system

Abstract

The system according to this embodiment aims to automate and efficiently perform task assignment, progress management, and resource optimization. [Solution] The system according to the embodiment comprises an assignment unit, a progress management unit, a resource allocation unit, and a problem prediction unit. The assignment unit assigns tasks. The progress management unit manages the progress of the tasks assigned by the assignment unit. The resource allocation unit efficiently allocates resources based on the progress managed by the progress management unit. The problem prediction unit predicts problems based on the resources efficiently allocated by the resource allocation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that task assignment, progress management, and resource optimization are performed manually, which takes time and effort.

[0005] The system according to the embodiment aims to automate and efficiently perform task assignment, progress management, and resource optimization.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an assignment unit, a progress management unit, a resource allocation unit, and a problem prediction unit. The assignment unit assigns tasks. The progress management unit manages the progress of the tasks assigned by the assignment unit. The resource allocation unit efficiently allocates resources based on the progress managed by the progress management unit. The problem prediction unit predicts problems based on the resources efficiently allocated by the resource allocation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate and efficiently perform task assignment, progress management, and resource optimization. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The project management system according to an embodiment of the present invention is a system that utilizes generative AI to assign tasks, manage progress, optimize resources, predict problems, and propose solutions. This system supports efficient project management for project managers, leaders, and project teams in small and medium-sized enterprises. First, the project management system uses schedule information as a reference to determine who should be assigned what through data analysis and prediction. The generative AI assigns optimal tasks based on each member's schedule and past task performance. For example, if a particular member has efficiently completed a similar task in the past, a similar task can be assigned to that member. Next, task progress management is automated. The generative AI monitors the progress of each member's tasks in real time and sends reminders as needed. For example, if a task deadline is approaching, the generative AI automatically sends a reminder to encourage the member to complete the task. Furthermore, resource optimization is achieved. The generative AI allocates resources optimally, taking into account each member's skill set and current task load. For example, if a particular member is overloaded, the generative AI optimizes overall resources by reassigning tasks to other members. Finally, it predicts problems and delays and proposes effective solutions. Generative AI analyzes past project data to identify patterns of problems and delays. For example, if a particular task tends to be delayed, the generative AI can predict the risks associated with that task and propose countermeasures in advance. In this way, by utilizing generative AI, task assignment and progress management, resource optimization, and problem prediction and solution proposals can be automated, supporting the smooth progress and success of projects. As a result, project management systems can automate task assignment, progress management, resource optimization, and problem prediction and solution proposals, enabling efficient project management.

[0029] The project management system according to this embodiment comprises an assignment unit, a progress management unit, a resource allocation unit, and a problem prediction unit. The assignment unit assigns tasks. The assignment unit assigns tasks based on, for example, schedule information. The assignment unit uses a generation AI to analyze each member's schedule and past task performance to assign the most suitable tasks. For example, if a particular member has efficiently completed a similar task in the past, a similar task can be assigned to that member. The assignment unit can also use a generation AI to allocate resources optimally, taking into account each member's skill set and current task load. The progress management unit manages the progress of tasks assigned by the assignment unit. The progress management unit uses a generation AI to monitor the progress of each member's tasks in real time and send reminders as needed. For example, if a task deadline is approaching, the generation AI automatically sends a reminder to encourage the member to complete the task. The resource allocation unit efficiently allocates resources based on the progress managed by the progress management unit. The resource allocation unit uses a generation AI to allocate resources optimally, taking into account each member's skill set and current task load. For example, if a particular member is overloaded, the generating AI optimizes overall resources by reassigning tasks to other members. The problem prediction unit predicts problems based on resources efficiently allocated by the resource allocation unit. The problem prediction unit uses the generating AI to analyze past project data and identify patterns of problems and delays. For example, if a particular task tends to be delayed, the generating AI can predict the risks associated with that task and propose countermeasures in advance. As a result, the project management system according to this embodiment can automate task assignment, progress management, resource optimization, and problem prediction, enabling efficient project management.

[0030] The assignment unit can assign tasks based on schedule information. Schedule information includes, but is not limited to, calendar information and Gantt charts. The assignment unit uses a generation AI to analyze the schedule information and assign tasks optimally. For example, if a particular member has efficiently completed similar tasks in the past, they can be assigned similar tasks. This enables efficient task management by assigning tasks based on schedule information.

[0031] The progress management department can monitor task progress in real time and send reminders as needed. Real time includes, but is not limited to, second-by-second or minute-by-minute updates. The progress management department uses a generation AI to monitor the progress of each member's tasks in real time and send reminders as needed. For example, if a task deadline is approaching, the generation AI will automatically send a reminder to encourage the member to complete the task. Reminders include, but are not limited to, email notifications and alerts. This allows for real-time monitoring of task progress and sending reminders to prevent task delays.

[0032] The resource allocation unit can efficiently allocate resources by considering each member's skill set and current task load. Skill sets include, but are not limited to, technical skills and soft skills. Task load includes, but are not limited to, working time and task difficulty. The resource allocation unit uses generative AI to optimize resource allocation by considering each member's skill set and current task load. For example, if a particular member is overloaded, the generative AI will reallocate tasks to other members to optimize overall resources. This enables efficient resource management by optimally allocating resources by considering each member's skill set and task load.

[0033] The problem prediction unit can analyze past project data to identify patterns of problems and delays. Past project data includes, but is not limited to, project progress records and deliverables. Patterns of problems and delays include, but are not limited to, frequency analysis and root cause analysis. The problem prediction unit uses generative AI to analyze past project data and identify patterns of problems and delays. For example, if a particular task tends to be delayed, the generative AI can predict the risks associated with that task and propose countermeasures in advance. This allows for proactive measures to be taken by analyzing past project data and identifying patterns of problems and delays.

