System

Abstract

An object of a system according to an embodiment is to efficiently perform progress management and task assignment of a project or business.SOLUTION: A system according to an embodiment includes a visualization unit, an allocation unit, a setting unit, a grasping unit, a sharing unit, and a management unit. The visualization unit visualizes the progress status. The allocating unit allocates a task based on the progress status visualized by the visualizing unit. The setting unit sets a priority of the task allocated by the allocating unit. The grasping unit grasps the situation of the member based on the priority set by the setting unit. The sharing unit shares the information based on the situation grasped by the grasping unit. The management unit manages the resources based on the information shared by the sharing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not efficiently manage the progress of projects and work or assign tasks, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently manage the progress of projects and work and to efficiently assign tasks. [Means for solving the problem]

[0006] The system according to the embodiment includes a visualization unit, an allocation unit, a setting unit, a grasping unit, a sharing unit, and a management unit. The visualization unit visualizes progress. The allocation unit assigns tasks based on the progress visualized by the visualization unit. The setting unit sets priorities for the tasks assigned by the allocation unit. The grasping unit grasps the status of members based on the priorities set by the setting unit. The sharing unit shares information based on the status grasped by the grasping unit. The management unit manages resources based on the information shared by the sharing unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage the progress of projects and work and assign tasks. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention efficiently manages projects and tasks, aiming to improve productivity. This system visualizes progress, assigns tasks, sets priorities, monitors member status, shares information, and manages resources. For example, progress visualization and automatic suggestion functions enable effective task management. The system visually displays the progress of each task and automatically makes suggestions for delayed tasks. Task assignment based on each member's skills and workload enables efficient task management. Furthermore, the information management function can be utilized during handovers, allowing even new members to begin work without worrying about prioritization. This system improves team productivity and promotes task efficiency. For example, progress visualization makes it easier to grasp the overall picture of a project, allowing for quick responses to delayed tasks. Task assignment and priority setting enable appropriate task management based on each member's skills and workload. Furthermore, the communication support function facilitates smooth information sharing within the team, and the resource management function ensures appropriate allocation of necessary resources.

[0029] A project management system according to an embodiment includes a visualization unit, an allocation unit, a setting unit, a comprehension unit, a sharing unit, and a management unit. The visualization unit visualizes progress. For example, the visualization unit visually displays the progress of each task. The visualization unit can also automatically make suggestions for tasks that are delayed. The allocation unit assigns tasks based on the progress visualized by the visualization unit. For example, the allocation unit assigns tasks based on the skills and workload of each member. The allocation unit can also optimize task allocation using AI. The setting unit sets priorities for tasks assigned by the allocation unit. For example, the setting unit sets priorities based on the urgency and importance of the tasks. The setting unit can also optimize the priority settings using AI. The comprehension unit comprehends the status of each member based on the priorities set by the setting unit. For example, the comprehension unit comprehends the work progress and health status of each member. The comprehension unit can also analyze the status of each member using AI. The sharing unit shares information based on the status perceived by the comprehension unit. For example, the sharing unit facilitates information sharing within the team. The sharing unit can also use AI to improve the efficiency of information sharing. The management unit manages resources based on the information shared by the sharing unit. For example, the management unit appropriately allocates necessary resources. The management unit can also optimize resource management using AI. As a result, the project management system according to the embodiment can efficiently visualize progress, assign tasks, set priorities, grasp member situations, share information, and manage resources.

[0030] The visualization unit can visually display the progress status of each task. For example, the visualization unit displays the progress status of each task using a Gantt chart. The visualization unit can also display the progress of a task using a bar graph. The visualization unit can also display the progress status of a task using a dashboard so that the progress status can be grasped at a glance. In this way, by visually displaying the progress status, the progress of a task can be grasped at a glance.

[0031] The allocation unit can allocate tasks based on the skills or workload of each member. For example, the allocation unit can allocate tasks based on the technical skills of each member. The allocation unit can also allocate tasks based on the communication skills of each member. The allocation unit can also allocate tasks based on the workload of each member. This makes it possible to allocate tasks appropriately according to the skills and workload of each member.

[0032] The setting unit can set the priority of the assigned task. For example, the setting unit sets the priority based on the urgency of the task. The setting unit can also set the priority based on the importance of the task. The setting unit can also set the priority based on the deadline of the task. In this way, by setting the priority of the task, important tasks can be processed preferentially.

