system

Abstract

We provide the system. [Solution] Methods for retrieving tasks from project information, A method for assigning tasks based on the skills and tasks of team members, A means of monitoring and displaying task progress information in real time, Means to adjust schedules and optimize meeting times, A means of sharing project-related information, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In project management, tasks such as manually assigning tasks, monitoring progress, adjusting schedules, and promoting communication performed by humans are highly complex and time-consuming and labor-intensive, making efficient operation difficult. Also, if these tasks are not properly performed, it may lead to project delays and a decrease in team productivity.

Means for Solving the Problems

[0005] This invention provides a system that automatically retrieves tasks from project information and appropriately assigns them to team members according to their skills. Furthermore, this system includes means for monitoring task progress in real time and visualizing and displaying it on a dashboard. It also has a function to optimize members' schedules and adjust meeting times, and streamlines project management by efficiently sharing important project-related information. In addition, it promotes communication among team members and improves the efficiency of project management through automatically generated reminders based on progress and a summarization function from audio data.

[0006] A "task" is a specific unit of work or activity that each member or team in a project is supposed to perform.

[0007] "Project information" refers to a set of data necessary for managing and operating a project, including tasks, resources, schedules, and progress status.

[0008] "Skills" refer to the specialized knowledge, experience, and abilities that team members possess, including the technical aptitude necessary to perform specific tasks.

[0009] "Progress monitoring" is the process of tracking the degree of completion and managing time spent in order to understand the progress of tasks and the overall project.

[0010] A "dashboard" is a screen display that visually organizes the progress of projects and tasks, as well as various related data, making it easy for administrators and members to grasp the information.

[0011] "Schedule adjustment" refers to the process of efficiently allocating meeting and task execution times while considering the schedules of all team members.

[0012] A "reminder" is an automated notification sent to team members to inform them of the deadline and status of a specific task and to prompt them to take necessary action.

[0013] The "Audio Data Summarization Function" is a system function that automatically analyzes audio information collected during meetings and other events, extracts important points, and summarizes them in text format. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single 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.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a 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, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system that efficiently automates various aspects of project management. By automatically assigning tasks, monitoring progress, and optimizing communication among team members, it streamlines project operations.

[0036] Task management implementation

[0037] The server retrieves unassigned task information from the project information database and automatically distributes tasks based on the team members' skill sets and current workload. This ensures that the right members receive the right tasks, leading to smoother project progress.

[0038] Implementation of progress monitoring

[0039] The terminal provides an interface for each member to update the progress of their ongoing tasks. This information is periodically sent to the server, which analyzes it in real time and provides it to the administrator as a visual dashboard.

[0040] Schedule adjustment

[0041] Users register their preferred meeting times via their calendars on their devices, and the server aggregates these and notifies the entire team of the optimal meeting time. This enables efficient meeting scheduling that takes into account the schedules of all members.

[0042] Reminders and communication facilitation

[0043] The server monitors progress and automatically sends reminders as deadlines for important tasks approach. It also analyzes audio data recorded during meetings, generates summaries, and distributes them to team members. This prevents information from being missed and ensures all team members are up-to-date.

[0044] Specific example

[0045] In a team project, when a new feature development task is registered on the server, the server automatically assigns this task to member A, who possesses "Python skills." Member A updates the progress to 50% using their terminal, and the server receives this information and reflects it on the administrator dashboard. If progress falls behind, the server sends a reminder to member A to help ensure smooth task completion.

[0046] This system minimizes manual operations in project management, enabling efficient and productive team management.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server connects to the project information database and retrieves unassigned tasks.

[0050] Step 2:

[0051] The server analyzes the acquired task content and required skill set, and compares it with the team members' skill profiles and current task load information.

[0052] Step 3:

[0053] The server automatically assigns tasks to the most suitable team members and notifies each member of the results on their device.

[0054] Step 4:

[0055] Users use an application on their device to access an interface for updating the progress of their assigned tasks.

[0056] Step 5:

[0057] The terminal receives the user's progress as input data and sends it to the server.

[0058] Step 6:

[0059] The server analyzes the received progress data and updates the overall progress in real time, visualizing it on the dashboard.

[0060] Step 7:

[0061] The user enters their preferred date and time for the next meeting into a calendar application via their device and sends it to the server.

[0062] Step 8:

[0063] The server aggregates each user's schedule and calculates the optimal meeting time using an optimization algorithm.

[0064] Step 9:

[0065] The server notifies each member of the project team of the agreed-upon meeting time and adjusts their schedules accordingly.

[0066] Step 10:

[0067] The server monitors the progress of tasks and automatically generates and sends reminder notifications to the device for tasks with approaching deadlines.

[0068] Step 11:

[0069] The device uses its recording function to acquire audio data during the meeting and uploads it to the server.

[0070] Step 12:

[0071] The server analyzes the received audio data, extracts key points, and generates a text summary.

[0072] Step 13:

[0073] The server sends the generated summary to each user's terminal to facilitate information sharing.

[0074] (Example 1)

[0075] 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."

[0076] Project management requires addressing the inefficiencies and wasted time resulting from manual processes such as task assignment, progress monitoring, scheduling, and information sharing. Furthermore, large teams are prone to communication breakdowns and misinterpretations of task priorities, which negatively impact the overall project progress. Therefore, automation and accurate information dissemination are essential.

[0077] 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.

[0078] In this invention, the server includes means for retrieving tasks from an information infrastructure that records project information, means for automatically distributing tasks by referring to the skills and workload of the members, and means for collecting and visualizing task progress information in real time via a display device for inputting progress status. This enables minimizing manual operations in project management and accurate information management in real time.

[0079] "Project information" refers to the information necessary for managing project tasks, schedules, resources, progress, and other related matters.

[0080] "Information infrastructure" refers to foundational systems for collecting, storing, and managing information, including databases and storage systems.

[0081] A "task" refers to a series of actions or activities performed to achieve a specific objective.

[0082] "Members" refers to the individual members or staff participating in the project.

[0083] "Skills" refers to the knowledge and technical abilities that individual members possess in a specific field.

[0084] "Workload status" refers to information indicating the current state of work, such as the tasks each member is currently assigned, their volume, and their difficulty level.

[0085] A "display device" refers to a device that visually displays information through the screen of a computer or terminal.

[0086] "Progress information" refers to data that shows the extent to which a task or project has been completed.

[0087] "Visualization" refers to the process of making data and information easier to understand by displaying them in the form of graphs, charts, diagrams, and other visual aids.

[0088] One embodiment of this invention provides a system for streamlining and automating project management. This system consists of the following hardware and software:

[0089] Task management implementation

[0090] The server retrieves project information from the underlying database management system (e.g., a common database software). Based on the skills and workload of the team members, the server automatically distributes tasks to the most suitable members. This process uses algorithms to optimize skill matching and workload reduction for each member.

[0091] Implementation of progress monitoring

[0092] The terminal provides an interface for members to input progress on their display devices. This interface is implemented as a browser-based web application and collects progress information. This information is sent to a server and analyzed in real time. The server uses visualization tools (e.g., common data visualization tools) to graph the progress data and provide it to the administrator.

[0093] Schedule adjustment

[0094] Users register their preferred meeting times using calendar software (e.g., a common calendar service) from their terminals. The server has a system that analyzes the preferred times of all members, adjusts the optimal meeting time, and notifies the user.

[0095] Reminders and communication facilitation

[0096] The server automatically generates and distributes reminders to members based on task progress. Furthermore, audio data from meetings can be converted to text using speech recognition software (e.g., a common speech recognition API), creating a summary that can then be distributed to the team.

[0097] Specific example

[0098] For example, when a new project is started, the server automatically retrieves tasks from the project information and assigns a task to develop a new feature to member A, who has Python skills. At this time, member A reports the progress as 50% via their terminal, and the server reflects this information on the dashboard and displays it to the administrator. If progress falls behind, the server automatically sends a task reminder to member A to help streamline the work.

[0099] Example of a prompt

[0100] "How can I distribute newly added tasks in the project management system based on the skills of the team members?"

[0101] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0102] Step 1:

[0103] The server retrieves task data from the project's information infrastructure. In this step, unassigned tasks are selected from the database and sent to the server via API queries. The input is the task information stored in the database, and the output is the task list used by the server. The server performs actions such as "SELECT FROM tasks WHERE assigned = FALSE" to collect the necessary task data.

[0104] Step 2:

[0105] The server collects skill information and workload status for each member. The input is information stored in the member profile database, and the output is a list of members considering their skill sets and current workload. The server retrieves this data using another API query, formats the data using a Python script, and prepares the optimal task assignment for each member.

[0106] Step 3:

[0107] The server assigns tasks to the most suitable members using a skill matching algorithm. The inputs are task data and member information, and the output is a list of tasks assigned to each member. In this process, the server runs an optimization algorithm to calculate the best match between tasks and skills.

[0108] Step 4:

[0109] The terminal notifies members of task assignment information. The input is the data for the assigned task, and the output is a screen displaying the notification for the member. The terminal displays task details via a web application and notifies members via email or a notification system.

[0110] Step 5:

[0111] The terminal provides an interface for members to input their progress. Input is progress information manually entered by the members, and output is progress data sent to the server. The terminal provides a form that runs in a browser and includes a function to input progress numerically.

[0112] Step 6:

[0113] The server receives progress information and updates a visual dashboard for administrators. The input is progress data submitted by members, and the output is a visualized dashboard. The server aggregates the data, uses visualization tools to generate graphs and charts, and displays the progress to administrators in real time.

[0114] Step 7:

[0115] Users enter their preferred meeting times into their device's calendar. The input is information entered into the user's calendar app, and the output is a unified list of preferred meeting times. By setting preferred meeting times using the calendar app, the preferred times of all members are centrally managed.

[0116] Step 8:

[0117] The server analyzes the aggregated meeting time requests and calculates the optimal time. The input is a list of each member's preferred times, and the output is the selection of the optimal meeting time. The server uses an algorithm to calculate non-overlapping time slots and determines the most suitable meeting time for everyone.

[0118] Step 9:

[0119] The server notifies members of optimized meeting time information. The input is the calculated meeting time, and the output is the group of members who received the notification. The server then communicates meeting details to members via email or message through the notification system.

[0120] (Application Example 1)

[0121] 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."

[0122] Project management systems are required to maximize the use of worker skills while achieving efficient task allocation and progress management in conjunction with automated equipment. Furthermore, challenges include real-time visualization of progress that reflects actual work status, automation of schedule adjustments, and reliable information sharing.

[0123] 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.

[0124] In this invention, the server includes means for acquiring tasks from project information, means for assigning tasks based on the worker's skills and task content, and means for monitoring and displaying task progress information in real time. This enables efficient operation through collaboration between workers and automated equipment, real-time monitoring of project progress, and maintenance of an optimal schedule.

[0125] A "task" is a specific set of tasks that should be performed within a project or work.

[0126] "Project information" refers to all information related to the project, including the work content, progress, and required resources.

[0127] A "worker" refers to an individual with the labor force and specialized skills to carry out tasks within a project.

[0128] "Skills" refer to the ability and knowledge that a worker possesses to perform a specific task.

[0129] "Automated equipment" refers to devices or systems that operate autonomously and do not require human intervention.

[0130] "Progress information" refers to data that shows how far along a task or project is in relation to the plan.

[0131] "Schedule adjustment" refers to modifying a plan to optimize the order and timing of scheduled tasks, making them more efficient.

[0132] "Real-time monitoring" refers to the process of instantly detecting and immediately understanding the ongoing situation.

[0133] "Information sharing" refers to the process of synchronizing and making available the same data and knowledge among stakeholders.

[0134] In this invention, the server provides an infrastructure for managing project information, from task acquisition and assignment to progress monitoring and information sharing. Specifically, the server is located in a cloud environment and uses a database management system (e.g., PostgreSQL) to store and retrieve various project-related information. The automatic task assignment incorporates an algorithm based on worker skills and work status data, and is processed efficiently using a Python program.

[0135] The terminal functions as a device (smartphone or tablet) used by each worker, providing an interface through a web browser or dedicated application. This interface provides integrated input and confirmation of progress information, receipt of reminders, and communication methods. The real-time monitoring function visualizes progress using a frontend built with JavaScript® and React.

