system

Abstract

The system according to this embodiment aims to efficiently plan and manage project progress and optimize resource allocation. [Solution] The system according to the embodiment comprises a communication unit, a task assignment unit, a progress confirmation unit, and a monitoring unit. The communication unit engages in natural language dialogue with team members. The task assignment unit assigns tasks based on the information obtained by the communication unit. The progress confirmation unit checks the progress of the tasks assigned by the task assignment unit. The monitoring unit monitors the progress confirmed by the progress confirmation unit.

Description

Technical Field

[0006] , , , ,

[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, the method including 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

Means for Solving the Problems

[0006] ​​​​​​The system according to this embodiment comprises a communication unit, a task assignment unit, a progress confirmation unit, and a monitoring unit. The communication unit engages in natural language dialogue with team members. The task assignment unit assigns tasks based on the information obtained by the communication unit. The progress confirmation unit checks the progress of the tasks assigned by the task assignment unit. The monitoring unit monitors the progress confirmed by the progress confirmation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently plan and manage project progress and optimize resource allocation. [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 multiple 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​project manager system according to an embodiment of the present invention is a system that automates project planning, progress management, and optimal resource allocation. This AI project manager system utilizes natural language understanding and generation to efficiently communicate with team members and automatically assign tasks and check progress. Furthermore, it grasps the project's progress in real time and supports the early detection and countermeasures for delays and problems. For example, the AI ​​project manager system understands questions and reports from team members in natural language and generates appropriate responses. This facilitates smooth communication with team members and ensures smooth project progress. Next, the AI ​​project manager system automatically assigns project tasks and checks the progress of each team member in real time. For example, if a task is behind schedule, the AI ​​project manager system identifies the cause and proposes appropriate countermeasures. This prevents project delays and enables efficient progress. Furthermore, the AI ​​project manager system constantly monitors the project's progress and responds immediately if delays or problems occur. For example, if excessive resource usage occurs, the AI ​​project manager system proposes reallocating resources to improve project efficiency. This improves project efficiency and enables cost reduction. The AI ​​project manager system reduces the burden on project managers and team members by monitoring every element of a project and automating task allocation and coordination. This improves project success rates and increases team productivity and reliability. The AI ​​project manager system automates project planning, progress management, and resource optimization, enabling efficient project management.

[0029] The AI ​​project manager system according to this embodiment comprises a communication unit, a task assignment unit, a progress confirmation unit, and a monitoring unit. The communication unit engages in natural language dialogue with team members. The communication unit understands questions and reports from team members using, for example, speech recognition technology and generates appropriate responses using natural language processing technology. For example, if a team member asks a question about the progress of the project, the AI ​​understands the question and generates an appropriate answer. The communication unit can also receive reports from team members, understand their contents, and reflect them in the project progress. For example, if a team member reports on the progress of a task, the communication unit understands the content of the report and provides the information to the progress confirmation unit. The task assignment unit assigns tasks based on the information obtained by the communication unit. The task assignment unit automatically analyzes the project tasks and assigns the optimal task based on each team member's skill set and current workload. For example, if team member A possesses a specific skill, the task assignment unit assigns a task suitable for that skill. The task assignment unit can also assign new tasks to other members to reduce the workload of team member B if B currently has many tasks. The progress monitoring unit checks the progress of tasks assigned by the task assignment unit. For example, the progress monitoring unit monitors the progress of each team member in real time and identifies the cause if progress is behind schedule. For example, if team member C is behind schedule on a task, the progress monitoring unit analyzes the cause and proposes appropriate countermeasures. The progress monitoring unit can also periodically report on progress and provide feedback to the project manager and team members. The monitoring unit monitors the progress confirmed by the progress monitoring unit. For example, the monitoring unit grasps the project's progress in real time and supports the early detection and countermeasures for delays and problems. For example, if the project is behind schedule, the monitoring unit identifies the cause and proposes appropriate countermeasures.Furthermore, the monitoring unit can also improve project efficiency by suggesting resource reallocation if excessive resource usage occurs. This enables the AI ​​project manager system, according to the embodiment, to automate project planning, progress management, and optimal resource allocation, resulting in efficient project management.

[0030] The communications department engages in natural language dialogue with team members. For example, it uses speech recognition technology to understand questions and reports from team members and natural language processing technology to generate appropriate responses. Specifically, speech recognition technology converts the voice spoken by team members into text data, and natural language processing technology analyzes that text data. Natural language processing technology performs morphological analysis, grammatical analysis, and semantic analysis to understand the context and intent, and generates appropriate responses. For example, if a team member asks, "How is the progress on this week's tasks?", the communications department analyzes the question, retrieves the latest information from the project progress database, and generates a response such as, "This week's tasks are progressing smoothly. Task A is 80% complete, and Task B is 60% complete." The communications department can also receive reports from team members, understand their content, and reflect them in the project progress. For example, if a team member reports, "Task C is complete," the communications department analyzes the report and provides the information to the progress monitoring department. The progress monitoring department updates the project progress based on that information, allowing for real-time progress tracking. Furthermore, the communications department can save the history of conversations with team members and refer to past conversations to provide more consistent responses. This enables the communications department to facilitate smooth communication with team members and support the progress of the project.

[0031] The task allocation unit assigns tasks based on information obtained by the communication unit. For example, the task allocation unit automatically analyzes project tasks and assigns the most suitable tasks based on each team member's skill set and current workload. Specifically, the task allocation unit retrieves each team member's skill profile, past performance, and current task progress from a database and uses an AI algorithm to perform optimal task allocation. For example, if team member A is proficient in a particular programming language, it will assign programming tasks suited to that skill. Also, if team member B currently has many tasks, it can assign new tasks to other members to reduce their workload. The task allocation unit also considers task priority and deadlines to optimize the overall project progress. For example, it prioritizes the assignment of tasks with approaching deadlines or high-priority tasks to prevent project delays. Furthermore, the task allocation unit can monitor task progress in real time and reassign tasks as needed. This allows the task allocation unit to efficiently manage project progress and evenly distribute the workload among team members. Furthermore, the task allocation unit can save the task allocation history and continuously improve the accuracy of task allocation based on past data. This allows the task allocation unit to optimize project progress and achieve efficient project management.

[0032] The progress monitoring unit checks the progress of tasks assigned by the task assignment unit. For example, the progress monitoring unit monitors the progress of each team member in real time and identifies the cause if progress is behind schedule. Specifically, the progress monitoring unit records the progress data and task completion status reported by each team member in a database and analyzes the progress using an AI algorithm. For example, if team member C is behind schedule on a task, the unit analyzes the cause and proposes appropriate countermeasures. The progress monitoring unit provides a dashboard to visualize the progress of tasks, allowing project managers and team members to check progress in real time. The dashboard displays the progress, completion rate, and causes of delays for each task, allowing for an overview of the entire project. The progress monitoring unit can also periodically report on progress and provide feedback to project managers and team members. For example, it can automatically generate weekly and monthly reports to share project progress and challenges. Furthermore, the progress monitoring unit can perform trend analysis based on past progress data to predict future risks and challenges. This allows the progress monitoring unit to monitor the project's progress in real time and take appropriate measures, thereby contributing to the project's success.