[0033] The grasping unit can grasp the status of each member. For example, the grasping unit grasps the work progress of each member. The grasping unit can also grasp the health status of each member. The grasping unit can also grasp the skill level of each member. By grasping the status of each member, appropriate support can be provided.

[0034] The sharing section allows smooth information sharing within the team. For example, the sharing section shares the progress status of a project within the team. The sharing section can also share the task assignment status within the team. The sharing section can also share the resource allocation status within the team. This allows smooth information sharing, improving communication within the team.

[0035] The management department can appropriately allocate the necessary resources. For example, the management department appropriately allocates the human resources required for a project. The management department can also appropriately allocate the equipment required for a project. The management department can also appropriately allocate the budget required for a project. This allows for appropriate allocation of resources, which improves the efficiency of the project.

[0036] When visualizing progress, the visualization unit can adjust the level of detail displayed based on the importance of the task. For example, the visualization unit displays detailed information for tasks with high importance and simplified information for tasks with low importance. The visualization unit can also highlight tasks with high importance by changing the color. The visualization unit can also display progress bars for tasks with high importance by dividing them into smaller sections. This allows the display to be tailored to the importance of the task, allowing users to focus on important tasks.

[0037] When visualizing progress, the visualization unit can apply different display formats depending on the task category. For example, the visualization unit can display development tasks in Gantt chart format and design tasks in Kanban format. The visualization unit can also display management tasks in list format and creative tasks in card format. The visualization unit can also display urgent tasks in pop-up format and regular tasks in timeline format. This improves visibility by providing display formats according to task category.

[0038] When visualizing the progress, the visualization unit can improve the accuracy of the display by referring to the user's past operation history. For example, the visualization unit can prioritize displaying tasks that the user has frequently checked in the past. The visualization unit can also automatically apply a display format that the user has used in the past. The visualization unit can also suggest an optimal display method based on the user's past operation history. In this way, by referring to the past operation history, the accuracy of the display can be improved.

[0039] When visualizing progress, the visualization unit can determine the display priority based on the submission date of the task. For example, the visualization unit can prioritize displaying tasks with an upcoming submission deadline. The visualization unit can also postpone displaying tasks with a distant submission deadline. The visualization unit can also display a warning for tasks whose submission deadline has passed. This makes it easier to meet submission deadlines by displaying tasks based on the submission date.

[0040] When visualizing progress, the visualization unit can adjust the display order based on the relevance of tasks. For example, the visualization unit can group and display related tasks. The visualization unit can also display highly related tasks adjacent to each other. The visualization unit can also display less related tasks in a separate section. This allows for easier task management by displaying tasks based on their relevance.

[0041] When visualizing the progress status, the visualization unit can customize the display content according to the user's level of expertise. For example, the visualization unit displays detailed technical information to a user with high expertise. The visualization unit can also display simplified information to a user with low expertise. The visualization unit can also adjust the depth of the information to be displayed according to the user's level of expertise. This improves visibility by providing display content according to the user's level of expertise.

[0042] When assigning tasks, the allocation unit can adjust the level of detail of the assignment based on the skill level of the member. For example, the allocation unit can assign detailed tasks to members with a high skill level. The allocation unit can also assign simplified tasks to members with a low skill level. The allocation unit can also adjust the difficulty of the task according to the skill level. This allows tasks to be assigned according to the skill level of the member, enabling efficient task management.

[0043] When allocating tasks, the allocation unit can improve the accuracy of allocation by referring to the past performance of members. For example, the allocation unit can assign important tasks to members who have demonstrated high performance in the past. The allocation unit can also assign support tasks to members who have demonstrated poor performance in the past. The allocation unit can also assign optimal tasks based on past performance data. This allows optimal task allocation by referring to past performance.

[0044] The allocation unit can allocate tasks while taking into account the current load status of the members. For example, the allocation unit will not allocate new tasks to members with a high current load. The allocation unit can also allocate additional tasks to members with a low current load. The allocation unit can also adjust task allocation according to the load status. This allows the burden on members to be reduced by taking into account the current load status.

[0045] The allocation unit can take into consideration the geographical location information of the members when allocating tasks. For example, the allocation unit can assign tasks that require on-site response to nearby members. The allocation unit can also assign tasks that can be handled remotely to members in remote locations. The allocation unit can also assign tasks to the most suitable members based on the geographical location information. In this way, tasks can be allocated to the most suitable members by taking geographical location information into consideration.

