system

Abstract

The system according to this embodiment aims to optimize task priorities by considering each member's schedule and workload, and to visualize the emotional state of the entire team. [Solution] The system according to the embodiment comprises a collection unit, an optimization unit, a monitoring unit, and a visualization unit. The collection unit collects task data. The optimization unit optimizes task priorities based on the task data collected by the collection unit, taking into account each member's schedule and workload. The monitoring unit works in conjunction with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. The visualization unit visualizes the emotional state of the entire team based on the health data monitored by the monitoring unit.

Description

Technical Field

[0006]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to fully consider the schedules and loads of each member when optimizing the task priorities, and there is also a problem that the means for grasping the emotional state of the entire team is limited.

[0005] The system according to the embodiment aims to optimize the task priorities in consideration of the schedules and loads of each member and visualize the emotional state of the entire team.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an optimization unit, a monitoring unit, and a visualization unit. The data collection unit collects task data. The optimization unit optimizes task priorities based on the task data collected by the data collection unit, taking into account each member's schedule and workload. The monitoring unit works in conjunction with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. The visualization unit visualizes the emotional state of the entire team based on the health data monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize task priorities by considering each member's schedule and workload, and can visualize the emotional state of the entire team. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[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 system according to an embodiment of the present invention is a system that uses an AI agent to optimize task priorities, monitors health data in conjunction with wearable devices, and visualizes the emotional state of the entire team. First, this system collects task data from task management tools such as Jira and automatically optimizes task priorities considering each member's schedule and workload. The AI ​​agent performs this task to maximize project efficiency. Next, it works in conjunction with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. This allows the system to understand the health status of each member and adjust tasks as needed. Furthermore, it provides an emotional sharing dashboard that visualizes the emotional state of the entire team. This promotes empathy among team members and enables efficient communication even in a remote work environment. For example, a project manager can check the task priorities automatically set by the AI ​​agent and equalize the workload of each member, thereby smoothly advancing the project. In addition, based on the health data obtained from wearable devices, the system makes suggestions for stress management and motivation improvement, promoting work style reform. This system solves the challenges of cumbersome task prioritization and difficult resource allocation, preventing stress and decreased motivation among team members. It also addresses the issue of difficulty in understanding the emotions and situations of other team members when working remotely. By optimizing task prioritization, monitoring health data, and visualizing emotional states, the system maximizes project efficiency and manages the team's health and emotional well-being.

[0029] The system according to the embodiment comprises a collection unit, an optimization unit, a monitoring unit, and a visualization unit. The collection unit collects task data. The collection unit collects task data from, for example, a task management tool such as Jira. The collection unit can collect information such as the task name, due date, assignee, and progress status. The collection unit can also obtain data using the API of the task management tool. For example, the collection unit periodically collects task data using the API of the task management tool. The collection unit can also collect task data in real time. For example, the collection unit detects and collects changes in task data in real time using the Webhook of the task management tool. The optimization unit optimizes task priorities based on the task data collected by the collection unit, taking into account each member's schedule and workload. For example, the optimization unit displays each member's schedule in a calendar format and optimizes task priorities. The optimization unit can also adjust task priorities considering each member's workload. For example, the optimization unit optimizes task priorities considering each member's work time and the difficulty of the task. The optimization unit can also optimize task priorities using AI algorithms. For example, the optimization unit can optimize task priorities using machine learning algorithms. The monitoring unit works in conjunction with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. For example, the monitoring unit can acquire heart rate data from smartwatches and monitor it in real time. The monitoring unit can also measure stress hormones and conduct surveys to measure stress levels. The monitoring unit can also monitor pedometers and calorie consumption to measure activity levels. The monitoring unit acquires data in real time to understand health status. The visualization unit visualizes the emotional state of the entire team based on the health data monitored by the monitoring unit. For example, the visualization unit visualizes health data using graphs and color coding. The visualization unit can also use surveys and facial recognition technology to evaluate emotional states.The visualization unit displays emotional states in real time, promoting empathy among team members. This allows the system, according to the embodiment, to optimize task priorities, monitor health data, and visualize emotional states, thereby maximizing project efficiency and managing the team's health and emotional state.

[0030] The data collection unit collects task data. For example, it collects task data from task management tools such as Jira. Specifically, the data collection unit uses the task management tool's API to periodically retrieve information such as task name, due date, assignee, and progress status. Using the API automates the retrieval of task data, eliminating the need for manual data entry. Furthermore, the data collection unit can use the task management tool's webhooks to detect changes in task data in real time and immediately reflect them in the database. This ensures that task progress and changes are always up-to-date. Additionally, the data collection unit can collect data from multiple task management tools, facilitating information sharing among team members using different tools. This allows for a comprehensive understanding of the overall project task status and enables efficient task management.

[0031] The optimization unit optimizes task priorities based on task data collected by the data collection unit, taking into account each member's schedule and workload. Specifically, the optimization unit displays each member's schedule in a calendar format and visually adjusts task priorities. Each member's schedule includes information such as already scheduled meetings and vacations, and tasks are assigned considering these factors. Furthermore, the optimization unit monitors each member's workload in real time and adjusts task priorities considering work time and task difficulty. For example, if a member is overloaded, the unit reassigns that member's tasks to other members to maintain overall team balance. The optimization unit can also optimize task priorities using AI algorithms. By using machine learning algorithms, it analyzes past task data and member performance data to automatically suggest optimal task assignments. This streamlines task prioritization and ensures smooth project progress. In addition, the optimization unit can track task progress in real time and readjust priorities as needed. This prevents project delays and enables efficient task management.

