system

Abstract

We provide the system. [Solution] A means of collecting and storing project progress data, A means of visualizing the collected progress data in real time, A means to automatically set task priorities and optimize resource allocation, A means of predicting potential problems and proposing countermeasures, A means of providing a conversational interface with the user, 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In many modern enterprises and organizations, project management has become a huge burden due to its complexity and diverse resource allocation. In particular, grasping the progress status, prioritizing tasks, appropriately allocating resources, and further quickly responding to unexpected situations are required. However, these tasks require a huge amount of time and labor, imposing excessive stress on project managers and team members. Also, in a remote work or global team environment, these issues are even more serious.

Means for Solving the Problems

[0005] This invention provides a system that allows for an intuitive understanding of project status by offering a means to collect and visualize project progress data in real time. It also reduces individual burdens by automatically prioritizing tasks and optimizing resource allocation while considering member skills and current workload. Furthermore, it minimizes project disruptions by having an AI agent predict potential problems and quickly provide countermeasures. Through a conversational interface with the user, it facilitates information acquisition and task management in natural language, improving overall efficiency. This series of measures significantly improves the accuracy and efficiency of project management.

[0006] "Project progress data" refers to a collection of information that shows the status, elapsed time, and degree of completion of each task within a project.

[0007] "Real-time visualization" means instantly displaying current data and situations visually, providing them in a format that is easy to understand at a glance.

[0008] "Automatically prioritizing tasks" means that the system determines the order in which tasks are processed based on their importance and urgency, according to defined criteria.

[0009] "Optimizing resource allocation" means arranging available resources such as personnel, time, and budget in the most efficient way possible.

[0010] "Predicting potential problems" means estimating potential obstacles and challenges in advance and preparing countermeasures accordingly.

[0011] "To propose countermeasures" means to suggest solutions or next steps for a predicted problem.

[0012] A "conversational interface" is a form of dialogue that allows users to interact with a system using natural language, enabling them to obtain information and give instructions.

[0013] An "AI agent" is a system or program that uses artificial intelligence technology to autonomously process information and make decisions. [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]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

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

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, 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] The system of this invention is designed to streamline and optimize project management. Based on a client-server architecture, it collects, analyzes, displays, predicts problems, and suggests countermeasures for each project.

[0036] First, the terminal provides an interface for project members to input the progress and status of tasks. When a user reports the status of a task, such as start, in progress, or completed, that information is automatically sent to the server. The server receives this data, stores it in a database, and manages the overall progress of the project.

[0037] The server then analyzes the collected data and visualizes the project's progress in real time. Users can visually check the project's progress and the status of individual tasks through the dashboard. This display is accessible across various platforms, including web applications and mobile apps.

[0038] Furthermore, the AI ​​agent optimizes the prioritization and resource allocation of each task. The server automatically determines the optimal task assignment, taking into account the urgency and importance of the tasks, the skills of the members, and the current workload. This result is notified to project members via their terminals, allowing them to focus on high-priority tasks.

[0039] Furthermore, the AI ​​agent predicts potential problems that may arise during project progress and provides prompt solutions. The server then notifies project managers and stakeholders of these solutions, prompting appropriate action.

[0040] As a concrete example, consider a web service development project at an IT company. This project involves multiple tasks and includes developers, designers, and testers. At the start of the project, each member is assigned tasks via a terminal and reports their progress to the server. An AI agent detects development bottlenecks and indicates priority tasks that developer A should tackle first. This improves the overall efficiency of the team and allows them to complete the project on time.

[0041] This system plays a crucial role in reducing the burden of project management and increasing efficiency.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The terminal provides an interface for users to input the status of task start, progress, and completion. Users enter task information, and this data is automatically sent to the server.

[0045] Step 2:

[0046] The server stores task progress data received from terminals in a database. This data includes information such as task ID, assignee, status, start time, and estimated completion time.

[0047] Step 3:

[0048] The server analyzes the stored data and generates information to visualize progress in real time. The generated visualization information is immediately reflected on the dashboard.

[0049] Step 4:

[0050] Users can check project progress through the dashboard and understand each member's tasks and the overall project overview. The dashboard can be accessed via a web browser or a dedicated app.

[0051] Step 5:

[0052] The server uses an AI agent to determine the priority of each task and optimizes resources considering the skills of the members and the current load. Based on these results, task assignments are updated.

[0053] Step 6:

[0054] The terminal notifies each member of the updated task assignment information. Users then proceed with tasks according to the new priorities.

[0055] Step 7:

[0056] The server predicts potential problems that may arise during project progress using AI agents and generates countermeasures. These countermeasures are then sent to the project manager and relevant team members.

[0057] Step 8:

[0058] The user reviews the countermeasures received from the server and decides on the steps to take. Additional actions may be taken as needed.

[0059] Step 9:

[0060] The server collects data after project completion and feeds it back as training data for the AI ​​agent. This improves the accuracy and efficiency of future project management.

[0061] (Example 1)

[0062] 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."

[0063] In project management, tracking progress, prioritizing tasks, and anticipating and addressing potential problems are crucial. However, these processes are often performed manually, making them time-consuming and labor-intensive. Furthermore, their reliance on human judgment can lead to inefficiencies and inaccuracies. As a result, project delays and inappropriate resource allocation can occur, leading to a decline in overall productivity.

[0064] 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.

[0065] In this invention, the server includes means for collecting progress information and storing it in a data storage device, means for visualizing the collected progress information in real time based on a generating AI model, and means for automatically setting the importance of tasks and optimizing the allocation of work resources. This makes it possible to efficiently manage project progress and objectively set task priorities. Furthermore, it is possible to improve the overall productivity of the project by predicting potential problems and addressing them in advance.

[0066] "Progress information" refers to data that shows the progress and degree of completion of each task in a project.

[0067] A "data storage device" is a system or device for storing information over a long period of time.

[0068] A "generative AI model" is an artificial intelligence algorithm designed to derive patterns and predictions from data.

[0069] "Visualizing in real time" means displaying information in a visually easy-to-understand format the moment it is received.

[0070] "Task importance" refers to the result of evaluating the priority and urgency of individual tasks within a project.

[0071] "Work resource allocation" refers to the efficient allocation of available resources, such as personnel and equipment, to each task within a project.

[0072] A "potential problem" refers to an issue or obstacle that is not currently apparent but may arise in the future.

[0073] "Presenting countermeasures" is the act of proposing solutions that should be implemented in advance to address anticipated problems.

[0074] An "interactive interface" is a user interface that allows users to communicate with the system in a natural, two-way manner.

[0075] This invention is a system for streamlining project management, in which a server, terminals, and users work together. The server is responsible for collecting progress information and storing it in a data storage device. In this process, a dedicated database management system is used to efficiently record and manage the received progress data. The server also utilizes a generative AI model to visualize the collected information in real time and provide the user with information on the project's progress.

[0076] The terminal provides an interface for users to input the status of tasks. Specifically, users input progress information into the terminal using a web application or mobile application. This information is automatically sent to the server and stored in a database. Users can view the progress in real time and visualized through the terminal.

[0077] Furthermore, the server uses an AI agent to automatically set the importance of tasks and optimize the allocation of work resources. The AI ​​agent uses a generative AI model to analyze the urgency, importance, and skill levels and workload of the team members. An example of a prompt message is input to the generative AI model: "Generate the optimal task assignment based on project progress data." The information generated in this way is then notified to project members via their terminals.

[0078] Regarding problem prediction, the server analyzes past project data and current issues to predict potential problems. Countermeasures for predicted problems are automatically notified to project managers and stakeholders. This enables rapid and efficient project management.

[0079] As a concrete example, when an IT company is working on a web service development project, developers, designers, and testers would use this system. Each member reports progress information to the server via their terminal, and the server uses AI to assign the most suitable tasks. As a result, the overall efficiency of the project improves, making it possible to complete the project on time.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The terminal provides an interface for users to report the progress and status of tasks. Users input the task progress using a web application or mobile application. The data obtained as input includes status information such as task start, in progress, and completed. This input data is sent to the server via a communication protocol.

[0083] Step 2:

[0084] The server stores progress information received from terminals in a database. Input data is processed into "INSERT" or "UPDATE" queries in the data storage device. This allows for centralized management of the entire project's progress in one location. The output is the latest progress status stored in the database.

[0085] Step 3:

[0086] The server analyzes stored progress information and uses a generated AI model to visualize progress in real time. This process uses progress information obtained from a database as input. The data is converted into graphs and charts for visualization and output to a dashboard. Users can view this visualized information through their devices. For example, the percentage of tasks in progress and the number of completed tasks are visualized.

[0087] Step 4:

[0088] The server automatically sets task priorities using a generative AI model. It collects project data, member skill information, and current workload as input, and performs data calculations based on the generative AI model. The output is the optimal task assignment, which is then notified to project members via their terminals. Specifically, the AI ​​model receives a prompt message such as, "Generate the optimal task assignment based on project progress data."

[0089] Step 5:

[0090] The server utilizes generative AI models to predict potential problems and propose solutions. Input includes historical project data and information on current issues. Data calculations identify potential risks and derive countermeasures. These countermeasures are then communicated to project managers and stakeholders. For example, if a specific task is behind schedule, the server will identify the cause and suggest corrective actions.

[0091] In this way, the system can process information efficiently and automatically at each step of project management, providing the optimal management method.

[0092] (Application Example 1)

[0093] 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."

[0094] In production environments, maximizing production efficiency is crucial. However, accurately tracking the progress of each work stage and allocating tasks optimally in real time is not easy. Furthermore, mechanisms for predicting problems before they occur and quickly providing countermeasures are often inadequate. As a result, bottlenecks and work delays can occur, potentially leading to decreased production efficiency.

[0095] 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.

[0096] In this invention, the server includes an information processing device for collecting and storing progress data, an information processing device for visualizing the collected progress data in real time, and an information processing device for monitoring work progress in the production process and dynamically reallocating tasks. This enables efficient progress management in the production process and rapid response through problem prediction.

[0097] "Progress data" refers to information about the work content and progress at each stage of the work process.

[0098] "Information processing equipment" refers to all devices equipped with the function of receiving and analyzing data, and includes servers and computer systems.

[0099] "Real-time visualization" means instantly displaying the state of data visually the moment it is collected.

[0100] "Dynamic task reallocation" is a process of instantly changing and optimizing the priority and assignment of tasks according to their progress and status.

[0101] "Production process" refers to a series of processes and procedures for manufacturing a product, encompassing the entire work involved at each stage.

[0102] An "interactive interface" is a means for a user to exchange information with a computer system in a two-way manner.

[0103] The system that realizes this invention employs advanced information processing technology to effectively manage, analyze, and utilize data. Its main components include a server, terminals, and a user interface.

[0104] The server utilizes software such as Apache® Kafka and TENSORFLOW® to collect and store progress data in real time. This allows for monitoring the progress at each stage of the work process. The server also provides a dashboard for immediate analysis and visualization of the progress data. This visualization enables visual monitoring of the status of each task in the production process.

[0105] Furthermore, the server dynamically redistributes tasks using an AI model to maximize the efficiency of the production process. The AI ​​agent automatically determines the optimal task assignment based on each member's skills and current workload. The results of this task assignment are immediately communicated to the workers via terminals.

[0106] The system's interactive interface allows users to interact seamlessly through input. This enables, for example, a production line supervisor to quickly follow instructions on the dashboard and perform the appropriate actions.

[0107] As a concrete example, in a car manufacturing plant, an AI agent detects body parts of a vehicle being assembled and optimizes the sequence of welding and painting. In this case, an example of a prompt message to the generated AI model would be, "Based on the data from the current production line, identify the bottleneck stage and readjust the optimal task allocation and priority for each robot." This prompt message prompts the AI ​​to analyze the relevant data and make appropriate suggestions for efficiency improvements.

[0108] As a whole system, these functions enable increased efficiency on the production floor and facilitate problem prediction and early response.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The server receives progress data in real time from each terminal within the factory. The input data includes the progress status and work details at each work stage. The server stores this data in a database and centrally manages progress information.

