "Please assign the UI design task to a member with extensive design experience in Project A." In this way, this system automates and streamlines each process in project management.

[0616] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0617] Step 1:

[0618] The server stores the skill information of each member, sent from their terminal, in a database. When a member logs in, they input their skill information using an input form. This information includes their programming language proficiency level and past project experience. The server receives this information and stores it in the database.

[0619] Step 2:

[0620] The server runs scheduling software to calculate the workload of its members. The server retrieves the current number and progress of tasks for each member from the database and calculates the workload for each member based on this data. The input here is task progress data, and the output is the workload value for each member.

[0621] Step 3:

[0622] The server uses a generative AI model to perform optimal task assignments. Based on the skill information obtained in step 1 and the load status in step 2, the AI ​​model selects appropriate members for each task. The model is given the prompt "Assign task B of the new project to members with skill set A." The output is a list of assigned tasks.

[0623] Step 4:

[0624] The terminal provides an interface for members to input progress. Once a member submits a progress report, this information is immediately sent to the server via an API. The input is progress report data for ongoing tasks, and the server output is updated dashboard data.

[0625] Step 5:

[0626] The server analyzes the project's progress and displays a visual progress report to the user. Based on this analysis, the dashboard is updated in real time. Users can access this dashboard through a web browser to view the project overview and issues.

[0627] Step 6:

[0628] The server notifies the user when it detects anomalies in the progress. It monitors progress data, and if problems such as delays occur, the server immediately sends a warning message to the user. This includes using external communication tools.

[0629] Step 7:

[0630] Upon project completion, the server generates an evaluation report based on all collected performance data. It analyzes the final deliverables and work efficiency of each team member, performs quantitative evaluations, and then provides the evaluation report to the user. This report is used by the user as a metric for performance evaluations.

[0631] (Application Example 1)

[0632] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0633] In factory production processes, it is difficult to assign tasks optimally, taking into account the characteristics and load conditions of each piece of equipment. Furthermore, without real-time progress monitoring and immediate detection / notification of anomalies, production efficiency may decline and quality problems may occur. Under these circumstances, there is a need to achieve efficient and stable production management.

[0634] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0635] In this invention, the server includes means for acquiring characteristic information of each work device, means for automatically assigning tasks based on the load status of each work device, and means for monitoring the progress of the production process in real time and displaying the progress status. This makes it possible to maximize the capabilities of each work device and improve the overall efficiency of the production process.

[0636] "Work equipment" is a general term for machines and devices designed to perform specific tasks within a factory.

[0637] "Characteristic information" refers to information that indicates the unique capabilities and special skills of each work device, and is used to improve work efficiency and optimize operations.

[0638] "Load status" refers to the current load level of each work device and serves as an indicator for efficient work allocation.

[0639] "Automatically assigning tasks" means distributing tasks to appropriate work devices based on pre-set algorithms or conditions.

[0640] "Progress in the production process" refers to the status of a series of tasks and processes being carried out within a factory, and is an important indicator for confirming whether things are progressing according to plan.

[0641] "Real-time monitoring" means being able to immediately check the current situation and take swift action as needed.

[0642] "Displaying progress" means visually showing the current status of the production process so that stakeholders can easily understand the current situation.

[0643] The system implementing this invention mainly consists of server, terminal, and user components. The server is responsible for retrieving characteristic information of each work device from a database and automatically optimizing and assigning tasks based on that information. This uses programming languages ​​and frameworks such as Python and Django. PostgreSQL is used as the database to manage characteristic information and load status in detail.

[0644] Terminals are used for real-time progress monitoring of the work equipment. These terminals provide information to administrators via a web interface, allowing them to visualize the progress of the production process. A dynamic dashboard is built using the Django framework to display the progress, making it viewable through a browser.

[0645] Furthermore, users can receive immediate notifications when anomalies or delays occur. When the server detects an anomaly, it will alert the user using email or mobile app notification features.

[0646] As a concrete example, in a factory, work machines specializing in welding and those specializing in painting would each handle tasks according to their respective strengths. Managers could monitor the progress of the production line in real time through smart glasses and grasp the overall status at any time.