[0032] The monitoring unit works in conjunction with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. Specifically, the monitoring unit acquires heart rate data from smartwatches and monitors it in real time. Heart rate data is an important indicator for evaluating members' stress levels and fatigue levels. The monitoring unit can also measure stress hormones and conduct questionnaires to measure stress levels. For example, it can conduct questionnaires regularly to evaluate members' subjective stress levels. It can also monitor pedometers and calorie consumption to measure activity levels. This allows for a comprehensive understanding of the members' health status. The monitoring unit not only acquires data in real time and understands health status, but can also issue immediate alerts if abnormal data is detected. For example, if the heart rate is abnormally high or the stress level suddenly rises, it can send a notification to the member prompting them to take a break. This helps maintain the members' health and prevents a decline in performance. Furthermore, the monitoring unit can analyze the collected data and provide data for long-term health management and stress countermeasures. This allows for the continuous improvement of the overall health status of the team.

[0033] The visualization unit visualizes the emotional state of the entire team based on health data monitored by the monitoring unit. Specifically, the visualization unit visualizes health data using graphs and color coding. For example, it graphs heart rate and stress level data over time, allowing for a visual understanding of changes in members' health. By color-coding members with high stress levels, the emotional state of the entire team can be grasped at a glance. The visualization unit can also use surveys and facial recognition technology to evaluate emotional states. For example, it can conduct surveys regularly to evaluate members' emotional states. It can also use facial recognition technology to analyze members' facial expressions during video conferences and evaluate their emotional states in real time. This can promote empathy among team members and improve the quality of communication. Furthermore, the visualization unit promotes cooperation among members by displaying emotional states in real time and sharing the emotional state of the entire team. For example, other members can provide support to members whose emotional state is low, thereby improving the overall performance of the team. As a result, the system according to this embodiment can optimize task prioritization, monitor health data, and visualize emotional states, maximizing project efficiency and managing the health and emotional state of the team.

[0034] The data collection unit can collect task data from task management tools such as Jira. For example, the unit can retrieve task data using the Jira API. The unit can collect information such as task name, due date, assignee, and progress. For example, the unit can periodically collect task data using the task management tool's API. Furthermore, the unit can collect task data in real time. For example, the unit can detect and collect changes to task data in real time using the task management tool's webhook. This streamlines task management by collecting task data from task management tools.

[0035] The optimization unit can optimize task priorities based on task data collected by the collection unit, taking into account each member's schedule and workload. For example, the optimization unit can display each member's schedule in a calendar format and optimize task priorities. The optimization unit can also adjust task priorities considering each member's workload. For example, the optimization unit can optimize task priorities considering each member's working time and the difficulty of the task. The optimization unit can also optimize task priorities using AI algorithms. For example, the optimization unit can optimize task priorities using machine learning algorithms. This improves project efficiency by optimizing task priorities while considering each member's schedule and workload.

[0036] The monitoring unit can connect with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. For example, the monitoring unit can acquire heart rate data from a smartwatch and monitor it in real time. To measure stress levels, the monitoring unit can also measure stress hormones and conduct questionnaires. To measure activity levels, the monitoring unit can also monitor pedometers and calorie consumption. The monitoring unit acquires data in real time and understands the health status. This allows for monitoring of each member's health status in real time by connecting with wearable devices.

[0037] The visualization unit can visualize the emotional state of the entire team based on health data monitored by the monitoring unit. For example, the visualization unit can visualize health data using graphs or color coding. The visualization unit can also use surveys or facial recognition technology to evaluate emotional states. The visualization unit displays emotional states in real time, promoting empathy among team members. By visualizing emotional states based on health data, it facilitates empathy among team members and enables efficient communication.

[0038] The data collection unit can analyze each member's past task history and select the optimal collection method. For example, it can prioritize collecting data from task management tools that were frequently used in the past. The data collection unit can also analyze each member's past task completion times to determine the optimal collection timing. Based on the types of tasks each member has performed in the past, the data collection unit can also prioritize collecting relevant data. This allows for efficient task data collection by analyzing past task history and selecting the optimal collection method.

[0039] The data collection unit can filter task data based on each member's current projects and areas of interest. For example, the unit can prioritize collecting task data related to projects each member is currently working on. The unit can also filter relevant task data based on each member's areas of interest. The unit can also collect necessary task data based on the progress of each member's current projects. This allows for the efficient collection of highly relevant data by filtering task data based on current projects and areas of interest.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering each member's geographical location when collecting task data. For example, the data collection unit can prioritize the collection of task data related to nearby projects based on each member's current location. The data collection unit can also collect region-specific task data based on each member's geographical location. The data collection unit can also collect highly relevant task data by considering each member's travel history. This enables efficient task data collection by considering geographical location and collecting highly relevant data.

[0041] The data collection unit can analyze each member's social media activity and collect relevant data when collecting task data. For example, the data collection unit can collect relevant task data based on each member's social media activity. The data collection unit can also analyze each member's social media interests and prioritize the collection of relevant task data. The data collection unit can also collect relevant task data considering each member's social media network. This enables efficient task data collection by analyzing social media activity and collecting relevant data.

[0042] The optimization unit can determine the optimal priority when optimizing tasks by referring to each member's past performance data. For example, the optimization unit can analyze each member's past task completion time to determine the optimal priority. The optimization unit can also prioritize related tasks based on the types of tasks each member has previously completed. The optimization unit can also determine the priority of the most efficient tasks based on each member's past performance data. This enables efficient task management by determining the optimal priority by referring to past performance data.

[0043] The optimization unit can assign tasks based on each member's skill set and expertise during task optimization. For example, the optimization unit can analyze each member's skill set and assign the most suitable tasks. The optimization unit can also prioritize the assignment of relevant tasks based on each member's expertise. The optimization unit can also assign tasks in the most efficient way, taking into account each member's skill set and expertise. This enables efficient task management by assigning tasks based on skill sets and expertise.