[0112] Step 2:

[0113] The server analyzes the collected progress data and visualizes it. It uses recently saved progress data as input. The output consists of graphs and charts displayed on a dashboard, allowing users to visually check the overall project progress.

[0114] Step 3:

[0115] The server runs a generative AI model to calculate the priority of each task. The inputs are task progress data and information on the skills and current workload of the team members. The AI ​​performs calculations based on this data and creates an optimal task assignment list as output.

[0116] Step 4:

[0117] The server sends the AI-generated task assignment results to the terminal. Upon receiving these results, the terminal displays a list of tasks that should be prioritized to the user and provides prompts to guide them on the next steps.

[0118] Step 5:

[0119] Users interact with the system through the terminal's interactive interface and update task progress as needed. Input consists of data based on the user's work reports and instructions, and the latest progress status is sent to the server as output.

[0120] Step 6:

[0121] The server feeds accumulated progress data into machine learning feedback to improve the accuracy of future project management. The input to this learning process is all the data obtained after the completion of a project. The output is an improved AI model for the next project.

[0122] 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.

[0123] This invention incorporates an emotion engine into a system that streamlines project management, enabling project operation that takes into account the user's emotional state. This system employs a client-server architecture and, in addition to managing progress data, setting priorities, optimizing resource allocation, and predicting and responding to problems, also recognizes the user's emotions.

[0124] The terminal provides an interface where users input task status and send data to the server in real time. Users not only input task start and progress, but also perform sentiment analysis using an emotion engine. This enables analysis of the user's facial expressions and voice through sensors such as a webcam and microphone.

[0125] The server integrates received task data and emotional data and stores it in a database. Project progress is displayed on a dashboard, allowing users to visually check the current status. The server's AI agent also considers the user's emotional state when determining task priorities and optimizing resource allocation. Users experiencing high stress levels are assigned tasks that reduce their workload, while users in a positive emotional state are given priority for important tasks.

[0126] Furthermore, the server utilizes an emotion engine to dynamically adjust the interface according to the user's emotional state, thereby improving the user experience. For example, if the user is feeling stressed, the interface is simplified and the number of operations is reduced to lessen the user's burden.

[0127] As a concrete example, consider a scenario where a project at a certain company requires the development of additional features. When a user feels their stress level is high, emotional data sent from their device is analyzed, and the server adjusts the user's tasks accordingly. This allows users to contribute to the project in a way that is optimal and considers their own emotions.

[0128] In this way, the present invention can reduce user stress and improve overall productivity by taking emotions into consideration in project management.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The device provides an interface for users to update the status of their tasks. Users input information such as task start, progress, and completion. The device also uses sensors to detect the user's facial expressions and voice, and acquires emotional data.

[0132] Step 2:

[0133] The device combines acquired task data and emotion data and sends it to the server. This allows for a comprehensive understanding of the user's current state.

[0134] Step 3:

[0135] The server stores the received data in a database. The database stores progress information for each task and the emotional state of each user.

[0136] Step 4:

[0137] The server updates the dashboard based on the collected data, providing real-time visibility into project progress. Through this, users can check their own progress and the overall status.

[0138] Step 5:

[0139] The server, including the AI ​​agent, determines task priorities. It assigns appropriate tasks, taking into account the user's emotional state. For example, it assigns easy tasks to users who are highly stressed, and prioritizes important tasks for users who are in a positive state.

[0140] Step 6:

[0141] The terminal notifies the user of task update information from the server. Based on this, the user can decide and proceed with the next action.

[0142] Step 7:

[0143] The server uses an emotion engine to adjust the user interface. Based on the user's emotional state, it optimizes the interface design and functionality as needed to improve the user experience.

[0144] Step 8:

[0145] Users can use the updated interface to complete tasks efficiently while reducing stress. After project completion, the results are fed back as training data for the AI ​​agent.

[0146] (Example 2)

[0147] 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".

[0148] Modern project management requires not only tracking progress and tasks, but also taking into account and adjusting the emotional state of team members. However, traditional systems have struggled to grasp individual emotions and reflect them in project management. This has led to increased stress among team members and decreased productivity.

[0149] 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.

[0150] In this invention, the server includes means for collecting and storing project progress data and sentiment data, means for visualizing the collected progress data and sentiment data in real time, and means for automatically setting task priorities and optimizing resource allocation considering the sentiment analysis results. This enables optimal project management that reflects not only project progress but also the individual emotional states.

[0151] "Project progress data" refers to information that shows the overall progress of a project, including data such as the completion rate of tasks and the percentage of time the project is on schedule.

[0152] "Emotional data" refers to information that indicates a user's emotional state, and includes emotional indicators obtained by analyzing facial expressions, voice, text, etc.

[0153] "Real-time visualization" means instantly representing data visually so that users can grasp the situation at a glance.

[0154] "Automatically setting" means that the system determines the order and importance of tasks based on predefined algorithms and rules, without manual intervention.

[0155] "Optimizing resource allocation" means efficiently and effectively allocating available resources such as labor, materials, and time within a project to maximize overall productivity.

[0156] "Considering the results of emotion analysis" means that the system makes judgments that reflect the user's emotional state, and uses those judgments as a basis for the decision-making process.

[0157] "Dynamically adjusting the user interface" means changing the screen layout and operability according to the user's emotional state to provide the optimal operating environment.

[0158] A description of the embodiment for carrying out the invention will be provided.

[0159] This system aims to manage projects while taking into account the user's emotional state. It is built using a client-server architecture, and each component plays the following role:

[0160] The terminal is a device that provides an interface for the user to input the status of a task. Through this interface, the user inputs the start time and ongoing state of the task. Furthermore, the terminal is equipped with sensors such as a webcam and microphone, which capture the user's facial expressions and voice, and send the data to the emotion engine. The emotion engine analyzes the user's emotions based on this data.

[0161] The server receives task and emotion data sent from terminals, integrates them into a database, and stores them. The server analyzes project progress and displays the results on a dashboard, allowing users to visually check their progress. Furthermore, the AI ​​agent installed on the server prioritizes tasks and optimizes resource allocation while considering the user's emotional state. In this process, it assigns lighter tasks to users with high stress levels, while prioritizing important tasks for users with positive emotional states.

[0162] Furthermore, the server improves the user experience by dynamically adjusting the interface according to the user's emotional state. For example, if a user is feeling stressed, measures such as simplifying the interface and reducing the number of operations are taken to alleviate the user's burden.

[0163] As a concrete example, consider a scenario where a company's project requires the development of a new feature. If the server determines that a user is experiencing stress, it adjusts the user's tasks based on emotional data transmitted from their device. This allows the user to reduce stress while effectively contributing to the project.

[0164] An example of a prompt to a generative AI model might be, "Please suggest the optimal task allocation when the user is feeling stressed."

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The user accesses an input interface on the device to enter the task's start status and ongoing status. Simultaneously, the user's facial expressions and voice are captured via webcam and microphone. The input data includes task-related information, facial recognition data, and emotion data extracted from the voice. Once this data is collected, emotion analysis begins on the device. For example, if the user enters "Start brainstorming a new project," the user's voice is recorded during this process.

[0168] Step 2:

[0169] The device passes facial and voice data collected to the emotion engine to analyze the user's emotional state. The emotion engine uses an AI model to analyze the emotional data and determine the user's emotional state. The input is raw facial and voice data, and the output is an indicator of the emotional state. For example, if the system recognizes that the user is smiling, it will output a "positive" emotional state.

[0170] Step 3:

[0171] The terminal sends analyzed emotion data and task data to the server. The server receives this data, integrates it into a database, and stores it. The input is the task status and emotion state from the terminal, and the output is the integrated data stored in the database. Specifically, the server records the emotion data "Project concept started" and "Positive" in the database.

[0172] Step 4:

[0173] The server analyzes information stored in the database and displays project progress on a dashboard. The analysis uses data including current progress and emotional status. The output is information on a visually verifiable dashboard. For example, if user A is at the "Project Concept Start" stage and has a "Positive" emotional state, this status will be displayed on the dashboard using graphs and other visuals.

[0174] Step 5:

[0175] The server runs an AI agent that optimizes task priorities and resource allocation based on the user's emotional state. Inputs are task and emotional data, while output is a prioritized task list and proposed resource allocation. Specifically, users exhibiting stress are automatically reassigned tasks that reduce their workload.

[0176] Step 6:

[0177] The server adjusts the user interface as needed, providing an operating environment suited to the user's emotional state. The input is the emotional state, and the output is the optimal interface presented to the user. For example, when the user is stressed, the interface switches to a simpler one with reduced unnecessary information.

[0178] (Application Example 2)

[0179] 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".

[0180] In project management, assigning tasks without considering the emotional state of project members presents a challenge: stress can build up and productivity can decline. Furthermore, dynamic task allocation that takes into account members' emotions is difficult, resulting in a failure to optimize individual performance and overall project efficiency.

[0181] 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.

[0182] In this invention, the server includes means for analyzing emotional states and collecting emotion recognition data, means for adjusting task priorities and resource allocation based on the analyzed emotional states, and means for providing a conversational interface with the user. This enables dynamic task assignment and resource allocation that takes into account the emotional states of project members.

[0183] "Progress data" refers to information that expresses the degree of completion and progress of each task in a project using numerical values ​​and indicators.

[0184] "Emotional state" refers to the psychological state that the user is currently experiencing, and is usually measured using indicators such as stress, concentration, and satisfaction.

[0185] "Emotion recognition data" refers to data that indicates an emotional state, obtained from information such as the user's facial expressions and voice.

[0186] "Priority" refers to a set of criteria for arranging tasks according to their importance and urgency based on specific conditions.

[0187] "Resource allocation" refers to appropriately allocating personnel, time, materials, and other resources to each task within a project.

[0188] A "conversational interface" is an interface that allows the user and the system to communicate with each other using natural language.

[0189] "Task assignment" refers to the act of instructing each project member on specific tasks.

[0190] A "term definition" is a document that clearly explains technical terms or concepts within a specific context or field.

[0191] In this invention, the terminal uses a personal device such as a smartphone to collect the user's facial expressions and voice through a webcam and microphone to analyze their emotional state. The analyzed emotion recognition data is transmitted to a server in real time, where it is integrated and stored in a database. An AI agent operates on the server, prioritizing tasks and optimizing resource allocation based on multiple data points, including emotional state. In particular, it can assign tasks that reduce the workload to users experiencing high stress levels, and important tasks to users in a positive emotional state. The server also uses the emotion recognition data to dynamically adjust the user interface and improve the user experience. For example, if a user is feeling stressed, it can provide an interface that simplifies operations.

[0192] As a concrete example, if analysis reveals that worker A is experiencing high stress levels due to a tight assembly schedule for a new product at a manufacturing site, the server will adjust A's workload accordingly. If analysis also reveals that worker B is more productive, the server will assign B more important tasks.

[0193] An example of a prompt message to use when employing a generative AI model would be: "Please suggest a project management application that analyzes emotional states based on voice and facial expression data and optimizes workload."

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice. The input consists of facial video frames and audio data, which are used for emotion analysis. Emotion analysis software analyzes this data in real time to identify the user's emotional state (e.g., stress or satisfaction). The output is a digital representation of the user's emotional state.

[0197] Step 2:

[0198] The terminal sends data, including analyzed emotional state information, to the server. This data also includes information about ongoing tasks and the user ID. After the data is sent, the server integrates this information into a database. The input is the user's emotional state data and task information, which the server stores in the database. The output is the integrated project data.

[0199] Step 3:

[0200] The server uses an AI agent to re-evaluate task priorities based on emotion recognition data and existing project data. Priorities are set considering factors such as the user's emotional state, task urgency, and resource availability. Input is data obtained from an integrated database, and the AI ​​module processes the data to determine task priorities. Output is updated task priority information.

[0201] Step 4:

[0202] The server adjusts resource allocation based on newly determined task priorities. Specifically, it assigns less burdensome tasks to users judged to be under high stress, and higher-priority tasks to users in a positive emotional state. The input is updated task priority information, and the output is optimized resource allocation information.