[0136] Users can access project information via their devices, checking the progress of specific tasks and requesting meetings. Schedule adjustments are handled by the server, and the results are sent to all members via push notifications. Additionally, audio data from meetings is converted into text using AI-based speech recognition technology (e.g., Google® Speech-to-Text API) to generate summary information.

[0137] A concrete example is its use in assembly lines where automated machinery operates. By monitoring the line's progress in real time, and automatically sending notifications to workers if schedule delays occur, appropriate actions can be taken. In such an environment, a prompt such as "How can we improve factory production efficiency using a robot task management system?" can be used, and the generated AI model can suggest ways to improve efficiency.

[0138] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0139] Step 1:

[0140] The server retrieves unassigned task information from the project information database. The input here is the project information database, and the output is unassigned task information. This information is extracted by executing a database query.

[0141] Step 2:

[0142] The server receives current skill data and workload information of workers as input and automatically assigns tasks to the most suitable workers. The output here is the assigned task information. An algorithm that matches workers' skills with task requirements is used to assign appropriate tasks to each worker.

[0143] Step 3:

[0144] The terminal receives task progress information input from the worker. The output is the updated progress information. This information is immediately sent to the server for progress visualization. The progress level can be changed on the terminal via the interface.

[0145] Step 4:

[0146] The server takes the collected progress information as input and generates data for a real-time dashboard. The output is a visual dashboard for administrators. Here, JavaScript is used to visually display the progress.

[0147] Step 5:

[0148] Users enter meeting requests using a terminal. The server uses this information to adjust the schedule, calculate the optimal meeting time, and notifies all members. The output of this process is the adjusted meeting time information.

[0149] Step 6:

[0150] The server monitors progress and schedule delays, and generates reminders for important tasks. Input is progress data, and output is reminder information. This notification is sent to the worker via their terminal.

[0151] Step 7:

[0152] During the meeting, the user's device collects audio data as input. The server processes this data, converts it to text using a summarization algorithm, and generates summary information. The output is a meeting summary text. This includes summarization using speech recognition technology.

[0153] 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.

[0154] This invention provides a system that highly automates project management while offering feedback that takes into account the user's emotional state. The system includes a function to retrieve tasks from project information and appropriately assign them to team members. Furthermore, it incorporates a function to identify the user's emotional state and optimize project management based on that state.

[0155] Task management implementation

[0156] The server retrieves new tasks from project information and analyzes the task content and the skill sets of the team members. Based on this, it automatically assigns tasks to the most suitable team members. Task progress is monitored in real time and provided to administrators and members through a progress dashboard.

[0157] Utilizing the Emotion Engine

[0158] The device activates an emotion engine based on user behavior data and interactions to identify the user's emotional state. The server uses the obtained emotional data to analyze the user's stress level and motivation, and adjusts tasks and communication methods as needed. This feedback function makes it possible to maximize the performance of team members.

[0159] Specific example

[0160] In one project, a device detects emotional indicators that a user is experiencing fatigue. Based on this information, the server adjusts the workload of tasks assigned to the user, switching them to less demanding tasks. Additionally, measures are taken to reduce the burden on members with high stress levels by extending the interval between reminder notifications.

[0161] This invention not only streamlines project management but also aims for optimal project operation while considering the emotional well-being of each team member.

[0162] The following describes the processing flow.

[0163] Step 1:

[0164] The server accesses the project information database and retrieves new unassigned tasks.

[0165] Step 2:

[0166] The server matches the required skill set for the acquired task with the skill profiles of the team members and assigns the task to the most suitable member.

[0167] Step 3:

[0168] The device activates an emotion engine based on user input data to analyze the user's emotional state in real time.

[0169] Step 4:

[0170] The server receives data from the emotion engine, reflects the user's emotional state in project management, and considers, for example, whether to reallocate tasks.

[0171] Step 5:

[0172] Users input the progress of their tasks through their device, and the device sends that data to the server.

[0173] Step 6:

[0174] The server analyzes the received progress data in real time and provides administrators with a dashboard that visualizes the results.

[0175] Step 7:

[0176] The server adjusts reminder notifications based on the user's emotional state, changing the notification interval as needed before sending them to the user.

[0177] Step 8:

[0178] The user obtains the audio data of the meeting through their device and uploads it to the server.

[0179] Step 9:

[0180] The server processes the meeting audio data, extracts key points, generates a summary, and distributes it to each member's terminal.

[0181] Step 10:

[0182] The server comprehensively evaluates the collected sentiment data and project progress, and makes continuous adjustments to optimize project efficiency.

[0183] (Example 2)

[0184] 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".

[0185] Modern project management requires not only efficient task assignment and progress monitoring, but also optimal project operation that takes into account the emotional state of team members. However, traditional systems struggle to adjust projects based on users' emotional states, failing to maximize the potential of team members. A new approach is needed to address this challenge.

[0186] 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.

[0187] In this invention, the server includes means for retrieving tasks from planning information, means for assigning tasks based on the abilities and work content of group members, and means for immediately observing and displaying work progress information. This enables efficient and effective project management that takes into account the emotional state of the members.

[0188] A "task" refers to a specific task or group of tasks that must be completed within a project.

[0189] "Planning information" refers to a collection of data and plans used to manage the overall progress of a project.

[0190] "Group members" refer to individual individuals involved in a project who have specific roles and responsibilities.

[0191] "Capability" refers to the technical skills and expertise possessed by individual members of a group.

[0192] "Work details" refers to the specific requirements and procedures needed for a particular task.

[0193] "Progress information" refers to data or status that indicates the degree of completion or state of a task.

[0194] "Emotional state" refers to the psychological or emotional condition exhibited by the user.

[0195] "Means of immediate observation and display" refers to a system that tracks the progress of work in real time and provides it visually.

[0196] This invention is a system that highly automates project management while taking into account the emotional state of team members. The system consists of the following elements:

[0197] Project Information Analysis

[0198] The server connects to project management tools (e.g., Jira or Trello) to collect current project information. This information includes details of new and ongoing tasks, required skill sets, and more. The collected information is stored in a database and processed by analysis algorithms as needed.

[0199] Task auto-assignment

[0200] The server analyzes the task's content and the team members' capabilities to assign the task to the most suitable member. An automated algorithm evaluates each member's skill set, ensuring effective work distribution.

[0201] Utilizing the Emotion Engine

[0202] The device collects user behavior data and uses an emotion engine to identify the user's emotional state. This process involves observing the user's behavior patterns through the interface. The emotional data is sent to a server to evaluate the user's stress level and motivation.

[0203] Project optimization

[0204] The server can redistribute tasks and adjust communication methods as needed based on the user's emotional state. For example, if a user indicates fatigue, the server can adjust the task content to reduce the workload and appropriately change the frequency of reminder notifications.

[0205] As a concrete example, the following prompt can be input into the AI ​​model: "In project management, please describe an automatic task redistribution function that takes into account the user's emotional state. Please describe in detail the roles of the server and terminal, including specific examples."

[0206] This system improves the efficiency of project management while enabling optimal project operation that takes into account the emotional well-being of team members.

[0207] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0208] Step 1:

[0209] The server retrieves project information from the project management tool.

[0210] As input, the server retrieves project data from a project management tool via an API. This includes a list of tasks, detailed information for each task, deadlines, and required skills. The output is the storage of this data in a database and its preparation for analysis. Specifically, the server periodically calls the API to collect the latest project information.

[0211] Step 2:

[0212] The server analyzes the task and assigns it optimally by comparing it to the capabilities of the group members.

[0213] As input, the server references the project information obtained in Step 1 and the competency database of the group members. For data processing, it evaluates the required skills for each task and matches them to the skill sets of the members. The output is assigning tasks to the most suitable members and notifying them of this information. Specifically, the assignment algorithm calculates based on skill matching and task priority.

[0214] Step 3:

[0215] The device collects user behavior data and uses an emotion engine to identify emotional states.

[0216] As input, the device receives keyboard and mouse inputs, as well as user facial expression data (if a camera is available). For data processing, this input data is fed into the emotion engine to identify stress levels and motivation. As output, the identified emotional state data is sent to the server. Specifically, the device uses the emotion engine's API to analyze the emotional state in real time.

[0217] Step 4:

[0218] The server redistributes tasks and adjusts communication based on emotional states.

[0219] As input, the server receives emotional state data obtained in step 3. As data processing, it redistributes tasks and adjusts notification frequency for members with high stress levels. It provides each member with feedback on the adjusted tasks and notification methods, which constitutes the output. In terms of specific operation, the server updates the schedule in real time according to the conditions and notifies the relevant members of the information.

[0220] This process streamlines project progress and enables management that takes into account the emotional well-being of users.

[0221] (Application Example 2)

[0222] 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".

[0223] In project management, it is essential to achieve efficient task allocation and work operations that take into account the emotional state of workers. While current systems are increasingly automating task assignment and progress management, they lack the functionality to redistribute tasks or adjust workloads based on workers' emotional states. Therefore, a method is needed to maximize overall project efficiency while reducing worker fatigue and stress.

[0224] 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.

[0225] In this invention, the server includes means for acquiring tasks from business information, means for assigning tasks based on individual skills and task content, and means for recognizing the emotional state of workers and optimizing task redistribution based on that state. This enables efficient business operations that take into account not only the progress of tasks but also the emotional state of workers.

[0226] "Business information" refers to all data and information related to a specific project or operation, including task details, deadlines, resources, and related documents.

[0227] "Individual skills" refer to the ability of a person to possess the knowledge, experience, and techniques necessary to perform a specific task.

[0228] "Task description" refers to the details of a specific task or job, including information such as its purpose, procedures, required resources, and criteria for completion.

[0229] "Worker's emotional state" refers to the psychological state of employees during work, and includes indicators such as stress, motivation, and fatigue.

[0230] "Redistribution" refers to reviewing the tasks assigned to each worker based on appropriate criteria and making changes as necessary.

[0231] "Optimization" refers to bringing a system or process to its most efficient and effective state in order to achieve a specific purpose or goal.

[0232] The system for implementing this invention is primarily composed of a server and terminals. The server retrieves tasks from business information and assigns appropriate tasks based on individual skills and task content. Furthermore, it recognizes the emotional state of workers and optimizes operations by redistributing tasks based on that data.

[0233] The terminal uses hardware such as smart glasses to collect the worker's emotional state in real time. Emotion recognition uses software equipped with an emotion engine to analyze the worker's psychological state.

[0234] On the server side, this data is integrated, and task allocation is managed in real time using project management software. This process uses programming languages ​​such as Python, and the emotion_recognition library is used for processing emotional state data.

[0235] For example, if worker A shows signs of fatigue, the server will use that emotional data to reduce worker A's workload and automatically switch to a simpler task. Furthermore, measures will be taken to adjust the interval and content of notifications for workers with high stress levels.

[0236] Examples of prompts for a generative AI model:

[0237] "Please tell me how to monitor the emotional state of factory workers and assign them to the most appropriate tasks."

[0238] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0239] Step 1:

[0240] The server receives business information and extracts new tasks from it. The input is business information, and the output is detailed task information. At this stage, database queries are used to retrieve relevant task data.

[0241] Step 2:

[0242] The server references individual skill sets, matches them with task content, and assigns tasks to the most suitable individuals. Inputs are detailed task information and individual skill data, while output is information on individual and task assignments. A machine learning algorithm calculates the optimal match.

[0243] Step 3:

[0244] The terminal collects the worker's emotional state in real time via sensors and transmits it to a server. The input is biometric data, and the output is the result of the emotional state analysis. Here, emotion recognition software analyzes the data and determines the psychological state.

[0245] Step 4:

[0246] The server analyzes collected emotional states and redistributes tasks as needed. Inputs are the results of the emotional state analysis and current task assignment information; output is the adjusted task assignment information. An algorithm is used to optimize worker states and task load.

[0247] Step 5:

[0248] The server updates the progress dashboard, displaying real-time task progress and worker sentiment status to administrators. Inputs are adjusted task assignment information and real-time sentiment status data, while outputs are visualized progress and sentiment status information. The dashboard uses web application technology to visualize the data.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] [Second Embodiment]

[0253] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0254] 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.