[0033] The monitoring department monitors the progress confirmed by the progress confirmation department. For example, the monitoring department grasps the project's progress in real time and supports the early detection and countermeasures for delays and problems. Specifically, the monitoring department continuously monitors the project's progress based on data provided by the progress confirmation department and detects abnormal patterns and signs of delays. For example, if the project is behind schedule, it identifies the cause and proposes appropriate countermeasures. The monitoring department uses AI algorithms to detect anomalies and detect unusual patterns and abnormal data early. For example, if a particular task is significantly behind schedule, it analyzes the cause and proposes reallocating resources or reassigning tasks. The monitoring department can also improve project efficiency by proposing resource reallocation if excessive resource use occurs. For example, if a particular team member is overloaded, it reassigns tasks to other members to alleviate that load. The monitoring department also provides reports and dashboards to visualize the project's progress, allowing project managers and team members to understand the situation in real time. This enables the monitoring department to efficiently manage project progress and support the early detection and countermeasures for delays and problems. Furthermore, the monitoring department can analyze the project's progress based on past data and predict future risks and challenges. This allows the monitoring department to continuously monitor the project's progress and take appropriate measures, thereby contributing to its success.

[0034] The monitoring department can grasp the project's progress in real time and support the early detection and resolution of delays and problems. For example, the monitoring department can monitor the project's progress in real time and identify the cause if it is behind schedule. For example, if the project is behind schedule, the monitoring department can analyze the cause and propose appropriate countermeasures. In addition, the monitoring department can increase the frequency of data updates in order to grasp the project's progress in real time. For example, by updating the project's progress every minute, the monitoring department can always have the latest information. This improves project efficiency by enabling real-time monitoring of the project's progress and supporting the early detection and resolution of delays and problems.

[0035] The task allocation unit can identify the cause of delays in task progress and propose appropriate countermeasures. For example, if a task is behind schedule, the task allocation unit can use data analysis techniques to identify the cause. For instance, the task allocation unit analyzes data from the task that is behind schedule to identify the cause, such as a lack of resources or technical problems. After identifying the cause, the task allocation unit can propose appropriate countermeasures. For example, if a lack of resources is the cause, the task allocation unit can propose allocating additional resources. If a technical problem is the cause, it can also propose seeking expert assistance. In this way, when task progress is behind schedule, the cause can be identified and appropriate countermeasures can be proposed to prevent project delays.

[0036] The progress monitoring unit can check the progress of each team member in real time. For example, the progress monitoring unit can monitor the progress of each team member in real time and identify the cause if progress is behind schedule. For example, if Team Member A's progress is behind schedule, the progress monitoring unit will analyze the cause and propose appropriate countermeasures. The progress monitoring unit can also report on progress regularly and provide feedback to the project manager and team members. For example, the progress monitoring unit can provide weekly progress reports and share the progress of the project. This allows for an accurate understanding of the project's progress by checking the progress of each team member in real time.

[0037] The communications department can understand questions and reports from team members in natural language and generate appropriate responses. For example, the communications department uses speech recognition technology to understand questions and reports from team members and natural language processing technology to generate appropriate responses. For instance, if a team member asks about the progress of a project, the communications department's AI will understand the question and generate an appropriate answer. The communications department can also receive reports from team members, understand their content, and reflect them in the project progress. For example, if a team member reports on the progress of a task, the communications department will understand the report and provide the information to the progress monitoring department. This allows for smoother communication by understanding questions and reports from team members in natural language and generating appropriate responses.

[0038] The monitoring unit can propose resource reallocation if excessive resource usage occurs. For example, the monitoring unit monitors resource usage in real time and identifies the cause of excessive usage. For instance, if a specific resource is being excessively used, the monitoring unit analyzes the cause and proposes appropriate countermeasures. Furthermore, the monitoring unit can improve project efficiency by proposing resource reallocation. For example, the monitoring unit proposes reallocating excessively used resources to other projects. The monitoring unit can also adjust resource allocation to optimize resource usage. This allows for improved project efficiency by proposing resource reallocation when excessive resource usage occurs.

[0039] The communications department can analyze the past communication history of team members and select the optimal response method. For example, the communications department can use AI to respond in a similar style based on response styles that team members have preferred in the past. For example, the communications department can avoid response styles that team members have found unpleasant in the past, and the AI ​​can respond in an appropriate style. The communications department can also analyze the past question patterns of team members, and the AI ​​can predict and prepare appropriate responses. In this way, the optimal response method can be selected by analyzing the past communication history of team members.

[0040] The communications department can use appropriate technical terms and explanations based on the expertise and skills of its team members. For example, if a team member is an engineer, the AI ​​will respond using technical terminology. If a team member is a marketing professional, the AI ​​will respond using marketing terminology. Furthermore, if a team member is a newcomer, the AI ​​can explain things in simple terms and provide detailed guidance. This enables more effective communication by using appropriate technical terms and explanations according to the expertise and skills of each team member.

[0041] The communications department can communicate in the appropriate time zone by considering the geographical location of team members. For example, if team members are in different time zones, the AI ​​will send messages according to the time zone of their respective locations. Similarly, if a team member is on a business trip, the AI ​​will communicate according to the time zone of their location. Furthermore, if a team member is working remotely, the AI ​​can contact them at an appropriate time according to the time zone of their location. This ensures that communication is conducted in the appropriate time zone by considering the geographical location of team members.

[0042] The communications department can analyze team members' social media activity and provide relevant information. For example, the communications department can use AI to provide relevant project information based on information shared by team members on social media. For example, the communications department can use AI to suggest relevant resources based on topics that team members have shown interest in on social media. The communications department can also use AI to communicate with team members at the appropriate time based on their social media activity. In this way, by analyzing team members' social media activity, relevant information can be provided.

[0043] The task assignment unit can analyze the past task history of team members and assign the most suitable tasks. For example, the task assignment unit can assign similar tasks based on tasks that team members have successfully completed in the past. For example, the task assignment unit can avoid tasks that team members have struggled with in the past, and the AI ​​can assign appropriate tasks. The task assignment unit can also analyze the past task history of team members and assign the most efficient tasks using AI. In this way, by analyzing the past task history of team members, the optimal tasks can be assigned.