[0647] An example of a prompt statement would be: "We want to design a system that can optimally assign tasks in real time based on the characteristics and load status of each robot in the factory. We need a function that can immediately notify us if an anomaly or delay occurs. We want to implement this system as a web application."

[0648] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0649] Step 1:

[0650] The server retrieves characteristic information and load status for each work device from the database. This input information includes work history and current task load. The server analyzes this data to understand the capabilities and load status of each work device. The output is a characteristic and load profile used for work assignment.

[0651] Step 2:

[0652] The server automatically assigns the most suitable task to each work device based on acquired characteristic information and load conditions. Inputs are characteristic information and load profiles, and output is the generation of specific task assignment instructions for each work device. In this process, the server maximizes the capabilities of each device and improves work efficiency.

[0653] Step 3:

[0654] The terminal receives task assignment instructions sent from the server and monitors the progress of the production process in real time. Inputs include task assignment information from the server and progress data from each work device. The terminal processes this data and dynamically displays the progress status, visually informing the user. Output is a progress status dashboard displayed in a browser.

[0655] Step 4:

[0656] Users monitor the progress dashboard from their terminals and respond immediately if an anomaly occurs. By directly observing the progress, users can always understand the status of the entire production process. The input is the progress dashboard, and the output is the decision on the corrective action.

[0657] Step 5:

[0658] The server sends a notification to the user if it detects an anomaly or delay. The input is progress data received from the terminal. The server analyzes this progress data and, if an anomaly is detected, sends an alert to the user via email or notification. The output is a notification message regarding the anomaly.

[0659] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0660] This invention provides a task management system that incorporates a new emotion engine. This allows for understanding the emotional state of each team member, thereby improving the efficiency and accuracy of project management.

[0661] First, the server retrieves existing skill information, load status, and newly added emotional information from the database. Emotional information is collected in real time from facial expression analysis of members and biometric sensors, and stored as data.

[0662] Next, the server analyzes the acquired emotional information and evaluates each member's current mental state. For example, if it determines that a member is experiencing stress, the server adjusts how tasks are assigned based on that information.

[0663] Furthermore, the server incorporates emotional information into project progress management. Based on emotional data, it automatically configures an interface appropriate to the project's progress, providing a comfortable working environment for each team member.

[0664] The emotion engine also helps in how to notify users of abnormal events or delays. For example, it can be configured to send appropriate messages to members whose emotional state is more receptive to encouragement than specific criticism.

[0665] For example, if emotional data is collected indicating that a user is feeling tired, the server will reassign that member's tasks to other members, thereby reducing their workload. This allows for maintaining the productivity of individual members while balancing the overall project.

[0666] Thus, by considering the emotional state of team members at each stage of project management, this invention enables more adaptive and flexible task management. By using the emotion engine in conjunction with this, it is expected that not only will the success rate of projects be improved, but the satisfaction and well-being of team members will also be maintained.

[0667] The following describes the processing flow.

[0668] Step 1:

[0669] The terminal collects real-time emotional data from its members. This includes facial recognition technology using cameras and biometric data obtained from wearable devices.

[0670] Step 2:

[0671] The server analyzes the collected emotional data to determine the emotional state of each member. This includes stress levels, concentration levels, and happiness ratings based on feedback.

[0672] Step 3:

[0673] The server adjusts the optimal task assignment for each member based on their determined emotional state. For example, it assigns less demanding tasks to members who are experiencing high stress levels.

[0674] Step 4:

[0675] The server displays progress along with sentiment information on a dashboard, allowing users to gain a comprehensive understanding of the project's status. Sentiment data is also visualized, making it possible to instantly check the current status of each team member.

[0676] Step 5:

[0677] During project execution, the server adjusts its notification methods for abnormal events based on emotional information. For example, it takes a situation-sensitive approach, such as sending encouraging messages to members who are feeling down.

[0678] Step 6:

[0679] Upon project completion, the server generates and provides a performance report, including sentiment data, to the user. This allows for objective identification of areas for improvement and contributions from team members, which can then be used to determine roles and responsibilities in future projects.

