system

Abstract

We provide the system. [Solution] A means of visualizing the progress of a task, A means for assigning optimal tasks based on the user's skills and workload, A means of information exchange to support communication within the team, A means of monitoring resource usage and proposing the optimal resource allocation, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern business environment, the complexity of task management, the lack of communication between team members, and the efficient allocation of resources are major issues. In particular, due to the lack of appropriate task allocation and prioritization considering the skills and loads of users, the progress of projects may be delayed and the overall productivity may decrease. In addition, due to insufficient visualization of progress, managers and members cannot comprehensively grasp the current situation, making it difficult to make quick decisions.

Means for Solving the Problems

[0005] To solve the aforementioned problems, the present invention provides a system equipped with means for visualizing task progress, means for assigning optimal tasks based on user skills and workload, means for information exchange to support communication within a team, and means for monitoring resource usage and proposing optimal resource allocation. This system allows users to easily grasp task progress, efficiently assign tasks according to their skills and workload, and improve productivity. Furthermore, by automatically calculating task priority and proposing optimal meeting times using a shared calendar, the system can enhance work efficiency and team collaboration.

[0006] "Methods for visualizing task progress" refer to mechanisms in task management that visually display the progress of each task, making it easier to understand the progress.

[0007] "Means for assigning optimal tasks based on the user's skills and workload" refers to methods for assigning tasks efficiently and effectively, taking into account the user's skill level and current workload.

[0008] "Information exchange tools to support communication within a team" refer to tools and systems that facilitate the smooth exchange of information among team members.

[0009] "A means of monitoring resource usage and proposing optimal resource allocation" refers to a system that monitors the consumption of available resources within an organization and proposes appropriate resource usage as needed.

[0010] A "means for automatically calculating task priority" refers to a function that automatically calculates and displays the priority of tasks based on their importance and urgency.

[0011] "A method for suggesting optimal meeting times from a shared calendar" is a system that takes into account the schedules of users and suggests an optimal meeting time that everyone can attend. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] 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.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] To implement the AI ​​task manager system of the present invention, a program with the following functions is mainly required: The combination of modules for progress management, task assignment, team communication support, and resource management enables efficient management of tasks and the entire project.

[0034] Progress management module

[0035] The server periodically collects progress data for each task from the database and generates a visualized progress report. This is provided to the user in a dashboard format, allowing them to quickly grasp the progress of tasks through progress bar graphs and Gantt charts. For example, if they can visually confirm that a project is 75% complete, they can efficiently allocate the remaining tasks and make adjustments to complete the project.

[0036] Task assignment and priority setting module

[0037] The server automatically assigns tasks to the most suitable member based on each member's skill information and current workload. This process includes calculating priority scores and suggesting an appropriate processing order based on the urgency and importance of the tasks. Specifically, when a new urgent task arises, it is automatically given a high priority and immediately assigned to a skilled member.

[0038] Communication support module

[0039] The device is equipped with a real-time messaging system, allowing users to instantly share information with team members. For example, if a user wants to report on task progress or ask a question, they can do so through this system, and all members can see the response in real time. Important notifications are also sent via push notifications through the device, ensuring that information is not missed.

[0040] Resource management module

[0041] The server monitors usage and proposes a calculated resource allocation to the user. This function helps prevent resource surpluses and shortages, improving the overall efficiency of the project. For example, when considering starting a new project, it verifies whether the current server capacity is adequate and determines whether the necessary resources can be secured.

[0042] In this way, each module works organically together, aiming to improve the productivity of the entire team. This frees users from cumbersome task management, enabling them to perform their work more effectively.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server retrieves project and task progress from the database. This collects data related to the current progress of each task.

[0046] Step 2:

[0047] The server analyzes the collected progress data and generates visual reports such as Gantt charts and progress bar graphs. This visualizes the progress of the tasks.

[0048] Step 3:

[0049] The server sends the generated visual report to the user's dashboard, allowing the user to check the progress at any time.

[0050] Step 4:

[0051] The server retrieves each member's skills and current workload from the database. This allows for the collection of member resource information.

[0052] Step 5:

[0053] The server analyzes the task content and determines the skills required for assignment. Then, based on skill suitability and workload, it calculates and proposes the optimal task assignment.

[0054] Step 6:

[0055] The user reviews the task assignment proposed by the server and approves or readjusts it as needed.

[0056] Step 7:

[0057] The terminal sends the task progress and completion reports entered by the user to the server. This updates the data.

[0058] Step 8:

[0059] The server maintains a real-time messaging system, instantly sending new messages within the team to the terminals of other relevant members.

[0060] Step 9:

[0061] The device displays received messages to the user in real time, allowing the user to check their content.

[0062] Step 10:

[0063] The server periodically updates resource usage and suggests resource allocation optimizations as needed.

[0064] Step 11:

[0065] The user reviews the proposed resource allocation and approves it if they deem it appropriate.

[0066] (Example 1)

[0067] 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."

[0068] In today's business environment, task management and information sharing within teams are crucial, but traditional manual management methods are often inefficient for monitoring progress, assigning tasks, and allocating resources. This leads to project delays, wasted human resources, and a decline in organizational productivity.

[0069] 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.

[0070] In this invention, the server includes means for collecting and visually displaying task progress data, means for optimally allocating tasks based on user capability information and current workload, and communication means for immediate information sharing among team members. This makes it possible to monitor project progress in real time, optimize task allocation, and achieve smooth communication within the team.

[0071] "Means of collecting and visually displaying task progress data" refers to technologies that retrieve task completion status from databases or other sources and visualize that information in a way that is easy for users to understand.

[0072] "Means for optimally allocating tasks based on user ability information and current workload" refers to a system that automates the optimal task allocation by taking into account each user's skills and the progress of the tasks they are currently working on.

[0073] "Communication methods for immediate information sharing among team members" refers to communication technologies that enable real-time information exchange among members, including those that ensure the immediacy of messaging and notifications.

[0074] "Means for monitoring resource utilization and proposing efficient resource allocation" refers to a function that analyzes the resource usage of the entire system and proposes the optimal resource allocation based on the results.

[0075] "Methods for automatically analyzing task priorities" refer to technologies that quantify the urgency and importance of each task and automatically calculate their priority in order to achieve efficient task processing.

[0076] "A method for suggesting efficient meeting times based on calendar information" refers to a system that analyzes schedule data to recommend the optimal date and time for meetings and events.

[0077] The system of this invention is a comprehensive platform that combines various modules to efficiently manage tasks and projects. This system operates primarily using servers and terminals, enabling users to effectively manage tasks and share information with teams.

[0078] In the progress management module, the server periodically collects task progress data from the database. This data is visualized using libraries such as Python's Matplotlib and provided to the user in a dashboard format. This allows the user to intuitively understand the progress of the project.

[0079] The task assignment and priority setting module analyzes user skill information and current task load. Based on this, it automatically calculates task priorities and distributes tasks to the most suitable members. This equalizes the workload for each member and improves project efficiency.

[0080] The communication support module consists of a real-time messaging system implemented in the terminal. Users can instantly share information with team members through this system. The server also has the functionality to push important notifications to the terminal, ensuring users receive information without missing anything.

[0081] In the resource management module, the server monitors the project's resource usage and proposes efficient resource allocation to the user. This enables optimal resource utilization and improves the overall project performance.

[0082] For example, if a user enters a prompt such as, "Check the latest progress of Project A and reassign Task X to the most suitable member," the system can utilize these modules to quickly provide the data necessary for project management and take appropriate action. By leveraging this generative AI model, users can achieve higher productivity.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server collects task progress data from the database. This data collection is performed using SQL queries to extract the necessary task IDs and progress percentages. The inputs are database connection information and queries, and the output is the collected task progress data.

[0086] Step 2:

[0087] The server visualizes the collected progress data. This process uses Python's Matplotlib library to generate bar graphs and Gantt charts to show progress. The input is progress data, and the output is a visualized graph image. Specifically, it calculates the length of the bar corresponding to the progress rate of each task and plots it on the chart.

[0088] Step 3:

[0089] The terminal displays a dashboard to the user using visualization data sent from the server. This dashboard allows the user to intuitively understand the project status. The input is graph image data from the server, and the output is a visual dashboard on the terminal.

[0090] Step 4:

[0091] The server retrieves user skill and workload information from a database and performs analysis. The input is skill and workload information from the database, and the output is analytical data for task allocation. Specifically, it calculates weights to select tasks appropriate for each member's skills.

[0092] Step 5:

[0093] The server uses the analysis data to calculate task priorities and assign tasks to the most suitable members. Inputs are task information and member skill / load information, while output is optimized task assignment information. Specifically, it scores the priority of each task based on an algorithm and assigns them to members in order of highest priority.

[0094] Step 6:

[0095] Using the communication tools implemented on the device, users share information with team members in real time. Input is the message entered by the user, and output is the message notified to other members. Specifically, the device sends the user's input to a server, which then distributes it to other members.

[0096] Step 7:

[0097] The server monitors resource usage and proposes efficient resource allocation to the user. The input is the system's resource usage log, and the output is the optimal resource allocation proposal. Specifically, it uses a model that analyzes CPU usage and memory consumption to evaluate resource surpluses and shortages.

[0098] (Application Example 1)

[0099] 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."

[0100] In modern factory environments, there is a demand for increased efficiency in production lines through the use of autonomous machinery. However, since each machine often operates independently, there are challenges in overall project management and workload adjustment. While efficient and seamless operation is required, real-time information sharing between machines and optimal task allocation are essential.

[0101] 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.

[0102] In this invention, the server includes means for visualizing the progress of tasks, means for assigning optimal tasks based on the user's abilities and workload, and means for supporting information exchange within the group using a communication device. This enables the adjustment of workload among autonomous machines and the efficient operation of the production line.

[0103] "Means for visualizing task progress" refers to a device or method that visually displays the current progress of a task, enabling users to quickly grasp the situation.

[0104] "A means of assigning tasks based on user capabilities and workload" refers to a technology that automatically assigns appropriate tasks according to each user's skills and current workload.

[0105] "Means of supporting information exchange within a group using communication devices" refers to a system that enables real-time information transmission within a group using network communication.

[0106] "Means of monitoring resource usage using instruments and proposing optimal resource allocation" refers to methods for detecting the usage status of each resource and performing efficient reallocation and optimization.

[0107] "Optimization means for adjusting workload between autonomous machines" refers to a method or system for leveling the workload between different machines and improving overall work efficiency.

[0108] This system is designed to improve operational efficiency on a factory production line. A server plays a central role, receiving data transmitted from each autonomous machine and efficiently displaying it using visualization tools to visualize task progress. Specifically, data visualization libraries such as Plotly are used to visualize progress and workload.

[0109] The terminals are equipped with communication devices, allowing each machine to exchange information in real time via these devices. A real-time messaging system to support information exchange within the group is implemented using frameworks such as Flask.

[0110] Furthermore, the server assigns optimal tasks based on the user's capabilities and workload. Machine learning algorithms using scikit-learn are helpful in this process. These algorithms utilize generative AI models to efficiently assign the most suitable tasks to each user.

[0111] Furthermore, it includes a function that monitors resource usage using instruments and proposes the optimal resource allocation. The server utilizes sensor modules such as Raspberry Pi to analyze resource monitoring data and reflects the information on a dashboard.

[0112] As a concrete example, when implemented in a small-scale machine assembly plant, 10 robots worked together to share tasks, enabling project completion in a short amount of time.

[0113] The introduction of such a system will allow the entire factory's production line to operate more efficiently. An example of a prompt message utilizing a generative AI model would be: "A new order has been added. Please generate machine assignments to process it efficiently."

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server collects progress data from each autonomous machine. The input includes work status information reported by each machine's sensors. This data is analyzed to extract numerical data representing the progress of each task. The output is the numerical progress data.