[0044] The task allocation unit can determine task priorities based on the importance and urgency of each task. For example, it can prioritize assigning tasks with high urgency. For example, it can prioritize assigning tasks with high importance. Furthermore, the task allocation unit can also assign the optimal task by considering the balance between urgency and importance. This enables efficient task management by determining task priorities based on the importance and urgency of each task.

[0045] The task assignment unit can assign appropriate tasks by considering the geographical location information of team members. For example, if team members are in different locations, the AI ​​will assign tasks suitable for each location. For instance, if a team member is on a business trip, the AI ​​will assign tasks that can be performed in that location. Furthermore, if a team member is working remotely, the AI ​​can assign tasks that can be performed efficiently in that location. In this way, appropriate task assignment becomes possible by considering the geographical location information of team members.

[0046] The task assignment unit can assign appropriate tasks based on the skill sets and expertise of team members. For example, the task assignment unit can use AI to assign the optimal task based on the skill sets of team members. For example, the task assignment unit can use AI to assign appropriate tasks considering the expertise of team members. Furthermore, the task assignment unit can comprehensively assess the skill sets and expertise of team members and assign the most efficient tasks using AI. This enables efficient task management by assigning appropriate tasks based on the skill sets and expertise of team members.

[0047] The progress monitoring unit can analyze the progress of each task in detail and identify problems. For example, the progress monitoring unit can analyze the progress of each task in real time and identify the causes of delays. For example, the progress monitoring unit can analyze the progress of each task in detail and identify excessive resource usage. The progress monitoring unit can also analyze the progress of each task and identify factors that hinder efficient progress. As a result, by analyzing the progress of each task in detail and identifying problems, efficient task management becomes possible.

[0048] The progress monitoring unit can perform progress checks while considering task dependencies. For example, the progress monitoring unit analyzes task dependencies to minimize the impact of delays. Furthermore, the progress monitoring unit can perform efficient progress checks by considering task dependencies. This enables efficient task management by considering task dependencies during progress checks.

[0049] The progress tracking unit visualizes the progress of each task and allows the entire team to share it. For example, the progress tracking unit visualizes the progress of each task using graphs and charts and shares it with the entire team. For example, the progress tracking unit displays the progress of each task in real time and shares it with the entire team. In addition, the progress tracking unit can visualize the progress of each task on a dashboard and share it with the entire team. This allows for accurate understanding of the project's progress by visualizing and sharing the progress of each task with the entire team.

[0050] The progress checking unit can refer to relevant literature and materials related to the task when checking progress. For example, the progress checking unit can refer to relevant literature and materials related to the task and check progress. For example, the progress checking unit can check progress based on relevant literature and materials related to the task. In addition, the progress checking unit can perform efficient progress checking by referring to relevant literature and materials related to the task. As a result, efficient task management becomes possible by checking progress by referring to relevant literature and materials related to the task.

[0051] The monitoring department can analyze the project's progress in detail and identify the causes of delays and problems. For example, the monitoring department can analyze the project's progress in real time and identify the causes of delays. For example, the monitoring department can analyze the project's progress in detail and identify excessive use of resources. The monitoring department can also analyze the project's progress and identify factors that hinder efficient progress. This enables efficient project management by allowing for a detailed analysis of the project's progress and identification of the causes of delays and problems.

[0052] The monitoring unit can analyze resource usage in detail during monitoring and propose the optimal resource allocation. For example, the monitoring unit can analyze resource usage in real time and propose the optimal resource allocation. For example, the monitoring unit can analyze resource usage in detail and make suggestions to prevent overuse. The monitoring unit can also analyze resource usage and propose efficient resource allocation. As a result, by analyzing resource usage in detail and proposing the optimal resource allocation, efficient resource management becomes possible.

[0053] The monitoring department can visualize the project's progress and share it with the entire team. For example, the monitoring department can visualize the project's progress using graphs and charts and share it with the entire team. For example, the monitoring department can display the project's progress in real time and share it with the entire team. The monitoring department can also visualize the project's progress on a dashboard and share it with the entire team. This allows for accurate understanding of the project's progress by visualizing and sharing it with the entire team.

[0054] The monitoring department can perform monitoring by referring to project-related literature and materials. For example, the monitoring department can perform monitoring by referring to project-related literature and materials. Furthermore, the monitoring department can perform efficient monitoring by referring to project-related literature and materials. This enables efficient project management by performing monitoring by referring to project-related literature and materials.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The AI ​​project manager system can also be equipped with a predictive analytics unit. This unit can predict future risks and problems based on project progress and historical data. For example, it can analyze past project data to predict that a particular task is likely to be delayed. It can also analyze resource usage to predict potential resource shortages in the future. Furthermore, it can use team member performance data to predict the risk of burnout for specific members and propose appropriate countermeasures. In this way, the predictive analytics unit can proactively predict future risks and problems, supporting the efficient progress of the project.

[0057] The AI ​​project manager system can also be equipped with a feedback collection unit. This unit can automatically collect feedback from team members and use it to improve the project. For example, it can conduct regular surveys to gather opinions and feedback from team members. It can also record problems and areas for improvement that arise during the project and incorporate them into future projects. Furthermore, it can evaluate team member satisfaction and propose appropriate improvement measures based on the project's progress. In this way, the feedback collection unit can reflect the opinions of team members and contribute to improving project quality.

[0058] The AI ​​project manager system can also include a learning support unit. This unit can provide learning content to support the skill development of team members. For example, it can analyze the skill sets of team members and suggest online courses to improve necessary skills. It can also provide learning content at appropriate times depending on the project's progress. Furthermore, it can monitor the learning progress of team members and provide additional support as needed. In this way, the learning support unit can support the skill development of team members and contribute to the success of the project.

[0059] The AI ​​project manager system can also include a risk management department. This department can assess project risks and propose measures to mitigate them. For example, it can analyze project progress and identify potential risks. It can also predict the probability of risk occurrence based on past project data and propose appropriate countermeasures. Furthermore, it can assess the impact of a risk if it occurs and provide concrete action plans for risk mitigation. This allows the risk management department to effectively manage project risks and improve the project's success rate.

[0060] The AI ​​project manager system can also be equipped with a schedule optimization unit. This unit can automatically optimize the project schedule, supporting efficient progress. For example, it can consider the dependencies of each task and generate an optimal schedule. It can also adjust the schedule to evenly distribute the workload among team members. Furthermore, it can update the schedule in real time according to the project's progress, minimizing delays. In this way, the schedule optimization unit can efficiently manage the project schedule and contribute to the project's success.

