system

Abstract

The system according to this embodiment aims to improve team efficiency through fair allocation of tasks and management of progress. [Solution] The system according to the embodiment comprises a task assignment unit, a progress monitoring unit, and a feedback provision unit. The task assignment unit assigns tasks. The progress monitoring unit monitors the progress of the tasks assigned by the task assignment unit. The feedback provision unit provides feedback based on the progress monitored by the progress monitoring unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there may be a bias in the workload within an organization or the work may be concentrated on specific members, which may reduce the efficiency of the team.

[0005] The system according to the embodiment aims to improve the efficiency of the team through fair allocation of work and progress management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a task assignment unit, a progress monitoring unit, and a feedback provision unit. The task assignment unit assigns tasks. The progress monitoring unit monitors the progress of the tasks assigned by the task assignment unit. The feedback provision unit provides feedback based on the progress monitored by the progress monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve team efficiency through fair allocation of tasks and management of progress. [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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving 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 Task Equalizer system according to an embodiment of the present invention is a system that utilizes generative AI to optimize the balance of work and collaboration within a team. The Task Equalizer system is designed to eliminate unfair situations in traditional organizations, such as work being concentrated on specific members or work progressing only among close friends. This solves problems such as uneven workload distribution, decreased motivation among other members, and reduced team efficiency. The Task Equalizer system consists of the following elements: Fair automatic task assignment: The generative AI analyzes each member's skills and workload and automatically distributes tasks to prevent workload imbalance. Analysis of communication imbalance: The generative AI analyzes the status of dialogue and collaboration within the team and visualizes and improves situations where work is progressing only among specific members. Real-time feedback: The generative AI constantly monitors the work status and notifies the leader of members who are overburdened or tasks that are behind schedule. For example, to prevent work from being concentrated on specific members, the generative AI assigns tasks considering each member's skill set and current workload. The generative AI analyzes chat logs and email exchanges and notifies the leader if communication is concentrated only among specific members. If a particular member is taking on an excessive workload or if a specific task is falling behind schedule, the generating AI sends an alert to the leader. This allows the Task Equalizer system to eliminate excessive burdens on specific members, ensuring that everyone contributes equally to the work. The generating AI prevents workload imbalances by assigning tasks considering each member's skills and workload. The generating AI analyzes communication imbalances and provides improvement suggestions, enhancing overall team collaboration. The generating AI monitors work status in real time and provides feedback to leaders, enabling quick responses. The Task Equalizer system leverages generating AI to improve work efficiency and fairness. This leads to increased productivity and motivation for the entire team, enabling efficient work operations.This allows the Task Equalizer system to optimize team workload balance and collaboration, resulting in fairer work distribution and increased productivity.

[0029] The Task Equalizer system according to this embodiment comprises a task assignment unit, a progress monitoring unit, and a feedback provision unit. The task assignment unit assigns tasks. The task assignment unit, for example, analyzes each member's skills and workload using a generating AI and automatically distributes tasks. The task assignment unit, for example, assigns tasks considering each member's skill set and current workload using a generating AI to prevent work from concentrating on specific members. The task assignment unit prevents workload imbalances by assigning tasks considering members' skills and workloads using a generating AI. The progress monitoring unit monitors the progress of tasks assigned by the task assignment unit. The progress monitoring unit, for example, uses a generating AI to constantly monitor the work status and notifies the leader of members who are overburdened or tasks that are behind schedule. The progress monitoring unit, for example, uses a generating AI to send an alert to the leader if a particular member is taking on an excessive amount of work or if the progress of a particular task is behind schedule. The progress monitoring unit enables quick responses by having a generating AI monitor the work status in real time and provide feedback to the leader. The feedback provision unit provides feedback based on the progress monitored by the progress monitoring unit. For example, the feedback provision unit uses a generating AI to constantly monitor the work status and notify leaders of members who are overburdened or tasks that are behind schedule. For example, if a particular member is taking on an excessive amount of work or if the progress of a particular task is behind schedule, the generating AI sends an alert to the leader. For example, the feedback provision unit enables quick responses by using a generating AI to monitor the work status in real time and provide feedback to leaders. As a result, the Task Equalizer system according to this embodiment can efficiently assign tasks, monitor progress, and provide feedback.

[0030] The task assignment unit assigns tasks. For example, the task assignment unit analyzes each member's skills and workload using a generative AI and automatically distributes tasks. Specifically, the generative AI retrieves each member's past work history, skill set, and current workload from a database and uses this information to make optimal task assignments. The generative AI uses natural language processing technology to analyze members' skills and experience and identify the member best suited for each task. The generative AI also monitors each member's current workload in real time and distributes tasks to prevent work from concentrating on any particular member. For example, if member A currently has 5 tasks while member B only has 2, the generative AI will adjust the assignment so that the next task is assigned to member B. Furthermore, the generative AI also takes into account task priority and deadlines, prioritizing the assignment of high-urgency tasks. This allows the task assignment unit to prevent workload imbalances and optimally utilize each member's skills and workload. The generative AI can also periodically evaluate the progress of tasks and reassign tasks as needed. For example, if a particular task is behind schedule, the generation AI can distribute that task to other members, thereby ensuring smooth progress across the entire project. This allows the task assignment department to manage tasks efficiently and flexibly, improving the overall productivity of the project.

[0031] The progress monitoring unit monitors the progress of tasks assigned by the task assignment unit. For example, the progress monitoring unit uses a generation AI to constantly monitor work status and notify leaders of members who are overburdened or tasks that are behind schedule. Specifically, the generation AI tracks the task progress of each member in real time and identifies tasks that are behind schedule or members who are overburdened. The generation AI analyzes task progress data and evaluates whether it is progressing according to schedule. For example, it compares the scheduled completion date of a task with the current progress rate and sends an alert to the leader if a delay occurs. The generation AI also analyzes each member's working hours and task completion status, and reports to the leader if a particular member is overburdened. This allows the leader to respond quickly and reassign tasks or adjust resources as needed. Furthermore, the progress monitoring unit can also perform trend analysis based on past data and predict future risks. For example, by analyzing past project data, if a particular task tends to be prone to delays, countermeasures can be taken in advance. This allows the progress monitoring unit to not only grasp the situation in real time but also handle long-term risk management, enabling the smooth progress of the entire project.

[0032] The Feedback Department provides feedback based on the progress monitored by the Progress Monitoring Department. For example, the Feedback Department uses a generating AI to constantly monitor work status and notify leaders of members who are overburdened or tasks that are behind schedule. Specifically, the generating AI analyzes the task progress of each member and provides specific feedback to the leader. For example, if a particular member is taking on an excessive amount of work, the generating AI will suggest ways to reduce that member's workload. Also, if a particular task is behind schedule, the generating AI will analyze the cause and suggest solutions to the leader. For example, if the delay in a task is due to a lack of resources, the generating AI will suggest allocating additional resources. Furthermore, the Feedback Department can also provide feedback to members themselves to encourage self-improvement. For example, the generating AI will evaluate each member's work performance and provide feedback on areas for improvement and success stories. This allows members to objectively understand their own work performance and strive for self-improvement. In addition, the Feedback Department generates regular reports to inform leaders of the overall project progress and risks. This allows leaders to grasp the overall picture of the project and make appropriate decisions. This allows the feedback department to contribute not only to real-time feedback but also to long-term business improvement, thereby contributing to the overall success of the project.

[0033] The task assignment unit can automatically distribute tasks by analyzing each member's skills and workload using a generating AI. For example, the task assignment unit assigns tasks considering each member's skill set and current workload. The task assignment unit prevents workload imbalances by having the generating AI consider members' skills and workloads when assigning tasks. For example, the task assignment unit automatically assigns the optimal task by having the generating AI analyze each member's skills and workload. This allows for automatic task distribution based on each member's skills and workload. Skills include, for example, technical skills and soft skills. Workload includes, for example, working time and task difficulty. Technologies such as deep learning and natural language processing are used in the generating AI.

[0034] The Communication Analysis Department uses AI to analyze team dialogue and collaboration, enabling the visualization and improvement of situations where work is progressing only among specific members. For example, the AI ​​analyzes chat logs and email exchanges, and if communication is concentrated among specific members, it notifies the leader. The Communication Analysis Department uses AI to analyze team dialogue and collaboration, enabling the visualization and improvement of situations where work is progressing only among specific members. For example, the AI ​​analyzes communication imbalances and provides improvement suggestions, thereby improving overall team cooperation. This allows for the visualization and improvement of situations where work is progressing only among specific members. Communication analysis includes, for example, analysis of dialogue frequency and content.

[0035] The feedback system can notify leaders of tasks that are behind schedule or members who are being unfairly burdened. For example, the feedback system's AI continuously monitors work status and notifies leaders of members who are being unfairly burdened or tasks that are behind schedule. For example, if a particular member is taking on an excessive amount of work or if a particular task is behind schedule, the AI ​​will send an alert to the leader. The feedback system enables quick responses by having the AI ​​monitor work status in real time and provide feedback to leaders. This allows leaders to be notified of tasks that are behind schedule or members who are being unfairly burdened. Being behind schedule includes, for example, a percentage behind schedule. Being unfairly burdened includes, for example, a disproportionate amount of work concentrated on a particular member.