[0117] Step 2:

[0118] The server processes progress data using visualization tools and generates Gantt charts and bar graphs that display the actual progress. Pre-processed progress data is used as input. The data is then processed using a data visualization library and output as a visualized dashboard.

[0119] Step 3:

[0120] Using communication devices connected to terminals, each machine transmits progress information to the server in real time. The input is progress updates from each machine. This information exchange allows for real-time monitoring of each other's progress and resource usage. The output is the received updated progress information.

[0121] Step 4:

[0122] The server acquires user capability and workload data and uses a generative AI model to calculate the next task to assign. Current capability assessments and workloads are referenced as input. Machine learning algorithms are applied to generate the optimal task assignment. The recalculated task assignment is obtained as output.

[0123] Step 5:

[0124] The server aggregates data from sensors to analyze resource usage and propose the optimal resource allocation. Input includes resource usage data from each machine. The data is analyzed to plan an efficient resource allocation. The output is a recommended resource allocation plan.

[0125] Step 6:

[0126] Users can review progress and assigned tasks through the generated dashboard and make adjustments or modifications as needed. The visualized dashboard information is used as input. Users review the data and develop appropriate plans for the next steps. The adjusted task plan is obtained as output.

[0127] 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.

[0128] This invention provides a system that further improves the efficiency of task management by combining an emotion engine with an AI task manager system. This system visualizes task progress, assigns appropriate tasks based on the user's skills and workload, supports smooth information exchange within the team, and understands resource usage to propose optimal allocation. The addition of the emotion engine automatically recognizes the user's emotional state and optimizes task assignment and communication suggestions accordingly.

[0129] Emotion Engine Module

[0130] The server uses an emotion engine that analyzes the user's voice tone and facial expression data to identify the user's emotional state. This data influences task assignment and priority decisions. For example, if a user is feeling stressed, the server might suggest reducing the workload of that task and assigning a support member.

[0131] Task assignment adjustment

[0132] Based on its recognized emotional state, the device suggests reassigning specific tasks to the user. These suggestions are adjusted to include less stressful tasks and tasks that promote teamwork. The user can review, approve, or re-evaluate these suggestions.

[0133] Suggestions for improving communication

[0134] The server analyzes emotional data and formulates suggestions to improve communication within the team. For example, if team members are generally showing signs of fatigue, the server might suggest holding refresh meetings or online workshops.

[0135] Specific example

[0136] In one project, the server detects that a user is under stress. Based on this information, the server alerts the project leader and suggests seeking assistance from other team members to alleviate the user's workload. This action equalizes the workload and ensures the overall project progresses smoothly.

[0137] Thus, embodiments of the present invention, by combining an emotion engine, optimize task management and team management in a way that takes emotions into consideration, thereby promoting improved work efficiency.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The server collects the user's voice tone and facial expression data in real time using an emotion engine. This allows data related to the user's emotional state to be obtained.

[0141] Step 2:

[0142] The server analyzes the collected emotional data to identify the user's current emotional state. This information is categorized into emotional states such as stress, happiness, and fatigue.

[0143] Step 3:

[0144] The server determines whether the task load needs to be adjusted based on the identified emotional state. For example, if the user is determined to be in a high-stress state, reducing the task load will be recommended.

[0145] Step 4:

[0146] Based on instructions from the server, the terminal presents the user with a proposed task redistribution. This proposal includes tasks with less workload and tasks appropriate to the user's emotional state.

[0147] Step 5:

[0148] The user reviews the proposal displayed on their device and, if necessary, approves it or requests a different task assignment from the server.

[0149] Step 6:

[0150] The server analyzes the recognized emotion data as a whole to understand the emotional trends within the team and generates suggestions for improving communication.

[0151] Step 7:

[0152] The server will notify team leaders and members with suggestions, such as, "Overall fatigue levels are high, so we recommend increasing break times."

[0153] Step 8:

[0154] Users accept this suggestion and work to improve the overall emotional state of the team by setting up meetings, adjusting work styles, and so on.

[0155] (Example 2)

[0156] 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".

[0157] In modern work environments, flexible task management that takes into account technical skills, workload, and emotional state is required for users to work efficiently. Visualization of work progress, promotion of appropriate communication, and optimal resource allocation are also crucial. However, methods for centrally managing these aspects are not yet well established, and work adjustments based on users' emotional states are particularly lacking. Therefore, the development of a system that allows users to perform at their maximum potential is necessary.

[0158] 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.

[0159] In this invention, the server includes means for visualizing the progress of tasks, means for assigning optimal tasks based on the user's skills and workload, means for exchanging information to support communication within the organization, means for monitoring resource usage and proposing optimal resource allocation, and means for analyzing the user's emotional state and optimizing task assignments and communication strategies based on this. This enables flexible and efficient work management that takes into account the user's emotions and technical factors.

[0160] "Means for visualizing task progress" refers to methods for visually displaying the progress of tasks that users are working on, thereby making it easy to grasp the degree of progress and any delays in the work.

[0161] "Means for assigning optimal tasks based on users' skills and workload" refers to a method that evaluates each user's skill level and current workload, and automatically assigns the most suitable tasks accordingly.

[0162] "Information exchange methods to support communication within an organization" refer to methods and tools that enable members of an organization to exchange information smoothly, with the aim of building effective cooperative relationships.

[0163] "A means of monitoring resource usage and proposing optimal resource allocation" refers to a method of monitoring the usage of various resources (personnel, time, equipment, etc.) within an organization in real time and proposing an efficient allocation of resources based on this.

[0164] "Means of analyzing users' emotional states and optimizing work assignments and communication strategies based on them" refers to a process of collecting and analyzing users' emotions as data, and then using the results to optimize work assignments and communication methods within the organization.

[0165] The system for implementing this invention utilizes advanced AI models and is designed to enable users to perform tasks efficiently. This system incorporates multiple modules, including an emotion engine, which perform analysis of various data and optimization based on that analysis.

[0166] Server-based data analysis and optimization

[0167] The server first collects and analyzes the user's voice tone and facial expression data using speech recognition software and facial recognition algorithms. Specifically, it uses a speech API for speech recognition and an image processing library for facial recognition. This allows the server to identify the user's emotional state and reflect it in the operation of the entire system.

[0168] The server integrates user emotional data and technical evaluation data, and uses this to dynamically adjust work assignments. If the emotional state indicates stress, it reduces the burden and reassigns tasks to seek cooperation from team members.

[0169] Suggestions and feedback via devices

[0170] The terminal presents the user with suggestions sent from the server, offering new task assignments and communication improvement measures. Users can provide feedback on these suggestions and request adjustments as needed. This feedback is used to update the system's learning model, designed to improve the accuracy of future suggestions.

[0171] Specific example

[0172] In one project, the server detects that a user is under excessive stress. Based on this information, the server alerts the project leader and encourages other members to participate in order to equalize the workload across the team. It also maintains work efficiency by assigning new tasks that do not cause stress.

[0173] Example of a prompt

[0174] "Design a system framework that assesses a user's emotional state and suggests appropriate task reassignment. Include examples of actions based on emotional state data."

[0175] As described above, this invention aims to optimize task management and team operations more effectively and efficiently by combining the functions of an emotion engine.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The server acquires the user's voice tone and facial expression data. This is done using speech recognition software and facial recognition algorithms. The server inputs this data into an emotion recognition model to identify the user's emotional state. As output, it obtains emotional state data, such as whether the user is relaxed or stressed. Specifically, the server captures audio and video in real time and performs analysis in the cloud.

[0179] Step 2:

[0180] The terminal uses emotional state data received from the server to evaluate the tasks assigned to the user, taking into account the current skill level and workload. Using emotional state, skill data, and workload data as input, the terminal proposes task redistribution as needed. It generates a new list of task priorities as output. Specifically, the terminal updates the visual task management tool on the user interface.

[0181] Step 3:

[0182] The server aggregates emotional data from all users and analyzes the overall team situation. It uses each user's emotional state data as input to understand overall team trends. As output, the server generates suggestions for improving communication, such as suggestions for refresh meetings. Specifically, the server presents these suggestions to the project leader through a dashboard.

[0183] Step 4:

[0184] Users receive new task suggestions and communication improvement measures from their devices. They review this information and provide feedback. The system receives suggestions as input and provides approval or modification feedback as output. Specifically, users record their feedback by performing button operations on the UI.

[0185] Step 5:

[0186] The server receives feedback from users and updates its learning model to improve the accuracy of future suggestions. It receives user feedback data as input and adjusts model parameters using machine learning techniques. The output is an improved model for future suggestions. Specifically, the server periodically trains the model and reflects the new learning outcomes throughout the entire system.

[0187] (Application Example 2)

[0188] 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".

[0189] In modern production environments, there is a lack of work management that takes into account the emotional state of workers, resulting in excessive stress that negatively impacts productivity. Furthermore, a lack of smooth information exchange between production machinery and workers can lead to imbalances in workload. These problems raise concerns about decreased production efficiency and health risks for workers.

[0190] 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.

[0191] In this invention, the server includes means for visualizing the progress of tasks, means for assigning optimal tasks based on the user's skills and workload, and means for providing an appropriate work environment by analyzing the worker's emotional state from voice and image data and suggesting workload and rest periods. This makes it possible to adjust the workload according to the worker's emotional state and improve productivity through smooth information exchange.

[0192] "Methods for visualizing task progress" refer to functions that allow users to understand the extent to which each task has been completed by visually displaying the progress of the work.

[0193] "Means for assigning optimal tasks based on the user's skills and workload" refers to a function that automatically distributes each task to the most suitable worker, taking into account each worker's abilities and current workload.

[0194] "A means of analyzing workers' emotional states from audio and image data and proposing workload and rest" refers to a function that reads workers' emotions from audio and video, adjusts their workload based on that, and proposes rest at appropriate times, thereby improving workers' health and productivity.

[0195] "Information exchange means to support communication between automated production machines and workers" refers to a function that effectively transmits necessary information between production machines and workers and strengthens cooperation.

[0196] "A means of monitoring resource usage and proposing the optimal resource allocation" refers to a function that constantly checks the status of available resources and proposes the most efficient allocation accordingly.

[0197] To implement this invention, a server plays a central role. The server maintains a database for visualizing the progress of work and executes algorithms to evaluate each worker's skills and current workload. This makes it possible to assign tasks that are suitable for each worker.

[0198] For emotion recognition, devices such as smart glasses and head-mounted displays are used. These devices capture the worker's facial expressions and voice in real time and send them to a server for analysis. Specifically, emotion recognition software such as OpenCV and DeepFace is used. Based on this data, the server identifies the emotional state and adjusts the workload appropriately to suggest breaks and work reallocation for the worker.

[0199] For example, if a worker shows signs of stress due to high workload while performing a particular task, the server will flexibly change the criteria for assigning new tasks to that worker. It will also notify the entire team as needed to facilitate communication among employees.

[0200] The system supports real-time information communication between production machines and workers as a means of information exchange. The server collects operational data from production machines and user work records, and improves overall productivity by proposing the optimal resource allocation in specific situations.

[0201] Example prompt: "How would you redistribute tasks if an operator is experiencing stress?"

[0202] Through this invention, improvements to the work environment that respond to the emotional state of workers will be realized, enabling more efficient production activities.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The server receives voice and facial expression data in real time from smart glasses or head-mounted displays. This provides input data to identify the worker's emotional state. The transmitted data is stored in a database for analysis.

[0206] Step 2:

[0207] The server uses OpenCV and DeepFace, emotion recognition software, to analyze the received audio and facial expression data. This analysis extracts characteristic patterns from each data point and compares them with existing emotion state mappings to perform data calculations that determine the worker's stress level and emotional state.

[0208] Step 3:

[0209] Based on the analysis results, the server proposes an optimal task reallocation, taking into account the worker's skill level and workload. This proposal includes prioritizing tasks and assigning new tasks, taking into account emotional states, and the proposal is sent as a notification to the worker's terminal.