[0046] When assigning tasks, the assignment unit can analyze the social media activities of members and assign relevant tasks. For example, the assignment unit assigns relevant tasks based on the content of social media activities. The assignment unit can also analyze skills and interests on social media and assign optimal tasks. The assignment unit can also assign tasks within a team taking into account social media networks. In this way, the optimal tasks can be assigned to members by analyzing social media activities.

[0047] When assigning tasks, the allocation unit can customize the allocation method by reflecting the member's past feedback. For example, the allocation unit can assign tasks related to the member's areas of expertise based on past feedback. The allocation unit can also assign tasks that avoid the member's areas of weakness based on past feedback. The allocation unit can also analyze feedback data and propose the optimal task allocation method. In this way, by reflecting past feedback, optimal task allocation can be performed for the member.

[0048] When setting task priorities, the setting unit can adjust the level of detail of the setting based on the importance of the task. For example, the setting unit sets detailed priorities for tasks with high importance. The setting unit can also set simplified priorities for tasks with low importance. The setting unit can also adjust the depth of priority setting based on the importance. This allows priorities to be set according to the importance of the task, allowing you to focus on important tasks.

[0049] When setting task priorities, the setting unit can apply different setting algorithms depending on the task category. For example, the setting unit can set priorities by applying a specific algorithm to development tasks. The setting unit can also set priorities by applying a different algorithm to design tasks. The setting unit can also set priorities by applying a different algorithm to management tasks. This allows for priority setting according to task category, enabling efficient task management.

[0050] When setting task priorities, the setting unit can improve the accuracy of the setting by referring to the user's past setting results. For example, the setting unit proposes optimal priorities based on the priorities set by the user in the past. The setting unit can also analyze the user's past setting results to improve the accuracy of priority setting. The setting unit can also adjust the priority setting algorithm by referring to the past setting results. In this way, the accuracy of priority setting is improved by referring to the past setting results.

[0051] When setting the priority of a task, the setting unit can determine the priority of the setting based on the submission time of the task. For example, the setting unit can set a task with an upcoming submission deadline as a priority. The setting unit can also set a task with a distant submission deadline to be postponed. The setting unit can also display a warning for a task whose submission deadline has passed. This makes it easier to meet the submission deadline by setting the priority based on the submission time.

[0052] The setting unit can adjust the setting order based on the relevance of the tasks when setting the priority of the tasks. For example, the setting unit groups related tasks and sets them. The setting unit can also set highly related tasks adjacent to each other. The setting unit can also set less related tasks in a separate section. This allows for priority setting based on the relevance of the tasks, making it easier to manage tasks.

[0053] When setting task priorities, the setting unit can customize the setting contents according to the user's level of expertise. For example, the setting unit sets detailed priorities for users with high levels of expertise. The setting unit can also set simplified priorities for users with low levels of expertise. The setting unit can also adjust the depth of the priorities to be set according to the user's level of expertise. This allows for efficient task management by setting priorities according to the user's level of expertise.

[0054] When assessing the status of a member, the assessment unit can improve the accuracy of the assessment by referring to the member's past performance. For example, the assessment unit assesses in detail the status of a member who has demonstrated high performance in the past. The assessment unit can also simplify and assess the status of a member who has demonstrated poor performance in the past. The assessment unit can also propose an optimal method of assessing the status based on past performance data. This allows optimal status assessment to be performed by referring to past performance.

[0055] The ascertaining unit can take into consideration the current load status of the member when ascertaining the status of the member. For example, the ascertaining unit can ascertain in detail the status of a member with a high current load. The ascertaining unit can also simplify and ascertain the status of a member with a low current load. The ascertaining unit can also adjust the depth of the status ascertainment depending on the load status. This can reduce the burden on the member by taking the current load status into consideration.

[0056] When grasping the situation of a member, the grasping unit can adjust the level of detail of the grasping based on the skill level of the member. For example, the grasping unit grasps the situation of a member with a high skill level in detail. The grasping unit can also grasp the situation of a member with a low skill level in a simplified manner. The grasping unit can also adjust the depth of the situation grasping according to the skill level. This allows for situation grasping according to the skill level of the member, thereby enabling efficient member management.