[0034] The problem prediction unit can predict risks for specific tasks and propose countermeasures in advance. Risks include, but are not limited to, technical risks and scheduling risks. Countermeasures include, but are not limited to, resource reallocation and schedule adjustments. The problem prediction unit uses generative AI to predict risks for specific tasks and propose countermeasures in advance. For example, if a particular task tends to be delayed, the generative AI can predict the risks for that task and propose countermeasures in advance. In this way, by predicting risks for specific tasks and proposing countermeasures in advance, project delays and problems can be prevented.

[0035] The assignment unit can analyze each member's past task completion time and make optimal task assignments. Task completion time includes, but is not limited to, actual work time and planned work time. The assignment unit uses a generation AI to analyze each member's past task completion time and make optimal task assignments. For example, the generation AI assigns the most suitable task based on each member's past task completion time. The generation AI can also perform efficient task assignments by analyzing past task completion times. Furthermore, the generation AI can adjust task assignments by considering each member's past task completion time. In this way, efficient task assignment becomes possible by analyzing each member's past task completion time.

[0036] The assignment unit can consider members' current health status and stress levels when assigning tasks. Health status includes, but is not limited to, medical data and self-reported information. Stress levels include, but are not limited to, stress checks and vital data. The assignment unit uses generative AI to consider members' current health status and stress levels when assigning tasks. For example, it can monitor members' health status and adjust task assignments based on that. It can also optimize task assignments by considering stress levels. Furthermore, it can assign tasks based on health status and stress levels. This makes it possible to assign tasks appropriately by considering members' health status and stress levels.

[0037] The assignment unit can assign the most suitable tasks to members by considering their geographical location information when assigning tasks. This geographical location information includes, but is not limited to, GPS data and location services. The assignment unit uses a generative AI to assign the most suitable tasks by considering the members' geographical location information. For example, the generative AI assigns the most suitable task based on the member's current location. The generative AI can also perform efficient task assignment by considering geographical location information. Furthermore, the generative AI can adjust task assignments based on the members' geographical location information. This enables efficient task assignment by considering the members' geographical location information.

[0038] The assignment unit can analyze members' social media activity when assigning tasks and assign relevant tasks accordingly. Social media activity includes, but is not limited to, posts and activity frequency. The assignment unit uses generative AI to analyze members' social media activity and assign relevant tasks when assigning tasks. For example, it analyzes members' social media activity and assigns relevant tasks. It can also assign the most suitable tasks based on social media activity. Furthermore, it can adjust task assignments by considering members' social media activity. This allows for efficient assignment of relevant tasks by analyzing members' social media activity.

[0039] The progress management department can analyze the progress of each task in detail and detect delays early. Detailed analysis includes, but is not limited to, data granularity and analysis methods. The progress management department uses generative AI to analyze the progress of each task in detail and detect delays early. For example, it can monitor the progress of each task in real time and have the generative AI detect delays. It can also analyze the progress in detail and have the generative AI detect delays early. Furthermore, the generative AI can predict delays based on the progress of each task. In this way, by analyzing the progress of each task in detail, delays can be detected early.

[0040] The progress management unit can evaluate the progress status while considering the dependencies between tasks. Task dependencies include, but are not limited to, precedence and interdependencies. The progress management unit uses generative AI to evaluate the progress status while considering the dependencies between tasks. For example, the generative AI evaluates the progress status while considering the dependencies between tasks. The generative AI can also evaluate the progress status in detail based on the dependencies. Furthermore, the generative AI can evaluate the progress status by analyzing the dependencies between tasks. In this way, by considering the dependencies between tasks, the progress status can be accurately evaluated.

[0041] The progress management department can send reminders while considering the geographical location information of members during progress management. Geographical location information includes, but is not limited to, GPS data and location services. The progress management department uses a generation AI to send reminders while considering the geographical location information of members during progress management. For example, the generation AI sends a reminder based on the member's current location. The generation AI can also send reminders efficiently while considering geographical location information. Furthermore, the generation AI can adjust the timing of reminder sending based on the member's geographical location information. This makes it possible to send reminders efficiently by considering the geographical location information of members.

[0042] The progress management department can analyze members' social media activities and evaluate their progress during progress management. Social media activities include, but are not limited to, the content and frequency of posts. The progress management department uses generative AI to analyze members' social media activities and evaluate their progress during progress management. For example, the generative AI analyzes members' social media activities and evaluates their progress. The generative AI can also evaluate progress in detail based on social media activities. Furthermore, the generative AI can evaluate progress while considering members' social media activities. This allows for an accurate evaluation of progress by analyzing members' social media activities.

[0043] The resource allocation unit can analyze each member's skill set in detail and allocate resources optimally. Skill sets include, but are not limited to, technical skills and soft skills. The resource allocation unit uses generative AI to analyze each member's skill set in detail and allocate resources optimally. For example, the generative AI allocates the optimal resources based on each member's skill set. It can also analyze skill sets in detail and have the generative AI perform efficient resource allocation. Furthermore, the generative AI can adjust resource allocation considering each member's skill set. In this way, efficient resource allocation becomes possible by analyzing each member's skill set in detail.