[0210] Step 4:

[0211] The terminal receives suggestions from the server and notifies the user. These notifications may include suggestions for adjusting work content or suggesting breaks, and the new task schedule is finalized once the user approves them. The server receives a reply as output, indicating the user's approval or re-evaluation.

[0212] Step 5:

[0213] The server receives replies from users and updates the overall work schedule. Based on this, it shares information with other workers and automated production machines as needed to optimize the entire project. This final data is output to the administrator interface and can be checked at any time.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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".

[0230] To implement the AI ​​task manager system of the present invention, a program with the following functions is mainly required: The combination of modules for progress management, task assignment, team communication support, and resource management enables efficient management of tasks and the entire project.

[0231] Progress management module

[0232] The server periodically collects progress data for each task from the database and generates a visualized progress report. This is provided to the user in a dashboard format, allowing them to quickly grasp the progress of tasks through progress bar graphs and Gantt charts. For example, if they can visually confirm that a project is 75% complete, they can efficiently allocate the remaining tasks and make adjustments to complete the project.

[0233] Task assignment and priority setting module

[0234] The server automatically assigns tasks to the most suitable member based on each member's skill information and current workload. This process includes calculating priority scores and suggesting an appropriate processing order based on the urgency and importance of the tasks. Specifically, when a new urgent task arises, it is automatically given a high priority and immediately assigned to a skilled member.

[0235] Communication support module

[0236] The device is equipped with a real-time messaging system, allowing users to instantly share information with team members. For example, if a user wants to report on task progress or ask a question, they can do so through this system, and all members can see the response in real time. Important notifications are also sent via push notifications through the device, ensuring that information is not missed.

[0237] Resource management module

[0238] The server monitors usage and proposes a calculated resource allocation to the user. This function helps prevent resource surpluses and shortages, improving the overall efficiency of the project. For example, when considering starting a new project, it verifies whether the current server capacity is adequate and determines whether the necessary resources can be secured.

[0239] In this way, each module works organically together, aiming to improve the productivity of the entire team. This frees users from cumbersome task management, enabling them to perform their work more effectively.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The server retrieves project and task progress from the database. This collects data related to the current progress of each task.

[0243] Step 2:

[0244] The server analyzes the collected progress data and generates visual reports such as Gantt charts and progress bar graphs. This visualizes the progress of the tasks.

[0245] Step 3:

[0246] The server sends the generated visual report to the user's dashboard, allowing the user to check the progress at any time.

[0247] Step 4:

[0248] The server retrieves each member's skills and current workload from the database. This allows for the collection of member resource information.

[0249] Step 5:

[0250] The server analyzes the task content and determines the skills required for assignment. Then, based on skill suitability and workload, it calculates and proposes the optimal task assignment.

[0251] Step 6:

[0252] The user reviews the task assignment proposed by the server and approves or readjusts it as needed.

[0253] Step 7:

[0254] The terminal sends the task progress and completion reports entered by the user to the server. This updates the data.

[0255] Step 8:

[0256] The server maintains a real-time messaging system, instantly sending new messages within the team to the terminals of other relevant members.

[0257] Step 9:

[0258] The device displays received messages to the user in real time, allowing the user to check their content.

[0259] Step 10:

[0260] The server periodically updates resource usage and suggests resource allocation optimizations as needed.

[0261] Step 11:

[0262] The user reviews the proposed resource allocation and approves it if they deem it appropriate.

[0263] (Example 1)

[0264] 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."

[0265] In today's business environment, task management and information sharing within teams are crucial, but traditional manual management methods are often inefficient for monitoring progress, assigning tasks, and allocating resources. This leads to project delays, wasted human resources, and a decline in organizational productivity.

[0266] 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.

[0267] In this invention, the server includes means for collecting and visually displaying task progress data, means for optimally allocating tasks based on user capability information and current workload, and communication means for immediate information sharing among team members. This makes it possible to monitor project progress in real time, optimize task allocation, and achieve smooth communication within the team.

[0268] "Means of collecting and visually displaying task progress data" refers to technologies that retrieve task completion status from databases or other sources and visualize that information in a way that is easy for users to understand.

[0269] "Means for optimally allocating tasks based on user ability information and current workload" refers to a system that automates the optimal task allocation by taking into account each user's skills and the progress of the tasks they are currently working on.

[0270] "Communication methods for immediate information sharing among team members" refers to communication technologies that enable real-time information exchange among members, including those that ensure the immediacy of messaging and notifications.

[0271] "Means for monitoring resource utilization and proposing efficient resource allocation" refers to a function that analyzes the resource usage of the entire system and proposes the optimal resource allocation based on the results.

[0272] "Methods for automatically analyzing task priorities" refer to technologies that quantify the urgency and importance of each task and automatically calculate their priority in order to achieve efficient task processing.

[0273] "A method for suggesting efficient meeting times based on calendar information" refers to a system that analyzes schedule data to recommend the optimal date and time for meetings and events.

[0274] The system of this invention is a comprehensive platform that combines various modules to efficiently manage tasks and projects. This system operates primarily using servers and terminals, enabling users to effectively manage tasks and share information with teams.

[0275] In the progress management module, the server periodically collects task progress data from the database. This data is visualized using libraries such as Python's Matplotlib and provided to the user in a dashboard format. This allows the user to intuitively understand the progress of the project.

[0276] The task assignment and priority setting module analyzes user skill information and current task load. Based on this, it automatically calculates task priorities and distributes tasks to the most suitable members. This equalizes the workload for each member and improves project efficiency.

[0277] The communication support module consists of a real-time messaging system implemented in the terminal. Users can instantly share information with team members through this system. The server also has the functionality to push important notifications to the terminal, ensuring users receive information without missing anything.

[0278] In the resource management module, the server monitors the resource usage status of the project and proposes efficient resource allocation to the user. This enables the optimal utilization of resources and improves the performance of the entire project.

[0279] For example, when the user inputs a prompt sentence such as "Check the latest progress of Project A and reassign Task X to the most suitable member", the system can utilize these modules to quickly provide the data necessary for project management and execute appropriate actions. By leveraging this generative AI model, users can achieve higher productivity.

[0280] The flow of the specific process in Example 1 will be described using FIG. 11.

[0281] Step 1:

[0282] The server collects the progress data of the tasks from the database. This data collection is performed using SQL queries to extract the necessary task IDs and progress rates. The input is the database connection information and the query, and the output is the collected task progress data.

[0283] Step 2:

[0284] The server performs visualization based on the collected progress data. In this process, bar graphs and Gantt charts showing the progress are generated using Python's Matplotlib. The input is the progress data, and the output is the visualized graph image. As a specific operation, the length of the bar corresponding to the progress rate of each task is calculated and plotted on the chart.

[0285] Step 3:

[0286] The terminal displays a dashboard for the user using the visualization data sent from the server. With this dashboard, the user can intuitively grasp the status of the project. The input is the graph image data from the server, and the output is the visual dashboard on the terminal.

[0287] Step 4:

[0288] The server retrieves the user's skill information and workload information from the database and performs analysis. The input is the skill information and workload information from the database, and the output is the analysis data for task allocation. As a specific operation, weights are calculated to select tasks suitable for each member's skills.

[0289] Step 5:

[0290] The server uses the analysis data to calculate the priority of tasks and assigns tasks to the optimal members. The input is the task information and the members' skill and workload information, and the output is the optimized task assignment information. As a specific operation, the priority of each task is scored based on an algorithm and assigned to members in descending order.

[0291] Step 6:

[0292] Using the communication tool implemented on the terminal, the user shares information with team members in real time. The input is the message entered by the user, and the output is the message notified to other members. As a specific operation, the terminal sends the user's input to the server, which then distributes it to other members.

[0293] Step 7:

[0294] The server monitors the resource usage status and proposes efficient resource allocation to the user. The input is the system's resource usage log, and the output is the optimal resource allocation proposal. As a specific operation, it uses a model to analyze the CPU usage rate and memory consumption and evaluate the excess or deficiency of resources.

[0295] (Application Example 1)

[0296] 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."

[0297] In modern factory environments, there is a demand for increased efficiency in production lines through the use of autonomous machinery. However, since each machine often operates independently, there are challenges in overall project management and workload adjustment. While efficient and seamless operation is required, real-time information sharing between machines and optimal task allocation are essential.

[0298] 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.

[0299] In this invention, the server includes means for visualizing the progress of tasks, means for assigning optimal tasks based on the user's abilities and workload, and means for supporting information exchange within the group using a communication device. This enables the adjustment of workload among autonomous machines and the efficient operation of the production line.

[0300] "Means for visualizing task progress" refers to a device or method that visually displays the current progress of a task, enabling users to quickly grasp the situation.

[0301] "A means of assigning tasks based on user capabilities and workload" refers to a technology that automatically assigns appropriate tasks according to each user's skills and current workload.

[0302] "Means of supporting information exchange within a group using communication devices" refers to a system that enables real-time information transmission within a group using network communication.

[0303] The means of "monitoring the usage status of resources using instruments and proposing optimal resource allocation" is a method for detecting the usage status of each resource and performing efficient relocation or optimization.

[0304] The "optimization means for adjusting the workload among autonomous machines" is a method or system for leveling the workload among different machines and improving the overall work efficiency.

[0305] This system is designed to improve the work efficiency in the production line of a factory. The server plays a central role. To visualize the progress of tasks, it receives data sent from each autonomous machine and efficiently displays it using visualization tools. Specifically, data visualization libraries such as Plotly are used for visualizing the progress status and workload. [[ID=[]]

[0306] The terminal is equipped with a communication device, and each machine can exchange information in real time through this communication device. The real-time messaging system for assisting information exchange within the group is implemented using frameworks such as Flask.

[0307] In addition, the server performs optimal task allocation based on the capabilities and workload of users. For this, machine learning algorithms using sklearn are useful. This algorithm utilizes a generative AI model to efficiently allocate optimal tasks to each user.

[0308] Furthermore, it is also equipped with a function of monitoring the resource usage status using instruments and proposing optimal resource allocation. The server utilizes a sensor module such as Raspberry Pi to analyze the resource monitoring data and reflects the information on the dashboard.

[0309] As a specific example, when introduced in a small-scale machine assembly factory, 10 robots cooperated to share the work and were able to complete the project in a short time.

[0310] The introduction of such a system will allow the entire factory's production line to operate more efficiently. An example of a prompt message utilizing a generative AI model would be: "A new order has been added. Please generate machine assignments to process it efficiently."

[0311] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0312] Step 1:

[0313] The server collects progress data from each autonomous machine. The input includes work status information reported by each machine's sensors. This data is analyzed to extract numerical data representing the progress of each task. The output is the numerical progress data.

[0314] Step 2:

[0315] The server processes progress data using visualization tools and generates Gantt charts and bar graphs that display the actual progress. Pre-processed progress data is used as input. The data is then processed using a data visualization library and output as a visualized dashboard.

[0316] Step 3:

[0317] Using communication devices connected to terminals, each machine transmits progress information to the server in real time. The input is progress updates from each machine. This information exchange allows for real-time monitoring of each other's progress and resource usage. The output is the received updated progress information.

[0318] Step 4:

[0319] The server acquires user capability and workload data and uses a generative AI model to calculate the next task to assign. Current capability assessments and workloads are referenced as input. Machine learning algorithms are applied to generate the optimal task assignment. The recalculated task assignment is obtained as output.

[0320] Step 5:

[0321] The server aggregates data from sensors to analyze resource usage and propose the optimal resource allocation. Input includes resource usage data from each machine. The data is analyzed to plan an efficient resource allocation. The output is a recommended resource allocation plan.

[0322] Step 6:

[0323] Users can review progress and assigned tasks through the generated dashboard and make adjustments or modifications as needed. The visualized dashboard information is used as input. Users review the data and develop appropriate plans for the next steps. The adjusted task plan is obtained as output.

[0324] 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.