[0057] The grasping unit can grasp the status of members by taking into consideration the geographical location information of the members. For example, the grasping unit prioritizes grasping the status of nearby members. The grasping unit can also postpone grasping the status of members in remote locations. The grasping unit can also grasp the status of members in the optimal order based on the geographical location information. In this way, by taking the geographical location information into consideration, it is possible to grasp the status of members in the optimal order.

[0058] When assessing the status of a member, the assessment unit can analyze the member's social media activity to assess related information. For example, the assessment unit assesses the status of a member based on the content of social media activity. The assessment unit can also analyze skills and interests on social media to suggest the optimal method for assessing the status. The assessment unit can also assess the status within the team, taking into account social media networks. In this way, by analyzing social media activity, the status of a member can be assessed more accurately.

[0059] When assessing a member's situation, the assessment unit can customize the assessment method by reflecting the member's past feedback. For example, the assessment unit assesses in detail the situation related to the member's areas of strength based on past feedback. The assessment unit can also simplify and assess the member's areas of weakness based on past feedback. The assessment unit can also analyze feedback data and propose the optimal method for assessing the situation. In this way, by reflecting past feedback, the optimal situation assessment can be performed for the member.

[0060] When sharing information, the sharing unit can adjust the level of detail of the information based on the importance of the information. For example, the sharing unit shares information of high importance in detail. The sharing unit can also simplify and share information of low importance. The sharing unit can also adjust the depth of the information to be shared based on its importance. This allows sharing based on the importance of the information, so that important information can be shared preferentially.

[0061] When sharing information, the sharing unit can apply different sharing formats depending on the category of information. For example, the sharing unit shares technical information in a document format. The sharing unit can also share design information in a visual format. The sharing unit can also share management information in a list format. This improves visibility by providing a sharing format according to the category of information.

[0062] When sharing information, the sharing unit can improve the accuracy of sharing by referring to the user's past sharing history. For example, the sharing unit can propose an optimal sharing method based on information the user has shared in the past. The sharing unit can also analyze the user's past sharing history to improve the accuracy of sharing. The sharing unit can also customize the sharing method by referring to the past sharing history. In this way, the accuracy of sharing is improved by referring to the past sharing history.

[0063] When sharing information, the sharing unit can determine the priority of sharing based on the time of submission of the information. For example, the sharing unit prioritizes sharing of information with an upcoming submission deadline. The sharing unit can also postpone sharing of information with a distant submission deadline. The sharing unit can also display a warning for information whose submission deadline has passed. This makes it easier to meet submission deadlines by sharing information based on the time of submission.

[0064] When sharing information, the sharing unit can adjust the sharing order based on the relevance of the information. For example, the sharing unit groups and shares related information. The sharing unit can also share highly related information adjacent to each other. The sharing unit can also share less related information in a separate section. This allows sharing based on the relevance of information, making it easier to manage information.

[0065] When sharing information, the sharing unit can customize the content to be shared according to the user's level of expertise. For example, the sharing unit can share detailed information with a user with a high level of expertise. The sharing unit can also share simplified information with a user with a low level of expertise. The sharing unit can also adjust the depth of the information to be shared according to the user's level of expertise. This allows information to be shared according to the user's level of expertise, improving visibility.

[0066] The management unit can adjust the level of detail of management based on the importance of the resource when managing resources. For example, the management unit manages highly important resources in detail. The management unit can also manage less important resources in a simplified manner. The management unit can also adjust the depth of resources to be managed based on their importance. This allows management based on the importance of resources, allowing important resources to be managed with priority.

[0067] When managing resources, the management unit can apply different management algorithms depending on the category of the resource. For example, the management unit can apply a specific algorithm to technical resources. The management unit can also apply a different algorithm to design resources. The management unit can also apply a different algorithm to managed resources. This allows management according to resource category, making efficient resource management possible.

[0068] When managing resources, the management unit can improve the accuracy of management by referring to the user's past management results. For example, the management unit proposes an optimal management method based on the resources that the user has managed in the past. The management unit can also analyze the user's past management results to improve the accuracy of management. The management unit can also customize the management method by referring to the past management results. In this way, the accuracy of management is improved by referring to the past management results.

[0069] When managing resources, the management unit can determine management priorities based on the submission dates of the resources. For example, the management unit prioritizes management of resources with upcoming submission deadlines. The management unit can also postpone management of resources with more distant submission deadlines. The management unit can also display a warning for resources whose submission deadlines have passed. This makes it easier to meet submission deadlines by managing resources based on submission dates.

