system

Abstract

The system according to this embodiment aims to monitor the progress of a project in real time and provide appropriate reminders. [Solution] The system according to the embodiment comprises a monitoring unit, a visualization unit, and a reminder unit. The monitoring unit monitors the progress of the task. The visualization unit visualizes the progress monitored by the monitoring unit. The reminder unit issues reminders based on the progress visualized by the visualization unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to monitor the progress of a project in real time and provide an appropriate reminder.

[0005] The system according to the embodiment aims to monitor the progress of a project in real time and provide an appropriate reminder.

Means for Solving the Problems

[0006] The system according to the embodiment includes a monitoring unit, a visualization unit, and a reminder unit. The monitoring unit monitors the progress of tasks. The visualization unit visualizes the progress monitored by the monitoring unit. The reminder unit sends out a reminder based on the progress visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the progress of a project in real time and provide appropriate reminders. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. 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).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The project management system according to an embodiment of the present invention is a system that improves the success rate of projects by having an AI agent monitor the progress of projects in real time and remind users of task progress. This project management system solves the problems of conventional manual progress management, which is inefficient and time-consuming, often leads to tasks being delayed, and causes significant delays in delivery dates. The AI ​​agent monitors the progress in real time and constantly monitors the status of tasks to solve these problems. Specifically, it consists of the following steps: First, the start date, deadline, priority, etc. are set for each task. Next, the AI ​​agent monitors the overall progress and collects data. The progress of tasks can be checked directly on the dashboard, and if the progress falls below a set value, a reminder is automatically sent. The AI ​​agent evaluates and optimizes the effectiveness of reminders through learning from user interactions and feedback from data. In addition, by collecting data from completed projects and reflecting it in the next project, the accuracy of progress management and the flexibility of interactive responses are improved. This system can be used to support project managers and assist in gathering information before project status meetings, as well as to support the daily work of development teams and to automatically generate progress reports for customers. This improves project success rates and enables more efficient business processes. The project management system can enhance project success rates by monitoring project progress in real time and providing task reminders.

[0029] The project management system according to this embodiment comprises a monitoring unit, a visualization unit, and a reminder unit. The monitoring unit monitors the progress of tasks. The monitoring unit can, for example, monitor the progress of tasks in real time. The monitoring unit can use sensors and data collection devices to monitor the progress of tasks. For example, the monitoring unit can use project management software to monitor the progress of tasks. The monitoring unit can use an AI agent to monitor the progress of tasks. The visualization unit visualizes the progress monitored by the monitoring unit. For example, the visualization unit can display the progress on a dashboard. The visualization unit can use graphs and charts to visualize the progress. The visualization unit can use colors and icons to visualize the progress. The visualization unit can use an AI agent to visualize the progress. The reminder unit issues reminders based on the progress visualized by the visualization unit. For example, the reminder unit can issue a reminder when the progress falls below a set value. The reminder function can use email or notifications to send reminders. The reminder function can also use alerts or pop-ups to send reminders. Furthermore, the reminder function can use an AI agent to send reminders. As a result, the project management system according to this embodiment can improve the success rate of projects by monitoring and visualizing task progress in real time and sending reminders.

[0030] The monitoring unit monitors the progress of tasks. For example, the monitoring unit can monitor task progress in real time. The monitoring unit can use sensors and data collection devices to monitor task progress. Specifically, it uses project management software to track the progress of each task in detail. The project management software centrally manages information such as the start date, end date, progress rate, and assigned person for each task, and updates this information in real time. This allows the project manager to accurately understand the progress of each task. In addition, the monitoring unit can use an AI agent to automatically analyze task progress and detect anomalies. The AI ​​agent learns from past data and patterns and issues alerts if progress is behind schedule or if resource shortages are predicted. Furthermore, the monitoring unit can also use sensors and data collection devices to monitor the physical progress of tasks. For example, in a manufacturing project, sensors can be used to monitor the operating status of machinery and the production status of products, and data can be collected in real time. This allows the monitoring unit to monitor the progress of the project from multiple angles and enable rapid response.

[0031] The visualization unit visualizes the progress monitored by the monitoring unit. For example, the visualization unit can display progress on a dashboard. The dashboard is designed to provide an overview of the project at a glance, graphically displaying the progress and key metrics of each task. The visualization unit can use graphs and charts to visualize progress. For example, a Gantt chart can be used to visually display the start date, end date, and progress of each task. Bar graphs and pie charts can also be used to show task progress rates and resource usage. Furthermore, the visualization unit can use colors and icons to visualize progress. For example, green can be used for tasks progressing smoothly, and red for tasks that are behind schedule, making it easier to visually understand the situation. Icons can also be used to indicate task status and important notifications. The visualization unit can also automate progress visualization using an AI agent. The AI ​​agent analyzes data, extracts key information, and displays it on the dashboard. This allows the visualization unit to intuitively understand project progress and support rapid decision-making.

[0032] The reminder unit sends reminders based on the progress visualized by the visualization unit. For example, the reminder unit can send a reminder if the progress falls below a set value. Specifically, it sends a reminder to the person in charge if the task is behind schedule or the deadline is approaching. The reminder unit can use email or notifications to send reminders. For example, it can send an email to the person in charge of a task that is behind schedule, prompting them to report on the progress. It can also send reminders in real time using the notification function of project management software. The reminder unit can use alerts or pop-ups to send reminders. For example, it can display an alert on the project management software dashboard to highlight the progress of an important task. It can also use a pop-up notification to prompt the person in charge to take immediate action. The reminder unit can also automate the sending of reminders using an AI agent. The AI ​​agent analyzes the progress and optimizes the timing and content of reminder sending. This allows the reminder function to efficiently manage project progress and prevent task delays.

[0033] The learning unit can learn user interactions. For example, the learning unit can learn user interactions such as clicks, taps, and scrolls. The learning unit can use an AI agent to learn user interactions. The learning unit can use machine learning algorithms to learn user interactions. The learning unit can use data collection devices to learn user interactions. As a result, the system's accuracy improves by learning user interactions.

[0034] The feedback unit can receive feedback from data. For example, the feedback unit can receive feedback such as user opinions or system responses. The feedback unit can use AI agents to receive feedback from data. The feedback unit can use machine learning algorithms to receive feedback from data. The feedback unit can use data collection devices to receive feedback from data. This allows the system's accuracy to improve by receiving feedback from data.