[0325] This invention provides a system that further improves the efficiency of task management by combining an emotion engine with an AI task manager system. This system visualizes task progress, assigns appropriate tasks based on the user's skills and workload, supports smooth information exchange within the team, and understands resource usage to propose optimal allocation. The addition of the emotion engine automatically recognizes the user's emotional state and optimizes task assignment and communication suggestions accordingly.

[0326] Emotion Engine Module

[0327] The server uses an emotion engine that analyzes the user's voice tone and facial expression data to identify the user's emotional state. This data influences task assignment and priority decisions. For example, if a user is feeling stressed, the server might suggest reducing the workload of that task and assigning a support member.

[0328] Task assignment adjustment

[0329] Based on its recognized emotional state, the device suggests reassigning specific tasks to the user. These suggestions are adjusted to include less stressful tasks and tasks that promote teamwork. The user can review, approve, or re-evaluate these suggestions.

[0330] Suggestions for improving communication

[0331] The server analyzes emotional data and formulates suggestions to improve communication within the team. For example, if team members are generally showing signs of fatigue, the server might suggest holding refresh meetings or online workshops.

[0332] Specific example

[0333] In one project, the server detects that a user is under stress. Based on this information, the server alerts the project leader and suggests seeking assistance from other team members to alleviate the user's workload. This action equalizes the workload and ensures the overall project progresses smoothly.

[0334] Thus, embodiments of the present invention, by combining an emotion engine, optimize task management and team management in a way that takes emotions into consideration, thereby promoting improved work efficiency.

[0335] The following describes the processing flow.

[0336] Step 1:

[0337] The server collects the user's voice tone and facial expression data in real time using an emotion engine. This allows data related to the user's emotional state to be obtained.

[0338] Step 2:

[0339] The server analyzes the collected emotional data to identify the user's current emotional state. This information is categorized into emotional states such as stress, happiness, and fatigue.

[0340] Step 3:

[0341] The server determines whether the task load needs to be adjusted based on the identified emotional state. For example, if the user is determined to be in a high-stress state, reducing the task load will be recommended.

[0342] Step 4:

[0343] Based on instructions from the server, the terminal presents the user with a proposed task redistribution. This proposal includes tasks with less workload and tasks appropriate to the user's emotional state.

[0344] Step 5:

[0345] The user reviews the proposal displayed on their device and, if necessary, approves it or requests a different task assignment from the server.

[0346] Step 6:

[0347] The server analyzes the recognized emotion data as a whole to understand the emotional trends within the team and generates suggestions for improving communication.

[0348] Step 7:

[0349] The server will notify team leaders and members with suggestions, such as, "Overall fatigue levels are high, so we recommend increasing break times."

[0350] Step 8:

[0351] Users accept this suggestion and work to improve the overall emotional state of the team by setting up meetings, adjusting work styles, and so on.

[0352] (Example 2)

[0353] 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".

[0354] In modern work environments, flexible task management that takes into account technical skills, workload, and emotional state is required for users to work efficiently. Visualization of work progress, promotion of appropriate communication, and optimal resource allocation are also crucial. However, methods for centrally managing these aspects are not yet well established, and work adjustments based on users' emotional states are particularly lacking. Therefore, the development of a system that allows users to perform at their maximum potential is necessary.

[0355] 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.

[0356] In this invention, the server includes means for visualizing the progress of tasks, means for assigning optimal tasks based on the user's skills and workload, means for exchanging information to support communication within the organization, means for monitoring resource usage and proposing optimal resource allocation, and means for analyzing the user's emotional state and optimizing task assignments and communication strategies based on this. This enables flexible and efficient work management that takes into account the user's emotions and technical factors.

[0357] "Means for visualizing task progress" refers to methods for visually displaying the progress of tasks that users are working on, thereby making it easy to grasp the degree of progress and any delays in the work.

[0358] "Means for assigning optimal tasks based on users' skills and workload" refers to a method that evaluates each user's skill level and current workload, and automatically assigns the most suitable tasks accordingly.

[0359] "Information exchange methods to support communication within an organization" refer to methods and tools that enable members of an organization to exchange information smoothly, with the aim of building effective cooperative relationships.

[0360] "A means of monitoring resource usage and proposing optimal resource allocation" refers to a method of monitoring the usage of various resources (personnel, time, equipment, etc.) within an organization in real time and proposing an efficient allocation of resources based on this.

[0361] "Means of analyzing users' emotional states and optimizing work assignments and communication strategies based on them" refers to a process of collecting and analyzing users' emotions as data, and then using the results to optimize work assignments and communication methods within the organization.

[0362] The system for implementing this invention utilizes advanced AI models and is designed to enable users to perform tasks efficiently. This system incorporates multiple modules, including an emotion engine, which perform analysis of various data and optimization based on that analysis.

[0363] Server-based data analysis and optimization

[0364] The server first collects and analyzes the user's voice tone and facial expression data using speech recognition software and facial recognition algorithms. Specifically, it uses a speech API for speech recognition and an image processing library for facial recognition. This allows the server to identify the user's emotional state and reflect it in the operation of the entire system.

[0365] The server integrates user emotional data and technical evaluation data, and uses this to dynamically adjust work assignments. If the emotional state indicates stress, it reduces the burden and reassigns tasks to seek cooperation from team members.

[0366] Suggestions and feedback via devices

[0367] The terminal presents the user with suggestions sent from the server, offering new task assignments and communication improvement measures. Users can provide feedback on these suggestions and request adjustments as needed. This feedback is used to update the system's learning model, designed to improve the accuracy of future suggestions.

[0368] Specific example

[0369] In one project, the server detects that a user is under excessive stress. Based on this information, the server alerts the project leader and encourages other members to participate in order to equalize the workload across the team. It also maintains work efficiency by assigning new tasks that do not cause stress.

[0370] Example of a prompt

[0371] "Design a system framework that assesses a user's emotional state and suggests appropriate task reassignment. Include examples of actions based on emotional state data."

[0372] As described above, this invention aims to optimize task management and team operations more effectively and efficiently by combining the functions of an emotion engine.

[0373] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0374] Step 1:

[0375] The server acquires the user's voice tone and facial expression data. This is done using speech recognition software and facial recognition algorithms. The server inputs this data into an emotion recognition model to identify the user's emotional state. As output, it obtains emotional state data, such as whether the user is relaxed or stressed. Specifically, the server captures audio and video in real time and performs analysis in the cloud.

[0376] Step 2:

[0377] The terminal uses emotional state data received from the server to evaluate the tasks assigned to the user, taking into account the current skill level and workload. Using emotional state, skill data, and workload data as input, the terminal proposes task redistribution as needed. It generates a new list of task priorities as output. Specifically, the terminal updates the visual task management tool on the user interface.

[0378] Step 3:

[0379] The server aggregates emotional data from all users and analyzes the overall team situation. It uses each user's emotional state data as input to understand overall team trends. As output, the server generates suggestions for improving communication, such as suggestions for refresh meetings. Specifically, the server presents these suggestions to the project leader through a dashboard.

[0380] Step 4:

[0381] Users receive new task suggestions and communication improvement measures from their devices. They review this information and provide feedback. The system receives suggestions as input and provides approval or modification feedback as output. Specifically, users record their feedback by performing button operations on the UI.

[0382] Step 5:

[0383] The server receives feedback from users and updates its learning model to improve the accuracy of future suggestions. It receives user feedback data as input and adjusts model parameters using machine learning techniques. The output is an improved model for future suggestions. Specifically, the server periodically trains the model and reflects the new learning outcomes throughout the entire system.

[0384] (Application Example 2)

[0385] 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."

[0386] In modern production environments, there is a lack of work management that takes into account the emotional state of workers, resulting in excessive stress that negatively impacts productivity. Furthermore, a lack of smooth information exchange between production machinery and workers can lead to imbalances in workload. These problems raise concerns about decreased production efficiency and health risks for workers.

[0387] 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.

[0388] In this invention, the server includes means for visualizing the progress of tasks, means for assigning optimal tasks based on the user's skills and workload, and means for providing an appropriate work environment by analyzing the worker's emotional state from voice and image data and suggesting workload and rest periods. This makes it possible to adjust the workload according to the worker's emotional state and improve productivity through smooth information exchange.

[0389] "Methods for visualizing task progress" refer to functions that allow users to understand the extent to which each task has been completed by visually displaying the progress of the work.

[0390] "Means for assigning optimal tasks based on the user's skills and workload" refers to a function that automatically distributes each task to the most suitable worker, taking into account each worker's abilities and current workload.

[0391] "A means of analyzing workers' emotional states from audio and image data and proposing workload and rest" refers to a function that reads workers' emotions from audio and video, adjusts their workload based on that, and proposes rest at appropriate times, thereby improving workers' health and productivity.

[0392] "Information exchange means to support communication between automated production machines and workers" refers to a function that effectively transmits necessary information between production machines and workers and strengthens cooperation.

[0393] "A means of monitoring resource usage and proposing the optimal resource allocation" refers to a function that constantly checks the status of available resources and proposes the most efficient allocation accordingly.

[0394] To implement this invention, a server plays a central role. The server maintains a database for visualizing the progress of work and executes algorithms to evaluate each worker's skills and current workload. This makes it possible to assign tasks that are suitable for each worker.

[0395] For emotion recognition, devices such as smart glasses and head-mounted displays are used. These devices capture the worker's facial expressions and voice in real time and send them to a server for analysis. Specifically, emotion recognition software such as OpenCV and DeepFace is used. Based on this data, the server identifies the emotional state and adjusts the workload appropriately to suggest breaks and work reallocation for the worker.

[0396] For example, if a worker shows signs of stress due to high workload while performing a particular task, the server will flexibly change the criteria for assigning new tasks to that worker. It will also notify the entire team as needed to facilitate communication among employees.

[0397] The system supports real-time information communication between production machines and workers as a means of information exchange. The server collects operational data from production machines and user work records, and improves overall productivity by proposing the optimal resource allocation in specific situations.

[0398] Example prompt: "How would you redistribute tasks if an operator is experiencing stress?"

[0399] Through this invention, improvements to the work environment that respond to the emotional state of workers will be realized, enabling more efficient production activities.

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The server receives voice and facial expression data in real time from smart glasses or head-mounted displays. This provides input data to identify the worker's emotional state. The transmitted data is stored in a database for analysis.

[0403] Step 2:

[0404] The server uses OpenCV and DeepFace, emotion recognition software, to analyze the received audio and facial expression data. This analysis extracts characteristic patterns from each data point and compares them with existing emotion state mappings to perform data calculations that determine the worker's stress level and emotional state.

[0405] Step 3:

[0406] Based on the analysis results, the server proposes an optimal task reallocation, taking into account the worker's skill level and workload. This proposal includes prioritizing tasks and assigning new tasks, taking into account emotional states, and the proposal is sent as a notification to the worker's terminal.

[0407] Step 4:

[0408] The terminal receives suggestions from the server and notifies the user. These notifications may include suggestions for adjusting work content or suggesting breaks, and the new task schedule is finalized once the user approves them. The server receives a reply as output, indicating the user's approval or re-evaluation.

[0409] Step 5:

[0410] The server receives replies from users and updates the overall work schedule. Based on this, it shares information with other workers and automated production machines as needed to optimize the entire project. This final data is output to the administrator interface and can be checked at any time.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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".

[0427] To implement the AI ​​task manager system of the present invention, a program with the following functions is mainly required: The combination of modules for progress management, task assignment, team communication support, and resource management enables efficient management of tasks and the entire project.

[0428] Progress management module

[0429] The server periodically collects progress data for each task from the database and generates a visualized progress report. This is provided to the user in a dashboard format, allowing them to quickly grasp the progress of tasks through progress bar graphs and Gantt charts. For example, if they can visually confirm that a project is 75% complete, they can efficiently allocate the remaining tasks and make adjustments to complete the project.