[0036] The Skill Analysis Unit can analyze each member's skill set using a generating AI and provide it to the Task Assignment Unit. For example, the Skill Analysis Unit uses a generating AI to analyze each member's skill set and provide it to the Task Assignment Unit. The Skill Analysis Unit uses a generating AI to analyze each member's skill set and provide information for optimal task assignment. By having the Skill Analysis Unit use a generating AI to analyze each member's skill set and provide it to the Task Assignment Unit, task assignment can be made more efficient. This allows the Skill Analysis Unit to analyze each member's skill set and provide it to the Task Assignment Unit. Skill sets include, for example, programming skills and management skills.

[0037] The workload analysis unit can analyze the workload of each member using a generating AI and provide it to the feedback unit. For example, the workload analysis unit uses a generating AI to analyze the workload of each member and provide it to the feedback unit. By having the workload analysis unit use a generating AI to analyze the workload of each member and provide it to the feedback unit, task assignment can be made more efficient. This allows the workload analysis unit to analyze the workload of each member and provide it to the feedback unit. The workload includes, for example, work time and task difficulty.

[0038] The task assignment unit can analyze each member's past task completion history and select the optimal task assignment method. For example, the task assignment unit can use a generating AI to assign similar tasks based on the types of tasks a member has successfully completed in the past. For example, the task assignment unit can use a generating AI to adjust tasks so that members avoid tasks they have struggled with in the past. For example, the task assignment unit can use a generating AI to assign an appropriate amount of tasks based on a member's past task completion speed. This allows the task assignment unit to select the optimal task assignment method based on past task completion history. Past task completion history includes, for example, the number of tasks completed and the time taken to complete them. The optimal task assignment method includes, for example, the type of algorithm and evaluation criteria. Some or all of the above processes in the task assignment unit may be performed using a generating AI, or not. For example, the task assignment unit can input past task completion history data into a generating AI and have the generating AI select the optimal task assignment method.

[0039] The task assignment unit can filter tasks based on members' current projects and areas of interest when assigning tasks. For example, the task assignment unit can prioritize assigning tasks related to projects that members are currently working on. For example, the task assignment unit can assign tasks that the generating AI finds interesting based on members' areas of interest. For example, the task assignment unit can assign tasks in areas that members have shown interest in in the past. This allows tasks to be filtered based on current projects and areas of interest. Current projects include, for example, project objectives and progress. Areas of interest include, for example, past research topics and survey results. Some or all of the above processing in the task assignment unit may be performed using, for example, the generating AI, or without using the generating AI. For example, the task assignment unit can input data on members' current projects and areas of interest into the generating AI and have the generating AI perform task filtering.

[0040] The task assignment unit can prioritize assigning tasks that are highly relevant to the member, taking into account the member's geographical location information. For example, if a member is in the office, the task assignment unit will assign tasks that can be completed within the office. If a member is working remotely, the task assignment unit will assign tasks that can be completed remotely. If a member is on a business trip, the task assignment unit will assign tasks relevant to the business trip location. This allows for the priority assignment of tasks that are highly relevant based on geographical location information. Geographical location information includes, for example, GPS data and location services. Highly relevant tasks include, for example, geographical proximity and the relevance of the task content. Some or all of the above processing in the task assignment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the task assignment unit can input the member's geographical location information data into a generative AI and have the generative AI perform the assignment of highly relevant tasks.

[0041] The task assignment unit can analyze members' social media activity and assign relevant tasks when assigning tasks. For example, the task assignment unit can assign tasks related to topics that members have shown interest in on social media. For example, the task assignment unit can assign tasks related to industries that members follow on social media. For example, the task assignment unit can assign tasks related to articles that members have shared on social media. This allows relevant tasks to be assigned based on social media activity. Social media activity includes, for example, the content of posts and the frequency of activity. Some or all of the above processing in the task assignment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the task assignment unit can input members' social media activity data into a generative AI and have the generative AI perform the assignment of relevant tasks.

[0042] The progress monitoring unit can predict current progress by referring to past progress data during progress monitoring. For example, the progress monitoring unit predicts the completion of the current task based on past progress data. For example, the progress monitoring unit predicts the possibility of delays based on past progress data. For example, the progress monitoring unit predicts the completion time of a task based on past progress data. This allows the current progress to be predicted based on past progress data. Past progress data includes, for example, past task completion rates and the degree of delay in progress. Predicting current progress includes, for example, prediction algorithms and evaluation criteria. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the progress monitoring unit can input past progress data into a generative AI and have the generative AI perform a prediction of current progress.

[0043] The progress monitoring unit can apply different monitoring methods to each task category during progress monitoring. For example, the progress monitoring unit can apply a monitoring method that emphasizes the quality of progress to creative tasks, a monitoring method that emphasizes the quantity of progress to technical tasks, and a monitoring method that emphasizes the schedule of progress to managerial tasks. This allows for the application of different monitoring methods to each task category. Task categories include, for example, project tasks and daily work tasks. Monitoring methods include, for example, quantitative monitoring and qualitative monitoring. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the progress monitoring unit can input task category data into a generative AI and have the generative AI apply the monitoring method.

[0044] The progress monitoring unit can adjust the frequency of monitoring based on the task submission date during progress monitoring. For example, the progress monitoring unit will frequently monitor the progress of tasks with approaching deadlines. For example, the progress monitoring unit will reduce the frequency of monitoring for tasks with distant deadlines. For example, the progress monitoring unit will monitor the progress of tasks with moderate deadlines at an appropriate frequency. This allows the monitoring frequency to be adjusted based on the task submission date. The task submission date includes, for example, the submission deadline and progress status. The monitoring frequency includes, for example, the degree of delay in progress and the importance of the task. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the progress monitoring unit can input task submission date data into a generation AI and have the generation AI adjust the monitoring frequency.

[0045] The progress monitoring unit can improve the accuracy of its monitoring by referring to relevant literature for the task during progress monitoring. For example, the progress monitoring unit can refer to relevant literature for the task and set progress evaluation criteria. For example, the progress monitoring unit can improve the accuracy of progress predictions based on relevant literature for the task. For example, the progress monitoring unit can refer to relevant literature for the task and identify problems in progress. This allows the progress monitoring unit to improve the accuracy of its monitoring by referring to relevant literature for the task. Relevant literature includes, for example, academic papers and technical reports. Monitoring accuracy includes, for example, data accuracy and analysis algorithms. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the progress monitoring unit can input relevant literature data into a generative AI and have the generative AI perform the improvement of monitoring accuracy.

[0046] The feedback provider can adjust the level of detail of the feedback based on the importance of the task when providing feedback. For example, the feedback provider can provide detailed feedback for important tasks, moderately detailed feedback for tasks of moderate importance, and concise feedback for tasks of low importance. This allows the level of detail of the feedback to be adjusted based on the importance of the task. Task importance includes, for example, project priority and task impact. The level of detail of the feedback includes, for example, detailed explanations and concise comments. Some or all of the above processing in the feedback provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provider can input task importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the feedback.

[0047] The feedback provider can apply different feedback algorithms depending on the task category when providing feedback. For example, the feedback provider can provide creative feedback for creative tasks, feedback that emphasizes creativity, feedback that emphasizes technical details for technical tasks, and feedback that emphasizes schedule management for managerial tasks. This allows for the application of different feedback algorithms depending on the task category. Task categories include, for example, project tasks and routine tasks. Feedback algorithms include, for example, machine learning algorithms and rule-based algorithms. Some or all of the above processing in the feedback provider may be performed using, for example, generative AI, or without using generative AI. For example, the feedback provider can input task category data into a generative AI and have the generative AI apply the feedback algorithm.

[0048] The feedback provider can determine the priority of feedback based on the task submission date when providing feedback. For example, the feedback provider can prioritize providing feedback to tasks with approaching deadlines. For example, it can lower the priority of feedback to tasks with distant deadlines. For example, it can provide feedback with a moderate priority to tasks with medium deadlines. This allows the feedback provider to determine the priority of feedback based on the task submission date. The task submission date includes, for example, the submission deadline and progress status. The priority of feedback includes, for example, the importance and urgency of the task. Some or all of the above processing in the feedback provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provider can input task submission date data into a generative AI and have the generative AI determine the priority of feedback.

[0049] The feedback provider can adjust the order of feedback based on the relevance of the tasks when providing feedback. For example, the feedback provider can provide feedback first to important tasks. For example, it can provide feedback in an appropriate order to tasks of moderate importance. For example, it can provide feedback last to tasks of low importance. This allows the feedback to be adjusted based on the relevance of the tasks. Task relevance includes, for example, the relevance of the task content or the relevance of the project. The order of feedback includes, for example, order by importance or order of submission. Some or all of the above processing in the feedback provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provider can input task relevance data into a generative AI and have the generative AI perform the adjustment of the feedback order.

[0050] The communication analysis unit can improve the accuracy of its analysis by considering the interrelationships between dialogues during communication analysis. For example, the communication analysis unit can analyze the frequency and content of dialogues between members and identify interrelationships. For example, the communication analysis unit can analyze the tone and emotions of dialogues between members and identify interrelationships. For example, the communication analysis unit can analyze the timing and context of dialogues between members and identify interrelationships. This allows the accuracy of the analysis to be improved by considering the interrelationships between dialogues. Interrelationships between dialogues include, for example, the frequency and content of the dialogues. Accuracy of the analysis includes, for example, the accuracy of the data and the analysis algorithm. Some or all of the above processing in the communication analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the communication analysis unit can input dialogue interrelationship data into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0051] The communication analysis unit can perform analysis while considering the attribute information of the members. For example, the communication analysis unit can analyze the importance of communication by considering the member's position and job responsibilities. For example, the communication analysis unit can analyze the content of communication by considering the member's years of experience and skill set. For example, the communication analysis unit can analyze the frequency of communication by considering the member's working hours and work location. This allows the analysis to be performed while considering the attribute information of the members. Attribute information includes, for example, age, job title, and field of expertise. Some or all of the above processing in the communication analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the communication analysis unit can input member attribute information data into a generative AI and have the generative AI perform the analysis.