[0044] The optimization unit can assign tasks while considering the geographical distribution of each member during task optimization. For example, the optimization unit can prioritize assigning tasks related to nearby projects based on each member's current location. The optimization unit can also assign region-specific tasks based on each member's geographical distribution. The optimization unit can also assign highly relevant tasks by considering each member's travel history. This enables efficient task management by assigning tasks while considering geographical distribution.

[0045] The optimization unit can adjust task priorities by referencing the progress of related projects during task optimization. For example, the optimization unit analyzes the progress of related projects and determines the optimal task priorities. Based on project progress, the optimization unit can also prioritize tasks with high urgency. The optimization unit can also prioritize tasks with high importance, taking project progress into consideration. This enables efficient task management by adjusting task priorities by referencing project progress.

[0046] The monitoring unit can detect abnormal values ​​by referring to each member's past health data during monitoring. For example, the monitoring unit can analyze each member's past heart rate data to detect abnormal values. The monitoring unit can also detect abnormal values ​​by referring to each member's past stress level data. The monitoring unit can also detect abnormal values ​​based on each member's past activity level data. This allows for the early detection of health abnormalities by referring to past health data.

[0047] The monitoring unit can improve the accuracy of monitoring based on each member's lifestyle and activity patterns. For example, the monitoring unit can analyze each member's lifestyle to improve monitoring accuracy. The monitoring unit can also improve monitoring accuracy based on each member's activity patterns. The monitoring unit can also select the most efficient monitoring method considering each member's lifestyle and activity patterns. This allows for a more accurate understanding of health status by improving monitoring accuracy based on lifestyle and activity patterns.

[0048] The monitoring unit can analyze health data while considering each member's geographical environment during monitoring. For example, the monitoring unit can analyze health data considering the geographical environment based on each member's current location. The monitoring unit can also analyze region-specific health risks based on each member's geographical environment. The monitoring unit can also analyze health data while considering each member's travel history. This allows for the identification of region-specific health risks by analyzing health data while considering the geographical environment.

[0049] The monitoring unit can improve the accuracy of monitoring by referring to each member's relevant medical data during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to each member's past medical data. The monitoring unit can also improve the accuracy of monitoring based on each member's current medical data. The monitoring unit can also select the most efficient monitoring method by considering each member's medical data. As a result, by improving the accuracy of monitoring by referring to relevant medical data, it becomes possible to grasp the health status more accurately.

[0050] The visualization unit can predict the current emotional state by referring to past emotional data when visualizing emotional states. For example, the visualization unit can analyze each member's past emotional data and predict their current emotional state. The visualization unit can also predict the current emotional state based on each member's past emotional patterns. The visualization unit can also predict the most likely emotional state based on each member's past emotional data. This allows for understanding changes in emotional states by predicting the current emotional state by referring to past emotional data.

[0051] The visualization unit can customize the displayed content based on each member's role and relationships when visualizing emotional states. For example, the visualization unit can customize the displayed emotional state content according to each member's role. The visualization unit can also adjust the displayed emotional state content based on each member's relationships. The visualization unit can also provide the most appropriate displayed content considering each member's role and relationships. This makes it possible to provide optimal information by customizing the displayed content based on roles and relationships.

[0052] The visualization unit can analyze changes in emotions based on each member's activity history when visualizing emotional states. For example, the visualization unit can analyze each member's past activity history and predict changes in emotions. The visualization unit can also display changes in emotions in real time based on each member's activity history. The visualization unit can also predict the most likely changes in emotions based on each member's activity history. This allows for understanding changes in emotional states by analyzing changes in emotions based on activity history.

[0053] The visualization unit can analyze emotional states by referencing progress data of related projects when visualizing emotional states. For example, the visualization unit can analyze progress data of related projects and predict emotional states. The visualization unit can also display emotional states in real time based on project progress data. The visualization unit can also predict the most likely emotional state based on project progress data. This allows for understanding changes in emotional states by analyzing them with reference to project progress data.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can determine the optimal collection timing by referring to each member's past performance data when collecting task data. For example, the data collection unit can analyze each member's past task completion time to determine the optimal collection timing. The data collection unit can also prioritize the collection of relevant data based on the types of tasks each member has previously completed. The data collection unit can also determine the most efficient collection timing based on each member's past performance data. This enables efficient task data collection by determining the optimal collection timing by referring to past performance data.

[0056] The optimization unit can assign tasks based on each member's skill set and expertise during task optimization. For example, the optimization unit can analyze each member's skill set and assign the most suitable tasks. The optimization unit can also prioritize the assignment of relevant tasks based on each member's expertise. The optimization unit can also assign tasks in the most efficient way, taking into account each member's skill set and expertise. This enables efficient task management by assigning tasks based on skill sets and expertise.

[0057] The monitoring unit can detect abnormal values ​​by referring to each member's past health data during monitoring. For example, the monitoring unit can analyze each member's past heart rate data to detect abnormal values. The monitoring unit can also detect abnormal values ​​by referring to each member's past stress level data. The monitoring unit can also detect abnormal values ​​based on each member's past activity level data. This allows for the early detection of health abnormalities by referring to past health data.

[0058] The visualization unit can customize the displayed content based on each member's role and relationships when visualizing emotional states. For example, the visualization unit can customize the displayed emotional state content according to each member's role. The visualization unit can also adjust the displayed emotional state content based on each member's relationships. The visualization unit can also provide the most appropriate displayed content considering each member's role and relationships. This makes it possible to provide optimal information by customizing the displayed content based on roles and relationships.