[0430] Task assignment and priority setting module

[0431] The server automatically assigns tasks to the most suitable member based on each member's skill information and current workload. This process includes calculating priority scores and suggesting an appropriate processing order based on the urgency and importance of the tasks. Specifically, when a new urgent task arises, it is automatically given a high priority and immediately assigned to a skilled member.

[0432] Communication support module

[0433] The device is equipped with a real-time messaging system, allowing users to instantly share information with team members. For example, if a user wants to report on task progress or ask a question, they can do so through this system, and all members can see the response in real time. Important notifications are also sent via push notifications through the device, ensuring that information is not missed.

[0434] Resource management module

[0435] The server monitors usage and proposes a calculated resource allocation to the user. This function helps prevent resource surpluses and shortages, improving the overall efficiency of the project. For example, when considering starting a new project, it verifies whether the current server capacity is adequate and determines whether the necessary resources can be secured.

[0436] In this way, each module works organically together, aiming to improve the productivity of the entire team. This frees users from cumbersome task management, enabling them to perform their work more effectively.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] The server retrieves project and task progress from the database. This collects data related to the current progress of each task.

[0440] Step 2:

[0441] The server analyzes the collected progress data and generates visual reports such as Gantt charts and progress bar graphs. This visualizes the progress of the tasks.

[0442] Step 3:

[0443] The server sends the generated visual report to the user's dashboard, allowing the user to check the progress at any time.

[0444] Step 4:

[0445] The server retrieves each member's skills and current workload from the database. This allows for the collection of member resource information.

[0446] Step 5:

[0447] The server analyzes the task content and determines the skills required for assignment. Then, based on skill suitability and workload, it calculates and proposes the optimal task assignment.

[0448] Step 6:

[0449] The user reviews the task assignment proposed by the server and approves or readjusts it as needed.

[0450] Step 7:

[0451] The terminal sends the task progress and completion reports entered by the user to the server. This updates the data.

[0452] Step 8:

[0453] The server maintains a real-time messaging system, instantly sending new messages within the team to the terminals of other relevant members.

[0454] Step 9:

[0455] The device displays received messages to the user in real time, allowing the user to check their content.

[0456] Step 10:

[0457] The server periodically updates resource usage and suggests resource allocation optimizations as needed.

[0458] Step 11:

[0459] The user reviews the proposed resource allocation and approves it if they deem it appropriate.

[0460] (Example 1)

[0461] 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."

[0462] In today's business environment, task management and information sharing within teams are crucial, but traditional manual management methods are often inefficient for monitoring progress, assigning tasks, and allocating resources. This leads to project delays, wasted human resources, and a decline in organizational productivity.

[0463] 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.

[0464] In this invention, the server includes means for collecting and visually displaying task progress data, means for optimally allocating tasks based on user capability information and current workload, and communication means for immediate information sharing among team members. This makes it possible to monitor project progress in real time, optimize task allocation, and achieve smooth communication within the team.

[0465] "Means of collecting and visually displaying task progress data" refers to technologies that retrieve task completion status from databases or other sources and visualize that information in a way that is easy for users to understand.

[0466] "Means for optimally allocating tasks based on user ability information and current workload" refers to a system that automates the optimal task allocation by taking into account each user's skills and the progress of the tasks they are currently working on.

[0467] "Communication methods for immediate information sharing among team members" refers to communication technologies that enable real-time information exchange among members, including those that ensure the immediacy of messaging and notifications.

[0468] "Means for monitoring resource utilization and proposing efficient resource allocation" refers to a function that analyzes the resource usage of the entire system and proposes the optimal resource allocation based on the results.

[0469] "Methods for automatically analyzing task priorities" refer to technologies that quantify the urgency and importance of each task and automatically calculate their priority in order to achieve efficient task processing.

[0470] "A method for suggesting efficient meeting times based on calendar information" refers to a system that analyzes schedule data to recommend the optimal date and time for meetings and events.

[0471] The system of this invention is a comprehensive platform that combines various modules to efficiently manage tasks and projects. This system operates primarily using servers and terminals, enabling users to effectively manage tasks and share information with teams.

[0472] In the progress management module, the server periodically collects task progress data from the database. This data is visualized using libraries such as Python's Matplotlib and provided to the user in a dashboard format. This allows the user to intuitively understand the progress of the project.

[0473] The task assignment and priority setting module analyzes user skill information and current task load. Based on this, it automatically calculates task priorities and distributes tasks to the most suitable members. This equalizes the workload for each member and improves project efficiency.

[0474] The communication support module consists of a real-time messaging system implemented in the terminal. Users can instantly share information with team members through this system. The server also has the functionality to push important notifications to the terminal, ensuring users receive information without missing anything.

[0475] In the resource management module, the server monitors the project's resource usage and proposes efficient resource allocation to the user. This enables optimal resource utilization and improves the overall project performance.

[0476] For example, if a user enters a prompt such as, "Check the latest progress of Project A and reassign Task X to the most suitable member," the system can utilize these modules to quickly provide the data necessary for project management and take appropriate action. By leveraging this generative AI model, users can achieve higher productivity.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The server collects task progress data from the database. This data collection is performed using SQL queries to extract the necessary task IDs and progress percentages. The inputs are database connection information and queries, and the output is the collected task progress data.

[0480] Step 2:

[0481] The server visualizes the collected progress data. This process uses Python's Matplotlib library to generate bar graphs and Gantt charts to show progress. The input is progress data, and the output is a visualized graph image. Specifically, it calculates the length of the bar corresponding to the progress rate of each task and plots it on the chart.

[0482] Step 3:

[0483] The terminal displays a dashboard to the user using visualization data sent from the server. This dashboard allows the user to intuitively understand the project status. The input is graph image data from the server, and the output is a visual dashboard on the terminal.

[0484] Step 4:

[0485] The server retrieves user skill and workload information from a database and performs analysis. The input is skill and workload information from the database, and the output is analytical data for task allocation. Specifically, it calculates weights to select tasks appropriate for each member's skills.

[0486] Step 5:

[0487] The server uses the analysis data to calculate task priorities and assign tasks to the most suitable members. Inputs are task information and member skill / load information, while output is optimized task assignment information. Specifically, it scores the priority of each task based on an algorithm and assigns them to members in order of highest priority.

[0488] Step 6:

[0489] Using the communication tools implemented on the device, users share information with team members in real time. Input is the message entered by the user, and output is the message notified to other members. Specifically, the device sends the user's input to a server, which then distributes it to other members.

[0490] Step 7:

[0491] The server monitors resource usage and proposes efficient resource allocation to the user. The input is the system's resource usage log, and the output is the optimal resource allocation proposal. Specifically, it uses a model that analyzes CPU usage and memory consumption to evaluate resource surpluses and shortages.

[0492] (Application Example 1)

[0493] 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."

[0494] In modern factory environments, there is a demand for increased efficiency in production lines through the use of autonomous machinery. However, since each machine often operates independently, there are challenges in overall project management and workload adjustment. While efficient and seamless operation is required, real-time information sharing between machines and optimal task allocation are essential.

[0495] 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.

[0496] In this invention, the server includes means for visualizing the progress of tasks, means for assigning optimal tasks based on the user's abilities and workload, and means for supporting information exchange within the group using a communication device. This enables the adjustment of workload among autonomous machines and the efficient operation of the production line.

[0497] "Means for visualizing task progress" refers to a device or method that visually displays the current progress of a task, enabling users to quickly grasp the situation.

[0498] "A means of assigning tasks based on user capabilities and workload" refers to a technology that automatically assigns appropriate tasks according to each user's skills and current workload.

[0499] "Means of supporting information exchange within a group using communication devices" refers to a system that enables real-time information transmission within a group using network communication.

[0500] "Means of monitoring resource usage using instruments and proposing optimal resource allocation" refers to methods for detecting the usage status of each resource and performing efficient reallocation and optimization.

[0501] "Optimization means for adjusting workload between autonomous machines" refers to a method or system for leveling the workload between different machines and improving overall work efficiency.

[0502] This system is designed to improve operational efficiency on a factory production line. A server plays a central role, receiving data transmitted from each autonomous machine and efficiently displaying it using visualization tools to visualize task progress. Specifically, data visualization libraries such as Plotly are used to visualize progress and workload.

[0503] The terminals are equipped with communication devices, allowing each machine to exchange information in real time via these devices. A real-time messaging system to support information exchange within the group is implemented using frameworks such as Flask.

[0504] Furthermore, the server assigns optimal tasks based on the user's capabilities and workload. Machine learning algorithms using scikit-learn are helpful in this process. These algorithms utilize generative AI models to efficiently assign the most suitable tasks to each user.

[0505] Furthermore, it includes a function that monitors resource usage using instruments and proposes the optimal resource allocation. The server utilizes sensor modules such as Raspberry Pi to analyze resource monitoring data and reflects the information on a dashboard.

[0506] As a concrete example, when implemented in a small-scale machine assembly plant, 10 robots worked together to share tasks, enabling project completion in a short amount of time.

[0507] The introduction of such a system will allow the entire factory's production line to operate more efficiently. An example of a prompt message utilizing a generative AI model would be: "A new order has been added. Please generate machine assignments to process it efficiently."

[0508] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0509] Step 1:

[0510] The server collects progress data from each autonomous machine. The input includes work status information reported by each machine's sensors. This data is analyzed to extract numerical data representing the progress of each task. The output is the numerical progress data.

[0511] Step 2:

[0512] The server processes progress data using visualization tools and generates Gantt charts and bar graphs that display the actual progress. Pre-processed progress data is used as input. The data is then processed using a data visualization library and output as a visualized dashboard.

[0513] Step 3:

[0514] Using communication devices connected to terminals, each machine transmits progress information to the server in real time. The input is progress updates from each machine. This information exchange allows for real-time monitoring of each other's progress and resource usage. The output is the received updated progress information.

[0515] Step 4:

[0516] The server acquires user capability and workload data and uses a generative AI model to calculate the next task to assign. Current capability assessments and workloads are referenced as input. Machine learning algorithms are applied to generate the optimal task assignment. The recalculated task assignment is obtained as output.

[0517] Step 5:

[0518] The server aggregates data from sensors to analyze resource usage and propose the optimal resource allocation. Input includes resource usage data from each machine. The data is analyzed to plan an efficient resource allocation. The output is a recommended resource allocation plan.

[0519] Step 6:

[0520] Users can review progress and assigned tasks through the generated dashboard and make adjustments or modifications as needed. The visualized dashboard information is used as input. Users review the data and develop appropriate plans for the next steps. The adjusted task plan is obtained as output.

[0521] 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.

[0522] This invention provides a system that further improves the efficiency of task management by combining an emotion engine with an AI task manager system. This system visualizes task progress, assigns appropriate tasks based on the user's skills and workload, supports smooth information exchange within the team, and understands resource usage to propose optimal allocation. The addition of the emotion engine automatically recognizes the user's emotional state and optimizes task assignment and communication suggestions accordingly.

[0523] Emotion Engine Module

[0524] The server uses an emotion engine that analyzes the user's voice tone and facial expression data to identify the user's emotional state. This data influences task assignment and priority decisions. For example, if a user is feeling stressed, the server might suggest reducing the workload of that task and assigning a support member.

[0525] Task assignment adjustment

[0526] Based on its recognized emotional state, the device suggests reassigning specific tasks to the user. These suggestions are adjusted to include less stressful tasks and tasks that promote teamwork. The user can review, approve, or re-evaluate these suggestions.

[0527] Suggestions for improving communication

[0528] The server analyzes emotional data and formulates suggestions to improve communication within the team. For example, if team members are generally showing signs of fatigue, the server might suggest holding refresh meetings or online workshops.

[0529] Specific example

[0530] In one project, the server detects that a user is under stress. Based on this information, the server alerts the project leader and suggests seeking assistance from other team members to alleviate the user's workload. This action equalizes the workload and ensures the overall project progresses smoothly.

[0531] Thus, embodiments of the present invention, by combining an emotion engine, optimize task management and team management in a way that takes emotions into consideration, thereby promoting improved work efficiency.