[0255] 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).

[0256] 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.

[0257] 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.

[0258] 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).

[0259] 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.

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] 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".

[0265] This invention is a system that efficiently automates various aspects of project management. By automatically assigning tasks, monitoring progress, and optimizing communication among team members, it streamlines project operations.

[0266] Task management implementation

[0267] The server retrieves unassigned task information from the project information database and automatically distributes tasks based on the team members' skill sets and current workload. This ensures that the right members receive the right tasks, leading to smoother project progress.

[0268] Implementation of progress monitoring

[0269] The terminal provides an interface for each member to update the progress of their ongoing tasks. This information is periodically sent to the server, which analyzes it in real time and provides it to the administrator as a visual dashboard.

[0270] Schedule adjustment

[0271] Users register their preferred meeting times via their calendars on their devices, and the server aggregates these and notifies the entire team of the optimal meeting time. This enables efficient meeting scheduling that takes into account the schedules of all members.

[0272] Reminders and communication facilitation

[0273] The server monitors progress and automatically sends reminders as deadlines for important tasks approach. It also analyzes audio data recorded during meetings, generates summaries, and distributes them to team members. This prevents information from being missed and ensures all team members are up-to-date.

[0274] Specific example

[0275] In a team project, when a new feature development task is registered on the server, the server automatically assigns this task to member A, who possesses "Python skills." Member A updates the progress to 50% using their terminal, and the server receives this information and reflects it on the administrator dashboard. If progress falls behind, the server sends a reminder to member A to help ensure smooth task completion.

[0276] This system minimizes manual operations in project management, enabling efficient and productive team management.

[0277] The following describes the process flow.

[0278] Step 1:

[0279] The server connects to the project information database and retrieves unassigned tasks.

[0280] Step 2:

[0281] The server analyzes the content of the retrieved tasks and the required skill set, and matches them with the skill profiles and current task load information of team members.

[0282] Step 3:

[0283] The server automatically assigns tasks to the optimal team members and notifies the results to the terminals of each member.

[0284] Step 4:

[0285] The user accesses the interface for updating the progress of the assigned tasks using the application on the terminal.

[0286] Step 5:

[0287] The terminal receives the user's progress status as input data and sends it to the server.

[0288] Step 6:

[0289] The server analyzes the received progress data, updates the overall progress status in real time, and visualizes it on the dashboard.

[0290] Step 7:

[0291] The user enters the desired date and time of the next meeting into the calendar application through the terminal and sends it to the server.

[0292] Step 8:

[0293] The server aggregates each user's schedule and calculates the optimal meeting time using an optimization algorithm.

[0294] Step 9:

[0295] The server notifies each member of the project team of the agreed-upon meeting time and adjusts their schedules accordingly.

[0296] Step 10:

[0297] The server monitors the progress of tasks and automatically generates and sends reminder notifications to the device for tasks with approaching deadlines.

[0298] Step 11:

[0299] The device uses its recording function to acquire audio data during the meeting and uploads it to the server.

[0300] Step 12:

[0301] The server analyzes the received audio data, extracts key points, and generates a text summary.

[0302] Step 13:

[0303] The server sends the generated summary to each user's terminal to facilitate information sharing.

[0304] (Example 1)

[0305] 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."

[0306] It is necessary to solve the inefficiencies and time waste caused by the manual execution of processes such as task assignment, progress monitoring, schedule adjustment, and information sharing in project management. In addition, in large teams, there is a high likelihood of insufficient communication among team members and misrecognition of task priorities, which affects the progress of the entire project. Therefore, it is required to achieve automation and accurate information transmission.

[0307] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Example 1 is realized by the following means.

[0308] In this invention, the server includes means for acquiring tasks from an information infrastructure that records project information, means for automatically allocating tasks by referring to the skills and load status of team members, and means for collecting and visualizing the progress information of tasks in real time via a display device for inputting the progress status. As a result, it becomes possible to minimize manual operations and perform accurate information management in real time in project management.

[0309] "Project information" refers to information necessary for the management of project tasks, schedules, resources, progress, etc.

[0310] "Information infrastructure" refers to a system that serves as a foundation for collecting, storing, and managing information, including databases and storage systems.

[0311] "Task" refers to a series of operations or activities carried out to achieve a specific goal.

[0312] "Team members" refer to individual members or staff who participate in a project.

[0313] "Skills" refer to the knowledge and technical abilities of individual team members in a specific field.

[0314] "Workload status" refers to information indicating the current state of work, such as the tasks each member is currently assigned, their volume, and their difficulty level.

[0315] A "display device" refers to a device that visually displays information through the screen of a computer or terminal.

[0316] "Progress information" refers to data that shows the extent to which a task or project has been completed.

[0317] "Visualization" refers to the process of making data and information easier to understand by displaying them in the form of graphs, charts, diagrams, and other visual aids.

[0318] One embodiment of this invention provides a system for streamlining and automating project management. This system consists of the following hardware and software:

[0319] Task management implementation

[0320] The server retrieves project information from the underlying database management system (e.g., a common database software). Based on the skills and workload of the team members, the server automatically distributes tasks to the most suitable members. This process uses algorithms to optimize skill matching and workload reduction for each member.

[0321] Implementation of progress monitoring

[0322] The terminal provides an interface for members to input progress on their display devices. This interface is implemented as a browser-based web application and collects progress information. This information is sent to a server and analyzed in real time. The server uses visualization tools (e.g., common data visualization tools) to graph the progress data and provide it to the administrator.

[0323] Schedule adjustment

[0324] Users register their preferred meeting times using calendar software (e.g., a common calendar service) from their terminals. The server has a system that analyzes the preferred times of all members, adjusts the optimal meeting time, and notifies the user.

[0325] Reminders and communication facilitation

[0326] The server automatically generates and distributes reminders to members based on task progress. Furthermore, audio data from meetings can be converted to text using speech recognition software (e.g., a common speech recognition API), creating a summary that can then be distributed to the team.

[0327] Specific example

[0328] For example, when a new project is started, the server automatically retrieves tasks from the project information and assigns a task to develop a new feature to member A, who has Python skills. At this time, member A reports the progress as 50% via their terminal, and the server reflects this information on the dashboard and displays it to the administrator. If progress falls behind, the server automatically sends a task reminder to member A to help streamline the work.

[0329] Example of a prompt

[0330] "How can I distribute newly added tasks in the project management system based on the skills of the team members?"

[0331] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0332] Step 1:

[0333] The server retrieves task data from the project's information infrastructure. In this step, unassigned tasks are selected from the database and sent to the server via API queries. The input is the task information stored in the database, and the output is the task list used by the server. The server performs actions such as "SELECT FROM tasks WHERE assigned = FALSE" to collect the necessary task data.

[0334] Step 2:

[0335] The server collects skill information and workload status for each member. The input is information stored in the member profile database, and the output is a list of members considering their skill sets and current workload. The server retrieves this data using another API query, formats the data using a Python script, and prepares the optimal task assignment for each member.

[0336] Step 3:

[0337] The server assigns tasks to the most suitable members using a skill matching algorithm. The inputs are task data and member information, and the output is a list of tasks assigned to each member. In this process, the server runs an optimization algorithm to calculate the best match between tasks and skills.

[0338] Step 4:

[0339] The terminal notifies members of task assignment information. The input is the data for the assigned task, and the output is a screen displaying the notification for the member. The terminal displays task details via a web application and notifies members via email or a notification system.

[0340] Step 5:

[0341] The terminal provides an interface for members to input their progress. Input is progress information manually entered by the members, and output is progress data sent to the server. The terminal provides a form that runs in a browser and includes a function to input progress numerically.

[0342] Step 6:

[0343] The server receives progress information and updates a visual dashboard for administrators. The input is progress data submitted by members, and the output is a visualized dashboard. The server aggregates the data, uses visualization tools to generate graphs and charts, and displays the progress to administrators in real time.

[0344] Step 7:

[0345] Users enter their preferred meeting times into their device's calendar. The input is information entered into the user's calendar app, and the output is a unified list of preferred meeting times. By setting preferred meeting times using the calendar app, the preferred times of all members are centrally managed.

[0346] Step 8:

[0347] The server analyzes the aggregated meeting time requests and calculates the optimal time. The input is a list of each member's preferred times, and the output is the selection of the optimal meeting time. The server uses an algorithm to calculate non-overlapping time slots and determines the most suitable meeting time for everyone.

[0348] Step 9:

[0349] The server notifies members of optimized meeting time information. The input is the calculated meeting time, and the output is the group of members who received the notification. The server then communicates meeting details to members via email or message through the notification system.

[0350] (Application Example 1)

[0351] 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."

[0352] Project management systems are required to maximize the use of worker skills while achieving efficient task allocation and progress management in conjunction with automated equipment. Furthermore, challenges include real-time visualization of progress that reflects actual work status, automation of schedule adjustments, and reliable information sharing.

[0353] 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.

[0354] In this invention, the server includes means for acquiring tasks from project information, means for assigning tasks based on the worker's skills and task content, and means for monitoring and displaying task progress information in real time. This enables efficient operation through collaboration between workers and automated equipment, real-time monitoring of project progress, and maintenance of an optimal schedule.

[0355] A "task" is a specific set of tasks that should be performed within a project or work.

[0356] "Project information" refers to all information related to the project, including the work content, progress, and required resources.

[0357] A "worker" refers to an individual with the labor force and specialized skills to carry out tasks within a project.

[0358] "Skills" refer to the ability and knowledge that a worker possesses to perform a specific task.

[0359] "Automated equipment" refers to devices or systems that operate autonomously and do not require human intervention.

[0360] "Progress information" refers to data that shows how far along a task or project is in relation to the plan.

[0361] "Schedule adjustment" refers to modifying a plan to optimize the order and timing of scheduled tasks, making them more efficient.

[0362] "Real-time monitoring" refers to the process of instantly detecting and immediately understanding the ongoing situation.

[0363] "Information sharing" refers to the process of synchronizing and making available the same data and knowledge among stakeholders.

[0364] In this invention, the server provides an infrastructure for managing project information, from task acquisition and assignment to progress monitoring and information sharing. Specifically, the server is located in a cloud environment and uses a database management system (e.g., PostgreSQL) to store and retrieve various project-related information. The automatic task assignment incorporates an algorithm based on worker skills and work status data, and is processed efficiently using a Python program.

[0365] The terminal functions as a device (smartphone or tablet) used by each worker, providing an interface through a web browser or dedicated application. This interface integrates the input and confirmation of progress information, the reception of reminders, and communication methods. The real-time monitoring function visualizes progress using a frontend built with JavaScript or React.

[0366] Users can access project information via their devices, checking the progress of specific tasks and requesting meetings. Schedule adjustments are handled by the server, and the results are sent to all members via push notifications. Additionally, audio data from meetings is converted into text using AI-based speech recognition technology (e.g., Google Speech-to-Text API) to generate summary information.

[0367] A concrete example is its use in assembly lines where automated machinery operates. By monitoring the line's progress in real time, and automatically sending notifications to workers if schedule delays occur, appropriate actions can be taken. In such an environment, a prompt such as "How can we improve factory production efficiency using a robot task management system?" can be used, and the generated AI model can suggest ways to improve efficiency.

[0368] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0369] Step 1:

[0370] The server retrieves unassigned task information from the project information database. The input here is the project information database, and the output is unassigned task information. This information is extracted by executing a database query.

[0371] Step 2:

[0372] The server receives current skill data and workload information of workers as input and automatically assigns tasks to the most suitable workers. The output here is the assigned task information. An algorithm that matches workers' skills with task requirements is used to assign appropriate tasks to each worker.

[0373] Step 3:

[0374] The terminal receives task progress information input from the worker. The output is the updated progress information. This information is immediately sent to the server for progress visualization. The progress level can be changed on the terminal via the interface.

[0375] Step 4:

[0376] The server takes the collected progress information as input and generates data for a real-time dashboard. The output is a visual dashboard for administrators. Here, JavaScript is used to visually display the progress.