[0044] The resource allocation unit can evaluate the current task load of members in real time during resource optimization. Task load includes, but is not limited to, work time and task difficulty. The resource allocation unit uses a generative AI to evaluate the current task load of members in real time during resource optimization. For example, it can monitor the current task load of members in real time and the generative AI adjusts resource allocation. It can also evaluate task load in real time and perform efficient resource allocation with the generative AI. Furthermore, the generative AI can optimize resource allocation based on the current task load of members. This enables efficient resource allocation by evaluating the current task load of members in real time.

[0045] The resource allocation unit can allocate resources while considering the geographical location information of members during resource optimization. Geographical location information includes, but is not limited to, GPS data and location information services. The resource allocation unit uses a generation AI to allocate resources while considering the geographical location information of members during resource optimization. For example, the generation AI allocates the optimal resources based on the member's current location. The generation AI can also perform efficient resource allocation while considering geographical location information. Furthermore, the generation AI can adjust resource allocation based on the member's geographical location information. This makes efficient resource allocation possible by considering the geographical location information of members.

[0046] The resource allocation unit can analyze members' social media activities and adjust resource allocation during resource optimization. Social media activities include, but are not limited to, posts and activity frequency. The resource allocation unit uses generative AI to analyze members' social media activities and adjust resource allocation during resource optimization. For example, the generative AI adjusts resource allocation after analyzing members' social media activities. Alternatively, the generative AI can perform optimal resource allocation based on social media activities. Furthermore, the generative AI can optimize resource allocation by considering members' social media activities. This enables efficient resource allocation by analyzing members' social media activities.

[0047] The problem prediction unit can identify problem occurrence patterns by analyzing past project data in detail. Past project data includes, but is not limited to, project progress records and deliverables. Problem occurrence patterns include, but are not limited to, frequency analysis and cause analysis. The problem prediction unit uses generative AI to analyze past project data in detail and identify problem occurrence patterns. For example, the generative AI identifies problem occurrence patterns based on past project data. The generative AI can also identify problem occurrence trends by analyzing project data in detail. Furthermore, the generative AI can predict problem occurrence patterns based on past data. In this way, problem occurrence patterns can be identified by analyzing past project data in detail.

[0048] The problem prediction unit can assess risk by considering task dependencies when predicting a problem. Task dependencies include, but are not limited to, contextual relationships and interdependencies. The problem prediction unit uses generative AI to assess risk by considering task dependencies when predicting a problem. For example, the generative AI assesses risk by considering task dependencies. The generative AI can also assess risk in detail based on dependencies. Furthermore, the generative AI can assess risk by analyzing task dependencies. This allows for accurate risk assessment by considering task dependencies.

[0049] The problem prediction unit can assess risk by considering the geographical location information of members when predicting a problem. Geographical location information includes, but is not limited to, GPS data and location information services. The problem prediction unit uses generative AI to assess risk by considering the geographical location information of members when predicting a problem. For example, the generative AI assesses risk based on the member's current location. The generative AI can also perform an efficient risk assessment by considering geographical location information. Furthermore, the generative AI can adjust the risk assessment based on the member's geographical location information. This makes efficient risk assessment possible by considering the geographical location information of members.

[0050] The problem prediction unit can analyze members' social media activities and assess risks when predicting problems. Social media activities include, but are not limited to, the content and frequency of posts. The problem prediction unit uses generative AI to analyze members' social media activities and assess risks when predicting problems. For example, the generative AI analyzes members' social media activities and assesses the risks. The generative AI can also perform a more detailed risk assessment based on social media activities. Furthermore, the generative AI can adjust the risk assessment by taking into account members' social media activities. This enables efficient risk assessment by analyzing members' social media activities.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The assignment unit can analyze each member's past task completion time and make optimal task assignments. For example, the generating AI can assign the most suitable task based on each member's past task completion time. The generating AI can also perform efficient task assignments by analyzing past task completion times. Furthermore, the generating AI can adjust task assignments considering each member's past task completion time. This allows for efficient task assignment by analyzing each member's past task completion time.

[0053] The assignment unit can consider the current health status and stress levels of team members when assigning tasks. For example, it can monitor the health status of team members and have the generating AI adjust task assignments accordingly. It can also optimize task assignments by considering stress levels. Furthermore, the generating AI can assign tasks based on health status and stress levels. This allows for appropriate task assignments by considering the health status and stress levels of team members.

[0054] The assignment unit can assign the most suitable tasks by considering the geographical location information of the members when assigning tasks. For example, the generating AI can assign the most suitable tasks based on the members' current location. The generating AI can also perform efficient task assignment by considering geographical location information. Furthermore, the generating AI can adjust task assignments based on the members' geographical location information. This enables efficient task assignment by considering the geographical location information of the members.

[0055] The progress management department can analyze the progress of each task in detail and detect delays early. For example, it can monitor the progress of each task in real time, and a generating AI can detect delays. It can also analyze the progress in detail, and the generating AI can detect delays early. Furthermore, the generating AI can predict delays based on the progress of each task. In this way, by analyzing the progress of each task in detail, delays can be detected early.