[0532] The following describes the processing flow.

[0533] Step 1:

[0534] The server collects the user's voice tone and facial expression data in real time using an emotion engine. This allows data related to the user's emotional state to be obtained.

[0535] Step 2:

[0536] The server analyzes the collected emotional data to identify the user's current emotional state. This information is categorized into emotional states such as stress, happiness, and fatigue.

[0537] Step 3:

[0538] The server determines whether the task load needs to be adjusted based on the identified emotional state. For example, if the user is determined to be in a high-stress state, reducing the task load will be recommended.

[0539] Step 4:

[0540] Based on instructions from the server, the terminal presents the user with a proposed task redistribution. This proposal includes tasks with less workload and tasks appropriate to the user's emotional state.

[0541] Step 5:

[0542] The user reviews the proposal displayed on their device and, if necessary, approves it or requests a different task assignment from the server.

[0543] Step 6:

[0544] The server analyzes the recognized emotion data as a whole to understand the emotional trends within the team and generates suggestions for improving communication.

[0545] Step 7:

[0546] The server will notify team leaders and members with suggestions, such as, "Overall fatigue levels are high, so we recommend increasing break times."

[0547] Step 8:

[0548] Users accept this suggestion and work to improve the overall emotional state of the team by setting up meetings, adjusting work styles, and so on.

[0549] (Example 2)

[0550] 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."

[0551] In modern work environments, flexible task management that takes into account technical skills, workload, and emotional state is required for users to work efficiently. Visualization of work progress, promotion of appropriate communication, and optimal resource allocation are also crucial. However, methods for centrally managing these aspects are not yet well established, and work adjustments based on users' emotional states are particularly lacking. Therefore, the development of a system that allows users to perform at their maximum potential is necessary.

[0552] 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.

[0553] In this invention, the server includes means for visualizing the progress of tasks, means for assigning optimal tasks based on the user's skills and workload, means for exchanging information to support communication within the organization, means for monitoring resource usage and proposing optimal resource allocation, and means for analyzing the user's emotional state and optimizing task assignments and communication strategies based on this. This enables flexible and efficient work management that takes into account the user's emotions and technical factors.

[0554] "Means for visualizing task progress" refers to methods for visually displaying the progress of tasks that users are working on, thereby making it easy to grasp the degree of progress and any delays in the work.

[0555] "Means for assigning optimal tasks based on users' skills and workload" refers to a method that evaluates each user's skill level and current workload, and automatically assigns the most suitable tasks accordingly.

[0556] "Information exchange methods to support communication within an organization" refer to methods and tools that enable members of an organization to exchange information smoothly, with the aim of building effective cooperative relationships.

[0557] "A means of monitoring resource usage and proposing optimal resource allocation" refers to a method of monitoring the usage of various resources (personnel, time, equipment, etc.) within an organization in real time and proposing an efficient allocation of resources based on this.

[0558] "Means of analyzing users' emotional states and optimizing work assignments and communication strategies based on them" refers to a process of collecting and analyzing users' emotions as data, and then using the results to optimize work assignments and communication methods within the organization.

[0559] The system for implementing this invention utilizes advanced AI models and is designed to enable users to perform tasks efficiently. This system incorporates multiple modules, including an emotion engine, which perform analysis of various data and optimization based on that analysis.

[0560] Server-based data analysis and optimization

[0561] The server first collects and analyzes the user's voice tone and facial expression data using speech recognition software and facial recognition algorithms. Specifically, it uses a speech API for speech recognition and an image processing library for facial recognition. This allows the server to identify the user's emotional state and reflect it in the operation of the entire system.

[0562] The server integrates user emotional data and technical evaluation data, and uses this to dynamically adjust work assignments. If the emotional state indicates stress, it reduces the burden and reassigns tasks to seek cooperation from team members.

[0563] Suggestions and feedback via devices

[0564] The terminal presents the user with suggestions sent from the server, offering new task assignments and communication improvement measures. Users can provide feedback on these suggestions and request adjustments as needed. This feedback is used to update the system's learning model, designed to improve the accuracy of future suggestions.

[0565] Specific example

[0566] In one project, the server detects that a user is under excessive stress. Based on this information, the server alerts the project leader and encourages other members to participate in order to equalize the workload across the team. It also maintains work efficiency by assigning new tasks that do not cause stress.

[0567] Example of a prompt

[0568] "Design a system framework that assesses a user's emotional state and suggests appropriate task reassignment. Include examples of actions based on emotional state data."

[0569] As described above, this invention aims to optimize task management and team operations more effectively and efficiently by combining the functions of an emotion engine.

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] The server acquires the user's voice tone and facial expression data. This is done using speech recognition software and facial recognition algorithms. The server inputs this data into an emotion recognition model to identify the user's emotional state. As output, it obtains emotional state data, such as whether the user is relaxed or stressed. Specifically, the server captures audio and video in real time and performs analysis in the cloud.

[0573] Step 2:

[0574] The terminal uses emotional state data received from the server to evaluate the tasks assigned to the user, taking into account the current skill level and workload. Using emotional state, skill data, and workload data as input, the terminal proposes task redistribution as needed. It generates a new list of task priorities as output. Specifically, the terminal updates the visual task management tool on the user interface.

[0575] Step 3:

[0576] The server aggregates emotional data from all users and analyzes the overall team situation. It uses each user's emotional state data as input to understand overall team trends. As output, the server generates suggestions for improving communication, such as suggestions for refresh meetings. Specifically, the server presents these suggestions to the project leader through a dashboard.

[0577] Step 4:

[0578] Users receive new task suggestions and communication improvement measures from their devices. They review this information and provide feedback. The system receives suggestions as input and provides approval or modification feedback as output. Specifically, users record their feedback by performing button operations on the UI.

[0579] Step 5:

[0580] The server receives feedback from users and updates its learning model to improve the accuracy of future suggestions. It receives user feedback data as input and adjusts model parameters using machine learning techniques. The output is an improved model for future suggestions. Specifically, the server periodically trains the model and reflects the new learning outcomes throughout the entire system.

[0581] (Application Example 2)

[0582] 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."

[0583] In modern production environments, there is a lack of work management that takes into account the emotional state of workers, resulting in excessive stress that negatively impacts productivity. Furthermore, a lack of smooth information exchange between production machinery and workers can lead to imbalances in workload. These problems raise concerns about decreased production efficiency and health risks for workers.

[0584] 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.

[0585] In this invention, the server includes means for visualizing the progress of tasks, means for assigning optimal tasks based on the user's skills and workload, and means for providing an appropriate work environment by analyzing the worker's emotional state from voice and image data and suggesting workload and rest periods. This makes it possible to adjust the workload according to the worker's emotional state and improve productivity through smooth information exchange.

[0586] "Methods for visualizing task progress" refer to functions that allow users to understand the extent to which each task has been completed by visually displaying the progress of the work.

[0587] "Means for assigning optimal tasks based on the user's skills and workload" refers to a function that automatically distributes each task to the most suitable worker, taking into account each worker's abilities and current workload.

[0588] "A means of analyzing workers' emotional states from audio and image data and proposing workload and rest" refers to a function that reads workers' emotions from audio and video, adjusts their workload based on that, and proposes rest at appropriate times, thereby improving workers' health and productivity.

[0589] "Information exchange means to support communication between automated production machines and workers" refers to a function that effectively transmits necessary information between production machines and workers and strengthens cooperation.

[0590] "A means of monitoring resource usage and proposing the optimal resource allocation" refers to a function that constantly checks the status of available resources and proposes the most efficient allocation accordingly.

[0591] To implement this invention, a server plays a central role. The server maintains a database for visualizing the progress of work and executes algorithms to evaluate each worker's skills and current workload. This makes it possible to assign tasks that are suitable for each worker.

[0592] For emotion recognition, devices such as smart glasses and head-mounted displays are used. These devices capture the worker's facial expressions and voice in real time and send them to a server for analysis. Specifically, emotion recognition software such as OpenCV and DeepFace is used. Based on this data, the server identifies the emotional state and adjusts the workload appropriately to suggest breaks and work reallocation for the worker.

[0593] For example, if a worker shows signs of stress due to high workload while performing a particular task, the server will flexibly change the criteria for assigning new tasks to that worker. It will also notify the entire team as needed to facilitate communication among employees.

[0594] The system supports real-time information communication between production machines and workers as a means of information exchange. The server collects operational data from production machines and user work records, and improves overall productivity by proposing the optimal resource allocation in specific situations.

[0595] Example prompt: "How would you redistribute tasks if an operator is experiencing stress?"

[0596] Through this invention, improvements to the work environment that respond to the emotional state of workers will be realized, enabling more efficient production activities.

[0597] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0598] Step 1:

[0599] The server receives voice and facial expression data in real time from smart glasses or head-mounted displays. This provides input data to identify the worker's emotional state. The transmitted data is stored in a database for analysis.

[0600] Step 2:

[0601] The server uses OpenCV and DeepFace, emotion recognition software, to analyze the received audio and facial expression data. This analysis extracts characteristic patterns from each data point and compares them with existing emotion state mappings to perform data calculations that determine the worker's stress level and emotional state.

[0602] Step 3:

[0603] Based on the analysis results, the server proposes an optimal task reallocation, taking into account the worker's skill level and workload. This proposal includes prioritizing tasks and assigning new tasks, taking into account emotional states, and the proposal is sent as a notification to the worker's terminal.

[0604] Step 4:

[0605] The terminal receives suggestions from the server and notifies the user. These notifications may include suggestions for adjusting work content or suggesting breaks, and the new task schedule is finalized once the user approves them. The server receives a reply as output, indicating the user's approval or re-evaluation.

[0606] Step 5:

[0607] The server receives replies from users and updates the overall work schedule. Based on this, it shares information with other workers and automated production machines as needed to optimize the entire project. This final data is output to the administrator interface and can be checked at any time.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] [Fourth Embodiment]

[0612] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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".

[0625] To implement the AI ​​task manager system of the present invention, a program with the following functions is mainly required: The combination of modules for progress management, task assignment, team communication support, and resource management enables efficient management of tasks and the entire project.

[0626] Progress management module

[0627] The server periodically collects progress data for each task from the database and generates a visualized progress report. This is provided to the user in a dashboard format, allowing them to quickly grasp the progress of tasks through progress bar graphs and Gantt charts. For example, if they can visually confirm that a project is 75% complete, they can efficiently allocate the remaining tasks and make adjustments to complete the project.

[0628] Task assignment and priority setting module

[0629] The server automatically assigns tasks to the most suitable member based on each member's skill information and current workload. This process includes calculating priority scores and suggesting an appropriate processing order based on the urgency and importance of the tasks. Specifically, when a new urgent task arises, it is automatically given a high priority and immediately assigned to a skilled member.

[0630] Communication support module

[0631] The device is equipped with a real-time messaging system, allowing users to instantly share information with team members. For example, if a user wants to report on task progress or ask a question, they can do so through this system, and all members can see the response in real time. Important notifications are also sent via push notifications through the device, ensuring that information is not missed.

[0632] Resource management module

[0633] The server monitors usage and proposes a calculated resource allocation to the user. This function helps prevent resource surpluses and shortages, improving the overall efficiency of the project. For example, when considering starting a new project, it verifies whether the current server capacity is adequate and determines whether the necessary resources can be secured.

[0634] In this way, each module works organically together, aiming to improve the productivity of the entire team. This frees users from cumbersome task management, enabling them to perform their work more effectively.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] The server retrieves project and task progress from the database. This collects data related to the current progress of each task.

[0638] Step 2:

[0639] The server analyzes the collected progress data and generates visual reports such as Gantt charts and progress bar graphs. This visualizes the progress of the tasks.

[0640] Step 3:

[0641] The server sends the generated visual report to the user's dashboard, allowing the user to check the progress at any time.