[0052] The communication analysis unit can perform analysis while considering the geographical distribution of dialogue. For example, if members are in different regions, the communication analysis unit can analyze the frequency of communication in each region. For example, if members are in the same office, the communication analysis unit can analyze the communication patterns within the office. For example, if members are working remotely, the communication analysis unit can analyze the communication patterns in the remote environment. This allows the analysis to be performed while considering the geographical distribution of dialogue. Geographical distribution includes, for example, GPS data and location information services. Some or all of the above processing in the communication analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the communication analysis unit can input the geographical distribution data of dialogue into a generative AI and have the generative AI perform the analysis.

[0053] The communication analysis unit can improve the accuracy of its analysis by referring to relevant literature during communication analysis. For example, the communication analysis unit can improve its analysis algorithm by referring to the latest research on communication. For example, the communication analysis unit can optimize its analysis results by referring to best practices in communication. For example, the communication analysis unit can improve the accuracy of its analysis by referring to past data on communication. This allows the accuracy of the analysis to be improved by referring to relevant literature. Relevant literature includes, for example, academic papers and technical reports. Accuracy of the analysis includes, for example, data accuracy and analysis algorithms. Some or all of the above processes in the communication analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the communication analysis unit can input relevant literature data into a generative AI and have the generative AI perform the improvement of analysis accuracy.

[0054] The skill analysis unit can predict current skills by referring to past skill data during skill analysis. For example, the skill analysis unit predicts the current skill level based on past skill data. For example, the skill analysis unit predicts the degree of skill improvement based on past skill data. For example, the skill analysis unit identifies areas of skill deficiency based on past skill data. This allows the current skills to be predicted based on past skill data. Past skill data includes, for example, the number of tasks completed and the time taken to complete them. Some or all of the above processes in the skill analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the skill analysis unit can input past skill data into a generative AI and have the generative AI perform a prediction of current skills.

[0055] The skill analysis unit can apply different analysis methods to each member category during skill analysis. For example, the skill analysis unit can apply an analysis method that emphasizes technical skills to technical members. For example, the skill analysis unit can apply an analysis method that emphasizes creativity to creative members. For example, the skill analysis unit can apply an analysis method that emphasizes management skills to management members. This allows different analysis methods to be applied to each member category. Member categories include, for example, technical, creative, and management. Some or all of the above processing in the skill analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the skill analysis unit can input member category data into a generative AI and have the generative AI perform the application of the analysis method.

[0056] The skill analysis unit can adjust the frequency of analysis based on the submission timing of members. For example, the skill analysis unit will analyze skills with approaching submission deadlines more frequently. For example, the skill analysis unit will analyze skills with distant submission deadlines less frequently. For example, the skill analysis unit will analyze skills with moderate submission deadlines at a moderate frequency. This allows the analysis frequency to be adjusted based on the submission timing. Submission timing includes, for example, the submission deadline and progress status. Some or all of the above processing in the skill analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the skill analysis unit can input submission timing data into a generating AI and have the generating AI adjust the analysis frequency.

[0057] The skill analysis unit can improve the accuracy of its analysis by referring to relevant literature during skill analysis. For example, the skill analysis unit can refer to the latest research on skills and improve its analysis algorithm. For example, the skill analysis unit can refer to best practices for skills and optimize the analysis results. For example, the skill analysis unit can refer to past data on skills and improve the accuracy of its analysis. This allows the skill analysis unit to improve the accuracy of its analysis by referring to relevant literature. Relevant literature includes, for example, academic papers and technical reports. Accuracy of the analysis includes, for example, data accuracy and analysis algorithms. Some or all of the above processes in the skill analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the skill analysis unit can input relevant literature data into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0058] The burden analysis unit can predict the current burden by referring to past burden data during burden analysis. For example, the burden analysis unit predicts the current burden level based on past burden data. For example, the burden analysis unit predicts increases or decreases in the burden based on past burden data. For example, the burden analysis unit identifies burden imbalances based on past burden data. This allows the current burden to be predicted based on past burden data. Past burden data includes, for example, work time and task difficulty. Some or all of the above processing in the burden analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the burden analysis unit can input past burden data into a generation AI and have the generation AI perform a prediction of the current burden.

[0059] The burden analysis unit can apply different analysis methods to each member category during burden analysis. For example, the burden analysis unit can apply an analysis method that emphasizes technical burden to technical members. For example, the burden analysis unit can apply an analysis method that emphasizes creative burden to creative members. For example, the burden analysis unit can apply an analysis method that emphasizes administrative burden to administrative members. This allows different analysis methods to be applied to each member category. Member categories include, for example, technical, creative, and administrative roles. Some or all of the above processing in the burden analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the burden analysis unit can input member category data into a generative AI and have the generative AI execute the application of the analysis method.

[0060] The workload analysis unit can adjust the frequency of analysis based on the submission timing of members during workload analysis. For example, the workload analysis unit performs analysis frequently for workloads with approaching deadlines. For example, the workload analysis unit reduces the frequency of analysis for workloads with distant deadlines. For example, the workload analysis unit performs analysis at a moderate frequency for workloads with moderate deadlines. This allows the frequency of analysis to be adjusted based on the submission timing. The submission timing includes, for example, the submission deadline and progress status. Some or all of the above processing in the workload analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the workload analysis unit can input submission timing data into a generating AI and have the generating AI adjust the frequency of analysis.

[0061] The burden analysis unit can improve the accuracy of its analysis by referring to relevant literature during the burden analysis. For example, the burden analysis unit can refer to the latest research on burden and improve its analysis algorithm. For example, the burden analysis unit can refer to best practices for burden and optimize the analysis results. For example, the burden analysis unit can refer to past data on burden and improve the accuracy of its analysis. This allows the accuracy of the analysis to be improved by referring to relevant literature. Relevant literature includes, for example, academic papers and technical reports. Accuracy of the analysis includes, for example, data accuracy and analysis algorithms. Some or all of the above processing in the burden analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the burden analysis unit can input relevant literature data into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The task assignment unit can analyze each member's past task completion history and select the optimal task assignment method. For example, the task assignment unit can use a generating AI to assign similar tasks based on the types of tasks a member has successfully completed in the past. For example, the task assignment unit can use a generating AI to adjust tasks so that members avoid tasks they have struggled with in the past. For example, the task assignment unit can use a generating AI to assign an appropriate amount of tasks based on a member's past task completion speed. This allows the task assignment unit to select the optimal task assignment method based on past task completion history. Past task completion history includes, for example, the number of tasks completed and the time taken to complete them. The optimal task assignment method includes, for example, the type of algorithm and evaluation criteria. Some or all of the above processes in the task assignment unit may be performed using a generating AI, or not. For example, the task assignment unit can input past task completion history data into a generating AI and have the generating AI select the optimal task assignment method.

[0064] The task assignment unit can filter tasks based on members' current projects and areas of interest when assigning tasks. For example, the task assignment unit can prioritize assigning tasks related to projects that members are currently working on. For example, the task assignment unit can assign tasks that the generating AI finds interesting based on members' areas of interest. For example, the task assignment unit can assign tasks in areas that members have shown interest in in the past. This allows tasks to be filtered based on current projects and areas of interest. Current projects include, for example, project objectives and progress. Areas of interest include, for example, past research topics and survey results. Some or all of the above processing in the task assignment unit may be performed using, for example, the generating AI, or without using the generating AI. For example, the task assignment unit can input data on members' current projects and areas of interest into the generating AI and have the generating AI perform task filtering.

[0065] The task assignment unit can prioritize assigning tasks that are highly relevant to the member, taking into account the member's geographical location information. For example, if a member is in the office, the task assignment unit will assign tasks that can be completed within the office. If a member is working remotely, the task assignment unit will assign tasks that can be completed remotely. If a member is on a business trip, the task assignment unit will assign tasks relevant to the business trip location. This allows for the priority assignment of tasks that are highly relevant based on geographical location information. Geographical location information includes, for example, GPS data and location services. Highly relevant tasks include, for example, geographical proximity and the relevance of the task content. Some or all of the above processing in the task assignment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the task assignment unit can input the member's geographical location information data into a generative AI and have the generative AI perform the assignment of highly relevant tasks.

[0066] The feedback provider can adjust the level of detail of the feedback based on the importance of the task when providing feedback. For example, the feedback provider can provide detailed feedback for important tasks, moderately detailed feedback for tasks of moderate importance, and concise feedback for tasks of low importance. This allows the level of detail of the feedback to be adjusted based on the importance of the task. Task importance includes, for example, project priority and task impact. The level of detail of the feedback includes, for example, detailed explanations and concise comments. Some or all of the above processing in the feedback provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provider can input task importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the feedback.

[0067] The feedback provider can apply different feedback algorithms depending on the task category when providing feedback. For example, the feedback provider can provide creative feedback for creative tasks, feedback that emphasizes creativity, feedback that emphasizes technical details for technical tasks, and feedback that emphasizes schedule management for managerial tasks. This allows for the application of different feedback algorithms depending on the task category. Task categories include, for example, project tasks and routine tasks. Feedback algorithms include, for example, machine learning algorithms and rule-based algorithms. Some or all of the above processing in the feedback provider may be performed using, for example, generative AI, or without using generative AI. For example, the feedback provider can input task category data into a generative AI and have the generative AI apply the feedback algorithm.