[0377] Step 5:

[0378] Users enter meeting requests using a terminal. The server uses this information to adjust the schedule, calculate the optimal meeting time, and notifies all members. The output of this process is the adjusted meeting time information.

[0379] Step 6:

[0380] The server monitors progress and schedule delays, and generates reminders for important tasks. Input is progress data, and output is reminder information. This notification is sent to the worker via their terminal.

[0381] Step 7:

[0382] During the meeting, the user's device collects audio data as input. The server processes this data, converts it to text using a summarization algorithm, and generates summary information. The output is a meeting summary text. This includes summarization using speech recognition technology.

[0383] 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.

[0384] This invention provides a system that highly automates project management while offering feedback that takes into account the user's emotional state. The system includes a function to retrieve tasks from project information and appropriately assign them to team members. Furthermore, it incorporates a function to identify the user's emotional state and optimize project management based on that state.

[0385] Task management implementation

[0386] The server retrieves new tasks from project information and analyzes the task content and the skill sets of the team members. Based on this, it automatically assigns tasks to the most suitable team members. Task progress is monitored in real time and provided to administrators and members through a progress dashboard.

[0387] Utilizing the Emotion Engine

[0388] The device activates an emotion engine based on user behavior data and interactions to identify the user's emotional state. The server uses the obtained emotional data to analyze the user's stress level and motivation, and adjusts tasks and communication methods as needed. This feedback function makes it possible to maximize the performance of team members.

[0389] Specific example

[0390] In one project, a device detects emotional indicators that a user is experiencing fatigue. Based on this information, the server adjusts the workload of tasks assigned to the user, switching them to less demanding tasks. Additionally, measures are taken to reduce the burden on members with high stress levels by extending the interval between reminder notifications.

[0391] This invention not only streamlines project management but also aims for optimal project operation while considering the emotional well-being of each team member.

[0392] The following describes the processing flow.

[0393] Step 1:

[0394] The server accesses the project information database and retrieves new unassigned tasks.

[0395] Step 2:

[0396] The server matches the required skill set for the acquired task with the skill profiles of the team members and assigns the task to the most suitable member.

[0397] Step 3:

[0398] The device activates an emotion engine based on user input data to analyze the user's emotional state in real time.

[0399] Step 4:

[0400] The server receives data from the emotion engine, reflects the user's emotional state in project management, and considers, for example, whether to reallocate tasks.

[0401] Step 5:

[0402] Users input the progress of their tasks through their device, and the device sends that data to the server.

[0403] Step 6:

[0404] The server analyzes the received progress data in real time and provides administrators with a dashboard that visualizes the results.

[0405] Step 7:

[0406] The server adjusts reminder notifications based on the user's emotional state, changing the notification interval as needed before sending them to the user.

[0407] Step 8:

[0408] The user obtains the audio data of the meeting through their device and uploads it to the server.

[0409] Step 9:

[0410] The server processes the meeting audio data, extracts key points, generates a summary, and distributes it to each member's terminal.

[0411] Step 10:

[0412] The server comprehensively evaluates the collected sentiment data and project progress, and makes continuous adjustments to optimize project efficiency.

[0413] (Example 2)

[0414] 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".

[0415] Modern project management requires not only efficient task assignment and progress monitoring, but also optimal project operation that takes into account the emotional state of team members. However, traditional systems struggle to adjust projects based on users' emotional states, failing to maximize the potential of team members. A new approach is needed to address this challenge.

[0416] 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.

[0417] In this invention, the server includes means for retrieving tasks from planning information, means for assigning tasks based on the abilities and work content of group members, and means for immediately observing and displaying work progress information. This enables efficient and effective project management that takes into account the emotional state of the members.

[0418] A "task" refers to a specific task or group of tasks that must be completed within a project.

[0419] "Planning information" refers to a collection of data and plans used to manage the overall progress of a project.

[0420] "Group members" refer to individual individuals involved in a project who have specific roles and responsibilities.

[0421] "Capability" refers to the technical skills and expertise possessed by individual members of a group.

[0422] "Work details" refers to the specific requirements and procedures needed for a particular task.

[0423] "Progress information" refers to data or status that indicates the degree of completion or state of a task.

[0424] "Emotional state" refers to the psychological or emotional condition exhibited by the user.

[0425] "Means of immediate observation and display" refers to a system that tracks the progress of work in real time and provides it visually.

[0426] This invention is a system that highly automates project management while taking into account the emotional state of team members. The system consists of the following elements:

[0427] Project Information Analysis

[0428] The server connects to project management tools (e.g., Jira or Trello) to collect current project information. This information includes details of new and ongoing tasks, required skill sets, and more. The collected information is stored in a database and processed by analysis algorithms as needed.

[0429] Task auto-assignment

[0430] The server analyzes the task's content and the team members' capabilities to assign the task to the most suitable member. An automated algorithm evaluates each member's skill set, ensuring effective work distribution.

[0431] Utilizing the Emotion Engine

[0432] The device collects user behavior data and uses an emotion engine to identify the user's emotional state. This process involves observing the user's behavior patterns through the interface. The emotional data is sent to a server to evaluate the user's stress level and motivation.

[0433] Project optimization

[0434] The server can redistribute tasks and adjust communication methods as needed based on the user's emotional state. For example, if a user indicates fatigue, the server can adjust the task content to reduce the workload and appropriately change the frequency of reminder notifications.

[0435] As a concrete example, the following prompt can be input into the AI ​​model: "In project management, please describe an automatic task redistribution function that takes into account the user's emotional state. Please describe in detail the roles of the server and terminal, including specific examples."

[0436] This system improves the efficiency of project management while enabling optimal project operation that takes into account the emotional well-being of team members.

[0437] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0438] Step 1:

[0439] The server retrieves project information from the project management tool.

[0440] As input, the server retrieves project data from a project management tool via an API. This includes a list of tasks, detailed information for each task, deadlines, and required skills. The output is the storage of this data in a database and its preparation for analysis. Specifically, the server periodically calls the API to collect the latest project information.

[0441] Step 2:

[0442] The server analyzes the task and assigns it optimally by comparing it to the capabilities of the group members.

[0443] As input, the server references the project information obtained in Step 1 and the competency database of the group members. For data processing, it evaluates the required skills for each task and matches them to the skill sets of the members. The output is assigning tasks to the most suitable members and notifying them of this information. Specifically, the assignment algorithm calculates based on skill matching and task priority.

[0444] Step 3:

[0445] The device collects user behavior data and uses an emotion engine to identify emotional states.

[0446] As input, the device receives keyboard and mouse inputs, as well as user facial expression data (if a camera is available). For data processing, this input data is fed into the emotion engine to identify stress levels and motivation. As output, the identified emotional state data is sent to the server. Specifically, the device uses the emotion engine's API to analyze the emotional state in real time.

[0447] Step 4:

[0448] The server redistributes tasks and adjusts communication based on emotional states.

[0449] As input, the server receives emotional state data obtained in step 3. As data processing, it redistributes tasks and adjusts notification frequency for members with high stress levels. It provides each member with feedback on the adjusted tasks and notification methods, which constitutes the output. In terms of specific operation, the server updates the schedule in real time according to the conditions and notifies the relevant members of the information.

[0450] This process streamlines project progress and enables management that takes into account the emotional well-being of users.

[0451] (Application Example 2)

[0452] 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."

[0453] In project management, it is essential to achieve efficient task allocation and work operations that take into account the emotional state of workers. While current systems are increasingly automating task assignment and progress management, they lack the functionality to redistribute tasks or adjust workloads based on workers' emotional states. Therefore, a method is needed to maximize overall project efficiency while reducing worker fatigue and stress.

[0454] 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.

[0455] In this invention, the server includes means for acquiring tasks from business information, means for assigning tasks based on individual skills and task content, and means for recognizing the emotional state of workers and optimizing task redistribution based on that state. This enables efficient business operations that take into account not only the progress of tasks but also the emotional state of workers.

[0456] "Business information" refers to all data and information related to a specific project or operation, including task details, deadlines, resources, and related documents.

[0457] "Individual skills" refer to the ability of a person to possess the knowledge, experience, and techniques necessary to perform a specific task.

[0458] "Task description" refers to the details of a specific task or job, including information such as its purpose, procedures, required resources, and criteria for completion.

[0459] "Worker's emotional state" refers to the psychological state of employees during work, and includes indicators such as stress, motivation, and fatigue.

[0460] "Redistribution" refers to reviewing the tasks assigned to each worker based on appropriate criteria and making changes as necessary.

[0461] "Optimization" refers to bringing a system or process to its most efficient and effective state in order to achieve a specific purpose or goal.

[0462] The system for implementing this invention is primarily composed of a server and terminals. The server retrieves tasks from business information and assigns appropriate tasks based on individual skills and task content. Furthermore, it recognizes the emotional state of workers and optimizes operations by redistributing tasks based on that data.

[0463] The terminal uses hardware such as smart glasses to collect the worker's emotional state in real time. Emotion recognition uses software equipped with an emotion engine to analyze the worker's psychological state.

[0464] On the server side, this data is integrated, and task allocation is managed in real time using project management software. This process uses programming languages ​​such as Python, and the emotion_recognition library is used for processing emotional state data.

[0465] For example, if worker A shows signs of fatigue, the server will use that emotional data to reduce worker A's workload and automatically switch to a simpler task. Furthermore, measures will be taken to adjust the interval and content of notifications for workers with high stress levels.

[0466] Examples of prompts for a generative AI model:

[0467] "Please tell me how to monitor the emotional state of factory workers and assign them to the most appropriate tasks."

[0468] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0469] Step 1:

[0470] The server receives business information and extracts new tasks from it. The input is business information, and the output is detailed task information. At this stage, database queries are used to retrieve relevant task data.

[0471] Step 2:

[0472] The server references individual skill sets, matches them with task content, and assigns tasks to the most suitable individuals. Inputs are detailed task information and individual skill data, while output is information on individual and task assignments. A machine learning algorithm calculates the optimal match.

[0473] Step 3:

[0474] The terminal collects the worker's emotional state in real time via sensors and transmits it to a server. The input is biometric data, and the output is the result of the emotional state analysis. Here, emotion recognition software analyzes the data and determines the psychological state.

[0475] Step 4:

[0476] The server analyzes collected emotional states and redistributes tasks as needed. Inputs are the results of the emotional state analysis and current task assignment information; output is the adjusted task assignment information. An algorithm is used to optimize worker states and task load.

[0477] Step 5:

[0478] The server updates the progress dashboard, displaying real-time task progress and worker sentiment status to administrators. Inputs are adjusted task assignment information and real-time sentiment status data, while outputs are visualized progress and sentiment status information. The dashboard uses web application technology to visualize the data.

[0479] 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.

[0480] 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.

[0481] 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.

[0482] [Third Embodiment]

[0483] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0484] 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.

[0485] 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).

[0486] 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.

[0487] 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.

[0488] 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).

[0489] 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.

[0490] 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.

[0491] 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.

[0492] 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.

[0493] 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.

[0494] 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".

[0495] This invention is a system that efficiently automates various aspects of project management. By automatically assigning tasks, monitoring progress, and optimizing communication among team members, it streamlines project operations.

[0496] Task management implementation

[0497] The server retrieves unassigned task information from the project information database and automatically distributes tasks based on the team members' skill sets and current workload. This ensures that the right members receive the right tasks, leading to smoother project progress.

[0498] Implementation of progress monitoring

[0499] The terminal provides an interface for each member to update the progress of their ongoing tasks. This information is periodically sent to the server, which analyzes it in real time and provides it to the administrator as a visual dashboard.

[0500] Schedule adjustment

[0501] Users register their preferred meeting times via their calendars on their devices, and the server aggregates these and notifies the entire team of the optimal meeting time. This enables efficient meeting scheduling that takes into account the schedules of all members.

[0502] Reminders and communication facilitation

[0503] The server monitors progress and automatically sends reminders as deadlines for important tasks approach. It also analyzes audio data recorded during meetings, generates summaries, and distributes them to team members. This prevents information from being missed and ensures all team members are up-to-date.