[0059] The data collection unit can prioritize the collection of highly relevant data by considering each member's geographical location when collecting task data. For example, the data collection unit can prioritize the collection of task data related to nearby projects based on each member's current location. The data collection unit can also collect region-specific task data based on each member's geographical location. The data collection unit can also collect highly relevant task data by considering each member's travel history. This enables efficient task data collection by considering geographical location and collecting highly relevant data.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects task data. The collection unit collects task data from a task management tool such as Jira. The collection unit can collect information such as the task name, due date, assignee, and progress status. The collection unit can also obtain data using the task management tool's API. For example, the collection unit periodically collects task data using the task management tool's API. The collection unit can also collect task data in real time. For example, the collection unit uses the task management tool's Webhook to detect and collect changes to task data in real time. Step 2: The optimization unit optimizes task priorities based on the task data collected by the collection unit, taking into account each member's schedule and workload. For example, the optimization unit can display each member's schedule in a calendar format and optimize task priorities. The optimization unit can also adjust task priorities considering each member's workload. For example, the optimization unit can optimize task priorities considering each member's work time and the difficulty of the tasks. The optimization unit can also optimize task priorities using AI algorithms. For example, the optimization unit can optimize task priorities using machine learning algorithms. Step 3: The monitoring unit works in conjunction with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. For example, the monitoring unit can acquire heart rate data from a smartwatch and monitor it in real time. The monitoring unit can also measure stress hormones and conduct questionnaires to measure stress levels. The monitoring unit can also monitor pedometers and calorie consumption to measure activity levels. The monitoring unit acquires data in real time and understands the user's health status. Step 4: The visualization unit visualizes the emotional state of the entire team based on the health data monitored by the monitoring unit. The visualization unit visualizes the health data, for example, using graphs or color coding. The visualization unit can also use surveys or facial recognition technology to evaluate emotional states. The visualization unit displays emotional states in real time, promoting empathy among team members.

[0062] (Example of form 2) The system according to an embodiment of the present invention is a system that uses an AI agent to optimize task priorities, monitors health data in conjunction with wearable devices, and visualizes the emotional state of the entire team. First, this system collects task data from task management tools such as Jira and automatically optimizes task priorities considering each member's schedule and workload. The AI ​​agent performs this task to maximize project efficiency. Next, it works in conjunction with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. This allows the system to understand the health status of each member and adjust tasks as needed. Furthermore, it provides an emotional sharing dashboard that visualizes the emotional state of the entire team. This promotes empathy among team members and enables efficient communication even in a remote work environment. For example, a project manager can check the task priorities automatically set by the AI ​​agent and equalize the workload of each member, thereby smoothly advancing the project. In addition, based on the health data obtained from wearable devices, the system makes suggestions for stress management and motivation improvement, promoting work style reform. This system solves the challenges of cumbersome task prioritization and difficult resource allocation, preventing stress and decreased motivation among team members. It also addresses the issue of difficulty in understanding the emotions and situations of other team members when working remotely. By optimizing task prioritization, monitoring health data, and visualizing emotional states, the system maximizes project efficiency and manages the team's health and emotional well-being.

[0063] The system according to the embodiment comprises a collection unit, an optimization unit, a monitoring unit, and a visualization unit. The collection unit collects task data. The collection unit collects task data from, for example, a task management tool such as Jira. The collection unit can collect information such as the task name, due date, assignee, and progress status. The collection unit can also obtain data using the API of the task management tool. For example, the collection unit periodically collects task data using the API of the task management tool. The collection unit can also collect task data in real time. For example, the collection unit detects and collects changes in task data in real time using the Webhook of the task management tool. The optimization unit optimizes task priorities based on the task data collected by the collection unit, taking into account each member's schedule and workload. For example, the optimization unit displays each member's schedule in a calendar format and optimizes task priorities. The optimization unit can also adjust task priorities considering each member's workload. For example, the optimization unit optimizes task priorities considering each member's work time and the difficulty of the task. The optimization unit can also optimize task priorities using AI algorithms. For example, the optimization unit can optimize task priorities using machine learning algorithms. The monitoring unit works in conjunction with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. For example, the monitoring unit can acquire heart rate data from smartwatches and monitor it in real time. The monitoring unit can also measure stress hormones and conduct surveys to measure stress levels. The monitoring unit can also monitor pedometers and calorie consumption to measure activity levels. The monitoring unit acquires data in real time to understand health status. The visualization unit visualizes the emotional state of the entire team based on the health data monitored by the monitoring unit. For example, the visualization unit visualizes health data using graphs and color coding. The visualization unit can also use surveys and facial recognition technology to evaluate emotional states.The visualization unit displays emotional states in real time, promoting empathy among team members. This allows the system, according to the embodiment, to optimize task priorities, monitor health data, and visualize emotional states, thereby maximizing project efficiency and managing the team's health and emotional state.

[0064] The data collection unit collects task data. For example, it collects task data from task management tools such as Jira. Specifically, the data collection unit uses the task management tool's API to periodically retrieve information such as task name, due date, assignee, and progress status. Using the API automates the retrieval of task data, eliminating the need for manual data entry. Furthermore, the data collection unit can use the task management tool's webhooks to detect changes in task data in real time and immediately reflect them in the database. This ensures that task progress and changes are always up-to-date. Additionally, the data collection unit can collect data from multiple task management tools, facilitating information sharing among team members using different tools. This allows for a comprehensive understanding of the overall project task status and enables efficient task management.

[0065] The optimization unit optimizes task priorities based on task data collected by the data collection unit, taking into account each member's schedule and workload. Specifically, the optimization unit displays each member's schedule in a calendar format and visually adjusts task priorities. Each member's schedule includes information such as already scheduled meetings and vacations, and tasks are assigned considering these factors. Furthermore, the optimization unit monitors each member's workload in real time and adjusts task priorities considering work time and task difficulty. For example, if a member is overloaded, the unit reassigns that member's tasks to other members to maintain overall team balance. The optimization unit can also optimize task priorities using AI algorithms. By using machine learning algorithms, it analyzes past task data and member performance data to automatically suggest optimal task assignments. This streamlines task prioritization and ensures smooth project progress. In addition, the optimization unit can track task progress in real time and readjust priorities as needed. This prevents project delays and enables efficient task management.