[0061] The AI ​​project manager system can also include a collaboration support unit. This unit can facilitate collaboration among team members and support the efficient progress of the project. For example, it can provide tools to streamline communication among team members. It can also analyze the skill sets of team members and suggest optimal collaboration partners. Furthermore, it can facilitate information sharing among team members and share project progress in real time. In this way, the collaboration support unit can promote collaboration among team members and contribute to the success of the project.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The communications department engages in natural language dialogue with team members. For example, it uses speech recognition technology to understand team members' questions and reports, and natural language processing technology to generate appropriate responses. If a team member asks about the project's progress, the AI ​​understands the question and generates an appropriate answer. It can also receive reports from team members, understand their content, and reflect them in the project's progress report. Step 2: The task allocation unit assigns tasks based on the information obtained by the communication unit. For example, it automatically analyzes the project tasks and assigns the most suitable tasks based on each team member's skill set and current workload. If team member A has a specific skill, it assigns them a task that suits that skill. Also, if team member B currently has many tasks, it can assign new tasks to other members to reduce their workload. Step 3: The progress monitoring unit checks the progress of tasks assigned by the task assignment unit. For example, it monitors the progress of each team member in real time and identifies the cause if progress is behind schedule. If team member C is behind schedule on a task, it analyzes the cause and proposes appropriate countermeasures. It can also periodically report on progress and provide feedback to the project manager and team members. Step 4: The monitoring unit monitors the progress confirmed by the progress verification unit. For example, it grasps the project's progress in real time and supports the early detection and resolution of delays and problems. If the project is behind schedule, it identifies the cause and proposes appropriate countermeasures. It can also propose reallocating resources if excessive resource usage occurs, thereby improving project efficiency.

[0064] (Example of form 2) The AI ​​project manager system according to an embodiment of the present invention is a system that automates project planning, progress management, and optimal resource allocation. This AI project manager system utilizes natural language understanding and generation to efficiently communicate with team members and automatically assign tasks and check progress. Furthermore, it grasps the project's progress in real time and supports the early detection and countermeasures for delays and problems. For example, the AI ​​project manager system understands questions and reports from team members in natural language and generates appropriate responses. This facilitates smooth communication with team members and ensures smooth project progress. Next, the AI ​​project manager system automatically assigns project tasks and checks the progress of each team member in real time. For example, if a task is behind schedule, the AI ​​project manager system identifies the cause and proposes appropriate countermeasures. This prevents project delays and enables efficient progress. Furthermore, the AI ​​project manager system constantly monitors the project's progress and responds immediately if delays or problems occur. For example, if excessive resource usage occurs, the AI ​​project manager system proposes reallocating resources to improve project efficiency. This improves project efficiency and enables cost reduction. The AI ​​project manager system reduces the burden on project managers and team members by monitoring every element of a project and automating task allocation and coordination. This improves project success rates and increases team productivity and reliability. The AI ​​project manager system automates project planning, progress management, and resource optimization, enabling efficient project management.

[0065] The AI ​​project manager system according to this embodiment comprises a communication unit, a task assignment unit, a progress confirmation unit, and a monitoring unit. The communication unit engages in natural language dialogue with team members. The communication unit understands questions and reports from team members using, for example, speech recognition technology and generates appropriate responses using natural language processing technology. For example, if a team member asks a question about the progress of the project, the AI ​​understands the question and generates an appropriate answer. The communication unit can also receive reports from team members, understand their contents, and reflect them in the project progress. For example, if a team member reports on the progress of a task, the communication unit understands the content of the report and provides the information to the progress confirmation unit. The task assignment unit assigns tasks based on the information obtained by the communication unit. The task assignment unit automatically analyzes the project tasks and assigns the optimal task based on each team member's skill set and current workload. For example, if team member A possesses a specific skill, the task assignment unit assigns a task suitable for that skill. The task assignment unit can also assign new tasks to other members to reduce the workload of team member B if B currently has many tasks. The progress monitoring unit checks the progress of tasks assigned by the task assignment unit. For example, the progress monitoring unit monitors the progress of each team member in real time and identifies the cause if progress is behind schedule. For example, if team member C is behind schedule on a task, the progress monitoring unit analyzes the cause and proposes appropriate countermeasures. The progress monitoring unit can also periodically report on progress and provide feedback to the project manager and team members. The monitoring unit monitors the progress confirmed by the progress monitoring unit. For example, the monitoring unit grasps the project's progress in real time and supports the early detection and countermeasures for delays and problems. For example, if the project is behind schedule, the monitoring unit identifies the cause and proposes appropriate countermeasures.Furthermore, the monitoring unit can also improve project efficiency by suggesting resource reallocation if excessive resource usage occurs. This enables the AI ​​project manager system, according to the embodiment, to automate project planning, progress management, and optimal resource allocation, resulting in efficient project management.

[0066] The communications department engages in natural language dialogue with team members. For example, it uses speech recognition technology to understand questions and reports from team members and natural language processing technology to generate appropriate responses. Specifically, speech recognition technology converts the voice spoken by team members into text data, and natural language processing technology analyzes that text data. Natural language processing technology performs morphological analysis, grammatical analysis, and semantic analysis to understand the context and intent, and generates appropriate responses. For example, if a team member asks, "How is the progress on this week's tasks?", the communications department analyzes the question, retrieves the latest information from the project progress database, and generates a response such as, "This week's tasks are progressing smoothly. Task A is 80% complete, and Task B is 60% complete." The communications department can also receive reports from team members, understand their content, and reflect them in the project progress. For example, if a team member reports, "Task C is complete," the communications department analyzes the report and provides the information to the progress monitoring department. The progress monitoring department updates the project progress based on that information, allowing for real-time progress tracking. Furthermore, the communications department can save the history of conversations with team members and refer to past conversations to provide more consistent responses. This enables the communications department to facilitate smooth communication with team members and support the progress of the project.

[0067] The task allocation unit assigns tasks based on information obtained by the communication unit. For example, the task allocation unit automatically analyzes project tasks and assigns the most suitable tasks based on each team member's skill set and current workload. Specifically, the task allocation unit retrieves each team member's skill profile, past performance, and current task progress from a database and uses an AI algorithm to perform optimal task allocation. For example, if team member A is proficient in a particular programming language, it will assign programming tasks suited to that skill. Also, if team member B currently has many tasks, it can assign new tasks to other members to reduce their workload. The task allocation unit also considers task priority and deadlines to optimize the overall project progress. For example, it prioritizes the assignment of tasks with approaching deadlines or high-priority tasks to prevent project delays. Furthermore, the task allocation unit can monitor task progress in real time and reassign tasks as needed. This allows the task allocation unit to efficiently manage project progress and evenly distribute the workload among team members. Furthermore, the task allocation unit can save the task allocation history and continuously improve the accuracy of task allocation based on past data. This allows the task allocation unit to optimize project progress and achieve efficient project management.