[0203] Step 5:

[0204] The server dynamically adjusts the user interface to improve the user experience based on the user's emotional state. For example, if the user is stressed, the displayed information is simplified and the number of steps required for operation is reduced. The input is emotional state data, and the output is a customized user interface.

[0205] An example of a prompt used when utilizing a generative AI model is: "Suggest a project management application that analyzes emotional states based on voice and facial expression data and optimizes workload."

[0206] 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.

[0207] 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] The system of this invention is designed to streamline and optimize project management. Based on a client-server architecture, it collects, analyzes, displays, predicts problems, and suggests countermeasures for each project.

[0223] First, the terminal provides an interface for project members to input the progress and status of tasks. When a user reports the status of a task, such as start, in progress, or completed, that information is automatically sent to the server. The server receives this data, stores it in a database, and manages the overall progress of the project.

[0224] The server then analyzes the collected data and visualizes the project's progress in real time. Users can visually check the project's progress and the status of individual tasks through the dashboard. This display is accessible across various platforms, including web applications and mobile apps.

[0225] Furthermore, the AI ​​agent optimizes the prioritization and resource allocation of each task. The server automatically determines the optimal task assignment, taking into account the urgency and importance of the tasks, the skills of the members, and the current workload. This result is notified to project members via their terminals, allowing them to focus on high-priority tasks.

[0226] Furthermore, the AI ​​agent predicts potential problems that may arise during project progress and provides prompt solutions. The server then notifies project managers and stakeholders of these solutions, prompting appropriate action.

[0227] As a concrete example, consider a web service development project at an IT company. This project involves multiple tasks and includes developers, designers, and testers. At the start of the project, each member is assigned tasks via a terminal and reports their progress to the server. An AI agent detects development bottlenecks and indicates priority tasks that developer A should tackle first. This improves the overall efficiency of the team and allows them to complete the project on time.

[0228] This system plays a crucial role in reducing the burden of project management and increasing efficiency.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The terminal provides an interface for users to input the status of task start, progress, and completion. Users enter task information, and this data is automatically sent to the server.

[0232] Step 2:

[0233] The server stores task progress data received from terminals in a database. This data includes information such as task ID, assignee, status, start time, and estimated completion time.

[0234] Step 3:

[0235] The server analyzes the stored data and generates information to visualize progress in real time. The generated visualization information is immediately reflected on the dashboard.

[0236] Step 4:

[0237] Users can check project progress through the dashboard and understand each member's tasks and the overall project overview. The dashboard can be accessed via a web browser or a dedicated app.

[0238] Step 5:

[0239] The server uses an AI agent to determine the priority of each task and optimizes resources considering the skills of the members and the current load. Based on these results, task assignments are updated.

[0240] Step 6:

[0241] The terminal notifies each member of the updated task assignment information. Users then proceed with tasks according to the new priorities.

[0242] Step 7:

[0243] The server predicts potential problems that may arise during project progress using AI agents and generates countermeasures. These countermeasures are then sent to the project manager and relevant team members.

[0244] Step 8:

[0245] The user reviews the countermeasures received from the server and decides on the steps to take. Additional actions may be taken as needed.

[0246] Step 9:

[0247] The server collects data after project completion and feeds it back as training data for the AI ​​agent. This improves the accuracy and efficiency of future project management.

[0248] (Example 1)

[0249] 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."

[0250] In project management, tracking progress, prioritizing tasks, and anticipating and addressing potential problems are crucial. However, these processes are often performed manually, making them time-consuming and labor-intensive. Furthermore, their reliance on human judgment can lead to inefficiencies and inaccuracies. As a result, project delays and inappropriate resource allocation can occur, leading to a decline in overall productivity.

[0251] 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.

[0252] In this invention, the server includes means for collecting progress information and storing it in a data storage device, means for visualizing the collected progress information in real time based on a generating AI model, and means for automatically setting the importance of tasks and optimizing the allocation of work resources. This makes it possible to efficiently manage project progress and objectively set task priorities. Furthermore, it is possible to improve the overall productivity of the project by predicting potential problems and addressing them in advance.

[0253] "Progress information" refers to data that shows the progress and degree of completion of each task in a project.

[0254] A "data storage device" is a system or device for storing information over a long period of time.

[0255] A "generative AI model" is an artificial intelligence algorithm designed to derive patterns and predictions from data.

[0256] "Visualizing in real time" means displaying information in a visually easy-to-understand format the moment it is received.

[0257] "Task importance" refers to the result of evaluating the priority and urgency of individual tasks within a project.

[0258] "Work resource allocation" refers to the efficient allocation of available resources, such as personnel and equipment, to each task within a project.

[0259] A "potential problem" refers to an issue or obstacle that is not currently apparent but may arise in the future.

[0260] "Presenting countermeasures" is the act of proposing solutions that should be implemented in advance to address anticipated problems.

[0261] An "interactive interface" is a user interface that allows users to communicate with the system in a natural, two-way manner.

[0262] This invention is a system for streamlining project management, in which a server, terminals, and users work together. The server is responsible for collecting progress information and storing it in a data storage device. In this process, a dedicated database management system is used to efficiently record and manage the received progress data. The server also utilizes a generative AI model to visualize the collected information in real time and provide the user with information on the project's progress.

[0263] The terminal provides an interface for users to input the status of tasks. Specifically, users input progress information into the terminal using a web application or mobile application. This information is automatically sent to the server and stored in a database. Users can view the progress in real time and visualized through the terminal.

[0264] Furthermore, the server uses an AI agent to automatically set the importance of tasks and optimize the allocation of work resources. The AI ​​agent uses a generative AI model to analyze the urgency, importance, and skill levels and workload of the team members. An example of a prompt message is input to the generative AI model: "Generate the optimal task assignment based on project progress data." The information generated in this way is then notified to project members via their terminals.

[0265] Regarding problem prediction, the server analyzes past project data and current issues to predict potential problems. Countermeasures for predicted problems are automatically notified to project managers and stakeholders. This enables rapid and efficient project management.

[0266] As a concrete example, when an IT company is working on a web service development project, developers, designers, and testers would use this system. Each member reports progress information to the server via their terminal, and the server uses AI to assign the most suitable tasks. As a result, the overall efficiency of the project improves, making it possible to complete the project on time.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The terminal provides an interface for users to report the progress and status of tasks. Users input the task progress using a web application or mobile application. The data obtained as input includes status information such as task start, in progress, and completed. This input data is sent to the server via a communication protocol.

[0270] Step 2:

[0271] The server stores progress information received from terminals in a database. Input data is processed into "INSERT" or "UPDATE" queries in the data storage device. This allows for centralized management of the entire project's progress in one location. The output is the latest progress status stored in the database.

[0272] Step 3:

[0273] The server analyzes stored progress information and uses a generated AI model to visualize progress in real time. This process uses progress information obtained from a database as input. The data is converted into graphs and charts for visualization and output to a dashboard. Users can view this visualized information through their devices. For example, the percentage of tasks in progress and the number of completed tasks are visualized.

[0274] Step 4:

[0275] The server automatically sets task priorities using a generative AI model. It collects project data, member skill information, and current workload as input, and performs data calculations based on the generative AI model. The output is the optimal task assignment, which is then notified to project members via their terminals. Specifically, the AI ​​model receives a prompt message such as, "Generate the optimal task assignment based on project progress data."

[0276] Step 5:

[0277] The server utilizes generative AI models to predict potential problems and propose solutions. Input includes historical project data and information on current issues. Data calculations identify potential risks and derive countermeasures. These countermeasures are then communicated to project managers and stakeholders. For example, if a specific task is behind schedule, the server will identify the cause and suggest corrective actions.

[0278] In this way, the system can process information efficiently and automatically at each step of project management, providing the optimal management method.

[0279] (Application Example 1)

[0280] 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."

[0281] In production environments, maximizing production efficiency is crucial. However, accurately tracking the progress of each work stage and allocating tasks optimally in real time is not easy. Furthermore, mechanisms for predicting problems before they occur and quickly providing countermeasures are often inadequate. As a result, bottlenecks and work delays can occur, potentially leading to decreased production efficiency.

[0282] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0283] In this invention, the server includes an information processing apparatus means for collecting and storing progress data, an information processing apparatus means for visualizing the collected progress data in real time, and an information processing apparatus means for monitoring the work progress in the production process and performing dynamic redistribution of tasks. Thereby, efficient progress management in the production process and prompt response based on problem prediction become possible.

[0284] "Progress data" is information regarding the work content and progress status at each work stage.

[0285] "Information processing apparatus" refers to all apparatuses having a function of receiving and analyzing data, including servers and computer systems.

[0286] "Visualization in real time" means visually displaying the state immediately at the moment when the data is collected. <00​​​​​​​​​​​​​​​​​​The server utilizes software such as Apache Kafka and TensorFlow to perform information processing, collecting and storing progress data in real time. This allows for monitoring the progress at each stage of the work. The server also provides a dashboard for immediate analysis and visualization of the progress data. This visualization enables visual monitoring of the status of each task in the production process.

[0292] Furthermore, the server dynamically redistributes tasks using an AI model to maximize the efficiency of the production process. The AI ​​agent automatically determines the optimal task assignment based on each member's skills and current workload. The results of this task assignment are immediately communicated to the workers via terminals.

[0293] The system's interactive interface allows users to interact seamlessly through input. This enables, for example, a production line supervisor to quickly follow instructions on the dashboard and perform the appropriate actions.

[0294] As a concrete example, in a car manufacturing plant, an AI agent detects body parts of a vehicle being assembled and optimizes the sequence of welding and painting. In this case, an example of a prompt message to the generated AI model would be, "Based on the data from the current production line, identify the bottleneck stage and readjust the optimal task allocation and priority for each robot." This prompt message prompts the AI ​​to analyze the relevant data and make appropriate suggestions for efficiency improvements.

[0295] As a whole system, these functions enable increased efficiency on the production floor and facilitate problem prediction and early response.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The server receives progress data in real time from each terminal within the factory. The input data includes the progress status and work content at each work stage. The server stores these data in a database and performs unified management of the progress information.

[0299] Step 2:

[0300] The server analyzes the collected progress data and visualizes the data. As input, the saved recent progress data is used. The output is graphs and charts displayed on the dashboard, through which users can visually confirm the progress of the entire project.

[0301] Step 3:

[0302] The server operates the generated AI model to calculate the priority of each task. The input is the progress data of the task and the skills and current load information of the team members. Based on this, the AI performs data calculations and creates an optimal task assignment list as the output.

[0303] Step 4:

[0304] <I The server sends the task assignment result by the AI to the terminal. The terminal that receives this result displays a list of tasks to be prioritized to the user and provides a prompt to indicate the next step.

[0305] Step 5:

[0306] The user interacts with the system through the interactive interface of the terminal and updates the progress of the task as needed. The input is data based on the user's work report and instructions, and the latest progress status is sent to the server as the output.

[0307] Step 6:

[0308] The server feeds accumulated progress data into machine learning feedback to improve the accuracy of future project management. The input to this learning process is all the data obtained after the completion of a project. The output is an improved AI model for the next project.

[0309] 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.

[0310] This invention incorporates an emotion engine into a system that streamlines project management, enabling project operation that takes into account the user's emotional state. This system employs a client-server architecture and, in addition to managing progress data, setting priorities, optimizing resource allocation, and predicting and responding to problems, also recognizes the user's emotions.

[0311] The terminal provides an interface where users input task status and send data to the server in real time. Users not only input task start and progress, but also perform sentiment analysis using an emotion engine. This enables analysis of the user's facial expressions and voice through sensors such as a webcam and microphone.

[0312] The server integrates received task data and emotional data and stores it in a database. Project progress is displayed on a dashboard, allowing users to visually check the current status. The server's AI agent also considers the user's emotional state when determining task priorities and optimizing resource allocation. Users experiencing high stress levels are assigned tasks that reduce their workload, while users in a positive emotional state are given priority for important tasks.