[0056] The resource allocation unit can evaluate the current task load of members in real time during resource optimization. For example, it can monitor the current task load of members in real time, and the generating AI adjusts resource allocation accordingly. It can also evaluate task load in real time, and the generating AI can perform efficient resource allocation. Furthermore, the generating AI can optimize resource allocation based on the current task load of members. This enables efficient resource allocation by evaluating the current task load of members in real time.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The assignment unit assigns tasks. The assignment unit assigns tasks based on, for example, schedule information. The assignment unit uses generative AI to analyze each member's schedule and past task performance to assign the most suitable tasks. For example, if a particular member has efficiently completed similar tasks in the past, a similar task can be assigned to that member. The assignment unit can also use generative AI to consider each member's skill set and current task load to optimize resource allocation. Step 2: The progress management department manages the progress of tasks assigned by the assignment department. The progress management department uses a generation AI to monitor the progress of each member's tasks in real time and send reminders as needed. For example, if a task deadline is approaching, the generation AI automatically sends a reminder to encourage the member to complete the task. Step 3: The resource allocation unit efficiently allocates resources based on the progress managed by the progress management unit. The resource allocation unit uses a generative AI to optimize resource allocation, taking into account each member's skill set and current task load. For example, if a particular member is overloaded, the generative AI optimizes overall resources by reassigning tasks to other members. Step 4: The problem prediction unit predicts problems based on the resources efficiently allocated by the resource allocation unit. The problem prediction unit uses generative AI to analyze past project data and identify patterns of problems and delays. For example, if a particular task tends to be delayed, the generative AI can predict the risks associated with that task and propose countermeasures in advance.

[0059] (Example of form 2) The project management system according to an embodiment of the present invention is a system that utilizes generative AI to assign tasks, manage progress, optimize resources, predict problems, and propose solutions. This system supports efficient project management for project managers, leaders, and project teams in small and medium-sized enterprises. First, the project management system uses schedule information as a reference to determine who should be assigned what through data analysis and prediction. The generative AI assigns optimal tasks based on each member's schedule and past task performance. For example, if a particular member has efficiently completed a similar task in the past, a similar task can be assigned to that member. Next, task progress management is automated. The generative AI monitors the progress of each member's tasks in real time and sends reminders as needed. For example, if a task deadline is approaching, the generative AI automatically sends a reminder to encourage the member to complete the task. Furthermore, resource optimization is achieved. The generative AI allocates resources optimally, taking into account each member's skill set and current task load. For example, if a particular member is overloaded, the generative AI optimizes overall resources by reassigning tasks to other members. Finally, it predicts problems and delays and proposes effective solutions. Generative AI analyzes past project data to identify patterns of problems and delays. For example, if a particular task tends to be delayed, the generative AI can predict the risks associated with that task and propose countermeasures in advance. In this way, by utilizing generative AI, task assignment and progress management, resource optimization, and problem prediction and solution proposals can be automated, supporting the smooth progress and success of projects. As a result, project management systems can automate task assignment, progress management, resource optimization, and problem prediction and solution proposals, enabling efficient project management.

[0060] The project management system according to this embodiment comprises an assignment unit, a progress management unit, a resource allocation unit, and a problem prediction unit. The assignment unit assigns tasks. The assignment unit assigns tasks based on, for example, schedule information. The assignment unit uses a generation AI to analyze each member's schedule and past task performance to assign the most suitable tasks. For example, if a particular member has efficiently completed a similar task in the past, a similar task can be assigned to that member. The assignment unit can also use a generation AI to allocate resources optimally, taking into account each member's skill set and current task load. The progress management unit manages the progress of tasks assigned by the assignment unit. The progress management unit uses a generation AI to monitor the progress of each member's tasks in real time and send reminders as needed. For example, if a task deadline is approaching, the generation AI automatically sends a reminder to encourage the member to complete the task. The resource allocation unit efficiently allocates resources based on the progress managed by the progress management unit. The resource allocation unit uses a generation AI to allocate resources optimally, taking into account each member's skill set and current task load. For example, if a particular member is overloaded, the generating AI optimizes overall resources by reassigning tasks to other members. The problem prediction unit predicts problems based on resources efficiently allocated by the resource allocation unit. The problem prediction unit uses the generating AI to analyze past project data and identify patterns of problems and delays. For example, if a particular task tends to be delayed, the generating AI can predict the risks associated with that task and propose countermeasures in advance. As a result, the project management system according to this embodiment can automate task assignment, progress management, resource optimization, and problem prediction, enabling efficient project management.

[0061] The assignment unit can assign tasks based on schedule information. Schedule information includes, but is not limited to, calendar information and Gantt charts. The assignment unit uses a generation AI to analyze the schedule information and assign tasks optimally. For example, if a particular member has efficiently completed similar tasks in the past, they can be assigned similar tasks. This enables efficient task management by assigning tasks based on schedule information.

[0062] The progress management department can monitor task progress in real time and send reminders as needed. Real time includes, but is not limited to, second-by-second or minute-by-minute updates. The progress management department uses a generation AI to monitor the progress of each member's tasks in real time and send reminders as needed. For example, if a task deadline is approaching, the generation AI will automatically send a reminder to encourage the member to complete the task. Reminders include, but are not limited to, email notifications and alerts. This allows for real-time monitoring of task progress and sending reminders to prevent task delays.

[0063] The resource allocation unit can efficiently allocate resources by considering each member's skill set and current task load. Skill sets include, but are not limited to, technical skills and soft skills. Task load includes, but are not limited to, working time and task difficulty. The resource allocation unit uses generative AI to optimize resource allocation by considering each member's skill set and current task load. For example, if a particular member is overloaded, the generative AI will reallocate tasks to other members to optimize overall resources. This enables efficient resource management by optimally allocating resources by considering each member's skill set and task load.

[0064] The problem prediction unit can analyze past project data to identify patterns of problems and delays. Past project data includes, but is not limited to, project progress records and deliverables. Patterns of problems and delays include, but are not limited to, frequency analysis and root cause analysis. The problem prediction unit uses generative AI to analyze past project data and identify patterns of problems and delays. For example, if a particular task tends to be delayed, the generative AI can predict the risks associated with that task and propose countermeasures in advance. This allows for proactive measures to be taken by analyzing past project data and identifying patterns of problems and delays.