[0066] The monitoring unit works in conjunction with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. Specifically, the monitoring unit acquires heart rate data from smartwatches and monitors it in real time. Heart rate data is an important indicator for evaluating members' stress levels and fatigue levels. The monitoring unit can also measure stress hormones and conduct questionnaires to measure stress levels. For example, it can conduct questionnaires regularly to evaluate members' subjective stress levels. It can also monitor pedometers and calorie consumption to measure activity levels. This allows for a comprehensive understanding of the members' health status. The monitoring unit not only acquires data in real time and understands health status, but can also issue immediate alerts if abnormal data is detected. For example, if the heart rate is abnormally high or the stress level suddenly rises, it can send a notification to the member prompting them to take a break. This helps maintain the members' health and prevents a decline in performance. Furthermore, the monitoring unit can analyze the collected data and provide data for long-term health management and stress countermeasures. This allows for the continuous improvement of the overall health status of the team.

[0067] The visualization unit visualizes the emotional state of the entire team based on health data monitored by the monitoring unit. Specifically, the visualization unit visualizes health data using graphs and color coding. For example, it graphs heart rate and stress level data over time, allowing for a visual understanding of changes in members' health. By color-coding members with high stress levels, the emotional state of the entire team can be grasped at a glance. The visualization unit can also use surveys and facial recognition technology to evaluate emotional states. For example, it can conduct surveys regularly to evaluate members' emotional states. It can also use facial recognition technology to analyze members' facial expressions during video conferences and evaluate their emotional states in real time. This can promote empathy among team members and improve the quality of communication. Furthermore, the visualization unit promotes cooperation among members by displaying emotional states in real time and sharing the emotional state of the entire team. For example, other members can provide support to members whose emotional state is low, thereby improving the overall performance of the team. As a result, the system according to this embodiment can optimize task prioritization, monitor health data, and visualize emotional states, maximizing project efficiency and managing the health and emotional state of the team.

[0068] The data collection unit can collect task data from task management tools such as Jira. For example, the unit can retrieve task data using the Jira API. The unit can collect information such as task name, due date, assignee, and progress. For example, the unit can periodically collect task data using the task management tool's API. Furthermore, the unit can collect task data in real time. For example, the unit can detect and collect changes to task data in real time using the task management tool's webhook. This streamlines task management by collecting task data from task management tools.

[0069] The optimization unit can optimize task priorities based on task data collected by the collection unit, taking into account each member's schedule and workload. For example, the optimization unit can display each member's schedule in a calendar format and optimize task priorities. The optimization unit can also adjust task priorities considering each member's workload. For example, the optimization unit can optimize task priorities considering each member's working time and the difficulty of the task. The optimization unit can also optimize task priorities using AI algorithms. For example, the optimization unit can optimize task priorities using machine learning algorithms. This improves project efficiency by optimizing task priorities while considering each member's schedule and workload.

[0070] The monitoring unit can connect with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. For example, the monitoring unit can acquire heart rate data from a smartwatch and monitor it in real time. To measure stress levels, the monitoring unit can also measure stress hormones and conduct questionnaires. To measure activity levels, the monitoring unit can also monitor pedometers and calorie consumption. The monitoring unit acquires data in real time and understands the health status. This allows for monitoring of each member's health status in real time by connecting with wearable devices.

[0071] The visualization unit can visualize the emotional state of the entire team based on health data monitored by the monitoring unit. For example, the visualization unit can visualize health data using graphs or color coding. The visualization unit can also use surveys or facial recognition technology to evaluate emotional states. The visualization unit displays emotional states in real time, promoting empathy among team members. By visualizing emotional states based on health data, it facilitates empathy among team members and enables efficient communication.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of task data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the burden. If the user is relaxed, the data collection unit can also speed up the collection timing to increase efficiency. If the user is in a hurry, the data collection unit can make the collection timing immediate to enable a quick response. In this way, by adjusting the timing of task data collection based on the user's emotions, the burden on the user is reduced and efficiency is improved. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The data collection unit can analyze each member's past task history and select the optimal collection method. For example, it can prioritize collecting data from task management tools that were frequently used in the past. The data collection unit can also analyze each member's past task completion times to determine the optimal collection timing. Based on the types of tasks each member has performed in the past, the data collection unit can also prioritize collecting relevant data. This allows for efficient task data collection by analyzing past task history and selecting the optimal collection method.

[0074] The data collection unit can filter task data based on each member's current projects and areas of interest. For example, the unit can prioritize collecting task data related to projects each member is currently working on. The unit can also filter relevant task data based on each member's areas of interest. The unit can also collect necessary task data based on the progress of each member's current projects. This allows for the efficient collection of highly relevant data by filtering task data based on current projects and areas of interest.

[0075] The data collection unit can estimate the user's emotions and determine the priority of task data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important task data. If the user is relaxed, the data collection unit can also prioritize collecting more important task data. If the user is in a hurry, the data collection unit can immediately collect urgent task data. This enables efficient task data collection by prioritizing task data based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering each member's geographical location when collecting task data. For example, the data collection unit can prioritize the collection of task data related to nearby projects based on each member's current location. The data collection unit can also collect region-specific task data based on each member's geographical location. The data collection unit can also collect highly relevant task data by considering each member's travel history. This enables efficient task data collection by considering geographical location and collecting highly relevant data.

[0077] The data collection unit can analyze each member's social media activity and collect relevant data when collecting task data. For example, the data collection unit can collect relevant task data based on each member's social media activity. The data collection unit can also analyze each member's social media interests and prioritize the collection of relevant task data. The data collection unit can also collect relevant task data considering each member's social media network. This enables efficient task data collection by analyzing social media activity and collecting relevant data.