[0504] Specific example

[0505] In a team project, when a new feature development task is registered on the server, the server automatically assigns this task to member A, who possesses "Python skills." Member A updates the progress to 50% using their terminal, and the server receives this information and reflects it on the administrator dashboard. If progress falls behind, the server sends a reminder to member A to help ensure smooth task completion.

[0506] This system minimizes manual operations in project management, enabling efficient and productive team management.

[0507] The following describes the processing flow.

[0508] Step 1:

[0509] The server connects to the project information database and retrieves unassigned tasks.

[0510] Step 2:

[0511] The server analyzes the acquired task content and required skill set, and compares it with the team members' skill profiles and current task load information.

[0512] Step 3:

[0513] The server automatically assigns tasks to the most suitable team members and notifies each member of the results on their device.

[0514] Step 4:

[0515] Users use an application on their device to access an interface for updating the progress of their assigned tasks.

[0516] Step 5:

[0517] The terminal receives the user's progress as input data and sends it to the server.

[0518] Step 6:

[0519] The server analyzes the received progress data and updates the overall progress in real time, visualizing it on the dashboard.

[0520] Step 7:

[0521] The user enters their preferred date and time for the next meeting into a calendar application via their device and sends it to the server.

[0522] Step 8:

[0523] The server aggregates each user's schedule and calculates the optimal meeting time using an optimization algorithm.

[0524] Step 9:

[0525] The server notifies each member of the project team of the agreed-upon meeting time and adjusts their schedules accordingly.

[0526] Step 10:

[0527] The server monitors the progress of tasks and automatically generates and sends reminder notifications to the device for tasks with approaching deadlines.

[0528] Step 11:

[0529] The device uses its recording function to acquire audio data during the meeting and uploads it to the server.

[0530] Step 12:

[0531] The server analyzes the received audio data, extracts key points, and generates a text summary.

[0532] Step 13:

[0533] The server sends the generated summary to each user's terminal to facilitate information sharing.

[0534] (Example 1)

[0535] 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."

[0536] Project management requires addressing the inefficiencies and wasted time resulting from manual processes such as task assignment, progress monitoring, scheduling, and information sharing. Furthermore, large teams are prone to communication breakdowns and misinterpretations of task priorities, which negatively impact the overall project progress. Therefore, automation and accurate information dissemination are essential.

[0537] 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.

[0538] In this invention, the server includes means for retrieving tasks from an information infrastructure that records project information, means for automatically distributing tasks by referring to the skills and workload of the members, and means for collecting and visualizing task progress information in real time via a display device for inputting progress status. This enables minimizing manual operations in project management and accurate information management in real time.

[0539] "Project information" refers to the information necessary for managing project tasks, schedules, resources, progress, and other related matters.

[0540] "Information infrastructure" refers to foundational systems for collecting, storing, and managing information, including databases and storage systems.

[0541] A "task" refers to a series of actions or activities performed to achieve a specific objective.

[0542] "Members" refers to the individual members or staff participating in the project.

[0543] "Skills" refers to the knowledge and technical abilities that individual members possess in a specific field.

[0544] "Workload status" refers to information indicating the current state of work, such as the tasks each member is currently assigned, their volume, and their difficulty level.

[0545] A "display device" refers to a device that visually displays information through the screen of a computer or terminal.

[0546] "Progress information" refers to data that shows the extent to which a task or project has been completed.

[0547] "Visualization" refers to the process of making data and information easier to understand by displaying them in the form of graphs, charts, diagrams, and other visual aids.

[0548] One embodiment of this invention provides a system for streamlining and automating project management. This system consists of the following hardware and software:

[0549] Task management implementation

[0550] The server retrieves project information from the underlying database management system (e.g., a common database software). Based on the skills and workload of the team members, the server automatically distributes tasks to the most suitable members. This process uses algorithms to optimize skill matching and workload reduction for each member.

[0551] Implementation of progress monitoring

[0552] The terminal provides an interface for members to input progress on their display devices. This interface is implemented as a browser-based web application and collects progress information. This information is sent to a server and analyzed in real time. The server uses visualization tools (e.g., common data visualization tools) to graph the progress data and provide it to the administrator.

[0553] Schedule adjustment

[0554] Users register their preferred meeting times using calendar software (e.g., a common calendar service) from their terminals. The server has a system that analyzes the preferred times of all members, adjusts the optimal meeting time, and notifies the user.

[0555] Reminders and communication facilitation

[0556] The server automatically generates and distributes reminders to members based on task progress. Furthermore, audio data from meetings can be converted to text using speech recognition software (e.g., a common speech recognition API), creating a summary that can then be distributed to the team.

[0557] Specific example

[0558] For example, when a new project is started, the server automatically retrieves tasks from the project information and assigns a task to develop a new feature to member A, who has Python skills. At this time, member A reports the progress as 50% via their terminal, and the server reflects this information on the dashboard and displays it to the administrator. If progress falls behind, the server automatically sends a task reminder to member A to help streamline the work.

[0559] Example of a prompt

[0560] "How can I distribute newly added tasks in the project management system based on the skills of the team members?"

[0561] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0562] Step 1:

[0563] The server retrieves task data from the project's information infrastructure. In this step, unassigned tasks are selected from the database and sent to the server via API queries. The input is the task information stored in the database, and the output is the task list used by the server. The server performs actions such as "SELECT FROM tasks WHERE assigned = FALSE" to collect the necessary task data.

[0564] Step 2:

[0565] The server collects skill information and workload status for each member. The input is information stored in the member profile database, and the output is a list of members considering their skill sets and current workload. The server retrieves this data using another API query, formats the data using a Python script, and prepares the optimal task assignment for each member.

[0566] Step 3:

[0567] The server assigns tasks to the most suitable members using a skill matching algorithm. The inputs are task data and member information, and the output is a list of tasks assigned to each member. In this process, the server runs an optimization algorithm to calculate the best match between tasks and skills.

[0568] Step 4:

[0569] The terminal notifies members of task assignment information. The input is the data for the assigned task, and the output is a screen displaying the notification for the member. The terminal displays task details via a web application and notifies members via email or a notification system.

[0570] Step 5:

[0571] The terminal provides an interface for members to input their progress. Input is progress information manually entered by the members, and output is progress data sent to the server. The terminal provides a form that runs in a browser and includes a function to input progress numerically.

[0572] Step 6:

[0573] The server receives progress information and updates a visual dashboard for administrators. The input is progress data submitted by members, and the output is a visualized dashboard. The server aggregates the data, uses visualization tools to generate graphs and charts, and displays the progress to administrators in real time.

[0574] Step 7:

[0575] Users enter their preferred meeting times into their device's calendar. The input is information entered into the user's calendar app, and the output is a unified list of preferred meeting times. By setting preferred meeting times using the calendar app, the preferred times of all members are centrally managed.

[0576] Step 8:

[0577] The server analyzes the aggregated meeting time requests and calculates the optimal time. The input is a list of each member's preferred times, and the output is the selection of the optimal meeting time. The server uses an algorithm to calculate non-overlapping time slots and determines the most suitable meeting time for everyone.

[0578] Step 9:

[0579] The server notifies members of optimized meeting time information. The input is the calculated meeting time, and the output is the group of members who received the notification. The server then communicates meeting details to members via email or message through the notification system.

[0580] (Application Example 1)

[0581] 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."

[0582] Project management systems are required to maximize the use of worker skills while achieving efficient task allocation and progress management in conjunction with automated equipment. Furthermore, challenges include real-time visualization of progress that reflects actual work status, automation of schedule adjustments, and reliable information sharing.

[0583] 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.

[0584] In this invention, the server includes means for acquiring tasks from project information, means for assigning tasks based on the worker's skills and task content, and means for monitoring and displaying task progress information in real time. This enables efficient operation through collaboration between workers and automated equipment, real-time monitoring of project progress, and maintenance of an optimal schedule.

[0585] A "task" is a specific set of tasks that should be performed within a project or work.

[0586] "Project information" refers to all information related to the project, including the work content, progress, and required resources.

[0587] A "worker" refers to an individual with the labor force and specialized skills to carry out tasks within a project.

[0588] "Skills" refer to the ability and knowledge that a worker possesses to perform a specific task.

[0589] "Automated equipment" refers to devices or systems that operate autonomously and do not require human intervention.

[0590] "Progress information" refers to data that shows how far along a task or project is in relation to the plan.

[0591] "Schedule adjustment" refers to modifying a plan to optimize the order and timing of scheduled tasks, making them more efficient.

[0592] "Real-time monitoring" refers to the process of instantly detecting and immediately understanding the ongoing situation.

[0593] "Information sharing" refers to the process of synchronizing and making available the same data and knowledge among stakeholders.

[0594] In this invention, the server provides an infrastructure for managing project information, from task acquisition and assignment to progress monitoring and information sharing. Specifically, the server is located in a cloud environment and uses a database management system (e.g., PostgreSQL) to store and retrieve various project-related information. The automatic task assignment incorporates an algorithm based on worker skills and work status data, and is processed efficiently using a Python program.

[0595] The terminal functions as a device (smartphone or tablet) used by each worker, providing an interface through a web browser or dedicated application. This interface integrates the input and confirmation of progress information, the reception of reminders, and communication methods. The real-time monitoring function visualizes progress using a frontend built with JavaScript or React.

[0596] Users can access project information via their devices, checking the progress of specific tasks and requesting meetings. Schedule adjustments are handled by the server, and the results are sent to all members via push notifications. Additionally, audio data from meetings is converted into text using AI-based speech recognition technology (e.g., Google Speech-to-Text API) to generate summary information.

[0597] A concrete example is its use in assembly lines where automated machinery operates. By monitoring the line's progress in real time, and automatically sending notifications to workers if schedule delays occur, appropriate actions can be taken. In such an environment, a prompt such as "How can we improve factory production efficiency using a robot task management system?" can be used, and the generated AI model can suggest ways to improve efficiency.

[0598] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0599] Step 1:

[0600] The server retrieves unassigned task information from the project information database. The input here is the project information database, and the output is unassigned task information. This information is extracted by executing a database query.

[0601] Step 2:

[0602] The server receives current skill data and workload information of workers as input and automatically assigns tasks to the most suitable workers. The output here is the assigned task information. An algorithm that matches workers' skills with task requirements is used to assign appropriate tasks to each worker.

[0603] Step 3:

[0604] The terminal receives task progress information input from the worker. The output is the updated progress information. This information is immediately sent to the server for progress visualization. The progress level can be changed on the terminal via the interface.

[0605] Step 4:

[0606] The server takes the collected progress information as input and generates data for a real-time dashboard. The output is a visual dashboard for administrators. Here, JavaScript is used to visually display the progress.

[0607] Step 5:

[0608] Users enter meeting requests using a terminal. The server uses this information to adjust the schedule, calculate the optimal meeting time, and notifies all members. The output of this process is the adjusted meeting time information.

[0609] Step 6:

[0610] The server monitors progress and schedule delays, and generates reminders for important tasks. Input is progress data, and output is reminder information. This notification is sent to the worker via their terminal.

[0611] Step 7:

[0612] During the meeting, the user's device collects audio data as input. The server processes this data, converts it to text using a summarization algorithm, and generates summary information. The output is a meeting summary text. This includes summarization using speech recognition technology.

[0613] 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.

[0614] This invention provides a system that highly automates project management while offering feedback that takes into account the user's emotional state. The system includes a function to retrieve tasks from project information and appropriately assign them to team members. Furthermore, it incorporates a function to identify the user's emotional state and optimize project management based on that state.

[0615] Task management implementation

[0616] The server retrieves new tasks from project information and analyzes the task content and the skill sets of the team members. Based on this, it automatically assigns tasks to the most suitable team members. Task progress is monitored in real time and provided to administrators and members through a progress dashboard.

[0617] Utilizing the Emotion Engine

[0618] The device activates an emotion engine based on user behavior data and interactions to identify the user's emotional state. The server uses the obtained emotional data to analyze the user's stress level and motivation, and adjusts tasks and communication methods as needed. This feedback function makes it possible to maximize the performance of team members.