[0035] The optimization unit can improve the accuracy of progress management. For example, the optimization unit can adjust algorithms and set parameters. The optimization unit can use AI agents to improve the accuracy of progress management. The optimization unit can use machine learning algorithms to improve the accuracy of progress management. The optimization unit can use data collection devices to improve the accuracy of progress management. As a result, improving the accuracy of progress management increases the project success rate.

[0036] The monitoring unit can monitor the progress of tasks in real time. For example, the monitoring unit can monitor task progress in seconds or minutes. The monitoring unit can use AI agents to monitor task progress in real time. The monitoring unit can use sensors and data acquisition devices to monitor task progress in real time. The monitoring unit can use project management software to monitor task progress in real time. This allows for accurate understanding of project progress by monitoring task progress in real time.

[0037] The visualization unit can display progress on a dashboard. For example, the visualization unit can display progress using graphs, charts, tables, etc. The visualization unit can use an AI agent to display progress on the dashboard. The visualization unit can use colors and icons to display progress on the dashboard. The visualization unit can use project management software to display progress on the dashboard. This allows for a quick overview of project progress by displaying it on the dashboard.

[0038] The reminder function can send reminders when the progress falls below a set value. For example, the reminder function can send reminders when the progress falls below a set progress rate threshold or deadline. The reminder function can use email, notifications, or alerts to send reminders. The reminder function can use an AI agent to send reminders. The reminder function can use project management software to send reminders. This prevents task delays by sending reminders when the progress falls below a set value.

[0039] The monitoring unit can prioritize monitoring tasks based on their importance when monitoring task progress. For example, it can prioritize monitoring high-priority tasks and gain a detailed understanding of their progress. It can reduce the monitoring frequency for low-priority tasks, allowing for efficient resource allocation. The monitoring unit can adjust the timing of monitoring according to task importance to perform optimal monitoring. The monitoring unit can use AI agents to assess task importance. The monitoring unit can use project management software to assess task importance. This allows for efficient resource allocation by prioritizing monitoring based on task importance.

[0040] The monitoring unit can detect anomalies by referring to past progress data when monitoring the progress of tasks. For example, the monitoring unit can detect anomalies if progress is behind schedule compared to past progress data. The monitoring unit can also detect anomalies if progress is too fast based on past progress data. The monitoring unit can analyze past progress data, learn anomaly patterns, and improve detection accuracy. The monitoring unit can use AI agents to perform anomaly detection. The monitoring unit can use project management software to perform anomaly detection. This allows for early detection of progress anomalies by referring to past progress data.

[0041] The monitoring unit can improve the accuracy of task monitoring by considering the user's geographical location. For example, if the user is in the office, the monitoring unit can perform detailed monitoring and accurately grasp the progress. If the user is out of the office, the monitoring unit can reduce the frequency of monitoring and allocate resources efficiently. The monitoring unit can determine the optimal monitoring timing based on the user's geographical location. The monitoring unit can use GPS data or location services to obtain geographical location information. The monitoring unit can use AI agents to improve the accuracy of monitoring by considering geographical location information. This allows for an accurate grasp of progress by improving the accuracy of monitoring while considering the user's geographical location.

[0042] The monitoring unit can analyze users' social media activity to identify relevant tasks when monitoring task progress. For example, the monitoring unit can identify relevant tasks from users' social media activity and add them to the monitoring target. The monitoring unit can analyze users' social media activity to identify factors that affect progress. The monitoring unit can re-evaluate task priorities based on users' social media activity. The monitoring unit can use AI agents to analyze social media activity. The monitoring unit can use project management software to analyze social media activity. This allows for an accurate understanding of progress by analyzing users' social media activity and identifying relevant tasks.

[0043] The visualization unit can prioritize the display of tasks based on their importance when visualizing task progress. For example, it can prioritize the display of high-importance tasks, allowing for a detailed understanding of their progress. The visualization unit can reduce the display frequency of low-importance tasks, enabling efficient resource allocation. The visualization unit can adjust the display timing according to task importance to provide optimal visualization. The visualization unit can use an AI agent to evaluate task importance. The visualization unit can use project management software to evaluate task importance. This allows for efficient resource allocation by prioritizing the display based on task importance.

[0044] The visualization unit can optimize the displayed content by referencing past progress data when visualizing the progress of a task. For example, the visualization unit can display a warning if progress is behind schedule compared to past progress data. The visualization unit can also display a warning if progress is too fast based on past progress data. The visualization unit can analyze past progress data and provide optimal display content. The visualization unit can use an AI agent to optimize the displayed content. The visualization unit can use project management software to optimize the displayed content. This allows for an accurate understanding of the progress status by optimizing the displayed content by referencing past progress data.

[0045] The visualization unit can select the optimal display method when visualizing task progress, taking into account the user's device information. For example, if the user is using a smartphone, the visualization unit can provide a display method that matches the screen size. If the user is using a tablet, the visualization unit can provide a display method optimized for a larger screen. If the user is using a desktop, the visualization unit can provide a display method that includes detailed information. The visualization unit can use device type, screen size, OS, etc., to acquire device information. The visualization unit can use an AI agent to select the optimal display method considering the device information. As a result, by selecting the optimal display method considering the user's device information, the progress can be accurately grasped.

[0046] The visualization unit can analyze users' social media activity and display relevant information when visualizing task progress. For example, the visualization unit can identify relevant tasks from users' social media activity and reflect this in the displayed content. The visualization unit can analyze users' social media activity and display factors that affect progress. Based on users' social media activity, the visualization unit can re-evaluate task priorities and adjust the displayed content. The visualization unit can use an AI agent to analyze social media activity. The visualization unit can use project management software to analyze social media activity. This allows for accurate understanding of progress by analyzing users' social media activity and displaying relevant information.

[0047] The reminder function can prioritize reminders based on their importance when sending them. For example, it can prioritize reminders for high-priority tasks, allowing for detailed tracking of their progress. It can also reduce the frequency of reminders for lower-priority tasks, enabling efficient resource allocation. The reminder function can adjust the timing of reminders according to task importance, providing optimal reminders. It can utilize AI agents to assess task importance. It can also utilize project management software to assess task importance. This allows for efficient resource allocation by prioritizing reminders based on task importance.

[0048] The reminder function can optimize the content of reminders by referencing the effectiveness of past reminders. For example, it can analyze the effectiveness of past reminders to determine the optimal content. Based on the effectiveness of past reminders, it can adjust the timing of reminders. It can evaluate the effectiveness of past reminders and improve the content of reminders. The reminder function can use an AI agent to optimize the content. It can also use project management software to optimize the content. This maximizes the effectiveness of reminders by optimizing the content based on the effectiveness of past reminders.