[0078] The optimization unit can estimate the user's emotions and adjust task priorities based on those emotions. For example, if the user is stressed, the optimization unit will postpone less important tasks. If the user is relaxed, the optimization unit can prioritize high-importance tasks. If the user is in a hurry, the optimization unit can immediately process urgent tasks. This allows for efficient task management by adjusting task priorities based on the user's emotions. 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.

[0079] The optimization unit can determine the optimal priority when optimizing tasks by referring to each member's past performance data. For example, the optimization unit can analyze each member's past task completion time to determine the optimal priority. The optimization unit can also prioritize related tasks based on the types of tasks each member has previously completed. The optimization unit can also determine the priority of the most efficient tasks based on each member's past performance data. This enables efficient task management by determining the optimal priority by referring to past performance data.

[0080] The optimization unit can assign tasks based on each member's skill set and expertise during task optimization. For example, the optimization unit can analyze each member's skill set and assign the most suitable tasks. The optimization unit can also prioritize the assignment of relevant tasks based on each member's expertise. The optimization unit can also assign tasks in the most efficient way, taking into account each member's skill set and expertise. This enables efficient task management by assigning tasks based on skill sets and expertise.

[0081] The optimization unit can estimate the user's emotions and adjust the order in which tasks are displayed based on the estimated emotions. For example, if the user is stressed, the optimization unit will display lower-priority tasks later. If the user is relaxed, the optimization unit can also prioritize displaying high-priority tasks. If the user is in a hurry, the optimization unit can immediately display urgent tasks. This allows for efficient task management by adjusting the order in which tasks are displayed based on the user's emotions. 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.

[0082] The optimization unit can assign tasks while considering the geographical distribution of each member during task optimization. For example, the optimization unit can prioritize assigning tasks related to nearby projects based on each member's current location. The optimization unit can also assign region-specific tasks based on each member's geographical distribution. The optimization unit can also assign highly relevant tasks by considering each member's travel history. This enables efficient task management by assigning tasks while considering geographical distribution.

[0083] The optimization unit can adjust task priorities by referencing the progress of related projects during task optimization. For example, the optimization unit analyzes the progress of related projects and determines the optimal task priorities. Based on project progress, the optimization unit can also prioritize tasks with high urgency. The optimization unit can also prioritize tasks with high importance, taking project progress into consideration. This enables efficient task management by adjusting task priorities by referencing project progress.

[0084] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency of health data based on the estimated emotions. For example, if the user is stressed, the monitoring unit can increase the monitoring frequency to gain a more detailed understanding of their health status. If the user is relaxed, the monitoring unit can also decrease the monitoring frequency to reduce their burden. If the user is in a hurry, the monitoring unit can make the monitoring frequency instantaneous to enable a quick response. This allows for a detailed understanding of the user's health status and a quick response by adjusting the monitoring frequency based on their emotions. 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.

[0085] The monitoring unit can detect abnormal values ​​by referring to each member's past health data during monitoring. For example, the monitoring unit can analyze each member's past heart rate data to detect abnormal values. The monitoring unit can also detect abnormal values ​​by referring to each member's past stress level data. The monitoring unit can also detect abnormal values ​​based on each member's past activity level data. This allows for the early detection of health abnormalities by referring to past health data.

[0086] The monitoring unit can improve the accuracy of monitoring based on each member's lifestyle and activity patterns. For example, the monitoring unit can analyze each member's lifestyle to improve monitoring accuracy. The monitoring unit can also improve monitoring accuracy based on each member's activity patterns. The monitoring unit can also select the most efficient monitoring method considering each member's lifestyle and activity patterns. This allows for a more accurate understanding of health status by improving monitoring accuracy based on lifestyle and activity patterns.

[0087] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated emotions. For example, if the user is stressed, the monitoring unit provides a simple and highly visible display method. If the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. If the user is in a hurry, the monitoring unit can also provide a display method that gets straight to the point. By adjusting the display method of the monitoring results based on the user's emotions, a highly visible display is possible. 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.

[0088] The monitoring unit can analyze health data while considering each member's geographical environment during monitoring. For example, the monitoring unit can analyze health data considering the geographical environment based on each member's current location. The monitoring unit can also analyze region-specific health risks based on each member's geographical environment. The monitoring unit can also analyze health data while considering each member's travel history. This allows for the identification of region-specific health risks by analyzing health data while considering the geographical environment.

[0089] The monitoring unit can improve the accuracy of monitoring by referring to each member's relevant medical data during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to each member's past medical data. The monitoring unit can also improve the accuracy of monitoring based on each member's current medical data. The monitoring unit can also select the most efficient monitoring method by considering each member's medical data. As a result, by improving the accuracy of monitoring by referring to relevant medical data, it becomes possible to grasp the health status more accurately.

[0090] The visualization unit can estimate the user's emotions and adjust the display method of the emotional state based on the estimated emotions. For example, if the user is stressed, the visualization unit can provide a display method with calm colors. If the user is relaxed, the visualization unit can also provide a display method with bright colors. If the user is in a hurry, the visualization unit can also provide a simple and highly visible display method. This allows for highly visible displays by adjusting the display method of the emotional state based on the user's emotions. 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.

[0091] The visualization unit can predict the current emotional state by referring to past emotional data when visualizing emotional states. For example, the visualization unit can analyze each member's past emotional data and predict their current emotional state. The visualization unit can also predict the current emotional state based on each member's past emotional patterns. The visualization unit can also predict the most likely emotional state based on each member's past emotional data. This allows for understanding changes in emotional states by predicting the current emotional state by referring to past emotional data.

[0092] The visualization unit can customize the displayed content based on each member's role and relationships when visualizing emotional states. For example, the visualization unit can customize the displayed emotional state content according to each member's role. The visualization unit can also adjust the displayed emotional state content based on each member's relationships. The visualization unit can also provide the most appropriate displayed content considering each member's role and relationships. This makes it possible to provide optimal information by customizing the displayed content based on roles and relationships.