[0680] (Example 2)

[0681] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0682] Modern project management requires considering and integrating not only the skills and workload of team members, but also their mental and emotional states. However, existing systems struggle to adequately grasp and provide feedback on emotions and mental states, leading to stress and decreased productivity. To address this, flexible task management and interface optimization utilizing emotional data are necessary.

[0683] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0684] In this invention, the server includes means for acquiring skill information and physiological status of each member, means for analyzing emotional information and evaluating the mental state of the members, and means for dynamically assigning and adjusting tasks based on the mental state of each member. This enables adaptive project management that takes into account the emotional state of the members and provides an interface that reduces stress and increases productivity.

[0685] "Each member" refers to all members participating in the project or task, and their skills information and physiological status are subject to management.

[0686] "Skills information" refers to data on the professional abilities and proficiency levels of team members, and is used for task assignment and coordination.

[0687] "Physiological state" refers to data indicating the physiological state of a member's body, such as heart rate and skin electrical response.

[0688] "Emotional information" refers to data that indicates the psychological and emotional state of the members, and is collected through facial expression analysis and sensors.

[0689] "Mental state" refers to the results of an assessment of the psychological stress and emotional state of the team members, and is used for task adjustment and feedback.

[0690] "Dynamic task assignment" refers to a process of flexibly rearranging tasks according to the current skill levels and mental state of team members.

[0691] "Project progress" refers to information indicating how well the entire project is progressing according to plan, and management that takes emotional information into account is required.

[0692] An "adaptive interface" refers to a user interface that automatically adjusts according to the emotional state of its members, enabling them to work in the optimal environment.

[0693] A "notification message" refers to information that a system sends to its members, including content that may include encouragement or warnings that correspond to their emotional state.

[0694] This invention is a task management system incorporating an emotion engine, and consists of a server, terminals, and users to achieve efficient project management.

[0695] The server is the core of the program, collecting skill information and physiological status data from each member. Hardware such as cameras and sensors connected to terminals are used for data collection. The camera captures facial expressions in real time using commonly used video processing libraries and analyzes emotional information. Physiological sensors measure heart rate and skin electrical responses and transmit this data to the server. The server integrates this information and utilizes generative AI models to evaluate the mental state of each member.

[0696] The terminal functions as a user interface used by members, and the server provides an optimized interface based on collected data. For example, the color scheme and layout dynamically change according to the member's emotional state, designed to improve work efficiency. The terminal also has the functionality to display emotional information and visually inform users of project progress.

[0697] Through this system, users can check the progress of their tasks and projects. The system displays feedback to help users understand their current emotional state and workload. Notification messages also include encouragement and suggestions for rest, thereby reducing user stress and maintaining their health.

[0698] For example, if a user is feeling fatigued, the generative AI model analyzes this information and distributes tasks to other team members. In this case, the user will see a notification on their device such as, "Please get enough rest."

[0699] An example of a prompt message might be, "If a team member's stress level is high, what actions will you take to adjust the task?" In this way, the task management system can take into account the emotional state of its members and optimally adjust the project's progress.

[0700] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0701] Step 1:

[0702] The server acquires skill information and physiological status of each member. It receives facial expression image data and physiological information sensor data acquired from terminals as input. The server stores this data in a database and applies a facial expression analysis algorithm to extract emotional information from facial expressions. This results in the output of the member's latest emotional information.

[0703] Step 2:

[0704] The server uses the extracted emotional information to evaluate the mental state of its members using a generative AI model. The input includes the emotional information obtained in step 1. The AI ​​model quantifies each emotional state and determines stress levels and fatigue levels. This evaluation outputs the mental state of each member.

[0705] Step 3:

[0706] The server reallocates tasks based on the outputted mental state data. It uses the current task list and mental state data of each member as input. Based on this information, the server optimizes the workload for each member and reassigns tasks to other members as needed. This results in an updated task assignment status being output.

[0707] Step 4:

[0708] The terminal optimizes the user interface based on updated task information and mental state received from the server. It receives the current mental state and task information of the members as input. The terminal dynamically adjusts the screen's color tone and layout to provide a comfortable working environment for the user. This process results in an optimized user interface.