[0068] The progress monitoring unit checks the progress of tasks assigned by the task assignment unit. For example, the progress monitoring unit monitors the progress of each team member in real time and identifies the cause if progress is behind schedule. Specifically, the progress monitoring unit records the progress data and task completion status reported by each team member in a database and analyzes the progress using an AI algorithm. For example, if team member C is behind schedule on a task, the unit analyzes the cause and proposes appropriate countermeasures. The progress monitoring unit provides a dashboard to visualize the progress of tasks, allowing project managers and team members to check progress in real time. The dashboard displays the progress, completion rate, and causes of delays for each task, allowing for an overview of the entire project. The progress monitoring unit can also periodically report on progress and provide feedback to project managers and team members. For example, it can automatically generate weekly and monthly reports to share project progress and challenges. Furthermore, the progress monitoring unit can perform trend analysis based on past progress data to predict future risks and challenges. This allows the progress monitoring unit to monitor the project's progress in real time and take appropriate measures, thereby contributing to the project's success.

[0069] The monitoring department monitors the progress confirmed by the progress confirmation department. For example, the monitoring department grasps the project's progress in real time and supports the early detection and countermeasures for delays and problems. Specifically, the monitoring department continuously monitors the project's progress based on data provided by the progress confirmation department and detects abnormal patterns and signs of delays. For example, if the project is behind schedule, it identifies the cause and proposes appropriate countermeasures. The monitoring department uses AI algorithms to detect anomalies and detect unusual patterns and abnormal data early. For example, if a particular task is significantly behind schedule, it analyzes the cause and proposes reallocating resources or reassigning tasks. The monitoring department can also improve project efficiency by proposing resource reallocation if excessive resource use occurs. For example, if a particular team member is overloaded, it reassigns tasks to other members to alleviate that load. The monitoring department also provides reports and dashboards to visualize the project's progress, allowing project managers and team members to understand the situation in real time. This enables the monitoring department to efficiently manage project progress and support the early detection and countermeasures for delays and problems. Furthermore, the monitoring department can analyze the project's progress based on past data and predict future risks and challenges. This allows the monitoring department to continuously monitor the project's progress and take appropriate measures, thereby contributing to its success.

[0070] The monitoring department can grasp the project's progress in real time and support the early detection and resolution of delays and problems. For example, the monitoring department can monitor the project's progress in real time and identify the cause if it is behind schedule. For example, if the project is behind schedule, the monitoring department can analyze the cause and propose appropriate countermeasures. In addition, the monitoring department can increase the frequency of data updates in order to grasp the project's progress in real time. For example, by updating the project's progress every minute, the monitoring department can always have the latest information. This improves project efficiency by enabling real-time monitoring of the project's progress and supporting the early detection and resolution of delays and problems.

[0071] The task allocation unit can identify the cause of delays in task progress and propose appropriate countermeasures. For example, if a task is behind schedule, the task allocation unit can use data analysis techniques to identify the cause. For instance, the task allocation unit analyzes data from the task that is behind schedule to identify the cause, such as a lack of resources or technical problems. After identifying the cause, the task allocation unit can propose appropriate countermeasures. For example, if a lack of resources is the cause, the task allocation unit can propose allocating additional resources. If a technical problem is the cause, it can also propose seeking expert assistance. In this way, when task progress is behind schedule, the cause can be identified and appropriate countermeasures can be proposed to prevent project delays.

[0072] The progress monitoring unit can check the progress of each team member in real time. For example, the progress monitoring unit can monitor the progress of each team member in real time and identify the cause if progress is behind schedule. For example, if Team Member A's progress is behind schedule, the progress monitoring unit will analyze the cause and propose appropriate countermeasures. The progress monitoring unit can also report on progress regularly and provide feedback to the project manager and team members. For example, the progress monitoring unit can provide weekly progress reports and share the progress of the project. This allows for an accurate understanding of the project's progress by checking the progress of each team member in real time.

[0073] The communications department can understand questions and reports from team members in natural language and generate appropriate responses. For example, the communications department uses speech recognition technology to understand questions and reports from team members and natural language processing technology to generate appropriate responses. For instance, if a team member asks about the progress of a project, the communications department's AI will understand the question and generate an appropriate answer. The communications department can also receive reports from team members, understand their content, and reflect them in the project progress. For example, if a team member reports on the progress of a task, the communications department will understand the report and provide the information to the progress monitoring department. This allows for smoother communication by understanding questions and reports from team members in natural language and generating appropriate responses.

[0074] The monitoring unit can propose resource reallocation if excessive resource usage occurs. For example, the monitoring unit monitors resource usage in real time and identifies the cause of excessive usage. For instance, if a specific resource is being excessively used, the monitoring unit analyzes the cause and proposes appropriate countermeasures. Furthermore, the monitoring unit can improve project efficiency by proposing resource reallocation. For example, the monitoring unit proposes reallocating excessively used resources to other projects. The monitoring unit can also adjust resource allocation to optimize resource usage. This allows for improved project efficiency by proposing resource reallocation when excessive resource usage occurs.

[0075] The communication department can estimate the emotions of team members and adjust the tone and content of responses based on the estimated emotions. For example, if a team member is stressed, the AI ​​will respond in a gentle tone and offer words of encouragement. For example, if a team member is relaxed, the AI ​​will respond in a casual tone and include jokes. Conversely, if a team member is tense, the AI ​​will respond in a calm tone to provide reassurance. This allows for more appropriate communication by adjusting the tone and content of responses according to the emotions of team members. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The communications department can analyze the past communication history of team members and select the optimal response method. For example, the communications department can use AI to respond in a similar style based on response styles that team members have preferred in the past. For example, the communications department can avoid response styles that team members have found unpleasant in the past, and the AI ​​can respond in an appropriate style. The communications department can also analyze the past question patterns of team members, and the AI ​​can predict and prepare appropriate responses. In this way, the optimal response method can be selected by analyzing the past communication history of team members.

[0077] The communications department can use appropriate technical terms and explanations based on the expertise and skills of its team members. For example, if a team member is an engineer, the AI ​​will respond using technical terminology. If a team member is a marketing professional, the AI ​​will respond using marketing terminology. Furthermore, if a team member is a newcomer, the AI ​​can explain things in simple terms and provide detailed guidance. This enables more effective communication by using appropriate technical terms and explanations according to the expertise and skills of each team member.

[0078] The communication department can estimate the emotions of team members and adjust the frequency of communication based on the estimated emotions. For example, if a team member is stressed, the AI ​​will reduce the frequency of communication to alleviate the burden. For example, if a team member is relaxed, the AI ​​will increase the frequency of communication and actively share information. Also, if a team member is tense, the AI ​​will communicate at an appropriate frequency to provide a sense of security. In this way, more appropriate communication becomes possible by adjusting the frequency of communication according to the emotions of team members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The communications department can communicate in the appropriate time zone by considering the geographical location of team members. For example, if team members are in different time zones, the AI ​​will send messages according to the time zone of their respective locations. Similarly, if a team member is on a business trip, the AI ​​will communicate according to the time zone of their location. Furthermore, if a team member is working remotely, the AI ​​can contact them at an appropriate time according to the time zone of their location. This ensures that communication is conducted in the appropriate time zone by considering the geographical location of team members.