[0068] The feedback provider can determine the priority of feedback based on the task submission date when providing feedback. For example, the feedback provider can prioritize providing feedback to tasks with approaching deadlines. For example, it can lower the priority of feedback to tasks with distant deadlines. For example, it can provide feedback with a moderate priority to tasks with medium deadlines. This allows the feedback provider to determine the priority of feedback based on the task submission date. The task submission date includes, for example, the submission deadline and progress status. The priority of feedback includes, for example, the importance and urgency of the task. Some or all of the above processing in the feedback provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provider can input task submission date data into a generative AI and have the generative AI determine the priority of feedback.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The task assignment unit assigns tasks. For example, it analyzes each member's skills and workload using a generation AI and automatically distributes tasks. To prevent work from concentrating on specific members, the generation AI assigns tasks considering each member's skill set and current workload. This prevents work imbalances. Step 2: The progress monitoring unit monitors the progress of tasks assigned by the task assignment unit. For example, the generation AI constantly monitors the work status and notifies the leader of members who are overburdened or tasks that are behind schedule. If a particular member is taking on an excessive amount of work or if the progress of a particular task is behind schedule, the generation AI sends an alert to the leader. This enables a quick response. Step 3: The Feedback Provision Unit provides feedback based on the progress monitored by the Progress Monitoring Unit. For example, the Generating AI constantly monitors the work status and notifies leaders of members who are overburdened or tasks that are behind schedule. If a particular member is taking on an excessive amount of work or if the progress of a particular task is behind schedule, the Generating AI sends an alert to the leader. This enables a quick response.

[0071] (Example of form 2) The Task Equalizer system according to an embodiment of the present invention is a system that utilizes generative AI to optimize the balance of work and collaboration within a team. The Task Equalizer system is designed to eliminate unfair situations in traditional organizations, such as work being concentrated on specific members or work progressing only among close friends. This solves problems such as uneven workload distribution, decreased motivation among other members, and reduced team efficiency. The Task Equalizer system consists of the following elements: Fair automatic task assignment: The generative AI analyzes each member's skills and workload and automatically distributes tasks to prevent workload imbalance. Analysis of communication imbalance: The generative AI analyzes the status of dialogue and collaboration within the team and visualizes and improves situations where work is progressing only among specific members. Real-time feedback: The generative AI constantly monitors the work status and notifies the leader of members who are overburdened or tasks that are behind schedule. For example, to prevent work from being concentrated on specific members, the generative AI assigns tasks considering each member's skill set and current workload. The generative AI analyzes chat logs and email exchanges and notifies the leader if communication is concentrated only among specific members. If a particular member is taking on an excessive workload or if a specific task is falling behind schedule, the generating AI sends an alert to the leader. This allows the Task Equalizer system to eliminate excessive burdens on specific members, ensuring that everyone contributes equally to the work. The generating AI prevents workload imbalances by assigning tasks considering each member's skills and workload. The generating AI analyzes communication imbalances and provides improvement suggestions, enhancing overall team collaboration. The generating AI monitors work status in real time and provides feedback to leaders, enabling quick responses. The Task Equalizer system leverages generating AI to improve work efficiency and fairness. This leads to increased productivity and motivation for the entire team, enabling efficient work operations.This allows the Task Equalizer system to optimize team workload balance and collaboration, resulting in fairer work distribution and increased productivity.

[0072] The Task Equalizer system according to this embodiment comprises a task assignment unit, a progress monitoring unit, and a feedback provision unit. The task assignment unit assigns tasks. The task assignment unit, for example, analyzes each member's skills and workload using a generating AI and automatically distributes tasks. The task assignment unit, for example, assigns tasks considering each member's skill set and current workload using a generating AI to prevent work from concentrating on specific members. The task assignment unit prevents workload imbalances by assigning tasks considering members' skills and workloads using a generating AI. The progress monitoring unit monitors the progress of tasks assigned by the task assignment unit. The progress monitoring unit, for example, uses a generating AI to constantly monitor the work status and notifies the leader of members who are overburdened or tasks that are behind schedule. The progress monitoring unit, for example, uses a generating AI to send an alert to the leader if a particular member is taking on an excessive amount of work or if the progress of a particular task is behind schedule. The progress monitoring unit enables quick responses by having a generating AI monitor the work status in real time and provide feedback to the leader. The feedback provision unit provides feedback based on the progress monitored by the progress monitoring unit. For example, the feedback provision unit uses a generating AI to constantly monitor the work status and notify leaders of members who are overburdened or tasks that are behind schedule. For example, if a particular member is taking on an excessive amount of work or if the progress of a particular task is behind schedule, the generating AI sends an alert to the leader. For example, the feedback provision unit enables quick responses by using a generating AI to monitor the work status in real time and provide feedback to leaders. As a result, the Task Equalizer system according to this embodiment can efficiently assign tasks, monitor progress, and provide feedback.

[0073] The task assignment unit assigns tasks. For example, the task assignment unit analyzes each member's skills and workload using a generative AI and automatically distributes tasks. Specifically, the generative AI retrieves each member's past work history, skill set, and current workload from a database and uses this information to make optimal task assignments. The generative AI uses natural language processing technology to analyze members' skills and experience and identify the member best suited for each task. The generative AI also monitors each member's current workload in real time and distributes tasks to prevent work from concentrating on any particular member. For example, if member A currently has 5 tasks while member B only has 2, the generative AI will adjust the assignment so that the next task is assigned to member B. Furthermore, the generative AI also takes into account task priority and deadlines, prioritizing the assignment of high-urgency tasks. This allows the task assignment unit to prevent workload imbalances and optimally utilize each member's skills and workload. The generative AI can also periodically evaluate the progress of tasks and reassign tasks as needed. For example, if a particular task is behind schedule, the generation AI can distribute that task to other members, thereby ensuring smooth progress across the entire project. This allows the task assignment department to manage tasks efficiently and flexibly, improving the overall productivity of the project.

[0074] The progress monitoring unit monitors the progress of tasks assigned by the task assignment unit. For example, the progress monitoring unit uses a generation AI to constantly monitor work status and notify leaders of members who are overburdened or tasks that are behind schedule. Specifically, the generation AI tracks the task progress of each member in real time and identifies tasks that are behind schedule or members who are overburdened. The generation AI analyzes task progress data and evaluates whether it is progressing according to schedule. For example, it compares the scheduled completion date of a task with the current progress rate and sends an alert to the leader if a delay occurs. The generation AI also analyzes each member's working hours and task completion status, and reports to the leader if a particular member is overburdened. This allows the leader to respond quickly and reassign tasks or adjust resources as needed. Furthermore, the progress monitoring unit can also perform trend analysis based on past data and predict future risks. For example, by analyzing past project data, if a particular task tends to be prone to delays, countermeasures can be taken in advance. This allows the progress monitoring unit to not only grasp the situation in real time but also handle long-term risk management, enabling the smooth progress of the entire project.

[0075] The Feedback Department provides feedback based on the progress monitored by the Progress Monitoring Department. For example, the Feedback Department uses a generating AI to constantly monitor work status and notify leaders of members who are overburdened or tasks that are behind schedule. Specifically, the generating AI analyzes the task progress of each member and provides specific feedback to the leader. For example, if a particular member is taking on an excessive amount of work, the generating AI will suggest ways to reduce that member's workload. Also, if a particular task is behind schedule, the generating AI will analyze the cause and suggest solutions to the leader. For example, if the delay in a task is due to a lack of resources, the generating AI will suggest allocating additional resources. Furthermore, the Feedback Department can also provide feedback to members themselves to encourage self-improvement. For example, the generating AI will evaluate each member's work performance and provide feedback on areas for improvement and success stories. This allows members to objectively understand their own work performance and strive for self-improvement. In addition, the Feedback Department generates regular reports to inform leaders of the overall project progress and risks. This allows leaders to grasp the overall picture of the project and make appropriate decisions. This allows the feedback department to contribute not only to real-time feedback but also to long-term business improvement, thereby contributing to the overall success of the project.

[0076] The task assignment unit can automatically distribute tasks by analyzing each member's skills and workload using a generating AI. For example, the task assignment unit assigns tasks considering each member's skill set and current workload. The task assignment unit prevents workload imbalances by having the generating AI consider members' skills and workloads when assigning tasks. For example, the task assignment unit automatically assigns the optimal task by having the generating AI analyze each member's skills and workload. This allows for automatic task distribution based on each member's skills and workload. Skills include, for example, technical skills and soft skills. Workload includes, for example, working time and task difficulty. Technologies such as deep learning and natural language processing are used in the generating AI.

[0077] The Communication Analysis Department uses AI to analyze team dialogue and collaboration, enabling the visualization and improvement of situations where work is progressing only among specific members. For example, the AI ​​analyzes chat logs and email exchanges, and if communication is concentrated among specific members, it notifies the leader. The Communication Analysis Department uses AI to analyze team dialogue and collaboration, enabling the visualization and improvement of situations where work is progressing only among specific members. For example, the AI ​​analyzes communication imbalances and provides improvement suggestions, thereby improving overall team cooperation. This allows for the visualization and improvement of situations where work is progressing only among specific members. Communication analysis includes, for example, analysis of dialogue frequency and content.