[0065] The problem prediction unit can predict risks for specific tasks and propose countermeasures in advance. Risks include, but are not limited to, technical risks and scheduling risks. Countermeasures include, but are not limited to, resource reallocation and schedule adjustments. The problem prediction unit uses generative AI to predict risks for specific tasks and propose countermeasures in advance. For example, if a particular task tends to be delayed, the generative AI can predict the risks for that task and propose countermeasures in advance. In this way, by predicting risks for specific tasks and proposing countermeasures in advance, project delays and problems can be prevented.

[0066] The assignment unit can estimate the user's emotions and adjust the task assignment method based on those emotions. Emotions include, but are not limited to, stress, relaxation, and urgency. The assignment unit uses generative AI to estimate the user's emotions and adjust the task assignment method based on those emotions. For example, if the user is stressed, the generative AI will reduce the number of tasks assigned to alleviate the burden. If the user is relaxed, the generative AI may assign new, challenging tasks. If the user is in a hurry, the generative AI will assign high-priority tasks to allow for quick response. By adjusting the task assignment method based on the user's emotions, the burden on the user is reduced, and efficient task management becomes possible.

[0067] The assignment unit can analyze each member's past task completion time and make optimal task assignments. Task completion time includes, but is not limited to, actual work time and planned work time. The assignment unit uses a generation AI to analyze each member's past task completion time and make optimal task assignments. For example, the generation AI assigns the most suitable task based on each member's past task completion time. The generation AI can also perform efficient task assignments by analyzing past task completion times. Furthermore, the generation AI can adjust task assignments by considering each member's past task completion time. In this way, efficient task assignment becomes possible by analyzing each member's past task completion time.

[0068] The assignment unit can consider members' current health status and stress levels when assigning tasks. Health status includes, but is not limited to, medical data and self-reported information. Stress levels include, but are not limited to, stress checks and vital data. The assignment unit uses generative AI to consider members' current health status and stress levels when assigning tasks. For example, it can monitor members' health status and adjust task assignments based on that. It can also optimize task assignments by considering stress levels. Furthermore, it can assign tasks based on health status and stress levels. This makes it possible to assign tasks appropriately by considering members' health status and stress levels.

[0069] The assignment unit can estimate the user's emotions and determine task priorities based on those emotions. Emotions include, but are not limited to, stress, relaxation, and urgency. The assignment unit uses generative AI to estimate the user's emotions and determine task priorities based on those emotions. For example, if the user is stressed, the generative AI will assign lower-priority tasks first. If the user is relaxed, the generative AI can assign higher-priority tasks first. If the user is in a hurry, the generative AI can prioritize assigning the most important tasks. This enables efficient task management by determining task priorities based on the user's emotions.

[0070] The assignment unit can assign the most suitable tasks to members by considering their geographical location information when assigning tasks. This geographical location information includes, but is not limited to, GPS data and location services. The assignment unit uses a generative AI to assign the most suitable tasks by considering the members' geographical location information. For example, the generative AI assigns the most suitable task based on the member's current location. The generative AI can also perform efficient task assignment by considering geographical location information. Furthermore, the generative AI can adjust task assignments based on the members' geographical location information. This enables efficient task assignment by considering the members' geographical location information.

[0071] The assignment unit can analyze members' social media activity when assigning tasks and assign relevant tasks accordingly. Social media activity includes, but is not limited to, posts and activity frequency. The assignment unit uses generative AI to analyze members' social media activity and assign relevant tasks when assigning tasks. For example, it analyzes members' social media activity and assigns relevant tasks. It can also assign the most suitable tasks based on social media activity. Furthermore, it can adjust task assignments by considering members' social media activity. This allows for efficient assignment of relevant tasks by analyzing members' social media activity.

[0072] The progress management unit can estimate the user's emotions and adjust the progress management method based on those emotions. Emotions include, but are not limited to, stress, relaxation, and urgency. The progress management unit uses generative AI to estimate the user's emotions and adjust the progress management method based on those emotions. For example, if the user is stressed, the generative AI reduces the frequency of progress management. If the user is relaxed, the generative AI can perform detailed progress management. If the user is in a hurry, the generative AI can perform rapid progress management. This allows for efficient progress management by adjusting the progress management method based on the user's emotions.

[0073] The progress management department can analyze the progress of each task in detail and detect delays early. Detailed analysis includes, but is not limited to, data granularity and analysis methods. The progress management department uses generative AI to analyze the progress of each task in detail and detect delays early. For example, it can monitor the progress of each task in real time and have the generative AI detect delays. It can also analyze the progress in detail and have the generative AI detect delays early. Furthermore, the generative AI can predict delays based on the progress of each task. In this way, by analyzing the progress of each task in detail, delays can be detected early.

[0074] The progress management unit can evaluate the progress status while considering the dependencies between tasks. Task dependencies include, but are not limited to, precedence and interdependencies. The progress management unit uses generative AI to evaluate the progress status while considering the dependencies between tasks. For example, the generative AI evaluates the progress status while considering the dependencies between tasks. The generative AI can also evaluate the progress status in detail based on the dependencies. Furthermore, the generative AI can evaluate the progress status by analyzing the dependencies between tasks. In this way, by considering the dependencies between tasks, the progress status can be accurately evaluated.

[0075] The progress management unit can estimate the user's emotions and adjust the timing of reminder sending based on those emotions. Emotions include, but are not limited to, stress, relaxation, and urgency. The progress management unit uses generative AI to estimate the user's emotions and adjust the timing of reminder sending based on those emotions. For example, if the user is stressed, the generative AI will reduce the frequency of reminder sending. If the user is relaxed, the generative AI can increase the frequency of reminder sending. If the user is in a hurry, the generative AI can send reminders quickly. This allows for efficient progress management by adjusting the timing of reminder sending based on the user's emotions.