[0313] Furthermore, the server utilizes an emotion engine to dynamically adjust the interface according to the user's emotional state, thereby improving the user experience. For example, if the user is feeling stressed, the interface is simplified and the number of operations is reduced to lessen the user's burden.

[0314] As a concrete example, consider a scenario where a project at a certain company requires the development of additional features. When a user feels their stress level is high, emotional data sent from their device is analyzed, and the server adjusts the user's tasks accordingly. This allows users to contribute to the project in a way that is optimal and considers their own emotions.

[0315] In this way, the present invention can reduce user stress and improve overall productivity by taking emotions into consideration in project management.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The device provides an interface for users to update the status of their tasks. Users input information such as task start, progress, and completion. The device also uses sensors to detect the user's facial expressions and voice, and acquires emotional data.

[0319] Step 2:

[0320] The device combines acquired task data and emotion data and sends it to the server. This allows for a comprehensive understanding of the user's current state.

[0321] Step 3:

[0322] The server stores the received data in a database. The database stores progress information for each task and the emotional state of each user.

[0323] Step 4:

[0324] The server updates the dashboard based on the collected data, providing real-time visibility into project progress. Through this, users can check their own progress and the overall status.

[0325] Step 5:

[0326] The server, including the AI ​​agent, determines task priorities. It assigns appropriate tasks, taking into account the user's emotional state. For example, it assigns easy tasks to users who are highly stressed, and prioritizes important tasks for users who are in a positive state.

[0327] Step 6:

[0328] The terminal notifies the user of task update information from the server. Based on this, the user can decide and proceed with the next action.

[0329] Step 7:

[0330] The server uses an emotion engine to adjust the user interface. Based on the user's emotional state, it optimizes the interface design and functionality as needed to improve the user experience.

[0331] Step 8:

[0332] Users can use the updated interface to complete tasks efficiently while reducing stress. After project completion, the results are fed back as training data for the AI ​​agent.

[0333] (Example 2)

[0334] 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".

[0335] Modern project management requires not only tracking progress and tasks, but also taking into account and adjusting the emotional state of team members. However, traditional systems have struggled to grasp individual emotions and reflect them in project management. This has led to increased stress among team members and decreased productivity.

[0336] 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.

[0337] In this invention, the server includes means for collecting and storing project progress data and sentiment data, means for visualizing the collected progress data and sentiment data in real time, and means for automatically setting task priorities and optimizing resource allocation considering the sentiment analysis results. This enables optimal project management that reflects not only project progress but also the individual emotional states.

[0338] "Project progress data" refers to information that shows the overall progress of a project, including data such as the completion rate of tasks and the percentage of time the project is on schedule.

[0339] "Emotional data" refers to information that indicates a user's emotional state, and includes emotional indicators obtained by analyzing facial expressions, voice, text, etc.

[0340] "Real-time visualization" means instantly representing data visually so that users can grasp the situation at a glance.

[0341] "Automatically setting" means that the system determines the order and importance of tasks based on predefined algorithms and rules, without manual intervention.

[0342] "Optimizing resource allocation" means efficiently and effectively allocating available resources such as labor, materials, and time within a project to maximize overall productivity.

[0343] "Considering the results of emotion analysis" means that the system makes judgments that reflect the user's emotional state, and uses those judgments as a basis for the decision-making process.

[0344] "Dynamically adjusting the user interface" means changing the screen layout and operability according to the user's emotional state to provide the optimal operating environment.

[0345] A description of the embodiment for carrying out the invention will be provided.

[0346] This system aims to manage projects while taking into account the user's emotional state. It is built using a client-server architecture, and each component plays the following role:

[0347] The terminal is a device that provides an interface for the user to input the status of a task. Through this interface, the user inputs the start time and ongoing state of the task. Furthermore, the terminal is equipped with sensors such as a webcam and microphone, which capture the user's facial expressions and voice, and send the data to the emotion engine. The emotion engine analyzes the user's emotions based on this data.

[0348] The server receives task and emotion data sent from terminals, integrates them into a database, and stores them. The server analyzes project progress and displays the results on a dashboard, allowing users to visually check their progress. Furthermore, the AI ​​agent installed on the server prioritizes tasks and optimizes resource allocation while considering the user's emotional state. In this process, it assigns lighter tasks to users with high stress levels, while prioritizing important tasks for users with positive emotional states.

[0349] Furthermore, the server improves the user experience by dynamically adjusting the interface according to the user's emotional state. For example, if a user is feeling stressed, measures such as simplifying the interface and reducing the number of operations are taken to alleviate the user's burden.

[0350] As a concrete example, consider a scenario where a company's project requires the development of a new feature. If the server determines that a user is experiencing stress, it adjusts the user's tasks based on emotional data transmitted from their device. This allows the user to reduce stress while effectively contributing to the project.

[0351] An example of a prompt to a generative AI model might be, "Please suggest the optimal task allocation when the user is feeling stressed."

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The user accesses an input interface on the device to enter the task's start status and ongoing status. Simultaneously, the user's facial expressions and voice are captured via webcam and microphone. The input data includes task-related information, facial recognition data, and emotion data extracted from the voice. Once this data is collected, emotion analysis begins on the device. For example, if the user enters "Start brainstorming a new project," the user's voice is recorded during this process.

[0355] Step 2:

[0356] The device passes facial and voice data collected to the emotion engine to analyze the user's emotional state. The emotion engine uses an AI model to analyze the emotional data and determine the user's emotional state. The input is raw facial and voice data, and the output is an indicator of the emotional state. For example, if the system recognizes that the user is smiling, it will output a "positive" emotional state.

[0357] Step 3:

[0358] The terminal sends analyzed emotion data and task data to the server. The server receives this data, integrates it into a database, and stores it. The input is the task status and emotion state from the terminal, and the output is the integrated data stored in the database. Specifically, the server records the emotion data "Project concept started" and "Positive" in the database.

[0359] Step 4:

[0360] The server analyzes information stored in the database and displays project progress on a dashboard. The analysis uses data including current progress and emotional status. The output is information on a visually verifiable dashboard. For example, if user A is at the "Project Concept Start" stage and has a "Positive" emotional state, this status will be displayed on the dashboard using graphs and other visuals.

[0361] Step 5:

[0362] The server runs an AI agent that optimizes task priorities and resource allocation based on the user's emotional state. Inputs are task and emotional data, while output is a prioritized task list and proposed resource allocation. Specifically, users exhibiting stress are automatically reassigned tasks that reduce their workload.

[0363] Step 6:

[0364] The server adjusts the user interface as needed, providing an operating environment suited to the user's emotional state. The input is the emotional state, and the output is the optimal interface presented to the user. For example, when the user is stressed, the interface switches to a simpler one with reduced unnecessary information.

[0365] (Application Example 2)

[0366] 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."

[0367] In project management, assigning tasks without considering the emotional state of project members presents a challenge: stress can build up and productivity can decline. Furthermore, dynamic task allocation that takes into account members' emotions is difficult, resulting in a failure to optimize individual performance and overall project efficiency.

[0368] 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.

[0369] In this invention, the server includes means for analyzing emotional states and collecting emotion recognition data, means for adjusting task priorities and resource allocation based on the analyzed emotional states, and means for providing a conversational interface with the user. This enables dynamic task assignment and resource allocation that takes into account the emotional states of project members.

[0370] "Progress data" refers to information that expresses the degree of completion and progress of each task in a project using numerical values ​​and indicators.

[0371] "Emotional state" refers to the psychological state that the user is currently experiencing, and is usually measured using indicators such as stress, concentration, and satisfaction.

[0372] "Emotion recognition data" refers to data that indicates an emotional state, obtained from information such as the user's facial expressions and voice.

[0373] "Priority" refers to a set of criteria for arranging tasks according to their importance and urgency based on specific conditions.

[0374] "Resource allocation" refers to appropriately allocating personnel, time, materials, and other resources to each task within a project.

[0375] A "conversational interface" is an interface that allows the user and the system to communicate with each other using natural language.

[0376] "Task assignment" refers to the act of instructing each project member on specific tasks.

[0377] A "term definition" is a document that clearly explains technical terms or concepts within a specific context or field.

[0378] In this invention, the terminal uses a personal device such as a smartphone to collect the user's facial expressions and voice through a webcam and microphone to analyze their emotional state. The analyzed emotion recognition data is transmitted to a server in real time, where it is integrated and stored in a database. An AI agent operates on the server, prioritizing tasks and optimizing resource allocation based on multiple data points, including emotional state. In particular, it can assign tasks that reduce the workload to users experiencing high stress levels, and important tasks to users in a positive emotional state. The server also uses the emotion recognition data to dynamically adjust the user interface and improve the user experience. For example, if a user is feeling stressed, it can provide an interface that simplifies operations.

[0379] As a concrete example, if analysis reveals that worker A is experiencing high stress levels due to a tight assembly schedule for a new product at a manufacturing site, the server will adjust A's workload accordingly. If analysis also reveals that worker B is more productive, the server will assign B more important tasks.

[0380] An example of a prompt message to use when employing a generative AI model would be: "Please suggest a project management application that analyzes emotional states based on voice and facial expression data and optimizes workload."

[0381] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0382] Step 1:

[0383] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice. The input consists of facial video frames and audio data, which are used for emotion analysis. Emotion analysis software analyzes this data in real time to identify the user's emotional state (e.g., stress or satisfaction). The output is a digital representation of the user's emotional state.

[0384] Step 2:

[0385] The terminal sends data, including analyzed emotional state information, to the server. This data also includes information about ongoing tasks and the user ID. After the data is sent, the server integrates this information into a database. The input is the user's emotional state data and task information, which the server stores in the database. The output is the integrated project data.

[0386] Step 3:

[0387] The server uses an AI agent to re-evaluate task priorities based on emotion recognition data and existing project data. Priorities are set considering factors such as the user's emotional state, task urgency, and resource availability. Input is data obtained from an integrated database, and the AI ​​module processes the data to determine task priorities. Output is updated task priority information.

[0388] Step 4:

[0389] The server adjusts resource allocation based on newly determined task priorities. Specifically, it assigns less burdensome tasks to users judged to be under high stress, and higher-priority tasks to users in a positive emotional state. The input is updated task priority information, and the output is optimized resource allocation information.

[0390] Step 5:

[0391] The server dynamically adjusts the user interface to improve the user experience based on the user's emotional state. For example, if the user is stressed, the displayed information is simplified and the number of steps required for operation is reduced. The input is emotional state data, and the output is a customized user interface.

[0392] An example of a prompt used when utilizing a generative AI model is: "Suggest a project management application that analyzes emotional states based on voice and facial expression data and optimizes workload."

[0393] 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.

[0394] 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.

[0395] 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.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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".

[0409] The system of this invention is designed to streamline and optimize project management. Based on a client-server architecture, it collects, analyzes, displays, predicts problems, and suggests countermeasures for each project.

[0410] First, the terminal provides an interface for project members to input the progress and status of tasks. When a user reports the status of a task, such as start, in progress, or completed, that information is automatically sent to the server. The server receives this data, stores it in a database, and manages the overall progress of the project.

[0411] The server then analyzes the collected data and visualizes the project's progress in real time. Users can visually check the project's progress and the status of individual tasks through the dashboard. This display is accessible across various platforms, including web applications and mobile apps.

[0412] Furthermore, the AI ​​agent optimizes the prioritization and resource allocation of each task. The server automatically determines the optimal task assignment, taking into account the urgency and importance of the tasks, the skills of the members, and the current workload. This result is notified to project members via their terminals, allowing them to focus on high-priority tasks.

[0413] Furthermore, the AI ​​agent predicts potential problems that may arise during project progress and provides prompt solutions. The server then notifies project managers and stakeholders of these solutions, prompting appropriate action.

[0414] As a concrete example, consider a web service development project at an IT company. This project involves multiple tasks and includes developers, designers, and testers. At the start of the project, each member is assigned tasks via a terminal and reports their progress to the server. An AI agent detects development bottlenecks and indicates priority tasks that developer A should tackle first. This improves the overall efficiency of the team and allows them to complete the project on time.