[0078] The feedback system can notify leaders of tasks that are behind schedule or members who are being unfairly burdened. For example, the feedback system's AI continuously monitors work status and notifies leaders of members who are being unfairly burdened or tasks that are behind schedule. For example, if a particular member is taking on an excessive amount of work or if a particular task is behind schedule, the AI ​​will send an alert to the leader. The feedback system enables quick responses by having the AI ​​monitor work status in real time and provide feedback to leaders. This allows leaders to be notified of tasks that are behind schedule or members who are being unfairly burdened. Being behind schedule includes, for example, a percentage behind schedule. Being unfairly burdened includes, for example, a disproportionate amount of work concentrated on a particular member.

[0079] The Skill Analysis Unit can analyze each member's skill set using a generating AI and provide it to the Task Assignment Unit. For example, the Skill Analysis Unit uses a generating AI to analyze each member's skill set and provide it to the Task Assignment Unit. The Skill Analysis Unit uses a generating AI to analyze each member's skill set and provide information for optimal task assignment. By having the Skill Analysis Unit use a generating AI to analyze each member's skill set and provide it to the Task Assignment Unit, task assignment can be made more efficient. This allows the Skill Analysis Unit to analyze each member's skill set and provide it to the Task Assignment Unit. Skill sets include, for example, programming skills and management skills.

[0080] The workload analysis unit can analyze the workload of each member using a generating AI and provide it to the feedback unit. For example, the workload analysis unit uses a generating AI to analyze the workload of each member and provide it to the feedback unit. By having the workload analysis unit use a generating AI to analyze the workload of each member and provide it to the feedback unit, task assignment can be made more efficient. This allows the workload analysis unit to analyze the workload of each member and provide it to the feedback unit. The workload includes, for example, work time and task difficulty.

[0081] The task assignment unit can estimate the emotions of its members and adjust the task assignment method based on those emotions. For example, if a member is stressed, the task assignment unit can use the generative AI to adjust the task load to reduce it. For example, if a member is relaxed, the task assignment unit can use the generative AI to assign challenging tasks. For example, if a member is tired, the task assignment unit can use the generative AI to prioritize assigning easy tasks. This allows the task assignment method to be adjusted based on the members' emotions. Emotions include, for example, facial expression analysis and text analysis. Some or all of the above processing in the task assignment unit may be performed using, for example, the generative AI, or without the generative AI. For example, the task assignment unit can input member emotion data into the generative AI and have the generative AI perform the adjustment of the task assignment method based on those emotions.

[0082] The task assignment unit can analyze each member's past task completion history and select the optimal task assignment method. For example, the task assignment unit can use a generating AI to assign similar tasks based on the types of tasks a member has successfully completed in the past. For example, the task assignment unit can use a generating AI to adjust tasks so that members avoid tasks they have struggled with in the past. For example, the task assignment unit can use a generating AI to assign an appropriate amount of tasks based on a member's past task completion speed. This allows the task assignment unit to select the optimal task assignment method based on past task completion history. Past task completion history includes, for example, the number of tasks completed and the time taken to complete them. The optimal task assignment method includes, for example, the type of algorithm and evaluation criteria. Some or all of the above processes in the task assignment unit may be performed using a generating AI, or not. For example, the task assignment unit can input past task completion history data into a generating AI and have the generating AI select the optimal task assignment method.

[0083] The task assignment unit can filter tasks based on members' current projects and areas of interest when assigning tasks. For example, the task assignment unit can prioritize assigning tasks related to projects that members are currently working on. For example, the task assignment unit can assign tasks that the generating AI finds interesting based on members' areas of interest. For example, the task assignment unit can assign tasks in areas that members have shown interest in in the past. This allows tasks to be filtered based on current projects and areas of interest. Current projects include, for example, project objectives and progress. Areas of interest include, for example, past research topics and survey results. Some or all of the above processing in the task assignment unit may be performed using, for example, the generating AI, or without using the generating AI. For example, the task assignment unit can input data on members' current projects and areas of interest into the generating AI and have the generating AI perform task filtering.

[0084] The task assignment unit can estimate the emotions of its members and determine task priorities based on those estimated emotions. For example, if a member is feeling stressed, the task assignment unit's generating AI will assign a low-priority task. For example, if a member is relaxed, the task assignment unit's generating AI will assign a high-priority task. For example, if a member is tired, the task assignment unit's generating AI will assign a medium-priority task. This allows the task assignment unit to determine task priorities based on the members' emotions. Emotions include, for example, facial expression analysis and text analysis. Task priorities include, for example, the importance and urgency of the task. Some or all of the above-described processes in the task assignment unit may be performed using, for example, the generating AI, or not using the generating AI. For example, the task assignment unit can input member emotion data into the generating AI and have the generating AI determine task priorities.

[0085] The task assignment unit can prioritize assigning tasks that are highly relevant to the member, taking into account the member's geographical location information. For example, if a member is in the office, the task assignment unit will assign tasks that can be completed within the office. If a member is working remotely, the task assignment unit will assign tasks that can be completed remotely. If a member is on a business trip, the task assignment unit will assign tasks relevant to the business trip location. This allows for the priority assignment of tasks that are highly relevant based on geographical location information. Geographical location information includes, for example, GPS data and location services. Highly relevant tasks include, for example, geographical proximity and the relevance of the task content. Some or all of the above processing in the task assignment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the task assignment unit can input the member's geographical location information data into a generative AI and have the generative AI perform the assignment of highly relevant tasks.

[0086] The task assignment unit can analyze members' social media activity and assign relevant tasks when assigning tasks. For example, the task assignment unit can assign tasks related to topics that members have shown interest in on social media. For example, the task assignment unit can assign tasks related to industries that members follow on social media. For example, the task assignment unit can assign tasks related to articles that members have shared on social media. This allows relevant tasks to be assigned based on social media activity. Social media activity includes, for example, the content of posts and the frequency of activity. Some or all of the above processing in the task assignment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the task assignment unit can input members' social media activity data into a generative AI and have the generative AI perform the assignment of relevant tasks.

[0087] The progress monitoring unit can estimate the emotions of its members and adjust the frequency of progress monitoring based on those emotions. For example, if a member is stressed, the progress monitoring unit can reduce the frequency of progress monitoring. For example, if a member is relaxed, the progress monitoring unit can increase the frequency of progress monitoring. For example, if a member is tired, the progress monitoring unit can maintain a moderate frequency of progress monitoring. This allows the frequency of progress monitoring to be adjusted based on the emotions of the members. Emotions include, for example, facial expression analysis and text analysis. The frequency of progress monitoring includes, for example, the degree of delay in progress and the importance of the task. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the progress monitoring unit can input member emotion data into a generative AI and have the generative AI adjust the frequency of progress monitoring.

[0088] The progress monitoring unit can predict current progress by referring to past progress data during progress monitoring. For example, the progress monitoring unit predicts the completion of the current task based on past progress data. For example, the progress monitoring unit predicts the possibility of delays based on past progress data. For example, the progress monitoring unit predicts the completion time of a task based on past progress data. This allows the current progress to be predicted based on past progress data. Past progress data includes, for example, past task completion rates and the degree of delay in progress. Predicting current progress includes, for example, prediction algorithms and evaluation criteria. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the progress monitoring unit can input past progress data into a generative AI and have the generative AI perform a prediction of current progress.

[0089] The progress monitoring unit can apply different monitoring methods to each task category during progress monitoring. For example, the progress monitoring unit can apply a monitoring method that emphasizes the quality of progress to creative tasks, a monitoring method that emphasizes the quantity of progress to technical tasks, and a monitoring method that emphasizes the schedule of progress to managerial tasks. This allows for the application of different monitoring methods to each task category. Task categories include, for example, project tasks and daily work tasks. Monitoring methods include, for example, quantitative monitoring and qualitative monitoring. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the progress monitoring unit can input task category data into a generative AI and have the generative AI apply the monitoring method.

[0090] The progress monitoring unit can estimate the emotions of its members and determine the priority of progress monitoring based on those emotions. For example, if a member is stressed, the progress monitoring unit will lower the priority of progress monitoring. For example, if a member is relaxed, the progress monitoring unit will raise the priority of progress monitoring. For example, if a member is tired, the progress monitoring unit will maintain a medium priority of progress monitoring. This allows the progress monitoring unit to determine the priority of progress monitoring based on the emotions of its members. Emotions include, for example, facial expression analysis and text analysis. Prioritization of progress monitoring includes, for example, the importance and urgency of the tasks. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the progress monitoring unit can input member emotion data into a generative AI and have the generative AI determine the priority of progress monitoring.

[0091] The progress monitoring unit can adjust the frequency of monitoring based on the task submission date during progress monitoring. For example, the progress monitoring unit will frequently monitor the progress of tasks with approaching deadlines. For example, the progress monitoring unit will reduce the frequency of monitoring for tasks with distant deadlines. For example, the progress monitoring unit will monitor the progress of tasks with moderate deadlines at an appropriate frequency. This allows the monitoring frequency to be adjusted based on the task submission date. The task submission date includes, for example, the submission deadline and progress status. The monitoring frequency includes, for example, the degree of delay in progress and the importance of the task. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the progress monitoring unit can input task submission date data into a generation AI and have the generation AI adjust the monitoring frequency.