[0619] Specific example

[0620] In one project, a device detects emotional indicators that a user is experiencing fatigue. Based on this information, the server adjusts the workload of tasks assigned to the user, switching them to less demanding tasks. Additionally, measures are taken to reduce the burden on members with high stress levels by extending the interval between reminder notifications.

[0621] This invention not only streamlines project management but also aims for optimal project operation while considering the emotional well-being of each team member.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] The server accesses the project information database and retrieves new unassigned tasks.

[0625] Step 2:

[0626] The server matches the required skill set for the acquired task with the skill profiles of the team members and assigns the task to the most suitable member.

[0627] Step 3:

[0628] The device activates an emotion engine based on user input data to analyze the user's emotional state in real time.

[0629] Step 4:

[0630] The server receives data from the emotion engine, reflects the user's emotional state in project management, and considers, for example, whether to reallocate tasks.

[0631] Step 5:

[0632] Users input the progress of their tasks through their device, and the device sends that data to the server.

[0633] Step 6:

[0634] The server analyzes the received progress data in real time and provides administrators with a dashboard that visualizes the results.

[0635] Step 7:

[0636] The server adjusts reminder notifications based on the user's emotional state, changing the notification interval as needed before sending them to the user.

[0637] Step 8:

[0638] The user obtains the audio data of the meeting through their device and uploads it to the server.

[0639] Step 9:

[0640] The server processes the meeting audio data, extracts key points, generates a summary, and distributes it to each member's terminal.

[0641] Step 10:

[0642] The server comprehensively evaluates the collected sentiment data and project progress, and makes continuous adjustments to optimize project efficiency.

[0643] (Example 2)

[0644] 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."

[0645] Modern project management requires not only efficient task assignment and progress monitoring, but also optimal project operation that takes into account the emotional state of team members. However, traditional systems struggle to adjust projects based on users' emotional states, failing to maximize the potential of team members. A new approach is needed to address this challenge.

[0646] 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.

[0647] In this invention, the server includes means for retrieving tasks from planning information, means for assigning tasks based on the abilities and work content of group members, and means for immediately observing and displaying work progress information. This enables efficient and effective project management that takes into account the emotional state of the members.

[0648] A "task" refers to a specific task or group of tasks that must be completed within a project.

[0649] "Planning information" refers to a collection of data and plans used to manage the overall progress of a project.

[0650] "Group members" refer to individual individuals involved in a project who have specific roles and responsibilities.

[0651] "Capability" refers to the technical skills and expertise possessed by individual members of a group.

[0652] "Work details" refers to the specific requirements and procedures needed for a particular task.

[0653] "Progress information" refers to data or status that indicates the degree of completion or state of a task.

[0654] "Emotional state" refers to the psychological or emotional condition exhibited by the user.

[0655] "Means of immediate observation and display" refers to a system that tracks the progress of work in real time and provides it visually.

[0656] This invention is a system that highly automates project management while taking into account the emotional state of team members. The system consists of the following elements:

[0657] Project Information Analysis

[0658] The server connects to project management tools (e.g., Jira or Trello) to collect current project information. This information includes details of new and ongoing tasks, required skill sets, and more. The collected information is stored in a database and processed by analysis algorithms as needed.

[0659] Task auto-assignment

[0660] The server analyzes the task's content and the team members' capabilities to assign the task to the most suitable member. An automated algorithm evaluates each member's skill set, ensuring effective work distribution.

[0661] Utilizing the Emotion Engine

[0662] The device collects user behavior data and uses an emotion engine to identify the user's emotional state. This process involves observing the user's behavior patterns through the interface. The emotional data is sent to a server to evaluate the user's stress level and motivation.

[0663] Project optimization

[0664] The server can redistribute tasks and adjust communication methods as needed based on the user's emotional state. For example, if a user indicates fatigue, the server can adjust the task content to reduce the workload and appropriately change the frequency of reminder notifications.

[0665] As a concrete example, the following prompt can be input into the AI ​​model: "In project management, please describe an automatic task redistribution function that takes into account the user's emotional state. Please describe in detail the roles of the server and terminal, including specific examples."

[0666] This system improves the efficiency of project management while enabling optimal project operation that takes into account the emotional well-being of team members.

[0667] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0668] Step 1:

[0669] The server retrieves project information from the project management tool.

[0670] As input, the server retrieves project data from a project management tool via an API. This includes a list of tasks, detailed information for each task, deadlines, and required skills. The output is the storage of this data in a database and its preparation for analysis. Specifically, the server periodically calls the API to collect the latest project information.

[0671] Step 2:

[0672] The server analyzes the task and assigns it optimally by comparing it to the capabilities of the group members.

[0673] As input, the server references the project information obtained in Step 1 and the competency database of the group members. For data processing, it evaluates the required skills for each task and matches them to the skill sets of the members. The output is assigning tasks to the most suitable members and notifying them of this information. Specifically, the assignment algorithm calculates based on skill matching and task priority.

[0674] Step 3:

[0675] The device collects user behavior data and uses an emotion engine to identify emotional states.

[0676] As input, the device receives keyboard and mouse inputs, as well as user facial expression data (if a camera is available). For data processing, this input data is fed into the emotion engine to identify stress levels and motivation. As output, the identified emotional state data is sent to the server. Specifically, the device uses the emotion engine's API to analyze the emotional state in real time.

[0677] Step 4:

[0678] The server redistributes tasks and adjusts communication based on emotional states.

[0679] As input, the server receives emotional state data obtained in step 3. As data processing, it redistributes tasks and adjusts notification frequency for members with high stress levels. It provides each member with feedback on the adjusted tasks and notification methods, which constitutes the output. In terms of specific operation, the server updates the schedule in real time according to the conditions and notifies the relevant members of the information.

[0680] This process streamlines project progress and enables management that takes into account the emotional well-being of users.

[0681] (Application Example 2)

[0682] 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."

[0683] In project management, it is essential to achieve efficient task allocation and work operations that take into account the emotional state of workers. While current systems are increasingly automating task assignment and progress management, they lack the functionality to redistribute tasks or adjust workloads based on workers' emotional states. Therefore, a method is needed to maximize overall project efficiency while reducing worker fatigue and stress.

[0684] 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.

[0685] In this invention, the server includes means for acquiring tasks from business information, means for assigning tasks based on individual skills and task content, and means for recognizing the emotional state of workers and optimizing task redistribution based on that state. This enables efficient business operations that take into account not only the progress of tasks but also the emotional state of workers.

[0686] "Business information" refers to all data and information related to a specific project or operation, including task details, deadlines, resources, and related documents.

[0687] "Individual skills" refer to the ability of a person to possess the knowledge, experience, and techniques necessary to perform a specific task.

[0688] "Task description" refers to the details of a specific task or job, including information such as its purpose, procedures, required resources, and criteria for completion.

[0689] "Worker's emotional state" refers to the psychological state of employees during work, and includes indicators such as stress, motivation, and fatigue.

[0690] "Redistribution" refers to reviewing the tasks assigned to each worker based on appropriate criteria and making changes as necessary.

[0691] "Optimization" refers to bringing a system or process to its most efficient and effective state in order to achieve a specific purpose or goal.

[0692] The system for implementing this invention is primarily composed of a server and terminals. The server retrieves tasks from business information and assigns appropriate tasks based on individual skills and task content. Furthermore, it recognizes the emotional state of workers and optimizes operations by redistributing tasks based on that data.

[0693] The terminal uses hardware such as smart glasses to collect the worker's emotional state in real time. Emotion recognition uses software equipped with an emotion engine to analyze the worker's psychological state.

[0694] On the server side, this data is integrated, and task allocation is managed in real time using project management software. This process uses programming languages ​​such as Python, and the emotion_recognition library is used for processing emotional state data.

[0695] For example, if worker A shows signs of fatigue, the server will use that emotional data to reduce worker A's workload and automatically switch to a simpler task. Furthermore, measures will be taken to adjust the interval and content of notifications for workers with high stress levels.

[0696] Examples of prompts for a generative AI model:

[0697] "Please tell me how to monitor the emotional state of factory workers and assign them to the most appropriate tasks."

[0698] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0699] Step 1:

[0700] The server receives business information and extracts new tasks from it. The input is business information, and the output is detailed task information. At this stage, database queries are used to retrieve relevant task data.

[0701] Step 2:

[0702] The server references individual skill sets, matches them with task content, and assigns tasks to the most suitable individuals. Inputs are detailed task information and individual skill data, while output is information on individual and task assignments. A machine learning algorithm calculates the optimal match.

[0703] Step 3:

[0704] The terminal collects the worker's emotional state in real time via sensors and transmits it to a server. The input is biometric data, and the output is the result of the emotional state analysis. Here, emotion recognition software analyzes the data and determines the psychological state.

[0705] Step 4:

[0706] The server analyzes collected emotional states and redistributes tasks as needed. Inputs are the results of the emotional state analysis and current task assignment information; output is the adjusted task assignment information. An algorithm is used to optimize worker states and task load.

[0707] Step 5:

[0708] The server updates the progress dashboard, displaying real-time task progress and worker sentiment status to administrators. Inputs are adjusted task assignment information and real-time sentiment status data, while outputs are visualized progress and sentiment status information. The dashboard uses web application technology to visualize the data.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] [Fourth Embodiment]

[0713] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0714] 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.

[0715] 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).

[0716] 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.

[0717] 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.

[0718] 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).

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] 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".

[0726] This invention is a system that efficiently automates various aspects of project management. By automatically assigning tasks, monitoring progress, and optimizing communication among team members, it streamlines project operations.

[0727] Task management implementation

[0728] The server retrieves unassigned task information from the project information database and automatically distributes tasks based on the team members' skill sets and current workload. This ensures that the right members receive the right tasks, leading to smoother project progress.

[0729] Implementation of progress monitoring

[0730] The terminal provides an interface for each member to update the progress of their ongoing tasks. This information is periodically sent to the server, which analyzes it in real time and provides it to the administrator as a visual dashboard.

[0731] Schedule adjustment

[0732] Users register their preferred meeting times via their calendars on their devices, and the server aggregates these and notifies the entire team of the optimal meeting time. This enables efficient meeting scheduling that takes into account the schedules of all members.

[0733] Reminders and communication facilitation

[0734] The server monitors progress and automatically sends reminders as deadlines for important tasks approach. It also analyzes audio data recorded during meetings, generates summaries, and distributes them to team members. This prevents information from being missed and ensures all team members are up-to-date.

[0735] Specific example

[0736] In a team project, when a new feature development task is registered on the server, the server automatically assigns this task to member A, who possesses "Python skills." Member A updates the progress to 50% using their terminal, and the server receives this information and reflects it on the administrator dashboard. If progress falls behind, the server sends a reminder to member A to help ensure smooth task completion.

[0737] This system minimizes manual operations in project management, enabling efficient and productive team management.

[0738] The following describes the processing flow.

[0739] Step 1:

[0740] The server connects to the project information database and retrieves unassigned tasks.

[0741] Step 2:

[0742] The server analyzes the acquired task content and required skill set, and compares it with the team members' skill profiles and current task load information.

[0743] Step 3:

[0744] The server automatically assigns tasks to the most suitable team members and notifies each member of the results on their device.

[0745] Step 4:

[0746] Users use an application on their device to access an interface for updating the progress of their assigned tasks.

[0747] Step 5:

[0748] The terminal receives the user's progress as input data and sends it to the server.

[0749] Step 6:

[0750] The server analyzes the received progress data and updates the overall progress in real time, visualizing it on the dashboard.

[0751] Step 7:

[0752] The user enters their preferred date and time for the next meeting into a calendar application via their device and sends it to the server.

[0753] Step 8:

[0754] The server aggregates each user's schedule and calculates the optimal meeting time using an optimization algorithm.

[0755] Step 9:

[0756] The server notifies each member of the project team of the agreed-upon meeting time and adjusts their schedules accordingly.

[0757] Step 10:

[0758] The server monitors the progress of tasks and automatically generates and sends reminder notifications to the device for tasks with approaching deadlines.

[0759] Step 11:

[0760] The device uses its recording function to acquire audio data during the meeting and uploads it to the server.

[0761] Step 12:

[0762] The server analyzes the received audio data, extracts key points, and generates a text summary.