[0415] This system plays a crucial role in reducing the burden of project management and increasing efficiency.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] The terminal provides an interface for users to input the status of task start, progress, and completion. Users enter task information, and this data is automatically sent to the server.

[0419] Step 2:

[0420] The server stores task progress data received from terminals in a database. This data includes information such as task ID, assignee, status, start time, and estimated completion time.

[0421] Step 3:

[0422] The server analyzes the stored data and generates information to visualize progress in real time. The generated visualization information is immediately reflected on the dashboard.

[0423] Step 4:

[0424] Users can check project progress through the dashboard and understand each member's tasks and the overall project overview. The dashboard can be accessed via a web browser or a dedicated app.

[0425] Step 5:

[0426] The server uses an AI agent to determine the priority of each task and optimizes resources considering the skills of the members and the current load. Based on these results, task assignments are updated.

[0427] Step 6:

[0428] The terminal notifies each member of the updated task assignment information. Users then proceed with tasks according to the new priorities.

[0429] Step 7:

[0430] The server predicts potential problems that may arise during project progress using AI agents and generates countermeasures. These countermeasures are then sent to the project manager and relevant team members.

[0431] Step 8:

[0432] The user reviews the countermeasures received from the server and decides on the steps to take. Additional actions may be taken as needed.

[0433] Step 9:

[0434] The server collects data after project completion and feeds it back as training data for the AI ​​agent. This improves the accuracy and efficiency of future project management.

[0435] (Example 1)

[0436] 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."

[0437] In project management, tracking progress, prioritizing tasks, and anticipating and addressing potential problems are crucial. However, these processes are often performed manually, making them time-consuming and labor-intensive. Furthermore, their reliance on human judgment can lead to inefficiencies and inaccuracies. As a result, project delays and inappropriate resource allocation can occur, leading to a decline in overall productivity.

[0438] 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.

[0439] In this invention, the server includes means for collecting progress information and storing it in a data storage device, means for visualizing the collected progress information in real time based on a generating AI model, and means for automatically setting the importance of tasks and optimizing the allocation of work resources. This makes it possible to efficiently manage project progress and objectively set task priorities. Furthermore, it is possible to improve the overall productivity of the project by predicting potential problems and addressing them in advance.

[0440] "Progress information" refers to data that shows the progress and degree of completion of each task in a project.

[0441] A "data storage device" is a system or device for storing information over a long period of time.

[0442] A "generative AI model" is an artificial intelligence algorithm designed to derive patterns and predictions from data.

[0443] "Visualizing in real time" means displaying information in a visually easy-to-understand format the moment it is received.

[0444] "Task importance" refers to the result of evaluating the priority and urgency of individual tasks within a project.

[0445] "Work resource allocation" refers to the efficient allocation of available resources, such as personnel and equipment, to each task within a project.

[0446] A "potential problem" refers to an issue or obstacle that is not currently apparent but may arise in the future.

[0447] "Presenting countermeasures" is the act of proposing solutions that should be implemented in advance to address anticipated problems.

[0448] An "interactive interface" is a user interface that allows users to communicate with the system in a natural, two-way manner.

[0449] This invention is a system for streamlining project management, in which a server, terminals, and users work together. The server is responsible for collecting progress information and storing it in a data storage device. In this process, a dedicated database management system is used to efficiently record and manage the received progress data. The server also utilizes a generative AI model to visualize the collected information in real time and provide the user with information on the project's progress.

[0450] The terminal provides an interface for users to input the status of tasks. Specifically, users input progress information into the terminal using a web application or mobile application. This information is automatically sent to the server and stored in a database. Users can view the progress in real time and visualized through the terminal.

[0451] Furthermore, the server uses an AI agent to automatically set the importance of tasks and optimize the allocation of work resources. The AI ​​agent uses a generative AI model to analyze the urgency, importance, and skill levels and workload of the team members. An example of a prompt message is input to the generative AI model: "Generate the optimal task assignment based on project progress data." The information generated in this way is then notified to project members via their terminals.

[0452] Regarding problem prediction, the server analyzes past project data and current issues to predict potential problems. Countermeasures for predicted problems are automatically notified to project managers and stakeholders. This enables rapid and efficient project management.

[0453] As a concrete example, when an IT company is working on a web service development project, developers, designers, and testers would use this system. Each member reports progress information to the server via their terminal, and the server uses AI to assign the most suitable tasks. As a result, the overall efficiency of the project improves, making it possible to complete the project on time.

[0454] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0455] Step 1:

[0456] The terminal provides an interface for users to report the progress and status of tasks. Users input the task progress using a web application or mobile application. The data obtained as input includes status information such as task start, in progress, and completed. This input data is sent to the server via a communication protocol.

[0457] Step 2:

[0458] The server stores progress information received from terminals in a database. Input data is processed into "INSERT" or "UPDATE" queries in the data storage device. This allows for centralized management of the entire project's progress in one location. The output is the latest progress status stored in the database.

[0459] Step 3:

[0460] The server analyzes stored progress information and uses a generated AI model to visualize progress in real time. This process uses progress information obtained from a database as input. The data is converted into graphs and charts for visualization and output to a dashboard. Users can view this visualized information through their devices. For example, the percentage of tasks in progress and the number of completed tasks are visualized.

[0461] Step 4:

[0462] The server automatically sets task priorities using a generative AI model. It collects project data, member skill information, and current workload as input, and performs data calculations based on the generative AI model. The output is the optimal task assignment, which is then notified to project members via their terminals. Specifically, the AI ​​model receives a prompt message such as, "Generate the optimal task assignment based on project progress data."

[0463] Step 5:

[0464] The server utilizes generative AI models to predict potential problems and propose solutions. Input includes historical project data and information on current issues. Data calculations identify potential risks and derive countermeasures. These countermeasures are then communicated to project managers and stakeholders. For example, if a specific task is behind schedule, the server will identify the cause and suggest corrective actions.

[0465] In this way, the system can process information efficiently and automatically at each step of project management, providing the optimal management method.

[0466] (Application Example 1)

[0467] 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."

[0468] In production environments, maximizing production efficiency is crucial. However, accurately tracking the progress of each work stage and allocating tasks optimally in real time is not easy. Furthermore, mechanisms for predicting problems before they occur and quickly providing countermeasures are often inadequate. As a result, bottlenecks and work delays can occur, potentially leading to decreased production efficiency.

[0469] 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.

[0470] In this invention, the server includes an information processing device for collecting and storing progress data, an information processing device for visualizing the collected progress data in real time, and an information processing device for monitoring work progress in the production process and dynamically reallocating tasks. This enables efficient progress management in the production process and rapid response through problem prediction.

[0471] "Progress data" refers to information about the work content and progress at each stage of the work process.

[0472] "Information processing equipment" refers to all devices equipped with the function of receiving and analyzing data, and includes servers and computer systems.

[0473] "Real-time visualization" means instantly displaying the state of data visually the moment it is collected.

[0474] "Dynamic task reallocation" is a process of instantly changing and optimizing the priority and assignment of tasks according to their progress and status.

[0475] "Production process" refers to a series of processes and procedures for manufacturing a product, encompassing the entire work involved at each stage.

[0476] An "interactive interface" is a means for a user to exchange information with a computer system in a two-way manner.

[0477] The system that realizes this invention employs advanced information processing technology to effectively manage, analyze, and utilize data. Its main components include a server, terminals, and a user interface.

[0478] The server utilizes software such as Apache Kafka and TensorFlow to perform information processing, collecting and storing progress data in real time. This allows for monitoring the progress at each stage of the work. The server also provides a dashboard for immediate analysis and visualization of the progress data. This visualization enables visual monitoring of the status of each task in the production process.

[0479] Furthermore, the server dynamically redistributes tasks using an AI model to maximize the efficiency of the production process. The AI ​​agent automatically determines the optimal task assignment based on each member's skills and current workload. The results of this task assignment are immediately communicated to the workers via terminals.

[0480] The system's interactive interface allows users to interact seamlessly through input. This enables, for example, a production line supervisor to quickly follow instructions on the dashboard and perform the appropriate actions.

[0481] As a concrete example, in a car manufacturing plant, an AI agent detects body parts of a vehicle being assembled and optimizes the sequence of welding and painting. In this case, an example of a prompt message to the generated AI model would be, "Based on the data from the current production line, identify the bottleneck stage and readjust the optimal task allocation and priority for each robot." This prompt message prompts the AI ​​to analyze the relevant data and make appropriate suggestions for efficiency improvements.

[0482] As a whole system, these functions enable increased efficiency on the production floor and facilitate problem prediction and early response.

[0483] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0484] Step 1:

[0485] The server receives progress data in real time from each terminal within the factory. The input data includes the progress status and work details at each work stage. The server stores this data in a database and centrally manages progress information.

[0486] Step 2:

[0487] The server analyzes the collected progress data and visualizes it. It uses recently saved progress data as input. The output consists of graphs and charts displayed on a dashboard, allowing users to visually check the overall project progress.

[0488] Step 3:

[0489] The server runs a generative AI model to calculate the priority of each task. The inputs are task progress data and information on the skills and current workload of the team members. The AI ​​performs calculations based on this data and creates an optimal task assignment list as output.

[0490] Step 4:

[0491] The server sends the AI-generated task assignment results to the terminal. Upon receiving these results, the terminal displays a list of tasks that should be prioritized to the user and provides prompts to guide them on the next steps.

[0492] Step 5:

[0493] Users interact with the system through the terminal's interactive interface and update task progress as needed. Input consists of data based on the user's work reports and instructions, and the latest progress status is sent to the server as output.

[0494] Step 6:

[0495] The server feeds accumulated progress data into machine learning feedback to improve the accuracy of future project management. The input to this learning process is all the data obtained after the completion of a project. The output is an improved AI model for the next project.

[0496] 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.

[0497] This invention incorporates an emotion engine into a system that streamlines project management, enabling project operation that takes into account the user's emotional state. This system employs a client-server architecture and, in addition to managing progress data, setting priorities, optimizing resource allocation, and predicting and responding to problems, also recognizes the user's emotions.

[0498] The terminal provides an interface where users input task status and send data to the server in real time. Users not only input task start and progress, but also perform sentiment analysis using an emotion engine. This enables analysis of the user's facial expressions and voice through sensors such as a webcam and microphone.

[0499] The server integrates received task data and emotional data and stores it in a database. Project progress is displayed on a dashboard, allowing users to visually check the current status. The server's AI agent also considers the user's emotional state when determining task priorities and optimizing resource allocation. Users experiencing high stress levels are assigned tasks that reduce their workload, while users in a positive emotional state are given priority for important tasks.

[0500] Furthermore, the server utilizes an emotion engine to dynamically adjust the interface according to the user's emotional state, thereby improving the user experience. For example, if the user is feeling stressed, the interface is simplified and the number of operations is reduced to lessen the user's burden.

[0501] As a concrete example, consider a scenario where a project at a certain company requires the development of additional features. When a user feels their stress level is high, emotional data sent from their device is analyzed, and the server adjusts the user's tasks accordingly. This allows users to contribute to the project in a way that is optimal and considers their own emotions.

[0502] In this way, the present invention can reduce user stress and improve overall productivity by taking emotions into consideration in project management.

[0503] The following describes the processing flow.

[0504] Step 1:

[0505] The device provides an interface for users to update the status of their tasks. Users input information such as task start, progress, and completion. The device also uses sensors to detect the user's facial expressions and voice, and acquires emotional data.

[0506] Step 2:

[0507] The device combines acquired task data and emotion data and sends it to the server. This allows for a comprehensive understanding of the user's current state.

[0508] Step 3:

[0509] The server stores the received data in a database. The database stores progress information for each task and the emotional state of each user.

[0510] Step 4:

[0511] The server updates the dashboard based on the collected data, providing real-time visibility into project progress. Through this, users can check their own progress and the overall status.

[0512] Step 5:

[0513] The server, including the AI ​​agent, determines task priorities. It assigns appropriate tasks, taking into account the user's emotional state. For example, it assigns easy tasks to users who are highly stressed, and prioritizes important tasks for users who are in a positive state.