[0092] The progress monitoring unit can improve the accuracy of its monitoring by referring to relevant literature for the task during progress monitoring. For example, the progress monitoring unit can refer to relevant literature for the task and set progress evaluation criteria. For example, the progress monitoring unit can improve the accuracy of progress predictions based on relevant literature for the task. For example, the progress monitoring unit can refer to relevant literature for the task and identify problems in progress. This allows the progress monitoring unit to improve the accuracy of its monitoring by referring to relevant literature for the task. Relevant literature includes, for example, academic papers and technical reports. Monitoring accuracy includes, for example, data accuracy and analysis algorithms. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the progress monitoring unit can input relevant literature data into a generative AI and have the generative AI perform the improvement of monitoring accuracy.

[0093] The feedback provider can estimate a member's emotions and adjust the way feedback is expressed based on those emotions. For example, if a member is stressed, the feedback provider will provide feedback in gentle words. For example, if a member is relaxed, the feedback provider will provide detailed feedback. For example, if a member is tired, the feedback provider will provide concise feedback. This allows the feedback provider to adjust the way feedback is expressed based on the member's emotions. Emotions include, for example, facial expression analysis and text analysis. Methods of expressing feedback include, for example, text feedback and voice feedback. Some or all of the above processing in the feedback provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provider can input the member's emotional data into a generative AI and have the generative AI adjust the way feedback is expressed.

[0094] The feedback provider can adjust the level of detail of the feedback based on the importance of the task when providing feedback. For example, the feedback provider can provide detailed feedback for important tasks, moderately detailed feedback for tasks of moderate importance, and concise feedback for tasks of low importance. This allows the level of detail of the feedback to be adjusted based on the importance of the task. Task importance includes, for example, project priority and task impact. The level of detail of the feedback includes, for example, detailed explanations and concise comments. Some or all of the above processing in the feedback provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provider can input task importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the feedback.

[0095] The feedback provider can apply different feedback algorithms depending on the task category when providing feedback. For example, the feedback provider can provide creative feedback for creative tasks, feedback that emphasizes creativity, feedback that emphasizes technical details for technical tasks, and feedback that emphasizes schedule management for managerial tasks. This allows for the application of different feedback algorithms depending on the task category. Task categories include, for example, project tasks and routine tasks. Feedback algorithms include, for example, machine learning algorithms and rule-based algorithms. Some or all of the above processing in the feedback provider may be performed using, for example, generative AI, or without using generative AI. For example, the feedback provider can input task category data into a generative AI and have the generative AI apply the feedback algorithm.

[0096] The feedback provider can estimate the member's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the member is stressed, the feedback provider will provide short feedback. For example, if the member is relaxed, the feedback provider will provide long feedback. For example, if the member is tired, the feedback provider will provide feedback of an appropriate length. This allows the feedback length to be adjusted based on the member's emotions. Emotions include, for example, facial expression analysis and text analysis. Feedback length includes, for example, detailed explanations and concise comments. Some or all of the above processing in the feedback provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provider can input the member's emotion data into a generative AI and have the generative AI adjust the length of the feedback.

[0097] The feedback provider can determine the priority of feedback based on the task submission date when providing feedback. For example, the feedback provider can prioritize providing feedback to tasks with approaching deadlines. For example, it can lower the priority of feedback to tasks with distant deadlines. For example, it can provide feedback with a moderate priority to tasks with medium deadlines. This allows the feedback provider to determine the priority of feedback based on the task submission date. The task submission date includes, for example, the submission deadline and progress status. The priority of feedback includes, for example, the importance and urgency of the task. Some or all of the above processing in the feedback provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provider can input task submission date data into a generative AI and have the generative AI determine the priority of feedback.

[0098] The feedback provider can adjust the order of feedback based on the relevance of the tasks when providing feedback. For example, the feedback provider can provide feedback first to important tasks. For example, it can provide feedback in an appropriate order to tasks of moderate importance. For example, it can provide feedback last to tasks of low importance. This allows the feedback to be adjusted based on the relevance of the tasks. Task relevance includes, for example, the relevance of the task content or the relevance of the project. The order of feedback includes, for example, order by importance or order of submission. Some or all of the above processing in the feedback provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provider can input task relevance data into a generative AI and have the generative AI perform the adjustment of the feedback order.

[0099] The communication analysis unit can estimate the emotions of members and adjust the criteria for communication analysis based on the estimated emotions. For example, if a member is stressed, the communication analysis unit will prioritize the frequency of communication in its analysis. For example, if a member is relaxed, the communication analysis unit will prioritize the content of communication in its analysis. For example, if a member is tired, the communication analysis unit will prioritize the tone of communication in its analysis. This allows the criteria for communication analysis to be adjusted based on the emotions of the members. Emotions include, for example, facial expression analysis and text analysis. Criteria for communication analysis include, for example, analysis of the frequency and content of conversations. Some or all of the above processing in the communication analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the communication analysis unit can input member emotion data into a generative AI and have the generative AI perform the adjustment of the criteria for communication analysis.

[0100] The communication analysis unit can improve the accuracy of its analysis by considering the interrelationships between dialogues during communication analysis. For example, the communication analysis unit can analyze the frequency and content of dialogues between members and identify interrelationships. For example, the communication analysis unit can analyze the tone and emotions of dialogues between members and identify interrelationships. For example, the communication analysis unit can analyze the timing and context of dialogues between members and identify interrelationships. This allows the accuracy of the analysis to be improved by considering the interrelationships between dialogues. Interrelationships between dialogues include, for example, the frequency and content of the dialogues. Accuracy of the analysis includes, for example, the accuracy of the data and the analysis algorithm. Some or all of the above processing in the communication analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the communication analysis unit can input dialogue interrelationship data into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0101] The communication analysis unit can perform analysis while considering the attribute information of the members. For example, the communication analysis unit can analyze the importance of communication by considering the member's position and job responsibilities. For example, the communication analysis unit can analyze the content of communication by considering the member's years of experience and skill set. For example, the communication analysis unit can analyze the frequency of communication by considering the member's working hours and work location. This allows the analysis to be performed while considering the attribute information of the members. Attribute information includes, for example, age, job title, and field of expertise. Some or all of the above processing in the communication analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the communication analysis unit can input member attribute information data into a generative AI and have the generative AI perform the analysis.

[0102] The communication analysis unit can estimate the emotions of members and adjust the order in which the communication analysis results are displayed based on the estimated emotions. For example, if a member is feeling stressed, the communication analysis unit will display important communication analysis results first. For example, if a member is relaxed, the communication analysis unit will display detailed communication analysis results. For example, if a member is tired, the communication analysis unit will display concise communication analysis results. This allows the order in which the communication analysis results are displayed to be adjusted based on the emotions of the members. Emotions include, for example, facial expression analysis and text analysis. The display order includes, for example, order of importance or order of submission. Some or all of the above processing in the communication analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the communication analysis unit can input member emotion data into a generative AI and have the generative AI adjust the display order.

[0103] The communication analysis unit can perform analysis while considering the geographical distribution of dialogue. For example, if members are in different regions, the communication analysis unit can analyze the frequency of communication in each region. For example, if members are in the same office, the communication analysis unit can analyze the communication patterns within the office. For example, if members are working remotely, the communication analysis unit can analyze the communication patterns in the remote environment. This allows the analysis to be performed while considering the geographical distribution of dialogue. Geographical distribution includes, for example, GPS data and location information services. Some or all of the above processing in the communication analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the communication analysis unit can input the geographical distribution data of dialogue into a generative AI and have the generative AI perform the analysis.

[0104] The communication analysis unit can improve the accuracy of its analysis by referring to relevant literature during communication analysis. For example, the communication analysis unit can improve its analysis algorithm by referring to the latest research on communication. For example, the communication analysis unit can optimize its analysis results by referring to best practices in communication. For example, the communication analysis unit can improve the accuracy of its analysis by referring to past data on communication. This allows the accuracy of the analysis to be improved by referring to relevant literature. Relevant literature includes, for example, academic papers and technical reports. Accuracy of the analysis includes, for example, data accuracy and analysis algorithms. Some or all of the above processes in the communication analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the communication analysis unit can input relevant literature data into a generative AI and have the generative AI perform the improvement of analysis accuracy.

[0105] The skill analysis unit can estimate the emotions of its members and adjust the skill analysis method based on the estimated emotions. For example, if a member is stressed, the skill analysis unit can reduce the frequency of skill analysis. For example, if a member is relaxed, the skill analysis unit can increase the frequency of skill analysis. For example, if a member is tired, the skill analysis unit can maintain a moderate frequency of skill analysis. This allows the skill analysis method to be adjusted based on the emotions of the members. Emotions include, for example, facial expression analysis and text analysis. Some or all of the above processing in the skill analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the skill analysis unit can input the members' emotional data into a generative AI and have the generative AI perform the adjustment of the skill analysis method.

[0106] The skill analysis unit can predict current skills by referring to past skill data during skill analysis. For example, the skill analysis unit predicts the current skill level based on past skill data. For example, the skill analysis unit predicts the degree of skill improvement based on past skill data. For example, the skill analysis unit identifies areas of skill deficiency based on past skill data. This allows the current skills to be predicted based on past skill data. Past skill data includes, for example, the number of tasks completed and the time taken to complete them. Some or all of the above processes in the skill analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the skill analysis unit can input past skill data into a generative AI and have the generative AI perform a prediction of current skills.