[0080] The communications department can analyze team members' social media activity and provide relevant information. For example, the communications department can use AI to provide relevant project information based on information shared by team members on social media. For example, the communications department can use AI to suggest relevant resources based on topics that team members have shown interest in on social media. The communications department can also use AI to communicate with team members at the appropriate time based on their social media activity. In this way, by analyzing team members' social media activity, relevant information can be provided.

[0081] The task assignment unit can estimate the emotions of team members and adjust the task assignment method based on the estimated emotions. For example, if a team member is feeling stressed, the AI ​​will assign a less burdensome task. For example, if a team member is relaxed, the AI ​​will assign a challenging task. Also, if a team member is tense, the AI ​​can assign a task that requires support. This allows for more appropriate task assignment by adjusting the task assignment method according to the emotions of team members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The task assignment unit can analyze the past task history of team members and assign the most suitable tasks. For example, the task assignment unit can assign similar tasks based on tasks that team members have successfully completed in the past. For example, the task assignment unit can avoid tasks that team members have struggled with in the past, and the AI ​​can assign appropriate tasks. The task assignment unit can also analyze the past task history of team members and assign the most efficient tasks using AI. In this way, by analyzing the past task history of team members, the optimal tasks can be assigned.

[0083] The task allocation unit can determine task priorities based on the importance and urgency of each task. For example, it can prioritize assigning tasks with high urgency. For example, it can prioritize assigning tasks with high importance. Furthermore, the task allocation unit can also assign the optimal task by considering the balance between urgency and importance. This enables efficient task management by determining task priorities based on the importance and urgency of each task.

[0084] The task assignment unit can estimate the emotions of team members and adjust the frequency of task assignments based on the estimated emotions. For example, if a team member is stressed, the AI ​​will reduce the frequency of task assignments. For example, if a team member is relaxed, the AI ​​will increase the frequency of task assignments. Also, if a team member is tense, the AI ​​can assign tasks at an appropriate frequency. This allows for more appropriate task assignments by adjusting the frequency of task assignments according to the emotions of team members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The task assignment unit can assign appropriate tasks by considering the geographical location information of team members. For example, if team members are in different locations, the AI ​​will assign tasks suitable for each location. For instance, if a team member is on a business trip, the AI ​​will assign tasks that can be performed in that location. Furthermore, if a team member is working remotely, the AI ​​can assign tasks that can be performed efficiently in that location. In this way, appropriate task assignment becomes possible by considering the geographical location information of team members.

[0086] The task assignment unit can assign appropriate tasks based on the skill sets and expertise of team members. For example, the task assignment unit can use AI to assign the optimal task based on the skill sets of team members. For example, the task assignment unit can use AI to assign appropriate tasks considering the expertise of team members. Furthermore, the task assignment unit can comprehensively assess the skill sets and expertise of team members and assign the most efficient tasks using AI. This enables efficient task management by assigning appropriate tasks based on the skill sets and expertise of team members.

[0087] The progress monitoring unit can estimate the emotions of team members and adjust the progress monitoring method based on the estimated emotions. For example, if a team member is stressed, the AI ​​will reduce the frequency of progress monitoring to alleviate the burden. For example, if a team member is relaxed, the AI ​​will increase the frequency of progress monitoring and actively share information. Also, if a team member is tense, the AI ​​will monitor progress at an appropriate frequency to provide reassurance. In this way, by adjusting the progress monitoring method according to the emotions of team members, more appropriate progress monitoring becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The progress monitoring unit can analyze the progress of each task in detail and identify problems. For example, the progress monitoring unit can analyze the progress of each task in real time and identify the causes of delays. For example, the progress monitoring unit can analyze the progress of each task in detail and identify excessive resource usage. The progress monitoring unit can also analyze the progress of each task and identify factors that hinder efficient progress. As a result, by analyzing the progress of each task in detail and identifying problems, efficient task management becomes possible.

[0089] The progress monitoring unit can perform progress checks while considering task dependencies. For example, the progress monitoring unit analyzes task dependencies to minimize the impact of delays. Furthermore, the progress monitoring unit can perform efficient progress checks by considering task dependencies. This enables efficient task management by considering task dependencies during progress checks.

[0090] The progress monitoring unit can estimate the emotions of team members and adjust the frequency of progress checks based on the estimated emotions. For example, if a team member is feeling stressed, the AI ​​will reduce the frequency of progress checks to alleviate their burden. For example, if a team member is relaxed, the AI ​​will increase the frequency of progress checks and actively share information. Also, if a team member is feeling tense, the AI ​​will perform progress checks at an appropriate frequency to provide reassurance. By adjusting the frequency of progress checks according to the emotions of team members, more appropriate progress monitoring becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The progress tracking unit visualizes the progress of each task and allows the entire team to share it. For example, the progress tracking unit visualizes the progress of each task using graphs and charts and shares it with the entire team. For example, the progress tracking unit displays the progress of each task in real time and shares it with the entire team. In addition, the progress tracking unit can visualize the progress of each task on a dashboard and share it with the entire team. This allows for accurate understanding of the project's progress by visualizing and sharing the progress of each task with the entire team.

[0092] The progress checking unit can refer to relevant literature and materials related to the task when checking progress. For example, the progress checking unit can refer to relevant literature and materials related to the task and check progress. For example, the progress checking unit can check progress based on relevant literature and materials related to the task. In addition, the progress checking unit can perform efficient progress checking by referring to relevant literature and materials related to the task. As a result, efficient task management becomes possible by checking progress by referring to relevant literature and materials related to the task.

[0093] The monitoring unit can estimate the emotions of team members and adjust its monitoring methods based on the estimated emotions. For example, if a team member is stressed, the AI ​​will reduce the frequency of monitoring to alleviate the burden. For example, if a team member is relaxed, the AI ​​will increase the frequency of monitoring and actively collect information. Also, if a team member is tense, the AI ​​will monitor at an appropriate frequency to provide a sense of security. In this way, more appropriate monitoring becomes possible by adjusting the monitoring method according to the emotions of team members. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The monitoring department can analyze the project's progress in detail and identify the causes of delays and problems. For example, the monitoring department can analyze the project's progress in real time and identify the causes of delays. For example, the monitoring department can analyze the project's progress in detail and identify excessive use of resources. The monitoring department can also analyze the project's progress and identify factors that hinder efficient progress. This enables efficient project management by allowing for a detailed analysis of the project's progress and identification of the causes of delays and problems.