[0049] The reminder function can select the optimal sending method when sending reminders, taking into account the user's geographical location. For example, if the user is in the office, the reminder function can send a detailed reminder to accurately track progress. If the user is out of the office, the reminder function can send a concise reminder to efficiently allocate resources. The reminder function can determine the optimal sending timing based on the user's geographical location. The reminder function can use an AI agent to select the sending method. The reminder function can use project management software to select the sending method. This maximizes the effectiveness of reminders by selecting the optimal sending method considering the user's geographical location.

[0050] The reminder function can analyze users' social media activity and include relevant information when sending reminders. For example, the reminder function can identify relevant tasks from users' social media activity and reflect them in the reminder content. The reminder function can analyze users' social media activity and include factors influencing progress in the reminder. The reminder function can re-evaluate task priorities and adjust reminder content based on users' social media activity. The reminder function can use AI agents to analyze social media activity. The reminder function can use project management software to analyze social media activity. This maximizes the effectiveness of reminders by analyzing users' social media activity and including relevant information.

[0051] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can analyze past learning data to improve the accuracy of the learning algorithm. The learning unit can adjust the parameters of the learning algorithm by referring to past learning data. The learning unit can use an AI agent to optimize the learning algorithm. The learning unit can use project management software to optimize the learning algorithm. This improves the accuracy of learning by optimizing the learning algorithm by referring to past learning data.

[0052] The learning unit can weight the training data based on the progress of the tasks during training. For example, the learning unit can increase the weight of training data for tasks that are behind schedule, and decrease the weight of training data for tasks that are progressing smoothly. The learning unit can adjust the weighting of the training data according to the progress of the tasks to perform optimal training. The learning unit can use an AI agent to weight the training data. The learning unit can use project management software to weight the training data. This improves the accuracy of training by weighting the training data based on the progress of the tasks.

[0053] The feedback unit can select the optimal feedback method by referring to past feedback data when receiving feedback. For example, the feedback unit can select the optimal feedback method based on past feedback data. The feedback unit can analyze past feedback data to improve the accuracy of the feedback method. The feedback unit can adjust the parameters of the feedback method by referring to past feedback data. The feedback unit can use an AI agent to select the feedback method. The feedback unit can use project management software to select the feedback method. This improves the accuracy of feedback by selecting the optimal feedback method by referring to past feedback data.

[0054] The feedback unit can select the optimal feedback method when receiving feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit can provide a feedback method adapted to the screen size. If the user is using a tablet, the feedback unit can provide a feedback method optimized for a larger screen. If the user is using a desktop, the feedback unit can provide a feedback method that includes detailed information. The feedback unit can use device type, screen size, OS, etc., to obtain device information. The feedback unit can use an AI agent to select the optimal feedback method considering the device information. This improves the accuracy of feedback by selecting the optimal feedback method considering the user's device information.

[0055] The optimization unit can improve its optimization algorithm by referring to past optimization data during the optimization process. For example, the optimization unit can select the optimal optimization algorithm based on past optimization data. The optimization unit can analyze past optimization data to improve the accuracy of the optimization algorithm. The optimization unit can adjust the parameters of the optimization algorithm by referring to past optimization data. The optimization unit can use AI agents to improve the optimization algorithm. The optimization unit can use project management software to improve the optimization algorithm. This improves the accuracy of optimization by improving the optimization algorithm by referring to past optimization data.

[0056] The optimization unit can determine optimization priorities based on the progress of tasks. For example, it can prioritize optimizing tasks that are behind schedule to improve their progress. It can also reduce the optimization frequency for tasks that are progressing smoothly, allowing for efficient resource allocation. The optimization unit can adjust optimization priorities according to the progress of tasks to perform optimal optimization. The optimization unit can use an AI agent to determine optimization priorities. The optimization unit can use project management software to determine optimization priorities. This allows for efficient resource allocation by determining optimization priorities based on the progress of tasks.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The monitoring unit can automatically re-evaluate task priorities based on task progress when monitoring the progress of tasks. For example, it can prioritize monitoring tasks that are behind schedule, allowing for a detailed understanding of their progress. Furthermore, it can reduce the monitoring frequency for tasks progressing smoothly, enabling efficient resource allocation. In addition, it can adjust the timing of monitoring according to the task progress to ensure optimal monitoring. This allows for efficient resource allocation by determining monitoring priorities based on task progress.

[0059] The monitoring unit can detect anomalies by referring to past progress data when monitoring the progress of a task. For example, it can detect anomalies if progress is behind schedule compared to past progress data. It can also detect anomalies if progress is too fast based on past progress data. By analyzing past progress data and learning anomaly patterns, detection accuracy can be improved. As a result, anomalies in progress can be detected early by referring to past progress data.

[0060] The visualization unit can optimize the display content by referencing past progress data when visualizing task progress. For example, it can display a warning if progress is behind schedule compared to past progress data. It can also display a warning if progress is too fast based on past progress data. By analyzing past progress data, it can provide the most optimal display content. This allows for an accurate understanding of progress by optimizing the display content by referring to past progress data.

[0061] The reminder function can optimize the content of reminders by referencing the effectiveness of past reminders. For example, it can analyze the effectiveness of past reminders to determine the optimal content. It can adjust the timing of reminders based on the effectiveness of past reminders. It can evaluate the effectiveness of past reminders and improve the content of reminders. In this way, the effectiveness of reminders can be maximized by optimizing the content of reminders by referencing the effectiveness of past reminders.

[0062] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, it can select the optimal learning algorithm based on past learning data. It can analyze past learning data to improve the accuracy of the learning algorithm. It can adjust the parameters of the learning algorithm by referring to past learning data. As a result, the accuracy of learning is improved by optimizing the learning algorithm by referring to past learning data.