[0514] Step 6:

[0515] The terminal notifies the user of task update information from the server. Based on this, the user can decide and proceed with the next action.

[0516] Step 7:

[0517] The server uses an emotion engine to adjust the user interface. Based on the user's emotional state, it optimizes the interface design and functionality as needed to improve the user experience.

[0518] Step 8:

[0519] Users can use the updated interface to complete tasks efficiently while reducing stress. After project completion, the results are fed back as training data for the AI ​​agent.

[0520] (Example 2)

[0521] 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."

[0522] Modern project management requires not only tracking progress and tasks, but also taking into account and adjusting the emotional state of team members. However, traditional systems have struggled to grasp individual emotions and reflect them in project management. This has led to increased stress among team members and decreased productivity.

[0523] 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.

[0524] In this invention, the server includes means for collecting and storing project progress data and sentiment data, means for visualizing the collected progress data and sentiment data in real time, and means for automatically setting task priorities and optimizing resource allocation considering the sentiment analysis results. This enables optimal project management that reflects not only project progress but also the individual emotional states.

[0525] "Project progress data" refers to information that shows the overall progress of a project, including data such as the completion rate of tasks and the percentage of time the project is on schedule.

[0526] "Emotional data" refers to information that indicates a user's emotional state, and includes emotional indicators obtained by analyzing facial expressions, voice, text, etc.

[0527] "Real-time visualization" means instantly representing data visually so that users can grasp the situation at a glance.

[0528] "Automatically setting" means that the system determines the order and importance of tasks based on predefined algorithms and rules, without manual intervention.

[0529] "Optimizing resource allocation" means efficiently and effectively allocating available resources such as labor, materials, and time within a project to maximize overall productivity.

[0530] "Considering the results of emotion analysis" means that the system makes judgments that reflect the user's emotional state, and uses those judgments as a basis for the decision-making process.

[0531] "Dynamically adjusting the user interface" means changing the screen layout and operability according to the user's emotional state to provide the optimal operating environment.

[0532] A description of the embodiment for carrying out the invention will be provided.

[0533] This system aims to manage projects while taking into account the user's emotional state. It is built using a client-server architecture, and each component plays the following role:

[0534] The terminal is a device that provides an interface for the user to input the status of a task. Through this interface, the user inputs the start time and ongoing state of the task. Furthermore, the terminal is equipped with sensors such as a webcam and microphone, which capture the user's facial expressions and voice, and send the data to the emotion engine. The emotion engine analyzes the user's emotions based on this data.

[0535] The server receives task and emotion data sent from terminals, integrates them into a database, and stores them. The server analyzes project progress and displays the results on a dashboard, allowing users to visually check their progress. Furthermore, the AI ​​agent installed on the server prioritizes tasks and optimizes resource allocation while considering the user's emotional state. In this process, it assigns lighter tasks to users with high stress levels, while prioritizing important tasks for users with positive emotional states.

[0536] Furthermore, the server improves the user experience by dynamically adjusting the interface according to the user's emotional state. For example, if a user is feeling stressed, measures such as simplifying the interface and reducing the number of operations are taken to alleviate the user's burden.

[0537] As a concrete example, consider a scenario where a company's project requires the development of a new feature. If the server determines that a user is experiencing stress, it adjusts the user's tasks based on emotional data transmitted from their device. This allows the user to reduce stress while effectively contributing to the project.

[0538] An example of a prompt to a generative AI model might be, "Please suggest the optimal task allocation when the user is feeling stressed."

[0539] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0540] Step 1:

[0541] The user accesses an input interface on the device to enter the task's start status and ongoing status. Simultaneously, the user's facial expressions and voice are captured via webcam and microphone. The input data includes task-related information, facial recognition data, and emotion data extracted from the voice. Once this data is collected, emotion analysis begins on the device. For example, if the user enters "Start brainstorming a new project," the user's voice is recorded during this process.

[0542] Step 2:

[0543] The device passes facial and voice data collected to the emotion engine to analyze the user's emotional state. The emotion engine uses an AI model to analyze the emotional data and determine the user's emotional state. The input is raw facial and voice data, and the output is an indicator of the emotional state. For example, if the system recognizes that the user is smiling, it will output a "positive" emotional state.

[0544] Step 3:

[0545] The terminal sends analyzed emotion data and task data to the server. The server receives this data, integrates it into a database, and stores it. The input is the task status and emotion state from the terminal, and the output is the integrated data stored in the database. Specifically, the server records the emotion data "Project concept started" and "Positive" in the database.

[0546] Step 4:

[0547] The server analyzes information stored in the database and displays project progress on a dashboard. The analysis uses data including current progress and emotional status. The output is information on a visually verifiable dashboard. For example, if user A is at the "Project Concept Start" stage and has a "Positive" emotional state, this status will be displayed on the dashboard using graphs and other visuals.

[0548] Step 5:

[0549] The server runs an AI agent that optimizes task priorities and resource allocation based on the user's emotional state. Inputs are task and emotional data, while output is a prioritized task list and proposed resource allocation. Specifically, users exhibiting stress are automatically reassigned tasks that reduce their workload.

[0550] Step 6:

[0551] The server adjusts the user interface as needed, providing an operating environment suited to the user's emotional state. The input is the emotional state, and the output is the optimal interface presented to the user. For example, when the user is stressed, the interface switches to a simpler one with reduced unnecessary information.

[0552] (Application Example 2)

[0553] 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."

[0554] In project management, assigning tasks without considering the emotional state of project members presents a challenge: stress can build up and productivity can decline. Furthermore, dynamic task allocation that takes into account members' emotions is difficult, resulting in a failure to optimize individual performance and overall project efficiency.

[0555] 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.

[0556] In this invention, the server includes means for analyzing emotional states and collecting emotion recognition data, means for adjusting task priorities and resource allocation based on the analyzed emotional states, and means for providing a conversational interface with the user. This enables dynamic task assignment and resource allocation that takes into account the emotional states of project members.

[0557] "Progress data" refers to information that expresses the degree of completion and progress of each task in a project using numerical values ​​and indicators.

[0558] "Emotional state" refers to the psychological state that the user is currently experiencing, and is usually measured using indicators such as stress, concentration, and satisfaction.

[0559] "Emotion recognition data" refers to data that indicates an emotional state, obtained from information such as the user's facial expressions and voice.

[0560] "Priority" refers to a set of criteria for arranging tasks according to their importance and urgency based on specific conditions.

[0561] "Resource allocation" refers to appropriately allocating personnel, time, materials, and other resources to each task within a project.

[0562] A "conversational interface" is an interface that allows the user and the system to communicate with each other using natural language.

[0563] "Task assignment" refers to the act of instructing each project member on specific tasks.

[0564] A "term definition" is a document that clearly explains technical terms or concepts within a specific context or field.

[0565] In this invention, the terminal uses a personal device such as a smartphone to collect the user's facial expressions and voice through a webcam and microphone to analyze their emotional state. The analyzed emotion recognition data is transmitted to a server in real time, where it is integrated and stored in a database. An AI agent operates on the server, prioritizing tasks and optimizing resource allocation based on multiple data points, including emotional state. In particular, it can assign tasks that reduce the workload to users experiencing high stress levels, and important tasks to users in a positive emotional state. The server also uses the emotion recognition data to dynamically adjust the user interface and improve the user experience. For example, if a user is feeling stressed, it can provide an interface that simplifies operations.

[0566] As a concrete example, if analysis reveals that worker A is experiencing high stress levels due to a tight assembly schedule for a new product at a manufacturing site, the server will adjust A's workload accordingly. If analysis also reveals that worker B is more productive, the server will assign B more important tasks.

[0567] An example of a prompt message to use when employing a generative AI model would be: "Please suggest a project management application that analyzes emotional states based on voice and facial expression data and optimizes workload."

[0568] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0569] Step 1:

[0570] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice. The input consists of facial video frames and audio data, which are used for emotion analysis. Emotion analysis software analyzes this data in real time to identify the user's emotional state (e.g., stress or satisfaction). The output is a digital representation of the user's emotional state.

[0571] Step 2:

[0572] The terminal sends data, including analyzed emotional state information, to the server. This data also includes information about ongoing tasks and the user ID. After the data is sent, the server integrates this information into a database. The input is the user's emotional state data and task information, which the server stores in the database. The output is the integrated project data.

[0573] Step 3:

[0574] The server uses an AI agent to re-evaluate task priorities based on emotion recognition data and existing project data. Priorities are set considering factors such as the user's emotional state, task urgency, and resource availability. Input is data obtained from an integrated database, and the AI ​​module processes the data to determine task priorities. Output is updated task priority information.

[0575] Step 4:

[0576] The server adjusts resource allocation based on newly determined task priorities. Specifically, it assigns less burdensome tasks to users judged to be under high stress, and higher-priority tasks to users in a positive emotional state. The input is updated task priority information, and the output is optimized resource allocation information.

[0577] Step 5:

[0578] The server dynamically adjusts the user interface to improve the user experience based on the user's emotional state. For example, if the user is stressed, the displayed information is simplified and the number of steps required for operation is reduced. The input is emotional state data, and the output is a customized user interface.

[0579] An example of a prompt used when utilizing a generative AI model is: "Suggest a project management application that analyzes emotional states based on voice and facial expression data and optimizes workload."

[0580] 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.

[0581] 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.

[0582] 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.

[0583] [Fourth Embodiment]

[0584] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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".

[0597] The system of this invention is designed to streamline and optimize project management. Based on a client-server architecture, it collects, analyzes, displays, predicts problems, and suggests countermeasures for each project.

[0598] First, the terminal provides an interface for project members to input the progress and status of tasks. When a user reports the status of a task, such as start, in progress, or completed, that information is automatically sent to the server. The server receives this data, stores it in a database, and manages the overall progress of the project.

[0599] The server then analyzes the collected data and visualizes the project's progress in real time. Users can visually check the project's progress and the status of individual tasks through the dashboard. This display is accessible across various platforms, including web applications and mobile apps.

[0600] Furthermore, the AI ​​agent optimizes the prioritization and resource allocation of each task. The server automatically determines the optimal task assignment, taking into account the urgency and importance of the tasks, the skills of the members, and the current workload. This result is notified to project members via their terminals, allowing them to focus on high-priority tasks.

[0601] Furthermore, the AI ​​agent predicts potential problems that may arise during project progress and provides prompt solutions. The server then notifies project managers and stakeholders of these solutions, prompting appropriate action.

[0602] As a concrete example, consider a web service development project at an IT company. This project involves multiple tasks and includes developers, designers, and testers. At the start of the project, each member is assigned tasks via a terminal and reports their progress to the server. An AI agent detects development bottlenecks and indicates priority tasks that developer A should tackle first. This improves the overall efficiency of the team and allows them to complete the project on time.

[0603] This system plays a crucial role in reducing the burden of project management and increasing efficiency.

[0604] The following describes the processing flow.

[0605] Step 1:

[0606] The terminal provides an interface for users to input the status of task start, progress, and completion. Users enter task information, and this data is automatically sent to the server.

[0607] Step 2:

[0608] The server stores task progress data received from terminals in a database. This data includes information such as task ID, assignee, status, start time, and estimated completion time.

[0609] Step 3:

[0610] The server analyzes the stored data and generates information to visualize progress in real time. The generated visualization information is immediately reflected on the dashboard.

[0611] Step 4:

[0612] Users can check project progress through the dashboard and understand each member's tasks and the overall project overview. The dashboard can be accessed via a web browser or a dedicated app.

[0613] Step 5:

[0614] The server uses an AI agent to determine the priority of each task and optimizes resources considering the skills of the members and the current load. Based on these results, task assignments are updated.

[0615] Step 6:

[0616] The terminal notifies each member of the updated task assignment information. Users then proceed with tasks according to the new priorities.

[0617] Step 7:

[0618] The server predicts potential problems that may arise during project progress using AI agents and generates countermeasures. These countermeasures are then sent to the project manager and relevant team members.