[0107] The skill analysis unit can apply different analysis methods to each member category during skill analysis. For example, the skill analysis unit can apply an analysis method that emphasizes technical skills to technical members. For example, the skill analysis unit can apply an analysis method that emphasizes creativity to creative members. For example, the skill analysis unit can apply an analysis method that emphasizes management skills to management members. This allows different analysis methods to be applied to each member category. Member categories include, for example, technical, creative, and management. Some or all of the above processing in the skill analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the skill analysis unit can input member category data into a generative AI and have the generative AI perform the application of the analysis method.

[0108] The skill analysis unit can estimate the emotions of its members and determine the priority of skill analysis based on those emotions. For example, if a member is stressed, the skill analysis unit will lower the priority of skill analysis. For example, if a member is relaxed, the skill analysis unit will raise the priority of skill analysis. For example, if a member is tired, the skill analysis unit will maintain a medium priority of skill analysis. This allows the skill analysis unit to determine the priority of skill analysis based on the member's emotions. Emotions include, for example, facial expression analysis and text analysis. Some or all of the above processing in the skill analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the skill analysis unit can input the member's emotional data into a generative AI and have the generative AI determine the priority of skill analysis.

[0109] The skill analysis unit can adjust the frequency of analysis based on the submission timing of members. For example, the skill analysis unit will analyze skills with approaching submission deadlines more frequently. For example, the skill analysis unit will analyze skills with distant submission deadlines less frequently. For example, the skill analysis unit will analyze skills with moderate submission deadlines at a moderate frequency. This allows the analysis frequency to be adjusted based on the submission timing. Submission timing includes, for example, the submission deadline and progress status. Some or all of the above processing in the skill analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the skill analysis unit can input submission timing data into a generating AI and have the generating AI adjust the analysis frequency.

[0110] The skill analysis unit can improve the accuracy of its analysis by referring to relevant literature during skill analysis. For example, the skill analysis unit can refer to the latest research on skills and improve its analysis algorithm. For example, the skill analysis unit can refer to best practices for skills and optimize the analysis results. For example, the skill analysis unit can refer to past data on skills and improve the accuracy of its analysis. This allows the skill analysis unit to improve the accuracy of its analysis by referring to relevant literature. Relevant literature includes, for example, academic papers and technical reports. Accuracy of the analysis includes, for example, data accuracy and analysis algorithms. Some or all of the above processes in the skill analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the skill analysis unit can input relevant literature data into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0111] The burden analysis unit can estimate the emotions of the members and adjust the burden analysis method based on the estimated emotions. For example, if a member is feeling stressed, the burden analysis unit can reduce the frequency of burden analysis. For example, if a member is relaxed, the burden analysis unit can increase the frequency of burden analysis. For example, if a member is tired, the burden analysis unit can maintain a moderate frequency of burden analysis. This allows the burden analysis method to be adjusted based on the emotions of the members. Emotions include, for example, facial expression analysis and text analysis. Some or all of the above processing in the burden analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the burden analysis unit can input the members' emotional data into a generative AI and have the generative AI perform the adjustment of the burden analysis method.

[0112] The burden analysis unit can predict the current burden by referring to past burden data during burden analysis. For example, the burden analysis unit predicts the current burden level based on past burden data. For example, the burden analysis unit predicts increases or decreases in the burden based on past burden data. For example, the burden analysis unit identifies burden imbalances based on past burden data. This allows the current burden to be predicted based on past burden data. Past burden data includes, for example, work time and task difficulty. Some or all of the above processing in the burden analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the burden analysis unit can input past burden data into a generation AI and have the generation AI perform a prediction of the current burden.

[0113] The burden analysis unit can apply different analysis methods to each member category during burden analysis. For example, the burden analysis unit can apply an analysis method that emphasizes technical burden to technical members. For example, the burden analysis unit can apply an analysis method that emphasizes creative burden to creative members. For example, the burden analysis unit can apply an analysis method that emphasizes administrative burden to administrative members. This allows different analysis methods to be applied to each member category. Member categories include, for example, technical, creative, and administrative roles. Some or all of the above processing in the burden analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the burden analysis unit can input member category data into a generative AI and have the generative AI execute the application of the analysis method.

[0114] The burden analysis unit can estimate the emotions of the members and determine the priority of the burden analysis based on the estimated emotions. For example, if a member is feeling stressed, the burden analysis unit will lower the priority of the burden analysis. For example, if a member is relaxed, the burden analysis unit will raise the priority of the burden analysis. For example, if a member is tired, the burden analysis unit will maintain a medium priority of the burden analysis. This allows the burden analysis priority to be determined based on the emotions of the members. Emotions include, for example, facial expression analysis and text analysis. Some or all of the above processing in the burden analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the burden analysis unit can input the members' emotional data into a generative AI and have the generative AI perform the determination of the burden analysis priority.

[0115] The workload analysis unit can adjust the frequency of analysis based on the submission timing of members during workload analysis. For example, the workload analysis unit performs analysis frequently for workloads with approaching deadlines. For example, the workload analysis unit reduces the frequency of analysis for workloads with distant deadlines. For example, the workload analysis unit performs analysis at a moderate frequency for workloads with moderate deadlines. This allows the frequency of analysis to be adjusted based on the submission timing. The submission timing includes, for example, the submission deadline and progress status. Some or all of the above processing in the workload analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the workload analysis unit can input submission timing data into a generating AI and have the generating AI adjust the frequency of analysis.

[0116] The burden analysis unit can improve the accuracy of its analysis by referring to relevant literature during the burden analysis. For example, the burden analysis unit can refer to the latest research on burden and improve its analysis algorithm. For example, the burden analysis unit can refer to best practices for burden and optimize the analysis results. For example, the burden analysis unit can refer to past data on burden and improve the accuracy of its analysis. This allows the accuracy of the analysis to be improved by referring to relevant literature. Relevant literature includes, for example, academic papers and technical reports. Accuracy of the analysis includes, for example, data accuracy and analysis algorithms. Some or all of the above processing in the burden analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the burden analysis unit can input relevant literature data into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The task assignment unit can estimate the emotions of its members and adjust the task assignment method based on those emotions. For example, if a member is stressed, the task assignment unit can use the generative AI to adjust the task load to reduce it. For example, if a member is relaxed, the task assignment unit can use the generative AI to assign challenging tasks. For example, if a member is tired, the task assignment unit can use the generative AI to prioritize assigning easy tasks. This allows the task assignment method to be adjusted based on the members' emotions. Emotions include, for example, facial expression analysis and text analysis. Some or all of the above processing in the task assignment unit may be performed using, for example, the generative AI, or without the generative AI. For example, the task assignment unit can input member emotion data into the generative AI and have the generative AI perform the adjustment of the task assignment method based on those emotions.

[0119] The task assignment unit can analyze each member's past task completion history and select the optimal task assignment method. For example, the task assignment unit can use a generating AI to assign similar tasks based on the types of tasks a member has successfully completed in the past. For example, the task assignment unit can use a generating AI to adjust tasks so that members avoid tasks they have struggled with in the past. For example, the task assignment unit can use a generating AI to assign an appropriate amount of tasks based on a member's past task completion speed. This allows the task assignment unit to select the optimal task assignment method based on past task completion history. Past task completion history includes, for example, the number of tasks completed and the time taken to complete them. The optimal task assignment method includes, for example, the type of algorithm and evaluation criteria. Some or all of the above processes in the task assignment unit may be performed using a generating AI, or not. For example, the task assignment unit can input past task completion history data into a generating AI and have the generating AI select the optimal task assignment method.

[0120] The task assignment unit can filter tasks based on members' current projects and areas of interest when assigning tasks. For example, the task assignment unit can prioritize assigning tasks related to projects that members are currently working on. For example, the task assignment unit can assign tasks that the generating AI finds interesting based on members' areas of interest. For example, the task assignment unit can assign tasks in areas that members have shown interest in in the past. This allows tasks to be filtered based on current projects and areas of interest. Current projects include, for example, project objectives and progress. Areas of interest include, for example, past research topics and survey results. Some or all of the above processing in the task assignment unit may be performed using, for example, the generating AI, or without using the generating AI. For example, the task assignment unit can input data on members' current projects and areas of interest into the generating AI and have the generating AI perform task filtering.

[0121] The task assignment unit can estimate the emotions of its members and determine task priorities based on those estimated emotions. For example, if a member is feeling stressed, the task assignment unit's generating AI will assign a low-priority task. For example, if a member is relaxed, the task assignment unit's generating AI will assign a high-priority task. For example, if a member is tired, the task assignment unit's generating AI will assign a medium-priority task. This allows the task assignment unit to determine task priorities based on the members' emotions. Emotions include, for example, facial expression analysis and text analysis. Task priorities include, for example, the importance and urgency of the task. Some or all of the above-described processes in the task assignment unit may be performed using, for example, the generating AI, or not using the generating AI. For example, the task assignment unit can input member emotion data into the generating AI and have the generating AI determine task priorities.

[0122] The task assignment unit can prioritize assigning tasks that are highly relevant to the member, taking into account the member's geographical location information. For example, if a member is in the office, the task assignment unit will assign tasks that can be completed within the office. If a member is working remotely, the task assignment unit will assign tasks that can be completed remotely. If a member is on a business trip, the task assignment unit will assign tasks relevant to the business trip location. This allows for the priority assignment of tasks that are highly relevant based on geographical location information. Geographical location information includes, for example, GPS data and location services. Highly relevant tasks include, for example, geographical proximity and the relevance of the task content. Some or all of the above processing in the task assignment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the task assignment unit can input the member's geographical location information data into a generative AI and have the generative AI perform the assignment of highly relevant tasks.