[0709] Step 5:

[0710] The server generates and sends notification messages based on emotional information to the terminal. It uses updated mental state and task status as input. The server creates messages to alleviate the psychological burden on members and sends notifications such as "Please take a rest" to the terminal. This ensures that appropriate feedback notifications are output.

[0711] (Application Example 2)

[0712] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0713] Traditional task management systems assign tasks based solely on members' skill levels and workload, neglecting their emotional state and potentially leading to decreased productivity and motivation. Furthermore, the lack of real-time notifications and progress tracking based on emotional data can cause stress and unnecessary burdens on members.

[0714] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0715] In this invention, the server includes means for acquiring member skill information, means for automatically reassigning tasks based on member workload and emotional information, and means for monitoring project progress in real time and displaying progress that takes emotional status into account. This enables flexible task management that takes member emotional status into account.

[0716] A "task management system" is a framework that provides means and processes for efficiently and effectively carrying out the tasks assigned to its members.

[0717] "Skills information" refers to data related to the abilities of team members in performing their duties, such as their specialized knowledge, skills, and experience.

[0718] "Workload status" refers to information indicating the amount of work currently assigned to each member and the mental and physical impact that work has on them.

[0719] "Emotional information" refers to data about the emotional state of a member at a given time, obtained through their facial expressions and biological responses.

[0720] "Automatic reassignment" refers to a function in which the system operates based on pre-set conditions and reassigns tasks according to the status and circumstances of the members.

[0721] "Means of monitoring progress in real time" refers to methods and functions for immediately grasping the progress of ongoing tasks and visualizing it.

[0722] "Displaying progress while considering emotional state" refers to a method of visually showing information indicating the progress of work while taking into account the emotional data of the team members.

[0723] "Notifications based on emotional data" are messages created based on data about the emotional state of members, and are designed to convey appropriate information according to the situation.

[0724] The system that implements this application consists of a server and terminals. The server acquires the skill information, workload status, and emotional information of its members and reassigns tasks based on this information. Emotional information is collected using biometric sensors and facial recognition cameras and analyzed in real time. Specifically, a facial analysis program using Python and OpenCV processes the emotional data, and it is stored in a database using Firebase.

[0725] The devices are primarily smartphones, each provided to a member to receive notifications from the server. These notifications, based on emotional information, include encouragement and suggestions tailored to the member's current emotional state. For example, if stress levels are high, a rest is suggested; if motivation is high, work is encouraged to leverage that state.

[0726] For example, if the system determines that a member is feeling fatigued, the server will send a notification to that member suggesting that their workload be reduced. An example of a prompt message from the generated AI model might be: "Signs of fatigue have been detected in member A's emotional data. How should the tasks be adjusted?"

[0727] This system enables flexible task management that takes into account the emotional state of each member, and creates a more comfortable work environment.

[0728] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0729] Step 1:

[0730] The server retrieves each member's skill information and workload status from a database. It also collects emotional information from members in real time using biometric sensors and facial recognition cameras. This provides skill information, workload status, and emotional information as input data. In particular, emotional information is analyzed using OpenCV with heart rate data from sensors and camera images, and output as an emotional state.

[0731] Step 2:

[0732] The server analyzes the emotional information obtained in Step 1 to evaluate the emotional state of each member. The server filters the input emotional data to identify emotional states such as stress and fatigue. As a result of the evaluation, a report on the emotional state of each member is generated, and task reassignment is considered based on this report.

[0733] Step 3:

[0734] The server re-evaluates the tasks assigned to each member based on their emotional state and reassigns them as needed. This reassignment is performed using an optimization algorithm based on a generative AI model. It processes the input emotional state and workload to output a new task assignment plan. For example, if fatigue is detected, specific mitigation measures are proposed.

[0735] Step 4:

[0736] The server sends notifications to each member's terminal based on the task reassignment results. The notification content is customized and communicated based on the emotional state. Prompt messages are generated according to the situation and designed to encourage appropriate action from the member. The outputted notification content provides the user with guidance on how to receive and respond.

[0737] Step 5:

[0738] Users perform tasks according to the reassigned tasks and notifications displayed on their devices. This system allows users to quickly understand and act upon tasks optimized for their emotional state. This enables the implementation of task assignments and corresponding emotional care.