[0619] Step 8:

[0620] The user reviews the countermeasures received from the server and decides on the steps to take. Additional actions may be taken as needed.

[0621] Step 9:

[0622] The server collects data after project completion and feeds it back as training data for the AI ​​agent. This improves the accuracy and efficiency of future project management.

[0623] (Example 1)

[0624] 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".

[0625] In project management, tracking progress, prioritizing tasks, and anticipating and addressing potential problems are crucial. However, these processes are often performed manually, making them time-consuming and labor-intensive. Furthermore, their reliance on human judgment can lead to inefficiencies and inaccuracies. As a result, project delays and inappropriate resource allocation can occur, leading to a decline in overall productivity.

[0626] 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.

[0627] In this invention, the server includes means for collecting progress information and storing it in a data storage device, means for visualizing the collected progress information in real time based on a generating AI model, and means for automatically setting the importance of tasks and optimizing the allocation of work resources. This makes it possible to efficiently manage project progress and objectively set task priorities. Furthermore, it is possible to improve the overall productivity of the project by predicting potential problems and addressing them in advance.

[0628] "Progress information" refers to data that shows the progress and degree of completion of each task in a project.

[0629] A "data storage device" is a system or device for storing information over a long period of time.

[0630] A "generative AI model" is an artificial intelligence algorithm designed to derive patterns and predictions from data.

[0631] "Visualizing in real time" means displaying information in a visually easy-to-understand format the moment it is received.

[0632] "Task importance" refers to the result of evaluating the priority and urgency of individual tasks within a project.

[0633] "Work resource allocation" refers to the efficient allocation of available resources, such as personnel and equipment, to each task within a project.

[0634] A "potential problem" refers to an issue or obstacle that is not currently apparent but may arise in the future.

[0635] "Presenting countermeasures" is the act of proposing solutions that should be implemented in advance to address anticipated problems.

[0636] An "interactive interface" is a user interface that allows users to communicate with the system in a natural, two-way manner.

[0637] This invention is a system for streamlining project management, in which a server, terminals, and users work together. The server is responsible for collecting progress information and storing it in a data storage device. In this process, a dedicated database management system is used to efficiently record and manage the received progress data. The server also utilizes a generative AI model to visualize the collected information in real time and provide the user with information on the project's progress.

[0638] The terminal provides an interface for users to input the status of tasks. Specifically, users input progress information into the terminal using a web application or mobile application. This information is automatically sent to the server and stored in a database. Users can view the progress in real time and visualized through the terminal.

[0639] Furthermore, the server uses an AI agent to automatically set the importance of tasks and optimize the allocation of work resources. The AI ​​agent uses a generative AI model to analyze the urgency, importance, and skill levels and workload of the team members. An example of a prompt message is input to the generative AI model: "Generate the optimal task assignment based on project progress data." The information generated in this way is then notified to project members via their terminals.

[0640] Regarding problem prediction, the server analyzes past project data and current issues to predict potential problems. Countermeasures for predicted problems are automatically notified to project managers and stakeholders. This enables rapid and efficient project management.

[0641] As a concrete example, when an IT company is working on a web service development project, developers, designers, and testers would use this system. Each member reports progress information to the server via their terminal, and the server uses AI to assign the most suitable tasks. As a result, the overall efficiency of the project improves, making it possible to complete the project on time.

[0642] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0643] Step 1:

[0644] The terminal provides an interface for users to report the progress and status of tasks. Users input the task progress using a web application or mobile application. The data obtained as input includes status information such as task start, in progress, and completed. This input data is sent to the server via a communication protocol.

[0645] Step 2:

[0646] The server stores progress information received from terminals in a database. Input data is processed into "INSERT" or "UPDATE" queries in the data storage device. This allows for centralized management of the entire project's progress in one location. The output is the latest progress status stored in the database.

[0647] Step 3:

[0648] The server analyzes stored progress information and uses a generated AI model to visualize progress in real time. This process uses progress information obtained from a database as input. The data is converted into graphs and charts for visualization and output to a dashboard. Users can view this visualized information through their devices. For example, the percentage of tasks in progress and the number of completed tasks are visualized.

[0649] Step 4:

[0650] The server automatically sets task priorities using a generative AI model. It collects project data, member skill information, and current workload as input, and performs data calculations based on the generative AI model. The output is the optimal task assignment, which is then notified to project members via their terminals. Specifically, the AI ​​model receives a prompt message such as, "Generate the optimal task assignment based on project progress data."

[0651] Step 5:

[0652] The server utilizes generative AI models to predict potential problems and propose solutions. Input includes historical project data and information on current issues. Data calculations identify potential risks and derive countermeasures. These countermeasures are then communicated to project managers and stakeholders. For example, if a specific task is behind schedule, the server will identify the cause and suggest corrective actions.

[0653] In this way, the system can process information efficiently and automatically at each step of project management, providing the optimal management method.

[0654] (Application Example 1)

[0655] 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".

[0656] In production environments, maximizing production efficiency is crucial. However, accurately tracking the progress of each work stage and allocating tasks optimally in real time is not easy. Furthermore, mechanisms for predicting problems before they occur and quickly providing countermeasures are often inadequate. As a result, bottlenecks and work delays can occur, potentially leading to decreased production efficiency.

[0657] 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.

[0658] In this invention, the server includes an information processing device for collecting and storing progress data, an information processing device for visualizing the collected progress data in real time, and an information processing device for monitoring work progress in the production process and dynamically reallocating tasks. This enables efficient progress management in the production process and rapid response through problem prediction.

[0659] "Progress data" refers to information about the work content and progress at each stage of the work process.

[0660] "Information processing equipment" refers to all devices equipped with the function of receiving and analyzing data, and includes servers and computer systems.

[0661] "Real-time visualization" means instantly displaying the state of data visually the moment it is collected.

[0662] "Dynamic task reallocation" is a process of instantly changing and optimizing the priority and assignment of tasks according to their progress and status.

[0663] "Production process" refers to a series of processes and procedures for manufacturing a product, encompassing the entire work involved at each stage.

[0664] An "interactive interface" is a means for a user to exchange information with a computer system in a two-way manner.

[0665] The system that realizes this invention employs advanced information processing technology to effectively manage, analyze, and utilize data. Its main components include a server, terminals, and a user interface.

[0666] The server utilizes software such as Apache Kafka and TensorFlow to perform information processing, collecting and storing progress data in real time. This allows for monitoring the progress at each stage of the work. The server also provides a dashboard for immediate analysis and visualization of the progress data. This visualization enables visual monitoring of the status of each task in the production process.

[0667] Furthermore, the server dynamically redistributes tasks using an AI model to maximize the efficiency of the production process. The AI ​​agent automatically determines the optimal task assignment based on each member's skills and current workload. The results of this task assignment are immediately communicated to the workers via terminals.

[0668] The system's interactive interface allows users to interact seamlessly through input. This enables, for example, a production line supervisor to quickly follow instructions on the dashboard and perform the appropriate actions.

[0669] As a concrete example, in a car manufacturing plant, an AI agent detects body parts of a vehicle being assembled and optimizes the sequence of welding and painting. In this case, an example of a prompt message to the generated AI model would be, "Based on the data from the current production line, identify the bottleneck stage and readjust the optimal task allocation and priority for each robot." This prompt message prompts the AI ​​to analyze the relevant data and make appropriate suggestions for efficiency improvements.

[0670] As a whole system, these functions enable increased efficiency on the production floor and facilitate problem prediction and early response.

[0671] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0672] Step 1:

[0673] The server receives progress data in real time from each terminal within the factory. The input data includes the progress status and work details at each work stage. The server stores this data in a database and centrally manages progress information.

[0674] Step 2:

[0675] The server analyzes the collected progress data and visualizes it. It uses recently saved progress data as input. The output consists of graphs and charts displayed on a dashboard, allowing users to visually check the overall project progress.

[0676] Step 3:

[0677] The server runs a generative AI model to calculate the priority of each task. The inputs are task progress data and information on the skills and current workload of the team members. The AI ​​performs calculations based on this data and creates an optimal task assignment list as output.

[0678] Step 4:

[0679] The server sends the AI-generated task assignment results to the terminal. Upon receiving these results, the terminal displays a list of tasks that should be prioritized to the user and provides prompts to guide them on the next steps.

[0680] Step 5:

[0681] Users interact with the system through the terminal's interactive interface and update task progress as needed. Input consists of data based on the user's work reports and instructions, and the latest progress status is sent to the server as output.

[0682] Step 6:

[0683] The server feeds accumulated progress data into machine learning feedback to improve the accuracy of future project management. The input to this learning process is all the data obtained after the completion of a project. The output is an improved AI model for the next project.

[0684] 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.

[0685] This invention incorporates an emotion engine into a system that streamlines project management, enabling project operation that takes into account the user's emotional state. This system employs a client-server architecture and, in addition to managing progress data, setting priorities, optimizing resource allocation, and predicting and responding to problems, also recognizes the user's emotions.

[0686] The terminal provides an interface where users input task status and send data to the server in real time. Users not only input task start and progress, but also perform sentiment analysis using an emotion engine. This enables analysis of the user's facial expressions and voice through sensors such as a webcam and microphone.

[0687] The server integrates received task data and emotional data and stores it in a database. Project progress is displayed on a dashboard, allowing users to visually check the current status. The server's AI agent also considers the user's emotional state when determining task priorities and optimizing resource allocation. Users experiencing high stress levels are assigned tasks that reduce their workload, while users in a positive emotional state are given priority for important tasks.

[0688] Furthermore, the server utilizes an emotion engine to dynamically adjust the interface according to the user's emotional state, thereby improving the user experience. For example, if the user is feeling stressed, the interface is simplified and the number of operations is reduced to lessen the user's burden.

[0689] As a concrete example, consider a scenario where a project at a certain company requires the development of additional features. When a user feels their stress level is high, emotional data sent from their device is analyzed, and the server adjusts the user's tasks accordingly. This allows users to contribute to the project in a way that is optimal and considers their own emotions.

[0690] In this way, the present invention can reduce user stress and improve overall productivity by taking emotions into consideration in project management.

[0691] The following describes the processing flow.

[0692] Step 1:

[0693] The device provides an interface for users to update the status of their tasks. Users input information such as task start, progress, and completion. The device also uses sensors to detect the user's facial expressions and voice, and acquires emotional data.

[0694] Step 2:

[0695] The device combines acquired task data and emotion data and sends it to the server. This allows for a comprehensive understanding of the user's current state.

[0696] Step 3:

[0697] The server stores the received data in a database. The database stores progress information for each task and the emotional state of each user.

[0698] Step 4:

[0699] The server updates the dashboard based on the collected data, providing real-time visibility into project progress. Through this, users can check their own progress and the overall status.

[0700] Step 5:

[0701] The server, including the AI ​​agent, determines task priorities. It assigns appropriate tasks, taking into account the user's emotional state. For example, it assigns easy tasks to users who are highly stressed, and prioritizes important tasks for users who are in a positive state.

[0702] Step 6:

[0703] The terminal notifies the user of task update information from the server. Based on this, the user can decide and proceed with the next action.

[0704] Step 7:

[0705] The server uses an emotion engine to adjust the user interface. Based on the user's emotional state, it optimizes the interface design and functionality as needed to improve the user experience.

[0706] Step 8:

[0707] Users can use the updated interface to complete tasks efficiently while reducing stress. After project completion, the results are fed back as training data for the AI ​​agent.

[0708] (Example 2)

[0709] 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".

[0710] Modern project management requires not only tracking progress and tasks, but also taking into account and adjusting the emotional state of team members. However, traditional systems have struggled to grasp individual emotions and reflect them in project management. This has led to increased stress among team members and decreased productivity.

[0711] 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.

[0712] In this invention, the server includes means for collecting and storing project progress data and sentiment data, means for visualizing the collected progress data and sentiment data in real time, and means for automatically setting task priorities and optimizing resource allocation considering the sentiment analysis results. This enables optimal project management that reflects not only project progress but also the individual emotional states.