[0076] The progress management department can send reminders while considering the geographical location information of members during progress management. Geographical location information includes, but is not limited to, GPS data and location services. The progress management department uses a generation AI to send reminders while considering the geographical location information of members during progress management. For example, the generation AI sends a reminder based on the member's current location. The generation AI can also send reminders efficiently while considering geographical location information. Furthermore, the generation AI can adjust the timing of reminder sending based on the member's geographical location information. This makes it possible to send reminders efficiently by considering the geographical location information of members.

[0077] The progress management department can analyze members' social media activities and evaluate their progress during progress management. Social media activities include, but are not limited to, the content and frequency of posts. The progress management department uses generative AI to analyze members' social media activities and evaluate their progress during progress management. For example, the generative AI analyzes members' social media activities and evaluates their progress. The generative AI can also evaluate progress in detail based on social media activities. Furthermore, the generative AI can evaluate progress while considering members' social media activities. This allows for an accurate evaluation of progress by analyzing members' social media activities.

[0078] The resource allocation unit can estimate the user's emotions and adjust the resource allocation method based on those emotions. Emotions include, but are not limited to, stress, relaxation, and urgency. The resource allocation unit uses generative AI to estimate the user's emotions and adjust the resource allocation method based on those emotions. For example, if the user is stressed, the generative AI reduces the resource allocation to alleviate the burden. If the user is relaxed, the generative AI can allocate new, challenging resources. If the user is in a hurry, the generative AI allocates high-priority resources to enable a quick response. This allows for efficient resource management by adjusting the resource allocation method based on the user's emotions.

[0079] The resource allocation unit can analyze each member's skill set in detail and allocate resources optimally. Skill sets include, but are not limited to, technical skills and soft skills. The resource allocation unit uses generative AI to analyze each member's skill set in detail and allocate resources optimally. For example, the generative AI allocates the optimal resources based on each member's skill set. It can also analyze skill sets in detail and have the generative AI perform efficient resource allocation. Furthermore, the generative AI can adjust resource allocation considering each member's skill set. In this way, efficient resource allocation becomes possible by analyzing each member's skill set in detail.

[0080] The resource allocation unit can evaluate the current task load of members in real time during resource optimization. Task load includes, but is not limited to, work time and task difficulty. The resource allocation unit uses a generative AI to evaluate the current task load of members in real time during resource optimization. For example, it can monitor the current task load of members in real time and the generative AI adjusts resource allocation. It can also evaluate task load in real time and perform efficient resource allocation with the generative AI. Furthermore, the generative AI can optimize resource allocation based on the current task load of members. This enables efficient resource allocation by evaluating the current task load of members in real time.

[0081] The resource allocation unit can estimate the user's emotions and determine resource priorities based on those emotions. Emotions include, but are not limited to, stress, relaxation, and urgency. The resource allocation unit uses generative AI to estimate the user's emotions and determines resource priorities based on those emotions. For example, if the user is stressed, the generative AI will allocate lower-priority resources first. If the user is relaxed, the generative AI can allocate higher-priority resources first. If the user is in a hurry, the generative AI can prioritize the allocation of the most important resources. This enables efficient resource management by determining resource priorities based on the user's emotions.

[0082] The resource allocation unit can allocate resources while considering the geographical location information of members during resource optimization. Geographical location information includes, but is not limited to, GPS data and location information services. The resource allocation unit uses a generation AI to allocate resources while considering the geographical location information of members during resource optimization. For example, the generation AI allocates the optimal resources based on the member's current location. The generation AI can also perform efficient resource allocation while considering geographical location information. Furthermore, the generation AI can adjust resource allocation based on the member's geographical location information. This makes efficient resource allocation possible by considering the geographical location information of members.

[0083] The resource allocation unit can analyze members' social media activities and adjust resource allocation during resource optimization. Social media activities include, but are not limited to, posts and activity frequency. The resource allocation unit uses generative AI to analyze members' social media activities and adjust resource allocation during resource optimization. For example, the generative AI adjusts resource allocation after analyzing members' social media activities. Alternatively, the generative AI can perform optimal resource allocation based on social media activities. Furthermore, the generative AI can optimize resource allocation by considering members' social media activities. This enables efficient resource allocation by analyzing members' social media activities.

[0084] The problem prediction unit can estimate the user's emotions and adjust its problem prediction method based on those emotions. Emotions include, but are not limited to, stress, relaxation, and urgency. The problem prediction unit uses generative AI to estimate the user's emotions and adjusts its problem prediction method based on those emotions. For example, if the user is stressed, the generative AI reduces the frequency of problem predictions. If the user is relaxed, the generative AI can also make more detailed problem predictions. If the user is in a hurry, the generative AI can also make quick problem predictions. By adjusting the problem prediction method based on the user's emotions, efficient problem prediction becomes possible.

[0085] The problem prediction unit can identify problem occurrence patterns by analyzing past project data in detail. Past project data includes, but is not limited to, project progress records and deliverables. Problem occurrence patterns include, but are not limited to, frequency analysis and cause analysis. The problem prediction unit uses generative AI to analyze past project data in detail and identify problem occurrence patterns. For example, the generative AI identifies problem occurrence patterns based on past project data. The generative AI can also identify problem occurrence trends by analyzing project data in detail. Furthermore, the generative AI can predict problem occurrence patterns based on past data. In this way, problem occurrence patterns can be identified by analyzing past project data in detail.