[0739] 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.

[0740] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0741] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0742] 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.

[0743] Figure 9 shows an 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.

[0744] 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.

[0745] 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.

[0746] 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, motorcycles, etc., 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, for example, based 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.

[0747] 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."

[0748] 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.

[0749] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0750] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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 the like 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.

[0759] 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 as being incorporated by reference.

[0760] The following is further disclosed regarding the embodiments described above.

[0761] (Claim 1)

[0762] In a task management system,

[0763] A means of obtaining skill information for each member,

[0764] A means for automatically assigning tasks based on the workload of each member,

[0765] A means of monitoring project progress in real time and displaying the progress status,

[0766] A means for generating an evaluation report based on collected performance information,

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, which dynamically updates task assignments according to the progress of the project.

[0770] (Claim 3)

[0771] The system according to claim 1, comprising a function to issue a warning when an abnormal event or delay is detected.

[0772] "Example 1"

[0773] (Claim 1)

[0774] A means of obtaining information on each member,

[0775] A means for calculating the workload status of the members,

[0776] A means of automatically assigning tasks using a generative AI model,

[0777] A means of analyzing project progress based on progress information received from the terminal,

[0778] A means of visually displaying project progress,

[0779] A means of notifying a warning when an abnormal event is detected,

[0780] A means for generating an evaluation report based on the collected information,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, which dynamically adjusts task assignments according to the status of the project.

[0784] (Claim 3)

[0785] The system according to claim 1, further comprising a function for communicating with an external system to issue a warning notification.

[0786] "Application Example 1"

[0787] (Claim 1)

[0788] A means for acquiring characteristic information of each work device,

[0789] A means for automatically assigning tasks based on the load status of each work device,

[0790] A means of monitoring the progress of the production process in real time and displaying the progress status,

[0791] A means for generating an evaluation report based on collected performance information,

[0792] A means of issuing notifications when abnormal events or delays are detected,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, which dynamically updates work assignments in accordance with the progress of the production process.

[0796] (Claim 3)

[0797] The system according to claim 1, comprising a function to issue a warning when an abnormal event or delay is detected.

[0798] "Example 2 of combining an emotion engine"

[0799] (Claim 1)

[0800] A means of acquiring the skills information and physiological status of each member,

[0801] A means of analyzing emotional information and evaluating the mental state of its members,

[0802] A means of dynamically assigning and adjusting tasks based on the mental state of each member,

[0803] A means to monitor project progress in real time and provide an adaptive interface based on sentiment data,

[0804] A means for generating and sending notification messages based on collected physiological information and emotional data,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, which dynamically optimizes task assignment according to the emotional state of its members.

[0808] (Claim 3)

[0809] The system according to claim 1, comprising a function to issue emotionally sensitive warning and encouragement notifications when abnormal events or delays are detected.

[0810] "Application example 2 when combining with an emotional engine"

[0811] (Claim 1)

[0812] In a task management system,

[0813] A means of obtaining skill information for each member,

[0814] A means for automatically reassigning tasks based on the workload and emotional information of each member,

[0815] A means of monitoring project progress in real time and displaying progress status that takes emotional state into account,

[0816] A means for generating an evaluation report based on collected performance information and sentiment data,

[0817] A means of generating and sending notifications based on emotional data,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, which dynamically updates task assignments according to project progress and adjusts notification content based on sentiment information.

[0821] (Claim 3)

[0822] The system according to claim 1, comprising a function to issue a warning or notification in accordance with the emotional state when an abnormal event or delay is detected. [Explanation of Symbols]

[0823] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring characteristic information of each work device, A means for automatically assigning tasks based on the load status of each work device, A means of monitoring the progress of the production process in real time and displaying the progress status, A means for generating an evaluation report based on collected performance information, A means of issuing notifications when abnormal events or delays are detected, A system that includes this.

2. The system according to claim 1, which dynamically updates work assignments in accordance with the progress of the production process.

3. The system according to claim 1, comprising a function to issue a warning when an abnormal event or delay is detected.

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