[0713] "Project progress data" refers to information that shows the overall progress of a project, including data such as the completion rate of tasks and the percentage of time the project is on schedule.

[0714] "Emotional data" refers to information that indicates a user's emotional state, and includes emotional indicators obtained by analyzing facial expressions, voice, text, etc.

[0715] "Real-time visualization" means instantly representing data visually so that users can grasp the situation at a glance.

[0716] "Automatically setting" means that the system determines the order and importance of tasks based on predefined algorithms and rules, without manual intervention.

[0717] "Optimizing resource allocation" means efficiently and effectively allocating available resources such as labor, materials, and time within a project to maximize overall productivity.

[0718] "Considering the results of emotion analysis" means that the system makes judgments that reflect the user's emotional state, and uses those judgments as a basis for the decision-making process.

[0719] "Dynamically adjusting the user interface" means changing the screen layout and operability according to the user's emotional state to provide the optimal operating environment.

[0720] A description of the embodiment for carrying out the invention will be provided.

[0721] This system aims to manage projects while taking into account the user's emotional state. It is built using a client-server architecture, and each component plays the following role:

[0722] The terminal is a device that provides an interface for the user to input the status of a task. Through this interface, the user inputs the start time and ongoing state of the task. Furthermore, the terminal is equipped with sensors such as a webcam and microphone, which capture the user's facial expressions and voice, and send the data to the emotion engine. The emotion engine analyzes the user's emotions based on this data.

[0723] The server receives task and emotion data sent from terminals, integrates them into a database, and stores them. The server analyzes project progress and displays the results on a dashboard, allowing users to visually check their progress. Furthermore, the AI ​​agent installed on the server prioritizes tasks and optimizes resource allocation while considering the user's emotional state. In this process, it assigns lighter tasks to users with high stress levels, while prioritizing important tasks for users with positive emotional states.

[0724] Furthermore, the server improves the user experience by dynamically adjusting the interface according to the user's emotional state. For example, if a user is feeling stressed, measures such as simplifying the interface and reducing the number of operations are taken to alleviate the user's burden.

[0725] As a concrete example, consider a scenario where a company's project requires the development of a new feature. If the server determines that a user is experiencing stress, it adjusts the user's tasks based on emotional data transmitted from their device. This allows the user to reduce stress while effectively contributing to the project.

[0726] An example of a prompt to a generative AI model might be, "Please suggest the optimal task allocation when the user is feeling stressed."

[0727] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0728] Step 1:

[0729] The user accesses an input interface on the device to enter the task's start status and ongoing status. Simultaneously, the user's facial expressions and voice are captured via webcam and microphone. The input data includes task-related information, facial recognition data, and emotion data extracted from the voice. Once this data is collected, emotion analysis begins on the device. For example, if the user enters "Start brainstorming a new project," the user's voice is recorded during this process.

[0730] Step 2:

[0731] The device passes facial and voice data collected to the emotion engine to analyze the user's emotional state. The emotion engine uses an AI model to analyze the emotional data and determine the user's emotional state. The input is raw facial and voice data, and the output is an indicator of the emotional state. For example, if the system recognizes that the user is smiling, it will output a "positive" emotional state.

[0732] Step 3:

[0733] The terminal sends analyzed emotion data and task data to the server. The server receives this data, integrates it into a database, and stores it. The input is the task status and emotion state from the terminal, and the output is the integrated data stored in the database. Specifically, the server records the emotion data "Project concept started" and "Positive" in the database.

[0734] Step 4:

[0735] The server analyzes information stored in the database and displays project progress on a dashboard. The analysis uses data including current progress and emotional status. The output is information on a visually verifiable dashboard. For example, if user A is at the "Project Concept Start" stage and has a "Positive" emotional state, this status will be displayed on the dashboard using graphs and other visuals.

[0736] Step 5:

[0737] The server runs an AI agent that optimizes task priorities and resource allocation based on the user's emotional state. Inputs are task and emotional data, while output is a prioritized task list and proposed resource allocation. Specifically, users exhibiting stress are automatically reassigned tasks that reduce their workload.

[0738] Step 6:

[0739] The server adjusts the user interface as needed, providing an operating environment suited to the user's emotional state. The input is the emotional state, and the output is the optimal interface presented to the user. For example, when the user is stressed, the interface switches to a simpler one with reduced unnecessary information.

[0740] (Application Example 2)

[0741] 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".

[0742] In project management, assigning tasks without considering the emotional state of project members presents a challenge: stress can build up and productivity can decline. Furthermore, dynamic task allocation that takes into account members' emotions is difficult, resulting in a failure to optimize individual performance and overall project efficiency.

[0743] 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.

[0744] In this invention, the server includes means for analyzing emotional states and collecting emotion recognition data, means for adjusting task priorities and resource allocation based on the analyzed emotional states, and means for providing a conversational interface with the user. This enables dynamic task assignment and resource allocation that takes into account the emotional states of project members.

[0745] "Progress data" refers to information that expresses the degree of completion and progress of each task in a project using numerical values ​​and indicators.

[0746] "Emotional state" refers to the psychological state that the user is currently experiencing, and is usually measured using indicators such as stress, concentration, and satisfaction.

[0747] "Emotion recognition data" refers to data that indicates an emotional state, obtained from information such as the user's facial expressions and voice.

[0748] "Priority" refers to a set of criteria for arranging tasks according to their importance and urgency based on specific conditions.

[0749] "Resource allocation" refers to appropriately allocating personnel, time, materials, and other resources to each task within a project.

[0750] A "conversational interface" is an interface that allows the user and the system to communicate with each other using natural language.

[0751] "Task assignment" refers to the act of instructing each project member on specific tasks.

[0752] A "term definition" is a document that clearly explains technical terms or concepts within a specific context or field.

[0753] In this invention, the terminal uses a personal device such as a smartphone to collect the user's facial expressions and voice through a webcam and microphone to analyze their emotional state. The analyzed emotion recognition data is transmitted to a server in real time, where it is integrated and stored in a database. An AI agent operates on the server, prioritizing tasks and optimizing resource allocation based on multiple data points, including emotional state. In particular, it can assign tasks that reduce the workload to users experiencing high stress levels, and important tasks to users in a positive emotional state. The server also uses the emotion recognition data to dynamically adjust the user interface and improve the user experience. For example, if a user is feeling stressed, it can provide an interface that simplifies operations.

[0754] As a concrete example, if analysis reveals that worker A is experiencing high stress levels due to a tight assembly schedule for a new product at a manufacturing site, the server will adjust A's workload accordingly. If analysis also reveals that worker B is more productive, the server will assign B more important tasks.

[0755] An example of a prompt message to use when employing a generative AI model would be: "Please suggest a project management application that analyzes emotional states based on voice and facial expression data and optimizes workload."

[0756] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0757] Step 1:

[0758] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice. The input consists of facial video frames and audio data, which are used for emotion analysis. Emotion analysis software analyzes this data in real time to identify the user's emotional state (e.g., stress or satisfaction). The output is a digital representation of the user's emotional state.

[0759] Step 2:

[0760] The terminal sends data, including analyzed emotional state information, to the server. This data also includes information about ongoing tasks and the user ID. After the data is sent, the server integrates this information into a database. The input is the user's emotional state data and task information, which the server stores in the database. The output is the integrated project data.

[0761] Step 3:

[0762] The server uses an AI agent to re-evaluate task priorities based on emotion recognition data and existing project data. Priorities are set considering factors such as the user's emotional state, task urgency, and resource availability. Input is data obtained from an integrated database, and the AI ​​module processes the data to determine task priorities. Output is updated task priority information.

[0763] Step 4:

[0764] The server adjusts resource allocation based on newly determined task priorities. Specifically, it assigns less burdensome tasks to users judged to be under high stress, and higher-priority tasks to users in a positive emotional state. The input is updated task priority information, and the output is optimized resource allocation information.

[0765] Step 5:

[0766] The server dynamically adjusts the user interface to improve the user experience based on the user's emotional state. For example, if the user is stressed, the displayed information is simplified and the number of steps required for operation is reduced. The input is emotional state data, and the output is a customized user interface.

[0767] An example of a prompt used when utilizing a generative AI model is: "Suggest a project management application that analyzes emotional states based on voice and facial expression data and optimizes workload."

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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."

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] The following is further disclosed regarding the embodiments described above.

[0790] (Claim 1)

[0791] A means of collecting and storing project progress data,

[0792] A means of visualizing the collected progress data in real time,

[0793] A means to automatically set task priorities and optimize resource allocation,

[0794] A means of predicting potential problems and proposing countermeasures,

[0795] A means of providing a conversational interface with the user,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, which takes into account the skills and current workload of members when setting task priorities.

[0799] (Claim 3)

[0800] The system according to claim 1, which uses data obtained after project completion to provide feedback to AI learning and improve the accuracy of project management.

[0801] "Example 1"

[0802] (Claim 1)

[0803] A means for collecting progress information and storing it in a data storage device,

[0804] A means of visualizing the collected progress information in real time based on a generating AI model,

[0805] A means to automatically set the importance of tasks and optimize the allocation of work resources,

[0806] A means of predicting potential problems and proposing appropriate solutions,

[0807] A means of providing an interactive interface with the user,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, which takes into account the skills of the members and the current workload when setting the importance of a task.

[0811] (Claim 3)

[0812] The system according to claim 1, which uses information obtained after project completion to provide feedback to AI learning and improve the effectiveness of project management.

[0813] "Application Example 1"

[0814] (Claim 1)

[0815] Information processing device means for collecting and storing progress data,

[0816] An information processing device that visualizes collected progress data in real time,

[0817] An information processing device means that automatically sets task priorities and optimizes resource allocation,

[0818] Information processing device means that predicts problems and presents countermeasures,

[0819] An information processing device means for monitoring work progress in the production process and dynamically reallocating tasks,

[0820] Information processing device means that provides an interactive interface with the user,

[0821] An automated system including

[0822] (Claim 2)

[0823] The automation system according to claim 1, which takes into account the skills and current workload of its members when setting task priorities.

[0824] (Claim 3)

[0825] The automation system according to claim 1, which uses data obtained after the completion of the production process to provide feedback to machine learning and improve management accuracy.

[0826] "Example 2 of combining an emotion engine"

[0827] (Claim 1)

[0828] A means of collecting and storing project progress data and sentiment data,

[0829] A means of visualizing the collected progress data and sentiment data in real time,

[0830] A means to automatically set task priorities and optimize resource allocation considering sentiment analysis results,

[0831] A means of predicting potential problems and proposing countermeasures,

[0832] A means of dynamically adjusting the user interface according to emotional state,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, which takes into account the emotional state and skills of members and their current workload when setting task priorities.

[0836] (Claim 3)

[0837] The system according to claim 1, which uses progress data and sentiment data obtained after project completion to provide feedback to AI learning and improve the accuracy of project management.

[0838] "Application example 2 when combining with an emotional engine"

[0839] (Claim 1)

[0840] A means of collecting and storing project progress data,

[0841] A means of visualizing the collected progress data in real time,

[0842] A means to automatically set task priorities and optimize resource allocation,

[0843] A means of predicting potential problems and proposing countermeasures,

[0844] A means of analyzing the user's emotional state and collecting emotion recognition data,

[0845] A means of adjusting task priorities and resource allocation based on the analyzed emotional state,

[0846] A means of providing a conversational interface with the user,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, which takes into account the analyzed emotional state of members in addition to their skills and current workload when setting task priorities.

[0850] (Claim 3)

[0851] The system according to claim 1, which uses data obtained after project completion to provide feedback to AI learning and improve the accuracy of project management. [Explanation of Symbols]

[0852] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting and storing project progress data, A means of visualizing the collected progress data in real time, A means to automatically set task priorities and optimize resource allocation, A means of predicting potential problems and proposing countermeasures, A means of providing a conversational interface with the user, A system that includes this.

2. The system according to claim 1, which takes into account the skills and current workload of members when setting task priorities.

3. The system according to claim 1, which uses data obtained after project completion to provide feedback to AI learning and improve the accuracy of project management.

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