[0093] The visualization unit can estimate the user's emotions and adjust the importance of the emotional state based on the estimated emotions. For example, if the user is stressed, the visualization unit will increase the importance of the emotional state. If the user is relaxed, the visualization unit can also decrease the importance of the emotional state. If the user is in a hurry, the visualization unit can immediately display the importance of the emotional state. This allows important information to be displayed preferentially by adjusting the importance of the emotional state based on the user's emotions. 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.

[0094] The visualization unit can analyze changes in emotions based on each member's activity history when visualizing emotional states. For example, the visualization unit can analyze each member's past activity history and predict changes in emotions. The visualization unit can also display changes in emotions in real time based on each member's activity history. The visualization unit can also predict the most likely changes in emotions based on each member's activity history. This allows for understanding changes in emotional states by analyzing changes in emotions based on activity history.

[0095] The visualization unit can analyze emotional states by referencing progress data of related projects when visualizing emotional states. For example, the visualization unit can analyze progress data of related projects and predict emotional states. The visualization unit can also display emotional states in real time based on project progress data. The visualization unit can also predict the most likely emotional state based on project progress data. This allows for understanding changes in emotional states by analyzing them with reference to project progress data.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The data collection unit can determine the optimal collection timing by referring to each member's past performance data when collecting task data. For example, the data collection unit can analyze each member's past task completion time to determine the optimal collection timing. The data collection unit can also prioritize the collection of relevant data based on the types of tasks each member has previously completed. The data collection unit can also determine the most efficient collection timing based on each member's past performance data. This enables efficient task data collection by determining the optimal collection timing by referring to past performance data.

[0098] The optimization unit can assign tasks based on each member's skill set and expertise during task optimization. For example, the optimization unit can analyze each member's skill set and assign the most suitable tasks. The optimization unit can also prioritize the assignment of relevant tasks based on each member's expertise. The optimization unit can also assign tasks in the most efficient way, taking into account each member's skill set and expertise. This enables efficient task management by assigning tasks based on skill sets and expertise.

[0099] The monitoring unit can detect abnormal values ​​by referring to each member's past health data during monitoring. For example, the monitoring unit can analyze each member's past heart rate data to detect abnormal values. The monitoring unit can also detect abnormal values ​​by referring to each member's past stress level data. The monitoring unit can also detect abnormal values ​​based on each member's past activity level data. This allows for the early detection of health abnormalities by referring to past health data.

[0100] The visualization unit can customize the displayed content based on each member's role and relationships when visualizing emotional states. For example, the visualization unit can customize the displayed emotional state content according to each member's role. The visualization unit can also adjust the displayed emotional state content based on each member's relationships. The visualization unit can also provide the most appropriate displayed content considering each member's role and relationships. This makes it possible to provide optimal information by customizing the displayed content based on roles and relationships.

[0101] The data collection unit can prioritize the collection of highly relevant data by considering each member's geographical location when collecting task data. For example, the data collection unit can prioritize the collection of task data related to nearby projects based on each member's current location. The data collection unit can also collect region-specific task data based on each member's geographical location. The data collection unit can also collect highly relevant task data by considering each member's travel history. This enables efficient task data collection by considering geographical location and collecting highly relevant data.

[0102] The data collection unit can estimate the user's emotions and adjust the timing of task data collection based on those emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the burden. If the user is relaxed, the data collection unit can also speed up the collection timing to increase efficiency. If the user is in a hurry, the data collection unit can make it instantaneous to enable a quick response. In this way, by adjusting the timing of task data collection based on the user's emotions, the burden on the user is reduced and efficiency is improved.

[0103] The optimization unit can estimate the user's emotions and adjust task priorities based on those emotions. For example, if the user is stressed, the optimization unit will postpone less important tasks. If the user is relaxed, the optimization unit can prioritize high-importance tasks. If the user is in a hurry, the optimization unit can immediately process urgent tasks. This allows for efficient task management by adjusting task priorities based on the user's emotions.

[0104] The monitoring unit can estimate the user's emotions and adjust the frequency of health data monitoring based on the estimated emotions. For example, if the user is stressed, the monitoring unit can increase the monitoring frequency to gain a more detailed understanding of their health status. If the user is relaxed, the monitoring unit can also decrease the monitoring frequency to reduce their burden. If the user is in a hurry, the monitoring unit can instantly adjust the monitoring frequency to enable a quick response. In this way, by adjusting the monitoring frequency based on the user's emotions, a detailed understanding of their health status can be obtained, enabling a quick response.

[0105] The visualization unit can estimate the user's emotions and adjust the display method of the emotional state based on the estimated emotions. For example, if the user is feeling stressed, the visualization unit can provide a display method with calming colors. If the user is relaxed, the visualization unit can also provide a display method with bright colors. If the user is in a hurry, the visualization unit can also provide a simple and highly visible display method. In this way, by adjusting the display method of the emotional state based on the user's emotions, a highly visible display becomes possible.