[0063] The optimization unit can improve its optimization algorithm by referring to past optimization data during the optimization process. For example, it can select the optimal optimization algorithm based on past optimization data. It can analyze past optimization data to improve the accuracy of the optimization algorithm. It can adjust the parameters of the optimization algorithm by referring to past optimization data. In this way, the accuracy of optimization is improved by improving the optimization algorithm by referring to past optimization data.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The monitoring unit monitors the progress of the task. The monitoring unit can, for example, monitor the progress of the task in real time. The monitoring unit can use sensors and data acquisition devices to monitor the progress of the task. For example, the monitoring unit can use project management software to monitor the progress of the task. The monitoring unit can use an AI agent to monitor the progress of the task. Step 2: The visualization unit visualizes the progress monitored by the monitoring unit. The visualization unit can, for example, display the progress on a dashboard. The visualization unit can use graphs and charts to visualize the progress. The visualization unit can use colors and icons to visualize the progress. The visualization unit can use an AI agent to visualize the progress. Step 3: The reminder unit sends reminders based on the progress visualized by the visualization unit. For example, the reminder unit can send a reminder when the progress falls below a set value. The reminder unit can use email or notifications to send reminders. The reminder unit can use alerts or pop-ups to send reminders. The reminder unit can use an AI agent to send reminders.

[0066] (Example of form 2) The project management system according to an embodiment of the present invention is a system that improves the success rate of projects by having an AI agent monitor the progress of projects in real time and remind users of task progress. This project management system solves the problems of conventional manual progress management, which is inefficient and time-consuming, often leads to tasks being delayed, and causes significant delays in delivery dates. The AI ​​agent monitors the progress in real time and constantly monitors the status of tasks to solve these problems. Specifically, it consists of the following steps: First, the start date, deadline, priority, etc. are set for each task. Next, the AI ​​agent monitors the overall progress and collects data. The progress of tasks can be checked directly on the dashboard, and if the progress falls below a set value, a reminder is automatically sent. The AI ​​agent evaluates and optimizes the effectiveness of reminders through learning from user interactions and feedback from data. In addition, by collecting data from completed projects and reflecting it in the next project, the accuracy of progress management and the flexibility of interactive responses are improved. This system can be used to support project managers and assist in gathering information before project status meetings, as well as to support the daily work of development teams and to automatically generate progress reports for customers. This improves project success rates and enables more efficient business processes. The project management system can enhance project success rates by monitoring project progress in real time and providing task reminders.

[0067] The project management system according to this embodiment comprises a monitoring unit, a visualization unit, and a reminder unit. The monitoring unit monitors the progress of tasks. The monitoring unit can, for example, monitor the progress of tasks in real time. The monitoring unit can use sensors and data collection devices to monitor the progress of tasks. For example, the monitoring unit can use project management software to monitor the progress of tasks. The monitoring unit can use an AI agent to monitor the progress of tasks. The visualization unit visualizes the progress monitored by the monitoring unit. For example, the visualization unit can display the progress on a dashboard. The visualization unit can use graphs and charts to visualize the progress. The visualization unit can use colors and icons to visualize the progress. The visualization unit can use an AI agent to visualize the progress. The reminder unit issues reminders based on the progress visualized by the visualization unit. For example, the reminder unit can issue a reminder when the progress falls below a set value. The reminder function can use email or notifications to send reminders. The reminder function can also use alerts or pop-ups to send reminders. Furthermore, the reminder function can use an AI agent to send reminders. As a result, the project management system according to this embodiment can improve the success rate of projects by monitoring and visualizing task progress in real time and sending reminders.

[0068] The monitoring unit monitors the progress of tasks. For example, the monitoring unit can monitor task progress in real time. The monitoring unit can use sensors and data collection devices to monitor task progress. Specifically, it uses project management software to track the progress of each task in detail. The project management software centrally manages information such as the start date, end date, progress rate, and assigned person for each task, and updates this information in real time. This allows the project manager to accurately understand the progress of each task. In addition, the monitoring unit can use an AI agent to automatically analyze task progress and detect anomalies. The AI ​​agent learns from past data and patterns and issues alerts if progress is behind schedule or if resource shortages are predicted. Furthermore, the monitoring unit can also use sensors and data collection devices to monitor the physical progress of tasks. For example, in a manufacturing project, sensors can be used to monitor the operating status of machinery and the production status of products, and data can be collected in real time. This allows the monitoring unit to monitor the progress of the project from multiple angles and enable rapid response.

[0069] The visualization unit visualizes the progress monitored by the monitoring unit. For example, the visualization unit can display progress on a dashboard. The dashboard is designed to provide an overview of the project at a glance, graphically displaying the progress and key metrics of each task. The visualization unit can use graphs and charts to visualize progress. For example, a Gantt chart can be used to visually display the start date, end date, and progress of each task. Bar graphs and pie charts can also be used to show task progress rates and resource usage. Furthermore, the visualization unit can use colors and icons to visualize progress. For example, green can be used for tasks progressing smoothly, and red for tasks that are behind schedule, making it easier to visually understand the situation. Icons can also be used to indicate task status and important notifications. The visualization unit can also automate progress visualization using an AI agent. The AI ​​agent analyzes data, extracts key information, and displays it on the dashboard. This allows the visualization unit to intuitively understand project progress and support rapid decision-making.

[0070] The reminder unit sends reminders based on the progress visualized by the visualization unit. For example, the reminder unit can send a reminder if the progress falls below a set value. Specifically, it sends a reminder to the person in charge if the task is behind schedule or the deadline is approaching. The reminder unit can use email or notifications to send reminders. For example, it can send an email to the person in charge of a task that is behind schedule, prompting them to report on the progress. It can also send reminders in real time using the notification function of project management software. The reminder unit can use alerts or pop-ups to send reminders. For example, it can display an alert on the project management software dashboard to highlight the progress of an important task. It can also use a pop-up notification to prompt the person in charge to take immediate action. The reminder unit can also automate the sending of reminders using an AI agent. The AI ​​agent analyzes the progress and optimizes the timing and content of reminder sending. This allows the reminder function to efficiently manage project progress and prevent task delays.

[0071] The learning unit can learn user interactions. For example, the learning unit can learn user interactions such as clicks, taps, and scrolls. The learning unit can use an AI agent to learn user interactions. The learning unit can use machine learning algorithms to learn user interactions. The learning unit can use data collection devices to learn user interactions. As a result, the system's accuracy improves by learning user interactions.

[0072] The feedback unit can receive feedback from data. For example, the feedback unit can receive feedback such as user opinions or system responses. The feedback unit can use AI agents to receive feedback from data. The feedback unit can use machine learning algorithms to receive feedback from data. The feedback unit can use data collection devices to receive feedback from data. This allows the system's accuracy to improve by receiving feedback from data.