[0763] Step 13:

[0764] The server sends the generated summary to each user's terminal to facilitate information sharing.

[0765] (Example 1)

[0766] 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".

[0767] Project management requires addressing the inefficiencies and wasted time resulting from manual processes such as task assignment, progress monitoring, scheduling, and information sharing. Furthermore, large teams are prone to communication breakdowns and misinterpretations of task priorities, which negatively impact the overall project progress. Therefore, automation and accurate information dissemination are essential.

[0768] 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.

[0769] In this invention, the server includes means for retrieving tasks from an information infrastructure that records project information, means for automatically distributing tasks by referring to the skills and workload of the members, and means for collecting and visualizing task progress information in real time via a display device for inputting progress status. This enables minimizing manual operations in project management and accurate information management in real time.

[0770] "Project information" refers to the information necessary for managing project tasks, schedules, resources, progress, and other related matters.

[0771] "Information infrastructure" refers to foundational systems for collecting, storing, and managing information, including databases and storage systems.

[0772] A "task" refers to a series of actions or activities performed to achieve a specific objective.

[0773] "Members" refers to the individual members or staff participating in the project.

[0774] "Skills" refers to the knowledge and technical abilities that individual members possess in a specific field.

[0775] "Workload status" refers to information indicating the current state of work, such as the tasks each member is currently assigned, their volume, and their difficulty level.

[0776] A "display device" refers to a device that visually displays information through the screen of a computer or terminal.

[0777] "Progress information" refers to data that shows the extent to which a task or project has been completed.

[0778] "Visualization" refers to the process of making data and information easier to understand by displaying them in the form of graphs, charts, diagrams, and other visual aids.

[0779] One embodiment of this invention provides a system for streamlining and automating project management. This system consists of the following hardware and software:

[0780] Task management implementation

[0781] The server retrieves project information from the underlying database management system (e.g., a common database software). Based on the skills and workload of the team members, the server automatically distributes tasks to the most suitable members. This process uses algorithms to optimize skill matching and workload reduction for each member.

[0782] Implementation of progress monitoring

[0783] The terminal provides an interface for members to input progress on their display devices. This interface is implemented as a browser-based web application and collects progress information. This information is sent to a server and analyzed in real time. The server uses visualization tools (e.g., common data visualization tools) to graph the progress data and provide it to the administrator.

[0784] Schedule adjustment

[0785] Users register their preferred meeting times using calendar software (e.g., a common calendar service) from their terminals. The server has a system that analyzes the preferred times of all members, adjusts the optimal meeting time, and notifies the user.

[0786] Reminders and communication facilitation

[0787] The server automatically generates and distributes reminders to members based on task progress. Furthermore, audio data from meetings can be converted to text using speech recognition software (e.g., a common speech recognition API), creating a summary that can then be distributed to the team.

[0788] Specific example

[0789] For example, when a new project is started, the server automatically retrieves tasks from the project information and assigns a task to develop a new feature to member A, who has Python skills. At this time, member A reports the progress as 50% via their terminal, and the server reflects this information on the dashboard and displays it to the administrator. If progress falls behind, the server automatically sends a task reminder to member A to help streamline the work.

[0790] Example of a prompt

[0791] "How can I distribute newly added tasks in the project management system based on the skills of the team members?"

[0792] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0793] Step 1:

[0794] The server retrieves task data from the project's information infrastructure. In this step, unassigned tasks are selected from the database and sent to the server via API queries. The input is the task information stored in the database, and the output is the task list used by the server. The server performs actions such as "SELECT FROM tasks WHERE assigned = FALSE" to collect the necessary task data.

[0795] Step 2:

[0796] The server collects skill information and workload status for each member. The input is information stored in the member profile database, and the output is a list of members considering their skill sets and current workload. The server retrieves this data using another API query, formats the data using a Python script, and prepares the optimal task assignment for each member.

[0797] Step 3:

[0798] The server assigns tasks to the most suitable members using a skill matching algorithm. The inputs are task data and member information, and the output is a list of tasks assigned to each member. In this process, the server runs an optimization algorithm to calculate the best match between tasks and skills.

[0799] Step 4:

[0800] The terminal notifies members of task assignment information. The input is the data for the assigned task, and the output is a screen displaying the notification for the member. The terminal displays task details via a web application and notifies members via email or a notification system.

[0801] Step 5:

[0802] The terminal provides an interface for members to input their progress. Input is progress information manually entered by the members, and output is progress data sent to the server. The terminal provides a form that runs in a browser and includes a function to input progress numerically.

[0803] Step 6:

[0804] The server receives progress information and updates a visual dashboard for administrators. The input is progress data submitted by members, and the output is a visualized dashboard. The server aggregates the data, uses visualization tools to generate graphs and charts, and displays the progress to administrators in real time.

[0805] Step 7:

[0806] Users enter their preferred meeting times into their device's calendar. The input is information entered into the user's calendar app, and the output is a unified list of preferred meeting times. By setting preferred meeting times using the calendar app, the preferred times of all members are centrally managed.

[0807] Step 8:

[0808] The server analyzes the aggregated meeting time requests and calculates the optimal time. The input is a list of each member's preferred times, and the output is the selection of the optimal meeting time. The server uses an algorithm to calculate non-overlapping time slots and determines the most suitable meeting time for everyone.

[0809] Step 9:

[0810] The server notifies members of optimized meeting time information. The input is the calculated meeting time, and the output is the group of members who received the notification. The server then communicates meeting details to members via email or message through the notification system.

[0811] (Application Example 1)

[0812] 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".

[0813] Project management systems are required to maximize the use of worker skills while achieving efficient task allocation and progress management in conjunction with automated equipment. Furthermore, challenges include real-time visualization of progress that reflects actual work status, automation of schedule adjustments, and reliable information sharing.

[0814] 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.

[0815] In this invention, the server includes means for acquiring tasks from project information, means for assigning tasks based on the worker's skills and task content, and means for monitoring and displaying task progress information in real time. This enables efficient operation through collaboration between workers and automated equipment, real-time monitoring of project progress, and maintenance of an optimal schedule.

[0816] A "task" is a specific set of tasks that should be performed within a project or work.

[0817] "Project information" refers to all information related to the project, including the work content, progress, and required resources.

[0818] A "worker" refers to an individual with the labor force and specialized skills to carry out tasks within a project.

[0819] "Skills" refer to the ability and knowledge that a worker possesses to perform a specific task.

[0820] "Automated equipment" refers to devices or systems that operate autonomously and do not require human intervention.

[0821] "Progress information" refers to data that shows how far along a task or project is in relation to the plan.

[0822] "Schedule adjustment" refers to modifying a plan to optimize the order and timing of scheduled tasks, making them more efficient.

[0823] "Real-time monitoring" refers to the process of instantly detecting and immediately understanding the ongoing situation.

[0824] "Information sharing" refers to the process of synchronizing and making available the same data and knowledge among stakeholders.

[0825] In this invention, the server provides an infrastructure for managing project information, from task acquisition and assignment to progress monitoring and information sharing. Specifically, the server is located in a cloud environment and uses a database management system (e.g., PostgreSQL) to store and retrieve various project-related information. The automatic task assignment incorporates an algorithm based on worker skills and work status data, and is processed efficiently using a Python program.

[0826] The terminal functions as a device (smartphone or tablet) used by each worker, providing an interface through a web browser or dedicated application. This interface integrates the input and confirmation of progress information, the reception of reminders, and communication methods. The real-time monitoring function visualizes progress using a frontend built with JavaScript or React.

[0827] Users can access project information via their devices, checking the progress of specific tasks and requesting meetings. Schedule adjustments are handled by the server, and the results are sent to all members via push notifications. Additionally, audio data from meetings is converted into text using AI-based speech recognition technology (e.g., Google Speech-to-Text API) to generate summary information.

[0828] A concrete example is its use in assembly lines where automated machinery operates. By monitoring the line's progress in real time, and automatically sending notifications to workers if schedule delays occur, appropriate actions can be taken. In such an environment, a prompt such as "How can we improve factory production efficiency using a robot task management system?" can be used, and the generated AI model can suggest ways to improve efficiency.

[0829] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0830] Step 1:

[0831] The server retrieves unassigned task information from the project information database. The input here is the project information database, and the output is unassigned task information. This information is extracted by executing a database query.

[0832] Step 2:

[0833] The server receives current skill data and workload information of workers as input and automatically assigns tasks to the most suitable workers. The output here is the assigned task information. An algorithm that matches workers' skills with task requirements is used to assign appropriate tasks to each worker.

[0834] Step 3:

[0835] The terminal receives task progress information input from the worker. The output is the updated progress information. This information is immediately sent to the server for progress visualization. The progress level can be changed on the terminal via the interface.

[0836] Step 4:

[0837] The server takes the collected progress information as input and generates data for a real-time dashboard. The output is a visual dashboard for administrators. Here, JavaScript is used to visually display the progress.

[0838] Step 5:

[0839] Users enter meeting requests using a terminal. The server uses this information to adjust the schedule, calculate the optimal meeting time, and notifies all members. The output of this process is the adjusted meeting time information.

[0840] Step 6:

[0841] The server monitors progress and schedule delays, and generates reminders for important tasks. Input is progress data, and output is reminder information. This notification is sent to the worker via their terminal.

[0842] Step 7:

[0843] During the meeting, the user's device collects audio data as input. The server processes this data, converts it to text using a summarization algorithm, and generates summary information. The output is a meeting summary text. This includes summarization using speech recognition technology.

[0844] 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.

[0845] This invention provides a system that highly automates project management while offering feedback that takes into account the user's emotional state. The system includes a function to retrieve tasks from project information and appropriately assign them to team members. Furthermore, it incorporates a function to identify the user's emotional state and optimize project management based on that state.

[0846] Task management implementation

[0847] The server retrieves new tasks from project information and analyzes the task content and the skill sets of the team members. Based on this, it automatically assigns tasks to the most suitable team members. Task progress is monitored in real time and provided to administrators and members through a progress dashboard.

[0848] Utilizing the Emotion Engine

[0849] The device activates an emotion engine based on user behavior data and interactions to identify the user's emotional state. The server uses the obtained emotional data to analyze the user's stress level and motivation, and adjusts tasks and communication methods as needed. This feedback function makes it possible to maximize the performance of team members.

[0850] Specific example

[0851] In one project, a device detects emotional indicators that a user is experiencing fatigue. Based on this information, the server adjusts the workload of tasks assigned to the user, switching them to less demanding tasks. Additionally, measures are taken to reduce the burden on members with high stress levels by extending the interval between reminder notifications.

[0852] This invention not only streamlines project management but also aims for optimal project operation while considering the emotional well-being of each team member.

[0853] The following describes the processing flow.

[0854] Step 1:

[0855] The server accesses the project information database and retrieves new unassigned tasks.

[0856] Step 2:

[0857] The server matches the required skill set for the acquired task with the skill profiles of the team members and assigns the task to the most suitable member.

[0858] Step 3:

[0859] The device activates an emotion engine based on user input data to analyze the user's emotional state in real time.

[0860] Step 4:

[0861] The server receives data from the emotion engine, reflects the user's emotional state in project management, and considers, for example, whether to reallocate tasks.

[0862] Step 5:

[0863] Users input the progress of their tasks through their device, and the device sends that data to the server.

[0864] Step 6:

[0865] The server analyzes the received progress data in real time and provides administrators with a dashboard that visualizes the results.

[0866] Step 7:

[0867] The server adjusts reminder notifications based on the user's emotional state, changing the notification interval as needed before sending them to the user.

[0868] Step 8:

[0869] The user obtains the audio data of the meeting through their device and uploads it to the server.

[0870] Step 9:

[0871] The server processes the meeting audio data, extracts key points, generates a summary, and distributes it to each member's terminal.

[0872] Step 10:

[0873] The server comprehensively evaluates the collected sentiment data and project progress, and makes continuous adjustments to optimize project efficiency.

[0874] (Example 2)

[0875] 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".