[0086] The problem prediction unit can assess risk by considering task dependencies when predicting a problem. Task dependencies include, but are not limited to, contextual relationships and interdependencies. The problem prediction unit uses generative AI to assess risk by considering task dependencies when predicting a problem. For example, the generative AI assesses risk by considering task dependencies. The generative AI can also assess risk in detail based on dependencies. Furthermore, the generative AI can assess risk by analyzing task dependencies. This allows for accurate risk assessment by considering task dependencies.

[0087] The problem prediction unit can estimate the user's emotions and determine risk priorities based on those estimated emotions. Emotions include, but are not limited to, stress, relaxation, and urgency. Risk priorities include, but are not limited to, impact and probability of occurrence. The problem prediction unit uses generative AI to estimate the user's emotions and determines risk priorities based on those estimated emotions. For example, if the user is stressed, the generative AI will evaluate lower-priority risks first. If the user is relaxed, the generative AI can also evaluate higher-priority risks first. If the user is in a hurry, the generative AI can also evaluate the most important risks first. This enables efficient risk management by determining risk priorities based on the user's emotions.

[0088] The problem prediction unit can assess risk by considering the geographical location information of members when predicting a problem. Geographical location information includes, but is not limited to, GPS data and location information services. The problem prediction unit uses generative AI to assess risk by considering the geographical location information of members when predicting a problem. For example, the generative AI assesses risk based on the member's current location. The generative AI can also perform an efficient risk assessment by considering geographical location information. Furthermore, the generative AI can adjust the risk assessment based on the member's geographical location information. This makes efficient risk assessment possible by considering the geographical location information of members.

[0089] The problem prediction unit can analyze members' social media activities and assess risks when predicting problems. Social media activities include, but are not limited to, the content and frequency of posts. The problem prediction unit uses generative AI to analyze members' social media activities and assess risks when predicting problems. For example, the generative AI analyzes members' social media activities and assesses the risks. The generative AI can also perform a more detailed risk assessment based on social media activities. Furthermore, the generative AI can adjust the risk assessment by taking into account members' social media activities. This enables efficient risk assessment by analyzing members' social media activities. === Hard Collateral 1-1 === Each of the multiple elements described above, including the assignment unit, progress management unit, resource allocation unit, and problem prediction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the assignment unit is implemented by the control unit 46A of the smart device 14, which uses generating AI to analyze each member's schedule and past task performance and assigns the optimal tasks. The progress management unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses generating AI to monitor the progress of each member's tasks in real time and sends reminders as needed. The resource allocation unit is implemented by the control unit 46A of the smart device 14, which allocates resources optimally considering each member's skill set and current task load. The problem prediction unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes past project data to identify patterns of problems and delays and proposes countermeasures in advance. === Hard Collateral 1-2 === Each of the multiple elements described above, including the assignment unit, progress management unit, resource allocation unit, and problem prediction unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the assignment unit is implemented by the control unit 46A of the smart glasses 214, which uses generating AI to analyze each member's schedule and past task performance and assigns the optimal tasks. The progress management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses generating AI to monitor the progress of each member's tasks in real time and sends reminders as needed. The resource allocation unit is implemented, for example, by the control unit 46A of the smart glasses 214, which allocates resources optimally considering each member's skill set and current task load. The problem prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes past project data to identify patterns of problems and delays and proposes countermeasures in advance. === Hard Collateral 1-3 === Each of the multiple elements described above, including the assignment unit, progress management unit, resource allocation unit, and problem prediction unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the assignment unit is implemented by the control unit 46A of the headset terminal 314, which uses generating AI to analyze each member's schedule and past task performance and assigns the optimal tasks. The progress management unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses generating AI to monitor the progress of each member's tasks in real time and sends reminders as needed. The resource allocation unit is implemented by, for example, the control unit 46A of the headset terminal 314, which allocates resources optimally considering each member's skill set and current task load. The problem prediction unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes past project data to identify patterns of problems and delays and proposes countermeasures in advance. === Hard Collateral 1-4 === Each of the multiple elements described above, including the assignment unit, progress management unit, resource allocation unit, and problem prediction unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the assignment unit is implemented by the control unit 46A of the robot 414, which uses generating AI to analyze each member's schedule and past task performance and assigns the optimal tasks. The progress management unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses generating AI to monitor the progress of each member's tasks in real time and sends reminders as needed. The resource allocation unit is implemented by, for example, the control unit 46A of the robot 414, which allocates resources optimally considering each member's skill set and current task load. The problem prediction unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes past project data to identify patterns of problems and delays and proposes countermeasures in advance.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The assignment unit can estimate the user's emotions and adjust the task assignment method based on those emotions. For example, if the user is stressed, the generative AI will reduce the number of tasks assigned to alleviate the burden. Conversely, if the user is relaxed, the generative AI may assign new, challenging tasks. If the user is in a hurry, the generative AI will assign high-priority tasks to allow for quick response. By adjusting the task assignment method based on the user's emotions, the system reduces the user's burden and enables efficient task management.

[0092] The progress management unit can estimate the user's emotions and adjust the progress management method based on those emotions. For example, if the user is stressed, the generating AI will reduce the frequency of progress management. If the user is relaxed, the generating AI can perform detailed progress management. If the user is in a hurry, the generating AI can perform rapid progress management. In this way, by adjusting the progress management method based on the user's emotions, efficient progress management becomes possible.