[0073] The optimization unit can improve the accuracy of progress management. For example, the optimization unit can adjust algorithms and set parameters. The optimization unit can use AI agents to improve the accuracy of progress management. The optimization unit can use machine learning algorithms to improve the accuracy of progress management. The optimization unit can use data collection devices to improve the accuracy of progress management. As a result, improving the accuracy of progress management increases the project success rate.

[0074] The monitoring unit can monitor the progress of tasks in real time. For example, the monitoring unit can monitor task progress in seconds or minutes. The monitoring unit can use AI agents to monitor task progress in real time. The monitoring unit can use sensors and data acquisition devices to monitor task progress in real time. The monitoring unit can use project management software to monitor task progress in real time. This allows for accurate understanding of project progress by monitoring task progress in real time.

[0075] The visualization unit can display progress on a dashboard. For example, the visualization unit can display progress using graphs, charts, tables, etc. The visualization unit can use an AI agent to display progress on the dashboard. The visualization unit can use colors and icons to display progress on the dashboard. The visualization unit can use project management software to display progress on the dashboard. This allows for a quick overview of project progress by displaying it on the dashboard.

[0076] The reminder function can send reminders when the progress falls below a set value. For example, the reminder function can send reminders when the progress falls below a set progress rate threshold or deadline. The reminder function can use email, notifications, or alerts to send reminders. The reminder function can use an AI agent to send reminders. The reminder function can use project management software to send reminders. This prevents task delays by sending reminders when the progress falls below a set value.

[0077] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit can reduce the monitoring frequency to alleviate the user's burden. If the user is relaxed, the monitoring unit can increase the monitoring frequency to gain a detailed understanding of their progress. If the user is in a hurry, the monitoring unit can increase the monitoring frequency to enable a quick response. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for a reduction in the user's burden by adjusting the monitoring frequency according to the user's emotions.

[0078] The monitoring unit can prioritize monitoring tasks based on their importance when monitoring task progress. For example, it can prioritize monitoring high-priority tasks and gain a detailed understanding of their progress. It can reduce the monitoring frequency for low-priority tasks, allowing for efficient resource allocation. The monitoring unit can adjust the timing of monitoring according to task importance to perform optimal monitoring. The monitoring unit can use AI agents to assess task importance. The monitoring unit can use project management software to assess task importance. This allows for efficient resource allocation by prioritizing monitoring based on task importance.

[0079] The monitoring unit can detect anomalies by referring to past progress data when monitoring the progress of tasks. For example, the monitoring unit can detect anomalies if progress is behind schedule compared to past progress data. The monitoring unit can also detect anomalies if progress is too fast based on past progress data. The monitoring unit can analyze past progress data, learn anomaly patterns, and improve detection accuracy. The monitoring unit can use AI agents to perform anomaly detection. The monitoring unit can use project management software to perform anomaly detection. This allows for early detection of progress anomalies by referring to past progress data.

[0080] The monitoring unit can estimate the user's emotions and adjust the notification method of monitoring results based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can reduce the burden by providing less frequent notifications. If the user is relaxed, the monitoring unit can provide detailed notifications to make it easier to understand the progress. If the user is in a hurry, the monitoring unit can provide rapid notifications to encourage immediate action. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This reduces the burden on the user by adjusting the notification method according to the user's emotions.

[0081] The monitoring unit can improve the accuracy of task monitoring by considering the user's geographical location. For example, if the user is in the office, the monitoring unit can perform detailed monitoring and accurately grasp the progress. If the user is out of the office, the monitoring unit can reduce the frequency of monitoring and allocate resources efficiently. The monitoring unit can determine the optimal monitoring timing based on the user's geographical location. The monitoring unit can use GPS data or location services to obtain geographical location information. The monitoring unit can use AI agents to improve the accuracy of monitoring by considering geographical location information. This allows for an accurate grasp of progress by improving the accuracy of monitoring while considering the user's geographical location.

[0082] The monitoring unit can analyze users' social media activity to identify relevant tasks when monitoring task progress. For example, the monitoring unit can identify relevant tasks from users' social media activity and add them to the monitoring target. The monitoring unit can analyze users' social media activity to identify factors that affect progress. The monitoring unit can re-evaluate task priorities based on users' social media activity. The monitoring unit can use AI agents to analyze social media activity. The monitoring unit can use project management software to analyze social media activity. This allows for an accurate understanding of progress by analyzing users' social media activity and identifying relevant tasks.

[0083] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated user emotions. For example, if the user is stressed, the visualization unit can provide a simple display method to reduce visual burden. If the user is relaxed, the visualization unit can provide a detailed display method to make it easier to grasp the progress. If the user is in a hurry, the visualization unit can provide a concise display method to encourage quick action. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This reduces visual burden by adjusting the display method according to the user's emotions.

[0084] The visualization unit can prioritize the display of tasks based on their importance when visualizing task progress. For example, it can prioritize the display of high-importance tasks, allowing for a detailed understanding of their progress. The visualization unit can reduce the display frequency of low-importance tasks, enabling efficient resource allocation. The visualization unit can adjust the display timing according to task importance to provide optimal visualization. The visualization unit can use an AI agent to evaluate task importance. The visualization unit can use project management software to evaluate task importance. This allows for efficient resource allocation by prioritizing the display based on task importance.

[0085] The visualization unit can optimize the displayed content by referencing past progress data when visualizing the progress of a task. For example, the visualization unit can display a warning if progress is behind schedule compared to past progress data. The visualization unit can also display a warning if progress is too fast based on past progress data. The visualization unit can analyze past progress data and provide optimal display content. The visualization unit can use an AI agent to optimize the displayed content. The visualization unit can use project management software to optimize the displayed content. This allows for an accurate understanding of the progress status by optimizing the displayed content by referencing past progress data.

[0086] The visualization unit can estimate the user's emotions and adjust the visualization's colors and design based on those emotions. For example, if the user is stressed, the visualization unit can provide a calming color scheme to reduce visual strain. If the user is relaxed, the visualization unit can provide a bright color scheme to make it easier to understand the progress. If the user is in a hurry, the visualization unit can provide a simple and highly visible design to encourage quick responses. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for reduced visual strain by adjusting colors and designs according to the user's emotions.

[0087] The visualization unit can select the optimal display method when visualizing task progress, taking into account the user's device information. For example, if the user is using a smartphone, the visualization unit can provide a display method that matches the screen size. If the user is using a tablet, the visualization unit can provide a display method optimized for a larger screen. If the user is using a desktop, the visualization unit can provide a display method that includes detailed information. The visualization unit can use device type, screen size, OS, etc., to acquire device information. The visualization unit can use an AI agent to select the optimal display method considering the device information. As a result, by selecting the optimal display method considering the user's device information, the progress can be accurately grasped.