[0106] The visualization unit can estimate the user's emotions and adjust the importance of the emotional state based on the estimated emotions. For example, if the user is stressed, the visualization unit will increase the importance of the emotional state. If the user is relaxed, the visualization unit can also decrease the importance of the emotional state. If the user is in a hurry, the visualization unit can immediately display the importance of the emotional state. In this way, by adjusting the importance of emotional states based on the user's emotions, important information can be displayed preferentially.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The collection unit collects task data. The collection unit collects task data from a task management tool such as Jira. The collection unit can collect information such as the task name, due date, assignee, and progress status. The collection unit can also obtain data using the task management tool's API. For example, the collection unit periodically collects task data using the task management tool's API. The collection unit can also collect task data in real time. For example, the collection unit uses the task management tool's Webhook to detect and collect changes to task data in real time. Step 2: The optimization unit optimizes task priorities based on the task data collected by the collection unit, taking into account each member's schedule and workload. For example, the optimization unit can display each member's schedule in a calendar format and optimize task priorities. The optimization unit can also adjust task priorities considering each member's workload. For example, the optimization unit can optimize task priorities considering each member's work time and the difficulty of the tasks. The optimization unit can also optimize task priorities using AI algorithms. For example, the optimization unit can optimize task priorities using machine learning algorithms. Step 3: The monitoring unit works in conjunction with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. For example, the monitoring unit can acquire heart rate data from a smartwatch and monitor it in real time. The monitoring unit can also measure stress hormones and conduct questionnaires to measure stress levels. The monitoring unit can also monitor pedometers and calorie consumption to measure activity levels. The monitoring unit acquires data in real time and understands the user's health status. Step 4: The visualization unit visualizes the emotional state of the entire team based on the health data monitored by the monitoring unit. The visualization unit visualizes the health data, for example, using graphs or color coding. The visualization unit can also use surveys or facial recognition technology to evaluate emotional states. The visualization unit displays emotional states in real time, promoting empathy among team members.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the data collection unit, optimization unit, monitoring unit, and visualization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects task data from a task management tool. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes task priorities based on the collected task data. The monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors health data in cooperation with a wearable device. The visualization unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes the emotional state of the entire team. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, optimization unit, monitoring unit, and visualization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects task data from a task management tool. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes task priorities based on the collected task data. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors health data in cooperation with a wearable device. The visualization unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes the emotional state of the entire team. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, optimization unit, monitoring unit, and visualization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects task data from a task management tool. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes task priorities based on the collected task data. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors health data in cooperation with a wearable device. The visualization unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes the emotional state of the entire team. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the data collection unit, optimization unit, monitoring unit, and visualization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects task data from a task management tool. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes task priorities based on the collected task data. The monitoring unit is implemented by the control unit 46A of the robot 414 and monitors health data in cooperation with a wearable device. The visualization unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes the emotional state of the entire team. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A collection unit that collects task data, Based on the task data collected by the aforementioned collection unit, an optimization unit optimizes task priorities considering the schedule and workload of each member. The monitoring unit works in conjunction with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. The system includes a visualization unit that visualizes the emotional state of the entire team based on health data monitored by the monitoring unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect task data from task management tools such as Jira. The system described in Appendix 1, characterized by the features described herein. (Note 3) The optimization unit, Based on the task data collected by the data collection unit, task priorities are optimized considering each member's schedule and workload. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, It connects with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned visualization unit, Based on health data monitored by the monitoring department, the emotional state of the entire team is visualized. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of task data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze each member's past task history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting task data, filter it based on each member's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of task data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting task data, the system prioritizes collecting highly relevant data by considering the geographical location of each member. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting task data, analyze each member's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The optimization unit, It estimates the user's emotions and adjusts task priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The optimization unit, When optimizing tasks, we refer to each member's past performance data to determine the optimal priority. The system described in Appendix 1, characterized by the features described herein. (Note 14) The optimization unit, When optimizing tasks, assign tasks based on each member's skill set and expertise. The system described in Appendix 1, characterized by the features described herein. (Note 15) The optimization unit, It estimates the user's emotions and adjusts the order in which tasks are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The optimization unit, When optimizing tasks, assign tasks considering the geographical distribution of each member. The system described in Appendix 1, characterized by the features described herein. (Note 17) The optimization unit, When optimizing tasks, adjust task priorities by referring to the progress of related projects. The system described in Appendix 1, characterized by the features described herein. (Note 18) The monitoring unit, It estimates the user's emotions and adjusts the frequency of health data monitoring based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The monitoring unit, During monitoring, abnormal values ​​are detected by referring to each member's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The monitoring unit, During monitoring, improve the accuracy of monitoring based on each member's lifestyle and activity patterns. The system described in Appendix 1, characterized by the features described herein. (Note 21) The monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The monitoring unit, During monitoring, health data is analyzed taking into account the geographical environment of each member. The system described in Appendix 1, characterized by the features described herein. (Note 23) The monitoring unit, During monitoring, we improve the accuracy of monitoring by referring to the relevant medical data of each member. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned visualization unit, It estimates the user's emotions and adjusts how the emotional state is displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned visualization unit, When visualizing emotional states, past emotional data is used to predict the current emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned visualization unit, When visualizing emotional states, the displayed content can be customized based on each member's role and relationship. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned visualization unit, It estimates the user's emotions and adjusts the importance of emotional states based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned visualization unit, When visualizing emotional states, we analyze emotional changes based on each member's activity history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned visualization unit, When visualizing emotional states, we analyze them by referring to progress data of related projects. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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 collection unit that collects task data, Based on the task data collected by the aforementioned collection unit, an optimization unit optimizes task priorities considering the schedule and workload of each member. The monitoring unit works in conjunction with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. The system includes a visualization unit that visualizes the emotional state of the entire team based on health data monitored by the monitoring unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect task data from task management tools such as Jira. The system according to feature 1.

3. The optimization unit, Based on the task data collected by the aforementioned collection unit, the task prioritization is optimized, taking into account each member's schedule and workload. The system according to feature 1.

4. The monitoring unit, It connects with wearable devices such as smartwatches and fitness trackers to monitor heart rate, stress levels, and activity levels in real time. The system according to feature 1.

5. The aforementioned visualization unit, Based on the health data monitored by the aforementioned monitoring unit, the emotional state of the entire team is visualized. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of task data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze each member's past task history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting task data, filter it based on each member's current projects and areas of interest. The system according to feature 1.

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