[0093] The resource allocation unit can estimate the user's emotions and adjust resource allocation based on those emotions. For example, if the user is stressed, the generating AI reduces resource allocation to alleviate the burden. Conversely, if the user is relaxed, the generating AI can allocate new, challenging resources. If the user is in a hurry, the generating AI allocates high-priority resources to enable a quick response. This allows for efficient resource management by adjusting resource allocation based on the user's emotions.

[0094] The problem prediction unit can estimate the user's emotions and adjust the problem prediction method based on the estimated emotions. For example, if the user is stressed, the generating AI will reduce the frequency of problem predictions. Conversely, if the user is relaxed, the generating AI can perform more detailed problem predictions. Furthermore, if the user is in a hurry, the generating AI can perform more rapid problem predictions. By adjusting the problem prediction method based on the user's emotions, efficient problem prediction becomes possible.

[0095] The problem prediction unit can estimate the user's emotions and determine risk priorities based on those emotions. For example, if the user is stressed, the generating AI will evaluate lower-priority risks first. Conversely, if the user is relaxed, the generating AI can evaluate higher-priority risks first. Furthermore, if the user is in a hurry, the generating AI can prioritize evaluating the most important risks. This enables efficient risk management by determining risk priorities based on the user's emotions.

[0096] The assignment unit can analyze each member's past task completion time and make optimal task assignments. For example, the generating AI can assign the most suitable task based on each member's past task completion time. The generating AI can also perform efficient task assignments by analyzing past task completion times. Furthermore, the generating AI can adjust task assignments considering each member's past task completion time. This allows for efficient task assignment by analyzing each member's past task completion time.

[0097] The assignment unit can consider the current health status and stress levels of team members when assigning tasks. For example, it can monitor the health status of team members and have the generating AI adjust task assignments accordingly. It can also optimize task assignments by considering stress levels. Furthermore, the generating AI can assign tasks based on health status and stress levels. This allows for appropriate task assignments by considering the health status and stress levels of team members.

[0098] The assignment unit can assign the most suitable tasks by considering the geographical location information of the members when assigning tasks. For example, the generating AI can assign the most suitable tasks based on the members' current location. The generating AI can also perform efficient task assignment by considering geographical location information. Furthermore, the generating AI can adjust task assignments based on the members' geographical location information. This enables efficient task assignment by considering the geographical location information of the members.

[0099] The progress management department can analyze the progress of each task in detail and detect delays early. For example, it can monitor the progress of each task in real time, and a generating AI can detect delays. It can also analyze the progress in detail, and the generating AI can detect delays early. Furthermore, the generating AI can predict delays based on the progress of each task. In this way, by analyzing the progress of each task in detail, delays can be detected early.

[0100] The resource allocation unit can evaluate the current task load of members in real time during resource optimization. For example, it can monitor the current task load of members in real time, and the generating AI adjusts resource allocation accordingly. It can also evaluate task load in real time, and the generating AI can perform efficient resource allocation. Furthermore, the generating AI can optimize resource allocation based on the current task load of members. This enables efficient resource allocation by evaluating the current task load of members in real time.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The assignment unit assigns tasks. The assignment unit assigns tasks based on, for example, schedule information. The assignment unit uses generative AI to analyze each member's schedule and past task performance to assign the most suitable tasks. For example, if a particular member has efficiently completed similar tasks in the past, a similar task can be assigned to that member. The assignment unit can also use generative AI to consider each member's skill set and current task load to optimize resource allocation. Step 2: The progress management department manages the progress of tasks assigned by the assignment department. The progress management department uses a generation AI to monitor the progress of each member's tasks in real time and send reminders as needed. For example, if a task deadline is approaching, the generation AI automatically sends a reminder to encourage the member to complete the task. Step 3: The resource allocation unit efficiently allocates resources based on the progress managed by the progress management unit. The resource allocation unit uses a generative AI to optimize resource allocation, taking into account each member's skill set and current task load. For example, if a particular member is overloaded, the generative AI optimizes overall resources by reassigning tasks to other members. Step 4: The problem prediction unit predicts problems based on the resources efficiently allocated by the resource allocation unit. The problem prediction unit uses generative AI to analyze past project data and identify patterns of problems and delays. For example, if a particular task tends to be delayed, the generative AI can predict the risks associated with that task and propose countermeasures in advance.

[0103] 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.

[0104] 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 the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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. 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.

[0121] 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.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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. 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.

[0137] 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.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

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

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

[0150] In 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.

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

[0152] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0154] The data processing system 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.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 assignment section for assigning tasks, A progress management unit manages the progress of tasks assigned by the aforementioned assignment unit, A resource allocation unit that efficiently allocates resources based on the progress status managed by the aforementioned progress management unit, A problem prediction unit that predicts problems based on resources efficiently allocated by the resource allocation unit, Equipped with A system characterized by the following features.

2. The aforementioned allocation unit is, Assign tasks based on schedule information. The system according to feature 1.

3. The aforementioned progress management unit, Monitor task progress in real time and send reminders as needed. The system according to feature 1.

4. The resource allocation unit, Efficiently allocate resources considering each member's skill set and current task load. The system according to feature 1.

5. The aforementioned problem prediction unit, Analyze past project data to identify patterns of problems and delays. The system according to feature 1.

6. The aforementioned problem prediction unit, Predict risks for specific tasks and propose preventative measures. The system according to feature 1.

7. The aforementioned allocation unit is, It estimates the user's emotions and adjusts how tasks are assigned based on those estimated emotions. The system according to feature 1.

8. The aforementioned allocation unit is, Analyze each member's past task completion times to determine the optimal task assignment. The system according to feature 1.

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