[0642] Step 4:

[0643] The server retrieves each member's skills and current workload from the database. This allows for the collection of member resource information.

[0644] Step 5:

[0645] The server analyzes the task content and determines the skills required for assignment. Then, based on skill suitability and workload, it calculates and proposes the optimal task assignment.

[0646] Step 6:

[0647] The user reviews the task assignment proposed by the server and approves or readjusts it as needed.

[0648] Step 7:

[0649] The terminal sends the task progress and completion reports entered by the user to the server. This updates the data.

[0650] Step 8:

[0651] The server maintains a real-time messaging system, instantly sending new messages within the team to the terminals of other relevant members.

[0652] Step 9:

[0653] The device displays received messages to the user in real time, allowing the user to check their content.

[0654] Step 10:

[0655] The server periodically updates resource usage and suggests resource allocation optimizations as needed.

[0656] Step 11:

[0657] The user reviews the proposed resource allocation and approves it if they deem it appropriate.

[0658] (Example 1)

[0659] 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".

[0660] In today's business environment, task management and information sharing within teams are crucial, but traditional manual management methods are often inefficient for monitoring progress, assigning tasks, and allocating resources. This leads to project delays, wasted human resources, and a decline in organizational productivity.

[0661] 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.

[0662] In this invention, the server includes means for collecting and visually displaying task progress data, means for optimally allocating tasks based on user capability information and current workload, and communication means for immediate information sharing among team members. This makes it possible to monitor project progress in real time, optimize task allocation, and achieve smooth communication within the team.

[0663] "Means of collecting and visually displaying task progress data" refers to technologies that retrieve task completion status from databases or other sources and visualize that information in a way that is easy for users to understand.

[0664] "Means for optimally allocating tasks based on user ability information and current workload" refers to a system that automates the optimal task allocation by taking into account each user's skills and the progress of the tasks they are currently working on.

[0665] "Communication methods for immediate information sharing among team members" refers to communication technologies that enable real-time information exchange among members, including those that ensure the immediacy of messaging and notifications.

[0666] "Means for monitoring resource utilization and proposing efficient resource allocation" refers to a function that analyzes the resource usage of the entire system and proposes the optimal resource allocation based on the results.

[0667] "Methods for automatically analyzing task priorities" refer to technologies that quantify the urgency and importance of each task and automatically calculate their priority in order to achieve efficient task processing.

[0668] "A method for suggesting efficient meeting times based on calendar information" refers to a system that analyzes schedule data to recommend the optimal date and time for meetings and events.

[0669] The system of this invention is a comprehensive platform that combines various modules to efficiently manage tasks and projects. This system operates primarily using servers and terminals, enabling users to effectively manage tasks and share information with teams.

[0670] In the progress management module, the server periodically collects task progress data from the database. This data is visualized using libraries such as Python's Matplotlib and provided to the user in a dashboard format. This allows the user to intuitively understand the progress of the project.

[0671] The task assignment and priority setting module analyzes user skill information and current task load. Based on this, it automatically calculates task priorities and distributes tasks to the most suitable members. This equalizes the workload for each member and improves project efficiency.

[0672] The communication support module consists of a real-time messaging system implemented in the terminal. Users can instantly share information with team members through this system. The server also has the functionality to push important notifications to the terminal, ensuring users receive information without missing anything.

[0673] In the resource management module, the server monitors the project's resource usage and proposes efficient resource allocation to the user. This enables optimal resource utilization and improves the overall project performance.

[0674] For example, if a user enters a prompt such as, "Check the latest progress of Project A and reassign Task X to the most suitable member," the system can utilize these modules to quickly provide the data necessary for project management and take appropriate action. By leveraging this generative AI model, users can achieve higher productivity.

[0675] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0676] Step 1:

[0677] The server collects task progress data from the database. This data collection is performed using SQL queries to extract the necessary task IDs and progress percentages. The inputs are database connection information and queries, and the output is the collected task progress data.

[0678] Step 2:

[0679] The server visualizes the collected progress data. This process uses Python's Matplotlib library to generate bar graphs and Gantt charts to show progress. The input is progress data, and the output is a visualized graph image. Specifically, it calculates the length of the bar corresponding to the progress rate of each task and plots it on the chart.

[0680] Step 3:

[0681] The terminal displays a dashboard to the user using visualization data sent from the server. This dashboard allows the user to intuitively understand the project status. The input is graph image data from the server, and the output is a visual dashboard on the terminal.

[0682] Step 4:

[0683] The server retrieves user skill and workload information from a database and performs analysis. The input is skill and workload information from the database, and the output is analytical data for task allocation. Specifically, it calculates weights to select tasks appropriate for each member's skills.

[0684] Step 5:

[0685] The server uses the analysis data to calculate task priorities and assign tasks to the most suitable members. Inputs are task information and member skill / load information, while output is optimized task assignment information. Specifically, it scores the priority of each task based on an algorithm and assigns them to members in order of highest priority.

[0686] Step 6:

[0687] Using the communication tools implemented on the device, users share information with team members in real time. Input is the message entered by the user, and output is the message notified to other members. Specifically, the device sends the user's input to a server, which then distributes it to other members.

[0688] Step 7:

[0689] The server monitors resource usage and proposes efficient resource allocation to the user. The input is the system's resource usage log, and the output is the optimal resource allocation proposal. Specifically, it uses a model that analyzes CPU usage and memory consumption to evaluate resource surpluses and shortages.

[0690] (Application Example 1)

[0691] 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".

[0692] In modern factory environments, there is a demand for increased efficiency in production lines through the use of autonomous machinery. However, since each machine often operates independently, there are challenges in overall project management and workload adjustment. While efficient and seamless operation is required, real-time information sharing between machines and optimal task allocation are essential.

[0693] 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.

[0694] In this invention, the server includes means for visualizing the progress of tasks, means for assigning optimal tasks based on the user's abilities and workload, and means for supporting information exchange within the group using a communication device. This enables the adjustment of workload among autonomous machines and the efficient operation of the production line.

[0695] "Means for visualizing task progress" refers to a device or method that visually displays the current progress of a task, enabling users to quickly grasp the situation.

[0696] "A means of assigning tasks based on user capabilities and workload" refers to a technology that automatically assigns appropriate tasks according to each user's skills and current workload.

[0697] "Means of supporting information exchange within a group using communication devices" refers to a system that enables real-time information transmission within a group using network communication.

[0698] "Means of monitoring resource usage using instruments and proposing optimal resource allocation" refers to methods for detecting the usage status of each resource and performing efficient reallocation and optimization.

[0699] "Optimization means for adjusting workload between autonomous machines" refers to a method or system for leveling the workload between different machines and improving overall work efficiency.

[0700] This system is designed to improve operational efficiency on a factory production line. A server plays a central role, receiving data transmitted from each autonomous machine and efficiently displaying it using visualization tools to visualize task progress. Specifically, data visualization libraries such as Plotly are used to visualize progress and workload.

[0701] The terminals are equipped with communication devices, allowing each machine to exchange information in real time via these devices. A real-time messaging system to support information exchange within the group is implemented using frameworks such as Flask.

[0702] Furthermore, the server assigns optimal tasks based on the user's capabilities and workload. Machine learning algorithms using scikit-learn are helpful in this process. These algorithms utilize generative AI models to efficiently assign the most suitable tasks to each user.

[0703] Furthermore, it includes a function that monitors resource usage using instruments and proposes the optimal resource allocation. The server utilizes sensor modules such as Raspberry Pi to analyze resource monitoring data and reflects the information on a dashboard.

[0704] As a concrete example, when implemented in a small-scale machine assembly plant, 10 robots worked together to share tasks, enabling project completion in a short amount of time.

[0705] The introduction of such a system will allow the entire factory's production line to operate more efficiently. An example of a prompt message utilizing a generative AI model would be: "A new order has been added. Please generate machine assignments to process it efficiently."

[0706] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0707] Step 1:

[0708] The server collects progress data from each autonomous machine. The input includes work status information reported by each machine's sensors. This data is analyzed to extract numerical data representing the progress of each task. The output is the numerical progress data.

[0709] Step 2:

[0710] The server processes progress data using visualization tools and generates Gantt charts and bar graphs that display the actual progress. Pre-processed progress data is used as input. The data is then processed using a data visualization library and output as a visualized dashboard.

[0711] Step 3:

[0712] Using communication devices connected to terminals, each machine transmits progress information to the server in real time. The input is progress updates from each machine. This information exchange allows for real-time monitoring of each other's progress and resource usage. The output is the received updated progress information.

[0713] Step 4:

[0714] The server acquires user capability and workload data and uses a generative AI model to calculate the next task to assign. Current capability assessments and workloads are referenced as input. Machine learning algorithms are applied to generate the optimal task assignment. The recalculated task assignment is obtained as output.

[0715] Step 5:

[0716] The server aggregates data from sensors to analyze resource usage and propose the optimal resource allocation. Input includes resource usage data from each machine. The data is analyzed to plan an efficient resource allocation. The output is a recommended resource allocation plan.

[0717] Step 6:

[0718] Users can review progress and assigned tasks through the generated dashboard and make adjustments or modifications as needed. The visualized dashboard information is used as input. Users review the data and develop appropriate plans for the next steps. The adjusted task plan is obtained as output.

[0719] 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.

[0720] This invention provides a system that further improves the efficiency of task management by combining an emotion engine with an AI task manager system. This system visualizes task progress, assigns appropriate tasks based on the user's skills and workload, supports smooth information exchange within the team, and understands resource usage to propose optimal allocation. The addition of the emotion engine automatically recognizes the user's emotional state and optimizes task assignment and communication suggestions accordingly.

[0721] Emotion Engine Module

[0722] The server uses an emotion engine that analyzes the user's voice tone and facial expression data to identify the user's emotional state. This data influences task assignment and priority decisions. For example, if a user is feeling stressed, the server might suggest reducing the workload of that task and assigning a support member.

[0723] Task assignment adjustment

[0724] Based on its recognized emotional state, the device suggests reassigning specific tasks to the user. These suggestions are adjusted to include less stressful tasks and tasks that promote teamwork. The user can review, approve, or re-evaluate these suggestions.

[0725] Suggestions for improving communication

[0726] The server analyzes emotional data and formulates suggestions to improve communication within the team. For example, if team members are generally showing signs of fatigue, the server might suggest holding refresh meetings or online workshops.

[0727] Specific example

[0728] In one project, the server detects that a user is under stress. Based on this information, the server alerts the project leader and suggests seeking assistance from other team members to alleviate the user's workload. This action equalizes the workload and ensures the overall project progresses smoothly.

[0729] Thus, embodiments of the present invention, by combining an emotion engine, optimize task management and team management in a way that takes emotions into consideration, thereby promoting improved work efficiency.

[0730] The following describes the processing flow.

[0731] Step 1:

[0732] The server collects the user's voice tone and facial expression data in real time using an emotion engine. This allows data related to the user's emotional state to be obtained.

[0733] Step 2:

[0734] The server analyzes the collected emotional data to identify the user's current emotional state. This information is categorized into emotional states such as stress, happiness, and fatigue.

[0735] Step 3:

[0736] The server determines whether the task load needs to be adjusted based on the identified emotional state. For example, if the user is determined to be in a high-stress state, reducing the task load will be recommended.

[0737] Step 4:

[0738] Based on instructions from the server, the terminal presents the user with a proposed task redistribution. This proposal includes tasks with less workload and tasks appropriate to the user's emotional state.

[0739] Step 5:

[0740] The user reviews the proposal displayed on their device and, if necessary, approves it or requests a different task assignment from the server.

[0741] Step 6:

[0742] The server analyzes the recognized emotion data as a whole to understand the emotional trends within the team and generates suggestions for improving communication.

[0743] Step 7:

[0744] The server will notify team leaders and members with suggestions, such as, "Overall fatigue levels are high, so we recommend increasing break times."

[0745] Step 8:

[0746] Users accept this suggestion and work to improve the overall emotional state of the team by setting up meetings, adjusting work styles, and so on.