[0095] The monitoring unit can analyze resource usage in detail during monitoring and propose the optimal resource allocation. For example, the monitoring unit can analyze resource usage in real time and propose the optimal resource allocation. For example, the monitoring unit can analyze resource usage in detail and make suggestions to prevent overuse. The monitoring unit can also analyze resource usage and propose efficient resource allocation. As a result, by analyzing resource usage in detail and proposing the optimal resource allocation, efficient resource management becomes possible.

[0096] The monitoring unit can estimate the emotions of team members and adjust the monitoring frequency based on the estimated emotions. For example, if a team member is stressed, the AI ​​will reduce the monitoring frequency to alleviate the burden. For example, if a team member is relaxed, the AI ​​will increase the monitoring frequency and actively collect information. Also, if a team member is tense, the AI ​​will monitor at an appropriate frequency to provide a sense of security. In this way, adjusting the monitoring frequency according to the emotions of team members enables more appropriate monitoring. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The monitoring department can visualize the project's progress and share it with the entire team. For example, the monitoring department can visualize the project's progress using graphs and charts and share it with the entire team. For example, the monitoring department can display the project's progress in real time and share it with the entire team. The monitoring department can also visualize the project's progress on a dashboard and share it with the entire team. This allows for accurate understanding of the project's progress by visualizing and sharing it with the entire team.

[0098] The monitoring department can perform monitoring by referring to project-related literature and materials. For example, the monitoring department can perform monitoring by referring to project-related literature and materials. Furthermore, the monitoring department can perform efficient monitoring by referring to project-related literature and materials. This enables efficient project management by performing monitoring by referring to project-related literature and materials.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The AI ​​project manager system can also be equipped with a predictive analytics unit. This unit can predict future risks and problems based on project progress and historical data. For example, it can analyze past project data to predict that a particular task is likely to be delayed. It can also analyze resource usage to predict potential resource shortages in the future. Furthermore, it can use team member performance data to predict the risk of burnout for specific members and propose appropriate countermeasures. In this way, the predictive analytics unit can proactively predict future risks and problems, supporting the efficient progress of the project.

[0101] The AI ​​project manager system can also be equipped with a feedback collection unit. This unit can automatically collect feedback from team members and use it to improve the project. For example, it can conduct regular surveys to gather opinions and feedback from team members. It can also record problems and areas for improvement that arise during the project and incorporate them into future projects. Furthermore, it can evaluate team member satisfaction and propose appropriate improvement measures based on the project's progress. In this way, the feedback collection unit can reflect the opinions of team members and contribute to improving project quality.

[0102] The AI ​​project manager system can also include a motivation management department. This department can automatically propose measures to maintain and improve team members' motivation. For example, it can analyze team members' performance data and suggest appropriate rewards and incentives. It can also estimate team members' emotions and suggest relaxation programs to reduce stress. Furthermore, it can evaluate team members' goal achievement and provide feedback based on their level of accomplishment. In this way, the motivation management department can maintain and improve team members' motivation, contributing to project success.

[0103] The AI ​​project manager system can also include a learning support unit. This unit can provide learning content to support the skill development of team members. For example, it can analyze the skill sets of team members and suggest online courses to improve necessary skills. It can also provide learning content at appropriate times depending on the project's progress. Furthermore, it can monitor the learning progress of team members and provide additional support as needed. In this way, the learning support unit can support the skill development of team members and contribute to the success of the project.

[0104] The AI ​​project manager system can also include a health management department. This department can monitor the health status of team members and provide advice for maintaining their health. For example, it can monitor team members' stress levels and suggest relaxation methods to reduce stress. It can also analyze team members' sleep patterns and provide advice to ensure adequate sleep. Furthermore, it can monitor team members' dietary and exercise habits and provide advice to maintain healthy lifestyles. This allows the health management department to maintain the health of team members and support the efficient progress of the project.

[0105] The AI ​​project manager system can also include a risk management department. This department can assess project risks and propose measures to mitigate them. For example, it can analyze project progress and identify potential risks. It can also predict the probability of risk occurrence based on past project data and propose appropriate countermeasures. Furthermore, it can assess the impact of a risk if it occurs and provide concrete action plans for risk mitigation. This allows the risk management department to effectively manage project risks and improve the project's success rate.

[0106] The AI ​​project manager system can also be equipped with an emotion analysis unit. This unit can analyze the emotions of team members in real time and identify factors that affect project progress. For example, it can analyze team members' statements and actions to detect signs of stress and anxiety. It can also visualize the emotional state of team members and provide feedback to the project manager. Furthermore, it can provide appropriate support and advice based on the emotions of team members. This allows the emotion analysis unit to understand the emotions of team members in real time and provide support to ensure smooth project progress.

[0107] The AI ​​project manager system can also be equipped with a schedule optimization unit. This unit can automatically optimize the project schedule, supporting efficient progress. For example, it can consider the dependencies of each task and generate an optimal schedule. It can also adjust the schedule to evenly distribute the workload among team members. Furthermore, it can update the schedule in real time according to the project's progress, minimizing delays. In this way, the schedule optimization unit can efficiently manage the project schedule and contribute to the project's success.

[0108] The AI ​​project manager system can also include a collaboration support unit. This unit can facilitate collaboration among team members and support the efficient progress of the project. For example, it can provide tools to streamline communication among team members. It can also analyze the skill sets of team members and suggest optimal collaboration partners. Furthermore, it can facilitate information sharing among team members and share project progress in real time. In this way, the collaboration support unit can promote collaboration among team members and contribute to the success of the project.