[0088] The visualization unit can analyze users' social media activity and display relevant information when visualizing task progress. For example, the visualization unit can identify relevant tasks from users' social media activity and reflect this in the displayed content. The visualization unit can analyze users' social media activity and display factors that affect progress. Based on users' social media activity, the visualization unit can re-evaluate task priorities and adjust the displayed content. The visualization unit can use an AI agent to analyze social media activity. The visualization unit can use project management software to analyze social media activity. This allows for accurate understanding of progress by analyzing users' social media activity and displaying relevant information.

[0089] The reminder function can estimate the user's emotions and adjust the timing of reminder sending based on the estimated emotions. For example, if the user is stressed, the reminder function can reduce the frequency of reminders to alleviate their burden. If the user is relaxed, the reminder function can increase the frequency of reminders to make it easier to track progress. If the user is in a hurry, the reminder function can increase the frequency of reminders to encourage a quick response. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the user's burden to be reduced by adjusting the timing of reminders according to their emotions.

[0090] The reminder function can prioritize reminders based on their importance when sending them. For example, it can prioritize reminders for high-priority tasks, allowing for detailed tracking of their progress. It can also reduce the frequency of reminders for lower-priority tasks, enabling efficient resource allocation. The reminder function can adjust the timing of reminders according to task importance, providing optimal reminders. It can utilize AI agents to assess task importance. It can also utilize project management software to assess task importance. This allows for efficient resource allocation by prioritizing reminders based on task importance.

[0091] The reminder function can optimize the content of reminders by referencing the effectiveness of past reminders. For example, it can analyze the effectiveness of past reminders to determine the optimal content. Based on the effectiveness of past reminders, it can adjust the timing of reminders. It can evaluate the effectiveness of past reminders and improve the content of reminders. The reminder function can use an AI agent to optimize the content. It can also use project management software to optimize the content. This maximizes the effectiveness of reminders by optimizing the content based on the effectiveness of past reminders.

[0092] The reminder function can estimate the user's emotions and adjust the content of the reminder based on those emotions. For example, if the user is stressed, the reminder function can provide a simple reminder to reduce their burden. If the user is relaxed, the reminder function can provide a detailed reminder to make it easier to understand the progress. If the user is in a hurry, the reminder function can provide a concise reminder to encourage a quick response. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the user's burden to be reduced by adjusting the content of the reminder according to their emotions.

[0093] The reminder function can select the optimal sending method when sending reminders, taking into account the user's geographical location. For example, if the user is in the office, the reminder function can send a detailed reminder to accurately track progress. If the user is out of the office, the reminder function can send a concise reminder to efficiently allocate resources. The reminder function can determine the optimal sending timing based on the user's geographical location. The reminder function can use an AI agent to select the sending method. The reminder function can use project management software to select the sending method. This maximizes the effectiveness of reminders by selecting the optimal sending method considering the user's geographical location.

[0094] The reminder function can analyze users' social media activity and include relevant information when sending reminders. For example, the reminder function can identify relevant tasks from users' social media activity and reflect them in the reminder content. The reminder function can analyze users' social media activity and include factors influencing progress in the reminder. The reminder function can re-evaluate task priorities and adjust reminder content based on users' social media activity. The reminder function can use AI agents to analyze social media activity. The reminder function can use project management software to analyze social media activity. This maximizes the effectiveness of reminders by analyzing users' social media activity and including relevant information.

[0095] The learning unit can estimate the user's emotions and select training data based on those estimated emotions. For example, if the user is stressed, the learning unit can select simple training data to reduce the burden. If the user is relaxed, the learning unit can select detailed training data to make it easier to understand the progress. If the user is in a hurry, the learning unit can select training data that gets straight to the point to encourage a quick response. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This maximizes the effectiveness of learning by selecting training data according to the user's emotions.

[0096] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can analyze past learning data to improve the accuracy of the learning algorithm. The learning unit can adjust the parameters of the learning algorithm by referring to past learning data. The learning unit can use an AI agent to optimize the learning algorithm. The learning unit can use project management software to optimize the learning algorithm. This improves the accuracy of learning by optimizing the learning algorithm by referring to past learning data.

[0097] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency to alleviate the burden. If the user is relaxed, the learning unit can increase the learning frequency to make it easier to track progress. If the user is in a hurry, the learning unit can increase the learning frequency to encourage quick responses. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for a reduction in user burden by adjusting the learning frequency according to the user's emotions.

[0098] The learning unit can weight the training data based on the progress of the tasks during training. For example, the learning unit can increase the weight of training data for tasks that are behind schedule, and decrease the weight of training data for tasks that are progressing smoothly. The learning unit can adjust the weighting of the training data according to the progress of the tasks to perform optimal training. The learning unit can use an AI agent to weight the training data. The learning unit can use project management software to weight the training data. This improves the accuracy of training by weighting the training data based on the progress of the tasks.

[0099] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is stressed, the feedback unit can provide simple feedback to reduce their burden. If the user is relaxed, the feedback unit can provide detailed feedback to make it easier to understand the progress. If the user is in a hurry, the feedback unit can provide concise feedback to encourage a quick response. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the feedback content to be adjusted according to the user's emotions, thereby reducing the user's burden.

[0100] The feedback unit can select the optimal feedback method by referring to past feedback data when receiving feedback. For example, the feedback unit can select the optimal feedback method based on past feedback data. The feedback unit can analyze past feedback data to improve the accuracy of the feedback method. The feedback unit can adjust the parameters of the feedback method by referring to past feedback data. The feedback unit can use an AI agent to select the feedback method. The feedback unit can use project management software to select the feedback method. This improves the accuracy of feedback by selecting the optimal feedback method by referring to past feedback data.

[0101] The feedback unit can estimate the user's emotions and adjust the frequency of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit can reduce the frequency of feedback to alleviate the burden. If the user is relaxed, the feedback unit can increase the frequency of feedback to make it easier to understand the progress. If the user is in a hurry, the feedback unit can increase the frequency of feedback to encourage a quick response. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for a reduction in user burden by adjusting the frequency of feedback according to the user's emotions.