[0123] The feedback provider can estimate a member's emotions and adjust the way feedback is expressed based on those emotions. For example, if a member is stressed, the feedback provider will provide feedback in gentle words. For example, if a member is relaxed, the feedback provider will provide detailed feedback. For example, if a member is tired, the feedback provider will provide concise feedback. This allows the feedback provider to adjust the way feedback is expressed based on the member's emotions. Emotions include, for example, facial expression analysis and text analysis. Methods of expressing feedback include, for example, text feedback and voice feedback. Some or all of the above processing in the feedback provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provider can input the member's emotional data into a generative AI and have the generative AI adjust the way feedback is expressed.

[0124] The feedback provider can adjust the level of detail of the feedback based on the importance of the task when providing feedback. For example, the feedback provider can provide detailed feedback for important tasks, moderately detailed feedback for tasks of moderate importance, and concise feedback for tasks of low importance. This allows the level of detail of the feedback to be adjusted based on the importance of the task. Task importance includes, for example, project priority and task impact. The level of detail of the feedback includes, for example, detailed explanations and concise comments. Some or all of the above processing in the feedback provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provider can input task importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the feedback.

[0125] The feedback provider can apply different feedback algorithms depending on the task category when providing feedback. For example, the feedback provider can provide creative feedback for creative tasks, feedback that emphasizes creativity, feedback that emphasizes technical details for technical tasks, and feedback that emphasizes schedule management for managerial tasks. This allows for the application of different feedback algorithms depending on the task category. Task categories include, for example, project tasks and routine tasks. Feedback algorithms include, for example, machine learning algorithms and rule-based algorithms. Some or all of the above processing in the feedback provider may be performed using, for example, generative AI, or without using generative AI. For example, the feedback provider can input task category data into a generative AI and have the generative AI apply the feedback algorithm.

[0126] The feedback provider can estimate the member's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the member is stressed, the feedback provider will provide short feedback. For example, if the member is relaxed, the feedback provider will provide long feedback. For example, if the member is tired, the feedback provider will provide feedback of an appropriate length. This allows the feedback length to be adjusted based on the member's emotions. Emotions include, for example, facial expression analysis and text analysis. Feedback length includes, for example, detailed explanations and concise comments. Some or all of the above processing in the feedback provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provider can input the member's emotion data into a generative AI and have the generative AI adjust the length of the feedback.

[0127] The feedback provider can determine the priority of feedback based on the task submission date when providing feedback. For example, the feedback provider can prioritize providing feedback to tasks with approaching deadlines. For example, it can lower the priority of feedback to tasks with distant deadlines. For example, it can provide feedback with a moderate priority to tasks with medium deadlines. This allows the feedback provider to determine the priority of feedback based on the task submission date. The task submission date includes, for example, the submission deadline and progress status. The priority of feedback includes, for example, the importance and urgency of the task. Some or all of the above processing in the feedback provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback provider can input task submission date data into a generative AI and have the generative AI determine the priority of feedback.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The task assignment unit assigns tasks. For example, it analyzes each member's skills and workload using a generation AI and automatically distributes tasks. To prevent work from concentrating on specific members, the generation AI assigns tasks considering each member's skill set and current workload. This prevents work imbalances. Step 2: The progress monitoring unit monitors the progress of tasks assigned by the task assignment unit. For example, the generation AI constantly monitors the work status and notifies the leader of members who are overburdened or tasks that are behind schedule. If a particular member is taking on an excessive amount of work or if the progress of a particular task is behind schedule, the generation AI sends an alert to the leader. This enables a quick response. Step 3: The Feedback Provision Unit provides feedback based on the progress monitored by the Progress Monitoring Unit. For example, the Generating AI constantly monitors the work status and notifies leaders of members who are overburdened or tasks that are behind schedule. If a particular member is taking on an excessive amount of work or if the progress of a particular task is behind schedule, the Generating AI sends an alert to the leader. This enables a quick response.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the task assignment unit, progress monitoring unit, feedback provision unit, communication analysis unit, skill analysis unit, and burden analysis unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the task assignment unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The progress monitoring unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The feedback provision unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The communication analysis unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The skill analysis unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The burden analysis unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the task assignment unit, progress monitoring unit, feedback provision unit, communication analysis unit, skill analysis unit, and burden analysis unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the task assignment unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The progress monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The feedback provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The communication analysis unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The skill analysis unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The load analysis unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the task assignment unit, progress monitoring unit, feedback provision unit, communication analysis unit, skill analysis unit, and burden analysis unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the task assignment unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The progress monitoring unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The feedback provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The communication analysis unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The skill analysis unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The load analysis unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Each of the multiple elements described above, including the task assignment unit, progress monitoring unit, feedback provision unit, communication analysis unit, skill analysis unit, and burden analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the task assignment unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The progress monitoring unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The feedback provision unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The communication analysis unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The skill analysis unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The burden analysis unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] (Note 1) Task assignment unit for assigning tasks, A progress monitoring unit monitors the progress of tasks assigned by the task assignment unit, The system includes a feedback providing unit that provides feedback based on the progress monitored by the progress monitoring unit. A system characterized by the following features. (Note 2) The task assignment unit, The system uses AI to analyze each member's skills and workload, and automatically distributes tasks. The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with a communication analysis department. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback provision unit, Notify the leader of tasks that are behind schedule or where the workload is unevenly distributed among team members. The system described in Appendix 1, characterized by the features described herein. (Note 5) Equipped with a skill analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 6) Equipped with a load analysis unit The system described in Appendix 1, characterized by the features described herein. (Note 7) The task assignment unit, Estimate the emotions of team members and adjust task assignment methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The task assignment unit, Analyze each member's past task completion history to select the optimal task assignment method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The task assignment unit, When assigning tasks, filter based on members' current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The task assignment unit, Estimate the emotions of team members and prioritize tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The task assignment unit, When assigning tasks, the system prioritizes assigning highly relevant tasks by considering the geographical location of the team members. The system described in Appendix 1, characterized by the features described herein. (Note 12) The task assignment unit, When assigning tasks, analyze members' social media activity and assign relevant tasks. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned progress monitoring unit, Estimate the emotions of team members and adjust the frequency of progress monitoring based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned progress monitoring unit, When monitoring progress, past progress data is used to predict current progress. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned progress monitoring unit, When monitoring progress, apply different monitoring methods to each task category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned progress monitoring unit, Estimate the emotions of team members and prioritize progress monitoring based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned progress monitoring unit, When monitoring progress, adjust the monitoring frequency based on when tasks are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned progress monitoring unit, When monitoring progress, refer to relevant literature for the task to improve the accuracy of the monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback provision unit, The system estimates the emotions of the members and adjusts the way feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback provision unit, When providing feedback, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback provision unit, When providing feedback, different feedback algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback provision unit, The system estimates the emotions of the members and adjusts the length of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback provision unit, When providing feedback, we prioritize feedback based on when the task was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback provision unit, When providing feedback, adjust the order of feedback based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned communication analysis unit, We estimate the emotions of the members and adjust the criteria for communication analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned communication analysis unit, When analyzing communication, consider the interrelationships between conversations to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned communication analysis unit, When analyzing communication, the analysis takes into account the attribute information of the members. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned communication analysis unit, It estimates the emotions of the members and adjusts the order in which the communication analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned communication analysis unit, When analyzing communication, the geographical distribution of dialogues should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned communication analysis unit, When analyzing communication, referencing relevant literature improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned skill analysis unit, We estimate the emotions of the members and adjust the skill analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned skill analysis unit, During skill analysis, past skill data is referenced to predict current skills. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned skill analysis unit, When analyzing skills, different analysis methods are applied to each member category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned skill analysis unit, The system estimates the emotions of the team members and determines the priority of skill analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned skill analysis unit, During skill analysis, the frequency of analysis will be adjusted based on when members submit their work. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned skill analysis unit, When analyzing skills, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned load analysis unit, We estimate the emotions of the members and adjust the burden analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned load analysis unit, During burden analysis, past burden data is referenced to predict the current burden. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned load analysis unit, When performing a burden analysis, different analysis methods are applied to each member category. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned load analysis unit, The system estimates the emotions of the members and determines the priority of the burden analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned load analysis unit, During the burden analysis, the frequency of analysis will be adjusted based on when members submit their data. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned load analysis unit, When performing burden analysis, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0202] 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. Task assignment unit for assigning tasks, A progress monitoring unit monitors the progress of tasks assigned by the task assignment unit, The system includes a feedback providing unit that provides feedback based on the progress monitored by the progress monitoring unit. A system characterized by the following features.

2. The task assignment unit, The system uses AI to analyze each member's skills and workload, and automatically distributes tasks. The system according to feature 1.

3. Equipped with a communication analysis department. The system according to feature 1.

4. The aforementioned feedback provision unit, Notify the leader of tasks that are behind schedule or where the workload is unevenly distributed among team members. The system according to feature 1.

5. Equipped with a skill analysis unit. The system according to feature 1.

6. Equipped with a load analysis unit The system according to feature 1.

7. The task assignment unit, Estimate the emotions of team members and adjust task assignment methods based on those estimated emotions. The system according to feature 1.

8. The task assignment unit, Analyze each member's past task completion history to select the optimal task assignment method. The system according to feature 1.

9. The task assignment unit, When assigning tasks, filter based on members' current projects and areas of interest. The system according to feature 1.

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