[0109] The AI ​​project manager system can also be equipped with an emotional feedback unit. This unit can provide real-time feedback on team members' emotions and identify factors affecting project progress. For example, it can analyze team members' statements and actions to detect signs of stress and anxiety. It can also visualize the emotional state of team members and provide feedback to the project manager. Furthermore, it can provide appropriate support and advice based on the team members' emotions. This allows the emotional feedback unit to understand team members' emotions in real time and provide support to ensure smooth project progress.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The communications department engages in natural language dialogue with team members. For example, it uses speech recognition technology to understand team members' questions and reports, and natural language processing technology to generate appropriate responses. If a team member asks about the project's progress, the AI ​​understands the question and generates an appropriate answer. It can also receive reports from team members, understand their content, and reflect them in the project's progress report. Step 2: The task allocation unit assigns tasks based on the information obtained by the communication unit. For example, it automatically analyzes the project tasks and assigns the most suitable tasks based on each team member's skill set and current workload. If team member A has a specific skill, it assigns them a task that suits that skill. Also, if team member B currently has many tasks, it can assign new tasks to other members to reduce their workload. Step 3: The progress monitoring unit checks the progress of tasks assigned by the task assignment unit. For example, it monitors the progress of each team member in real time and identifies the cause if progress is behind schedule. If team member C is behind schedule on a task, it analyzes the cause and proposes appropriate countermeasures. It can also periodically report on progress and provide feedback to the project manager and team members. Step 4: The monitoring unit monitors the progress confirmed by the progress verification unit. For example, it grasps the project's progress in real time and supports the early detection and resolution of delays and problems. If the project is behind schedule, it identifies the cause and proposes appropriate countermeasures. It can also propose reallocating resources if excessive resource usage occurs, thereby improving project efficiency.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the communication unit, task assignment unit, progress confirmation unit, and monitoring unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the communication unit uses the processor 46 and microphone 38B of the smart device 14 to understand questions and reports from team members and generates appropriate responses by the control unit 46A. The task assignment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically analyzes project tasks and assigns the most suitable tasks to each team member. The progress confirmation unit is implemented by, for example, the control unit 46A of the smart device 14, which monitors the progress of each team member in real time. The monitoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which grasps the progress of the project in real time and supports the early detection and countermeasures for delays and problems. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the communication unit, task assignment unit, progress confirmation unit, and monitoring unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the communication unit uses the processor 46 and microphone 238 of the smart glasses 214 to understand questions and reports from team members and generates appropriate responses by the control unit 46A. The task assignment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which automatically analyzes project tasks and assigns the most suitable tasks to each team member. The progress confirmation unit is implemented, for example, by the control unit 46A of the smart glasses 214, which monitors the progress of each team member in real time. The monitoring unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which grasps the progress of the project in real time and supports the early detection and countermeasures for delays and problems. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the communication unit, task assignment unit, progress confirmation unit, and monitoring unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the communication unit uses the processor 46 and microphone 238 of the headset terminal 314 to understand questions and reports from team members, and the control unit 46A generates an appropriate response. The task assignment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically analyzes project tasks and assigns the most suitable tasks to each team member. The progress confirmation unit is implemented by, for example, the control unit 46A of the headset terminal 314, which monitors the progress of each team member in real time. The monitoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which grasps the progress of the project in real time and supports the early detection and countermeasures for delays and problems. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the communication unit, task assignment unit, progress confirmation unit, and monitoring unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the communication unit uses the robot 414's processor 46 and microphone 238 to understand questions and reports from team members, and the control unit 46A generates an appropriate response. The task assignment unit is implemented in the data processing unit 12, for example, by the specific processing unit 290, which automatically analyzes project tasks and assigns the most suitable tasks to each team member. The progress confirmation unit is implemented in the robot 414, for example, by the control unit 46A, which monitors the progress of each team member in real time. The monitoring unit is implemented in the data processing unit 12, for example, by the specific processing unit 290, which grasps the project's progress in real time and supports the early detection and countermeasures for delays and problems. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) The communications department, which handles natural language dialogue with team members, A task assignment unit that assigns tasks based on the information obtained by the communication unit, A progress confirmation unit that checks the progress of tasks assigned by the task assignment unit, The system includes a monitoring unit that monitors the progress status confirmed by the progress confirmation unit. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, We provide real-time support for monitoring project progress, enabling early detection and resolution of delays and problems. The system described in Appendix 1, characterized by the features described herein. (Note 3) The task allocation unit, If a task is behind schedule, identify the cause and propose appropriate countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned progress confirmation unit, Check the progress of each team member in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned communications department, It understands questions and reports from team members in natural language and generates appropriate responses. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, If resource overuse occurs, we will propose reallocating resources. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned communications department, Estimate the emotions of team members and adjust the tone and content of responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned communications department, Analyze the past communication history of team members and select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned communications department, Use appropriate technical terms and explanations based on the expertise and skills of team members. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned communications department, The system estimates the emotions of team members and adjusts the frequency of communication based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned communications department, Consider the geographical location of team members and communicate within the appropriate time zone. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned communications department, Analyze team members' social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The task allocation 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 14) The task allocation unit, Analyze the past task history of team members and assign them the most suitable tasks. The system described in Appendix 1, characterized by the features described herein. (Note 15) The task allocation unit, Prioritize tasks based on their importance and urgency. The system described in Appendix 1, characterized by the features described herein. (Note 16) The task allocation unit, Estimate the emotions of team members and adjust the frequency of task assignments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The task allocation unit, Assign appropriate tasks to team members, taking their geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The task allocation unit, Assign appropriate tasks based on the skill sets and expertise of team members. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned progress confirmation unit, Estimate the emotions of team members and adjust the progress monitoring method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned progress confirmation unit, Analyze the progress of each task in detail and identify any problems. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned progress confirmation unit, When checking progress, consider the dependencies between tasks. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned progress confirmation unit, Estimate the emotions of team members and adjust the frequency of progress checks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned progress confirmation unit, Visualize the progress of each task and share it with the entire team. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned progress confirmation unit, When checking progress, refer to relevant literature and materials related to the task. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned monitoring unit, Estimate the emotions of team members and adjust monitoring methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned monitoring unit, Analyze the project's progress in detail and identify the causes of delays and problems. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit, During monitoring, we analyze resource usage in detail and propose the optimal resource allocation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned monitoring unit, Estimate the emotions of team members and adjust the frequency of monitoring based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned monitoring unit, Visualize the project's progress and share it with the entire team. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned monitoring unit, During monitoring, refer to relevant project literature and documents. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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. The communications department, which handles natural language dialogue with team members, A task assignment unit that assigns tasks based on the information obtained by the communication unit, A progress confirmation unit that checks the progress of tasks assigned by the task assignment unit, The system includes a monitoring unit that monitors the progress status confirmed by the progress confirmation unit. A system characterized by the following features.

2. The aforementioned monitoring unit, We provide real-time support for monitoring project progress, enabling early detection and resolution of delays and problems. The system according to feature 1.

3. The task allocation unit, If a task is behind schedule, identify the cause and propose appropriate countermeasures. The system according to feature 1.

4. The aforementioned progress confirmation unit, Check the progress of each team member in real time. The system according to feature 1.

5. The aforementioned communications department, It understands questions and reports from team members in natural language and generates appropriate responses. The system according to feature 1.

6. The aforementioned monitoring unit, If resource overuse occurs, we will propose reallocating resources. The system according to feature 1.

7. The aforementioned communications department, Estimate the emotions of team members and adjust the tone and content of responses based on those estimated emotions. The system according to feature 1.

8. The aforementioned communications department, Analyze the past communication history of team members and select the most appropriate response method. The system according to feature 1.

9. The aforementioned communications department, Use appropriate technical terms and explanations based on the expertise and skills of team members. The system according to feature 1.

10. The aforementioned communications department, The system estimates the emotions of team members and adjusts the frequency of communication based on those estimated emotions. The system according to feature 1.

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