[0102] The feedback unit can select the optimal feedback method when receiving feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit can provide a feedback method adapted to the screen size. If the user is using a tablet, the feedback unit can provide a feedback method optimized for a larger screen. If the user is using a desktop, the feedback unit can provide a feedback method that includes detailed information. The feedback unit can use device type, screen size, OS, etc., to obtain device information. The feedback unit can use an AI agent to select the optimal feedback method considering the device information. This improves the accuracy of feedback by selecting the optimal feedback method considering the user's device information.

[0103] The optimization unit can estimate the user's emotions and adjust the optimization parameters based on the estimated emotions. For example, if the user is stressed, the optimization unit can adjust the optimization parameters to reduce the burden. If the user is relaxed, the optimization unit can adjust the optimization parameters to make it easier to understand the progress. If the user is in a hurry, the optimization unit can adjust the optimization parameters to encourage a quick response. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for a reduction in the user's burden by adjusting the optimization parameters according to the user's emotions.

[0104] The optimization unit can improve its optimization algorithm by referring to past optimization data during the optimization process. For example, the optimization unit can select the optimal optimization algorithm based on past optimization data. The optimization unit can analyze past optimization data to improve the accuracy of the optimization algorithm. The optimization unit can adjust the parameters of the optimization algorithm by referring to past optimization data. The optimization unit can use AI agents to improve the optimization algorithm. The optimization unit can use project management software to improve the optimization algorithm. This improves the accuracy of optimization by improving the optimization algorithm by referring to past optimization data.

[0105] The optimization unit can estimate the user's emotions and adjust the optimization frequency based on the estimated emotions. For example, if the user is stressed, the optimization unit can reduce the optimization frequency to alleviate the burden. If the user is relaxed, the optimization unit can increase the optimization frequency to make it easier to understand the progress. If the user is in a hurry, the optimization unit can increase the optimization frequency to encourage a quick response. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the user's burden to be reduced by adjusting the optimization frequency according to the user's emotions.

[0106] The optimization unit can determine optimization priorities based on the progress of tasks. For example, it can prioritize optimizing tasks that are behind schedule to improve their progress. It can also reduce the optimization frequency for tasks that are progressing smoothly, allowing for efficient resource allocation. The optimization unit can adjust optimization priorities according to the progress of tasks to perform optimal optimization. The optimization unit can use an AI agent to determine optimization priorities. The optimization unit can use project management software to determine optimization priorities. This allows for efficient resource allocation by determining optimization priorities based on the progress of tasks.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The monitoring unit can automatically re-evaluate task priorities based on task progress when monitoring the progress of tasks. For example, it can prioritize monitoring tasks that are behind schedule, allowing for a detailed understanding of their progress. Furthermore, it can reduce the monitoring frequency for tasks progressing smoothly, enabling efficient resource allocation. In addition, it can adjust the timing of monitoring according to the task progress to ensure optimal monitoring. This allows for efficient resource allocation by determining monitoring priorities based on task progress.

[0109] The learning unit can estimate the user's emotions and adjust the learning content based on those emotions. For example, if the user is stressed, it can provide simple learning content to reduce the burden. If the user is relaxed, it can provide detailed learning content to make it easier to track progress. If the user is in a hurry, it can provide concise learning content to encourage quick responses. In this way, the effectiveness of learning can be maximized by adjusting the learning content according to the user's emotions.

[0110] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is stressed, simple feedback can be provided to reduce their burden. If the user is relaxed, detailed feedback can be provided to make it easier for them to understand the progress. If the user is in a hurry, concise feedback can be provided to encourage a quick response. In this way, the user's burden can be reduced by adjusting the content of the feedback according to their emotions.

[0111] The optimization unit can estimate the user's emotions and adjust the optimization parameters based on those emotions. For example, if the user is stressed, the optimization parameters can be adjusted to reduce their burden. If the user is relaxed, the optimization parameters can be adjusted to make it easier to understand the progress. If the user is in a hurry, the optimization parameters can be adjusted to encourage a quick response. In this way, by adjusting the optimization parameters according to the user's emotions, the user's burden can be reduced.

[0112] The reminder function can estimate the user's emotions and adjust the timing of reminder sending based on those emotions. For example, if the user is stressed, the frequency of reminders can be reduced to lessen their burden. If the user is relaxed, the frequency of reminders can be increased to make it easier to track progress. If the user is in a hurry, the frequency of reminders can be increased to encourage a quick response. In this way, by adjusting the timing of reminders according to the user's emotions, the burden on the user can be reduced.

[0113] The monitoring unit can detect anomalies by referring to past progress data when monitoring the progress of a task. For example, it can detect anomalies if progress is behind schedule compared to past progress data. It can also detect anomalies if progress is too fast based on past progress data. By analyzing past progress data and learning anomaly patterns, detection accuracy can be improved. As a result, anomalies in progress can be detected early by referring to past progress data.

[0114] The visualization unit can optimize the display content by referencing past progress data when visualizing task progress. For example, it can display a warning if progress is behind schedule compared to past progress data. It can also display a warning if progress is too fast based on past progress data. By analyzing past progress data, it can provide the most optimal display content. This allows for an accurate understanding of progress by optimizing the display content by referring to past progress data.

[0115] The reminder function can optimize the content of reminders by referencing the effectiveness of past reminders. For example, it can analyze the effectiveness of past reminders to determine the optimal content. It can adjust the timing of reminders based on the effectiveness of past reminders. It can evaluate the effectiveness of past reminders and improve the content of reminders. In this way, the effectiveness of reminders can be maximized by optimizing the content of reminders by referencing the effectiveness of past reminders.

[0116] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, it can select the optimal learning algorithm based on past learning data. It can analyze past learning data to improve the accuracy of the learning algorithm. It can adjust the parameters of the learning algorithm by referring to past learning data. As a result, the accuracy of learning is improved by optimizing the learning algorithm by referring to past learning data.

[0117] The optimization unit can improve its optimization algorithm by referring to past optimization data during the optimization process. For example, it can select the optimal optimization algorithm based on past optimization data. It can analyze past optimization data to improve the accuracy of the optimization algorithm. It can adjust the parameters of the optimization algorithm by referring to past optimization data. In this way, the accuracy of optimization is improved by improving the optimization algorithm by referring to past optimization data.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The monitoring unit monitors the progress of the task. The monitoring unit can, for example, monitor the progress of the task in real time. The monitoring unit can use sensors and data acquisition devices to monitor the progress of the task. For example, the monitoring unit can use project management software to monitor the progress of the task. The monitoring unit can use an AI agent to monitor the progress of the task. Step 2: The visualization unit visualizes the progress monitored by the monitoring unit. The visualization unit can, for example, display the progress on a dashboard. The visualization unit can use graphs and charts to visualize the progress. The visualization unit can use colors and icons to visualize the progress. The visualization unit can use an AI agent to visualize the progress. Step 3: The reminder unit sends reminders based on the progress visualized by the visualization unit. For example, the reminder unit can send a reminder when the progress falls below a set value. The reminder unit can use email or notifications to send reminders. The reminder unit can use alerts or pop-ups to send reminders. The reminder unit can use an AI agent to send reminders.