[0876] Modern project management requires not only efficient task assignment and progress monitoring, but also optimal project operation that takes into account the emotional state of team members. However, traditional systems struggle to adjust projects based on users' emotional states, failing to maximize the potential of team members. A new approach is needed to address this challenge.

[0877] 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.

[0878] In this invention, the server includes means for retrieving tasks from planning information, means for assigning tasks based on the abilities and work content of group members, and means for immediately observing and displaying work progress information. This enables efficient and effective project management that takes into account the emotional state of the members.

[0879] A "task" refers to a specific task or group of tasks that must be completed within a project.

[0880] "Planning information" refers to a collection of data and plans used to manage the overall progress of a project.

[0881] "Group members" refer to individual individuals involved in a project who have specific roles and responsibilities.

[0882] "Capability" refers to the technical skills and expertise possessed by individual members of a group.

[0883] "Work details" refers to the specific requirements and procedures needed for a particular task.

[0884] "Progress information" refers to data or status that indicates the degree of completion or state of a task.

[0885] "Emotional state" refers to the psychological or emotional condition exhibited by the user.

[0886] "Means of immediate observation and display" refers to a system that tracks the progress of work in real time and provides it visually.

[0887] This invention is a system that highly automates project management while taking into account the emotional state of team members. The system consists of the following elements:

[0888] Project Information Analysis

[0889] The server connects to project management tools (e.g., Jira or Trello) to collect current project information. This information includes details of new and ongoing tasks, required skill sets, and more. The collected information is stored in a database and processed by analysis algorithms as needed.

[0890] Task auto-assignment

[0891] The server analyzes the task's content and the team members' capabilities to assign the task to the most suitable member. An automated algorithm evaluates each member's skill set, ensuring effective work distribution.

[0892] Utilizing the Emotion Engine

[0893] The device collects user behavior data and uses an emotion engine to identify the user's emotional state. This process involves observing the user's behavior patterns through the interface. The emotional data is sent to a server to evaluate the user's stress level and motivation.

[0894] Project optimization

[0895] The server can redistribute tasks and adjust communication methods as needed based on the user's emotional state. For example, if a user indicates fatigue, the server can adjust the task content to reduce the workload and appropriately change the frequency of reminder notifications.

[0896] As a concrete example, the following prompt can be input into the AI ​​model: "In project management, please describe an automatic task redistribution function that takes into account the user's emotional state. Please describe in detail the roles of the server and terminal, including specific examples."

[0897] This system improves the efficiency of project management while enabling optimal project operation that takes into account the emotional well-being of team members.

[0898] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0899] Step 1:

[0900] The server retrieves project information from the project management tool.

[0901] As input, the server retrieves project data from a project management tool via an API. This includes a list of tasks, detailed information for each task, deadlines, and required skills. The output is the storage of this data in a database and its preparation for analysis. Specifically, the server periodically calls the API to collect the latest project information.

[0902] Step 2:

[0903] The server analyzes the task and assigns it optimally by comparing it to the capabilities of the group members.

[0904] As input, the server references the project information obtained in Step 1 and the competency database of the group members. For data processing, it evaluates the required skills for each task and matches them to the skill sets of the members. The output is assigning tasks to the most suitable members and notifying them of this information. Specifically, the assignment algorithm calculates based on skill matching and task priority.

[0905] Step 3:

[0906] The device collects user behavior data and uses an emotion engine to identify emotional states.

[0907] As input, the device receives keyboard and mouse inputs, as well as user facial expression data (if a camera is available). For data processing, this input data is fed into the emotion engine to identify stress levels and motivation. As output, the identified emotional state data is sent to the server. Specifically, the device uses the emotion engine's API to analyze the emotional state in real time.

[0908] Step 4:

[0909] The server redistributes tasks and adjusts communication based on emotional states.

[0910] As input, the server receives emotional state data obtained in step 3. As data processing, it redistributes tasks and adjusts notification frequency for members with high stress levels. It provides each member with feedback on the adjusted tasks and notification methods, which constitutes the output. In terms of specific operation, the server updates the schedule in real time according to the conditions and notifies the relevant members of the information.

[0911] This process streamlines project progress and enables management that takes into account the emotional well-being of users.

[0912] (Application Example 2)

[0913] 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".

[0914] In project management, it is essential to achieve efficient task allocation and work operations that take into account the emotional state of workers. While current systems are increasingly automating task assignment and progress management, they lack the functionality to redistribute tasks or adjust workloads based on workers' emotional states. Therefore, a method is needed to maximize overall project efficiency while reducing worker fatigue and stress.

[0915] 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.

[0916] In this invention, the server includes means for acquiring tasks from business information, means for assigning tasks based on individual skills and task content, and means for recognizing the emotional state of workers and optimizing task redistribution based on that state. This enables efficient business operations that take into account not only the progress of tasks but also the emotional state of workers.

[0917] "Business information" refers to all data and information related to a specific project or operation, including task details, deadlines, resources, and related documents.

[0918] "Individual skills" refer to the ability of a person to possess the knowledge, experience, and techniques necessary to perform a specific task.

[0919] "Task description" refers to the details of a specific task or job, including information such as its purpose, procedures, required resources, and criteria for completion.

[0920] "Worker's emotional state" refers to the psychological state of employees during work, and includes indicators such as stress, motivation, and fatigue.

[0921] "Redistribution" refers to reviewing the tasks assigned to each worker based on appropriate criteria and making changes as necessary.

[0922] "Optimization" refers to bringing a system or process to its most efficient and effective state in order to achieve a specific purpose or goal.

[0923] The system for implementing this invention is primarily composed of a server and terminals. The server retrieves tasks from business information and assigns appropriate tasks based on individual skills and task content. Furthermore, it recognizes the emotional state of workers and optimizes operations by redistributing tasks based on that data.

[0924] The terminal uses hardware such as smart glasses to collect the worker's emotional state in real time. Emotion recognition uses software equipped with an emotion engine to analyze the worker's psychological state.

[0925] On the server side, this data is integrated, and task allocation is managed in real time using project management software. This process uses programming languages ​​such as Python, and the emotion_recognition library is used for processing emotional state data.

[0926] For example, if worker A shows signs of fatigue, the server will use that emotional data to reduce worker A's workload and automatically switch to a simpler task. Furthermore, measures will be taken to adjust the interval and content of notifications for workers with high stress levels.

[0927] Examples of prompts for a generative AI model:

[0928] "Please tell me how to monitor the emotional state of factory workers and assign them to the most appropriate tasks."

[0929] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0930] Step 1:

[0931] The server receives business information and extracts new tasks from it. The input is business information, and the output is detailed task information. At this stage, database queries are used to retrieve relevant task data.

[0932] Step 2:

[0933] The server references individual skill sets, matches them with task content, and assigns tasks to the most suitable individuals. Inputs are detailed task information and individual skill data, while output is information on individual and task assignments. A machine learning algorithm calculates the optimal match.

[0934] Step 3:

[0935] The terminal collects the worker's emotional state in real time via sensors and transmits it to a server. The input is biometric data, and the output is the result of the emotional state analysis. Here, emotion recognition software analyzes the data and determines the psychological state.

[0936] Step 4:

[0937] The server analyzes collected emotional states and redistributes tasks as needed. Inputs are the results of the emotional state analysis and current task assignment information; output is the adjusted task assignment information. An algorithm is used to optimize worker states and task load.

[0938] Step 5:

[0939] The server updates the progress dashboard, displaying real-time task progress and worker sentiment status to administrators. Inputs are adjusted task assignment information and real-time sentiment status data, while outputs are visualized progress and sentiment status information. The dashboard uses web application technology to visualize the data.

[0940] 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.

[0941] 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.

[0942] 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.

[0943] 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.

[0944] 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.

[0945] 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.

[0946] 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.

[0947] 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.

[0948] 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."

[0949] 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.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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.

[0954] 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.

[0955] 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.

[0956] 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.

[0957] 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.

[0958] 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.

[0959] 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.

[0960] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0961] The following is further disclosed regarding the embodiments described above.

[0962] (Claim 1)

[0963] Methods for retrieving tasks from project information,

[0964] A method for assigning tasks based on the skills and tasks of team members,

[0965] A means of monitoring and displaying task progress information in real time,

[0966] Means to adjust schedules and optimize meeting times,

[0967] A means of sharing project-related information,

[0968] A system that includes this.

[0969] (Claim 2)

[0970] The system according to claim 1, comprising means for automatically generating reminders based on the progress of a task and sending them to each team member.

[0971] (Claim 3)

[0972] The system according to claim 1, comprising means for recognizing audio data during a meeting, summarizing important information therein, and distributing it to team members.

[0973] "Example 1"

[0974] (Claim 1)

[0975] A means of retrieving tasks from an information infrastructure that records project information,

[0976] A means of automatically distributing tasks by referring to the skills and workload of the members,

[0977] A means for collecting and visualizing task progress information in real time via a display device for inputting progress status,

[0978] A means of collecting preferred meeting times using a time management platform and adjusting the optimal meeting time,

[0979] A means of integrating and communicating project-related information among members,

[0980] A system that includes this.

[0981] (Claim 2)

[0982] The system according to claim 1, comprising means for automatically generating and distributing notifications to each member based on the progress of a task.

[0983] (Claim 3)

[0984] The system according to claim 1, comprising means for analyzing audio signals during a meeting, summarizing important information therein, and communicating it to the members.

[0985] "Application Example 1"

[0986] (Claim 1)

[0987] Methods for retrieving tasks from project information,

[0988] A means of assigning tasks based on the worker's skills and the task content,

[0989] A means of monitoring and displaying task progress information in real time,

[0990] Means to adjust schedules and optimize meeting times,

[0991] A means of managing the operation of automated equipment operating in the workplace and visualizing progress using real-time data,

[0992] A means of automatically adjusting the schedule and providing notifications based on the work,

[0993] A means of sharing project-related information,

[0994] A system that includes this.

[0995] (Claim 2)

[0996] The system according to claim 1, comprising means for automatically generating reminders based on the progress of a task and sending them to each worker.

[0997] (Claim 3)

[0998] The system according to claim 1, comprising means for recognizing audio data during a meeting, summarizing important information therein, and distributing it to workers.

[0999] "Example 2 of combining an emotion engine"

[1000] (Claim 1)

[1001] A means of obtaining tasks from planning information,

[1002] A means of assigning tasks based on the abilities and tasks of the group members,

[1003] A means of instantly observing and displaying work progress information,

[1004] A device that recognizes the emotional state from the user's behavior,

[1005] An adjustment mechanism to optimize work based on emotional state,

[1006] Means of sharing information,

[1007] A system that includes this.

[1008] (Claim 2)

[1009] The system according to claim 1, comprising means for automatically generating notifications based on the progress of work and sending them to each group member.

[1010] (Claim 3)

[1011] The system according to claim 1, comprising means for analyzing audio information during a meeting, summarizing important information therein, and distributing it to members of the group.

[1012] "Application example 2 when combining with an emotional engine"

[1013] (Claim 1)

[1014] Methods for obtaining tasks from business information,

[1015] A method for assigning tasks based on individual skills and task content,

[1016] A means of monitoring and displaying task progress information in real time,

[1017] Means to adjust schedules and optimize meeting times,

[1018] Means for sharing business-related information,

[1019] A means of recognizing the emotional state of workers and optimizing task redistribution based on that state,

[1020] A means of adjusting workload by considering nonverbal information using emotional data,

[1021] A system that includes this.

[1022] (Claim 2)

[1023] The system according to claim 1, comprising means for automatically generating and sending reminders to each individual based on the progress of a task.

[1024] (Claim 3)

[1025] The system according to claim 1, comprising means for recognizing audio data during a meeting, summarizing important information therein, and distributing it to individuals. [Explanation of Symbols]

[1026] 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. Methods for retrieving tasks from project information, A method for assigning tasks based on the skills and tasks of team members, A means of monitoring and displaying task progress information in real time, Means to adjust schedules and optimize meeting times, A means of sharing project-related information, A system that includes this.

2. The system according to claim 1, comprising means for automatically generating reminders based on the progress of a task and sending them to each team member.

3. The system according to claim 1, comprising means for recognizing audio data during a meeting, summarizing important information therein, and distributing it to team members.

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