[0747] (Example 2)

[0748] 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".

[0749] In modern work environments, flexible task management that takes into account technical skills, workload, and emotional state is required for users to work efficiently. Visualization of work progress, promotion of appropriate communication, and optimal resource allocation are also crucial. However, methods for centrally managing these aspects are not yet well established, and work adjustments based on users' emotional states are particularly lacking. Therefore, the development of a system that allows users to perform at their maximum potential is necessary.

[0750] 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.

[0751] In this invention, the server includes means for visualizing the progress of tasks, means for assigning optimal tasks based on the user's skills and workload, means for exchanging information to support communication within the organization, means for monitoring resource usage and proposing optimal resource allocation, and means for analyzing the user's emotional state and optimizing task assignments and communication strategies based on this. This enables flexible and efficient work management that takes into account the user's emotions and technical factors.

[0752] "Means for visualizing task progress" refers to methods for visually displaying the progress of tasks that users are working on, thereby making it easy to grasp the degree of progress and any delays in the work.

[0753] "Means for assigning optimal tasks based on users' skills and workload" refers to a method that evaluates each user's skill level and current workload, and automatically assigns the most suitable tasks accordingly.

[0754] "Information exchange methods to support communication within an organization" refer to methods and tools that enable members of an organization to exchange information smoothly, with the aim of building effective cooperative relationships.

[0755] "A means of monitoring resource usage and proposing optimal resource allocation" refers to a method of monitoring the usage of various resources (personnel, time, equipment, etc.) within an organization in real time and proposing an efficient allocation of resources based on this.

[0756] "Means of analyzing users' emotional states and optimizing work assignments and communication strategies based on them" refers to a process of collecting and analyzing users' emotions as data, and then using the results to optimize work assignments and communication methods within the organization.

[0757] The system for implementing this invention utilizes advanced AI models and is designed to enable users to perform tasks efficiently. This system incorporates multiple modules, including an emotion engine, which perform analysis of various data and optimization based on that analysis.

[0758] Server-based data analysis and optimization

[0759] The server first collects and analyzes the user's voice tone and facial expression data using speech recognition software and facial recognition algorithms. Specifically, it uses a speech API for speech recognition and an image processing library for facial recognition. This allows the server to identify the user's emotional state and reflect it in the operation of the entire system.

[0760] The server integrates user emotional data and technical evaluation data, and uses this to dynamically adjust work assignments. If the emotional state indicates stress, it reduces the burden and reassigns tasks to seek cooperation from team members.

[0761] Suggestions and feedback via devices

[0762] The terminal presents the user with suggestions sent from the server, offering new task assignments and communication improvement measures. Users can provide feedback on these suggestions and request adjustments as needed. This feedback is used to update the system's learning model, designed to improve the accuracy of future suggestions.

[0763] Specific example

[0764] In one project, the server detects that a user is under excessive stress. Based on this information, the server alerts the project leader and encourages other members to participate in order to equalize the workload across the team. It also maintains work efficiency by assigning new tasks that do not cause stress.

[0765] Example of a prompt

[0766] "Design a system framework that assesses a user's emotional state and suggests appropriate task reassignment. Include examples of actions based on emotional state data."

[0767] As described above, this invention aims to optimize task management and team operations more effectively and efficiently by combining the functions of an emotion engine.

[0768] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0769] Step 1:

[0770] The server acquires the user's voice tone and facial expression data. This is done using speech recognition software and facial recognition algorithms. The server inputs this data into an emotion recognition model to identify the user's emotional state. As output, it obtains emotional state data, such as whether the user is relaxed or stressed. Specifically, the server captures audio and video in real time and performs analysis in the cloud.

[0771] Step 2:

[0772] The terminal uses emotional state data received from the server to evaluate the tasks assigned to the user, taking into account the current skill level and workload. Using emotional state, skill data, and workload data as input, the terminal proposes task redistribution as needed. It generates a new list of task priorities as output. Specifically, the terminal updates the visual task management tool on the user interface.

[0773] Step 3:

[0774] The server aggregates emotional data from all users and analyzes the overall team situation. It uses each user's emotional state data as input to understand overall team trends. As output, the server generates suggestions for improving communication, such as suggestions for refresh meetings. Specifically, the server presents these suggestions to the project leader through a dashboard.

[0775] Step 4:

[0776] Users receive new task suggestions and communication improvement measures from their devices. They review this information and provide feedback. The system receives suggestions as input and provides approval or modification feedback as output. Specifically, users record their feedback by performing button operations on the UI.

[0777] Step 5:

[0778] The server receives feedback from users and updates its learning model to improve the accuracy of future suggestions. It receives user feedback data as input and adjusts model parameters using machine learning techniques. The output is an improved model for future suggestions. Specifically, the server periodically trains the model and reflects the new learning outcomes throughout the entire system.

[0779] (Application Example 2)

[0780] 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".

[0781] In modern production environments, there is a lack of work management that takes into account the emotional state of workers, resulting in excessive stress that negatively impacts productivity. Furthermore, a lack of smooth information exchange between production machinery and workers can lead to imbalances in workload. These problems raise concerns about decreased production efficiency and health risks for workers.

[0782] 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.

[0783] In this invention, the server includes means for visualizing the progress of tasks, means for assigning optimal tasks based on the user's skills and workload, and means for providing an appropriate work environment by analyzing the worker's emotional state from voice and image data and suggesting workload and rest periods. This makes it possible to adjust the workload according to the worker's emotional state and improve productivity through smooth information exchange.

[0784] "Methods for visualizing task progress" refer to functions that allow users to understand the extent to which each task has been completed by visually displaying the progress of the work.

[0785] "Means for assigning optimal tasks based on the user's skills and workload" refers to a function that automatically distributes each task to the most suitable worker, taking into account each worker's abilities and current workload.

[0786] "A means of analyzing workers' emotional states from audio and image data and proposing workload and rest" refers to a function that reads workers' emotions from audio and video, adjusts their workload based on that, and proposes rest at appropriate times, thereby improving workers' health and productivity.

[0787] "Information exchange means to support communication between automated production machines and workers" refers to a function that effectively transmits necessary information between production machines and workers and strengthens cooperation.

[0788] "A means of monitoring resource usage and proposing the optimal resource allocation" refers to a function that constantly checks the status of available resources and proposes the most efficient allocation accordingly.

[0789] To implement this invention, a server plays a central role. The server maintains a database for visualizing the progress of work and executes algorithms to evaluate each worker's skills and current workload. This makes it possible to assign tasks that are suitable for each worker.

[0790] For emotion recognition, devices such as smart glasses and head-mounted displays are used. These devices capture the worker's facial expressions and voice in real time and send them to a server for analysis. Specifically, emotion recognition software such as OpenCV and DeepFace is used. Based on this data, the server identifies the emotional state and adjusts the workload appropriately to suggest breaks and work reallocation for the worker.

[0791] For example, if a worker shows signs of stress due to high workload while performing a particular task, the server will flexibly change the criteria for assigning new tasks to that worker. It will also notify the entire team as needed to facilitate communication among employees.

[0792] The system supports real-time information communication between production machines and workers as a means of information exchange. The server collects operational data from production machines and user work records, and improves overall productivity by proposing the optimal resource allocation in specific situations.

[0793] Example prompt: "How would you redistribute tasks if an operator is experiencing stress?"

[0794] Through this invention, improvements to the work environment that respond to the emotional state of workers will be realized, enabling more efficient production activities.

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The server receives voice and facial expression data in real time from smart glasses or head-mounted displays. This provides input data to identify the worker's emotional state. The transmitted data is stored in a database for analysis.

[0798] Step 2:

[0799] The server uses OpenCV and DeepFace, emotion recognition software, to analyze the received audio and facial expression data. This analysis extracts characteristic patterns from each data point and compares them with existing emotion state mappings to perform data calculations that determine the worker's stress level and emotional state.

[0800] Step 3:

[0801] Based on the analysis results, the server proposes an optimal task reallocation, taking into account the worker's skill level and workload. This proposal includes prioritizing tasks and assigning new tasks, taking into account emotional states, and the proposal is sent as a notification to the worker's terminal.

[0802] Step 4:

[0803] The terminal receives suggestions from the server and notifies the user. These notifications may include suggestions for adjusting work content or suggesting breaks, and the new task schedule is finalized once the user approves them. The server receives a reply as output, indicating the user's approval or re-evaluation.

[0804] Step 5:

[0805] The server receives replies from users and updates the overall work schedule. Based on this, it shares information with other workers and automated production machines as needed to optimize the entire project. This final data is output to the administrator interface and can be checked at any time.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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."

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] The following is further disclosed regarding the embodiments described above.

[0828] (Claim 1)

[0829] A means of visualizing the progress of a task,

[0830] A means for assigning optimal tasks based on the user's skills and workload,

[0831] A means of information exchange to support communication within the team,

[0832] A means of monitoring resource usage and proposing the optimal resource allocation,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, further comprising means for automatically calculating the priority of a task.

[0836] (Claim 3)

[0837] The system according to claim 1, comprising means for suggesting the optimal time for a meeting from a shared calendar.

[0838] "Example 1"

[0839] (Claim 1)

[0840] A means of collecting and visually displaying task progress data,

[0841] A means of assigning optimal tasks based on user ability information and current workload,

[0842] A means of communication for instantly sharing information among team members,

[0843] A means of monitoring resource utilization and proposing efficient resource allocation,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, further comprising means for automatically analyzing task priorities.

[0847] (Claim 3)

[0848] The system according to claim 1, comprising means for suggesting efficient meeting times based on calendar information.

[0849] "Application Example 1"

[0850] (Claim 1)

[0851] A means of visualizing the progress of a task,

[0852] A means for assigning optimal tasks based on the user's abilities and workload,

[0853] A means of supporting information exchange within a group using communication devices,

[0854] A means of monitoring resource usage using instruments and proposing the optimal resource allocation,

[0855] An optimization means for adjusting the workload between autonomous operating machines,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, further comprising means for automatically calculating the priority of a task.

[0859] (Claim 3)

[0860] The system according to claim 1, comprising means for suggesting the optimal time for a meeting from a shared schedule.

[0861] "Example 2 of combining an emotion engine"

[0862] (Claim 1)

[0863] A means of visualizing the progress of a task,

[0864] A means for assigning the most suitable tasks based on the user's skills and workload,

[0865] Information exchange means to support communication within an organization,

[0866] A means of monitoring resource usage and proposing optimal resource allocation,

[0867] A means for analyzing the user's emotional state and optimizing work assignments and communication strategies based on this analysis,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, further comprising means for automatically calculating the priority of tasks.

[0871] (Claim 3)

[0872] The system according to claim 1, comprising means for suggesting the optimal time for a meeting from a shared schedule.

[0873] "Application example 2 of combining emotional engines"

[0874] (Claim 1)

[0875] A means of visualizing the progress of a task,

[0876] A means of assigning the most suitable tasks based on the user's skills and workload,

[0877] This method analyzes the emotional state of workers from audio and image data and proposes appropriate work loads and rest periods to provide a suitable work environment.

[0878] Information exchange means to support communication between automated production machines and employees,

[0879] A means of monitoring resource usage and proposing optimal resource allocation,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, further comprising means for automatically calculating the priority of tasks and re-evaluating the priority according to the emotional state of the workers.

[0883] (Claim 3)

[0884] The system according to claim 1, comprising means for proposing appropriate rest periods and production plans based on worker emotional data. [Explanation of symbols]

[0885] 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 visualizing the progress of a task, A means for assigning optimal tasks based on the user's skills and workload, A means of information exchange to support communication within the team, A means of monitoring resource usage and proposing the optimal resource allocation, A system that includes this.

2. The system according to claim 1, further comprising means for automatically calculating the priority of a task.

3. The system according to claim 1, comprising means for suggesting the optimal time for a meeting from a shared calendar.

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