[0120] 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.

[0121] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0122] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements described above, including the monitoring unit, visualization unit, reminder unit, learning unit, feedback unit, and optimization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the progress of the task using the camera 42 and sensors of the smart device 14 and collects data in real time using the control unit 46A. The visualization unit displays the progress on a dashboard using the display 40A of the smart device 14. The reminder unit sends reminders using the notification function of the smart device 14. The learning unit learns user interactions using the specific processing unit 290 of the data processing unit 12. The feedback unit receives feedback from the data using the specific processing unit 290 of the data processing unit 12. The optimization unit improves the accuracy of progress management using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] 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.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] 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.

[0131] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the monitoring unit, visualization unit, reminder unit, learning unit, feedback unit, and optimization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the progress of the task using the camera 42 and sensors of the smart glasses 214 and collects data in real time using the control unit 46A. The visualization unit displays the progress on a dashboard using the display of the smart glasses 214. The reminder unit sends reminders using the notification function of the smart glasses 214. The learning unit learns user interactions using the specific processing unit 290 of the data processing unit 12. The feedback unit receives feedback from the data using the specific processing unit 290 of the data processing unit 12. The optimization unit improves the accuracy of progress management using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] 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.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] 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.

[0147] 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.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the monitoring unit, visualization unit, reminder unit, learning unit, feedback unit, and optimization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the progress of the task using the camera 42 and sensors of the headset terminal 314 and collects data in real time using the control unit 46A. The visualization unit displays the progress on a dashboard using the display 343 of the headset terminal 314. The reminder unit sends reminders using the notification function of the headset terminal 314. The learning unit learns user interactions using the specific processing unit 290 of the data processing unit 12. The feedback unit receives feedback from the data using the specific processing unit 290 of the data processing unit 12. The optimization unit improves the accuracy of progress management using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0159] 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.

[0160] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] 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.

[0163] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0164] 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.

[0165] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the monitoring unit, visualization unit, reminder unit, learning unit, feedback unit, and optimization unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the progress of the task using the camera 42 and sensors of the robot 414 and collects data in real time using the control unit 46A. The visualization unit displays the progress on a dashboard using the display of the robot 414. The reminder unit sends reminders using the notification function of the robot 414. The learning unit learns user interactions using the specific processing unit 290 of the data processing unit 12. The feedback unit receives feedback from the data using the specific processing unit 290 of the data processing unit 12. The optimization unit improves the accuracy of progress management using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0173] 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.

[0174] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0175] 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.

[0176] 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.

[0177] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0178] 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."

[0179] 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.

[0180] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0189] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0190] 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.

[0191] (Note 1) A monitoring unit that monitors the progress of tasks, A visualization unit that visualizes the progress monitored by the aforementioned monitoring unit, The system includes a reminder unit that issues reminders based on the progress visualized by the visualization unit. A system characterized by the following features. (Note 2) It includes a learning unit that learns user interactions. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a feedback unit that receives data feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an optimization unit to improve the accuracy of progress management. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned monitoring unit, Monitor task progress in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned visualization unit, Display progress on the dashboard The system described in Appendix 1, characterized by the features described herein. (Note 7) The reminder unit is, A reminder will be sent if the progress falls below the set value. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, When monitoring task progress, prioritize monitoring based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, When monitoring task progress, past progress data is referenced to detect anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the notification method of monitoring results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, When monitoring task progress, consider the user's geographical location to improve monitoring accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned monitoring unit, When monitoring task progress, analyze users' social media activity to identify relevant tasks. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned visualization unit, It estimates the user's emotions and adjusts the display method of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned visualization unit, When visualizing task progress, prioritize the display based on the importance of each task. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned visualization unit, When visualizing task progress, we optimize the display by referring to past progress data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned visualization unit, It estimates the user's emotions and adjusts the visualization's colors and design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned visualization unit, When visualizing task progress, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visualization unit, When visualizing task progress, analyze users' social media activity and display relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The reminder unit is, It estimates the user's emotions and adjusts the timing of sending reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The reminder unit is, When sending reminders, prioritize them based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 22) The reminder unit is, When sending reminders, the system optimizes the message content by referencing the effectiveness of past reminders. The system described in Appendix 1, characterized by the features described herein. (Note 23) The reminder unit is, It estimates the user's emotions and adjusts the content of reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The reminder unit is, When sending reminders, the system selects the optimal sending method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The reminder unit is, When sending reminders, analyze users' social media activity and include relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, During training, the training data is weighted based on the progress of the task. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is When receiving feedback, we refer to past feedback data to select the most suitable feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is It estimates the user's emotions and adjusts the frequency of feedback based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is When receiving feedback, the optimal feedback method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The optimization unit, It estimates the user's emotions and adjusts the optimization parameters based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The optimization unit, When performing optimization, we improve the optimization algorithm by referring to past optimization data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The optimization unit, It estimates the user's emotions and adjusts the optimization frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The optimization unit, When performing optimization, prioritize optimizations based on the progress of the tasks. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring unit that monitors the progress of tasks, A visualization unit that visualizes the progress monitored by the aforementioned monitoring unit, The system includes a reminder unit that issues reminders based on the progress visualized by the visualization unit. A system characterized by the following features.

2. It includes a learning unit that learns user interactions. The system according to feature 1.

3. It is equipped with a feedback unit that receives data feedback. The system according to feature 1.

4. It includes an optimization unit to improve the accuracy of progress management. The system according to feature 1.

5. The aforementioned monitoring unit, Monitor task progress in real time. The system according to feature 1.

6. The aforementioned visualization unit, Display progress on the dashboard The system according to feature 1.

7. The reminder unit is, A reminder will be sent if the progress falls below the set value. The system according to feature 1.

8. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

9. The aforementioned monitoring unit, When monitoring task progress, prioritize monitoring based on the importance of the task. The system according to feature 1.

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