[0070] During resource management, the management unit can adjust the management order based on the relevance of resources. For example, the management unit manages related resources by grouping them together. The management unit can also manage highly related resources adjacent to each other. The management unit can also manage less related resources by dividing them into separate sections. This makes it easier to manage resources by managing them based on the relevance of resources.

[0071] The management unit can customize the management content according to the user's level of expertise when managing resources. For example, the management unit performs detailed resource management for users with high levels of expertise. The management unit can also perform simplified resource management for users with low levels of expertise. The management unit can also adjust the depth of the resources to be managed according to the user's level of expertise. This allows resource management according to the user's level of expertise, thereby enabling efficient resource management.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The project management system may further include a notification section. The notification section sends notifications to members regarding the progress of tasks and important events. For example, the notification section sends reminders when a task deadline is approaching. The notification section can also send warnings when task progress is behind schedule. The notification section can also send reminders before important meetings or events. This allows members to manage tasks efficiently without missing important information.

[0074] The allocation unit can take into account the geographical location information of members when allocating tasks. For example, a task that requires on-site response can be assigned to a nearby member. A task that can be handled remotely can also be assigned to a member in a remote location. Furthermore, the allocation unit can assign tasks to the most suitable member based on the geographical location information. This allows tasks to be assigned to the most suitable member by taking geographical location information into consideration.

[0075] When assessing a member's situation, the assessment unit can analyze the member's social media activity to ascertain relevant information. For example, it can assess a member's situation based on their social media activity. It can also analyze their social media skills and interests to suggest the optimal method for assessing their situation. It can also assess the situation within the team, taking into account their social media network. This allows for a more accurate assessment of the member's situation by analyzing their social media activity.

[0076] When managing resources, the management unit can determine management priorities based on the submission date of the resource. For example, resources with an upcoming submission deadline can be managed preferentially. Resources with a distant submission deadline can also be managed later. A warning can also be displayed for resources whose submission deadline has passed. This makes it easier to meet submission deadlines by managing resources based on submission date.

[0077] When visualizing progress, the visualization unit can apply different display formats depending on the task category. For example, development tasks can be displayed in Gantt chart format, and design tasks in Kanban format. Management tasks can also be displayed in list format, and creative tasks in card format. Urgent tasks can also be displayed in pop-up format, and regular tasks in timeline format. This improves visibility by providing display formats according to task category.

[0078] When setting task priorities, the setting unit can adjust the setting order based on the relevance of the tasks. For example, related tasks can be grouped and set. Also, highly related tasks can be set adjacent to each other. Furthermore, less related tasks can be set in a separate section. This allows for prioritization based on task relevance, making task management easier.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The visualization unit visualizes the progress status. For example, it can visually display the progress status of each task and automatically make suggestions for tasks that are delayed. Step 2: The allocation unit allocates tasks based on the progress visualized by the visualization unit. For example, tasks can be allocated based on the skills and workload of each member, and AI can be used to optimize task allocation. Step 3: The setting unit sets the priority of the tasks assigned by the assignment unit. For example, the priority can be set based on the urgency or importance of the task, and the priority setting can be optimized using AI. Step 4: The understanding unit understands the status of each member based on the priority set by the setting unit. For example, it can understand each member's work progress and health condition and use AI to analyze the member's status. Step 5: The sharing unit shares information based on the situation grasped by the grasping unit. For example, it can facilitate information sharing within a team and improve the efficiency of information sharing using AI. Step 6: The management unit manages resources based on the information shared by the sharing unit. For example, it can appropriately allocate the necessary resources and optimize resource management using AI.

[0081] (Example 2) A system according to an embodiment of the present invention efficiently manages projects and tasks, aiming to improve productivity. This system visualizes progress, assigns tasks, sets priorities, monitors member status, shares information, and manages resources. For example, progress visualization and automatic suggestion functions enable effective task management. The system visually displays the progress of each task and automatically makes suggestions for delayed tasks. Task assignment based on each member's skills and workload enables efficient task management. Furthermore, the information management function can be utilized during handovers, allowing even new members to begin work without worrying about prioritization. This system improves team productivity and promotes task efficiency. For example, progress visualization makes it easier to grasp the overall picture of a project, allowing for quick responses to delayed tasks. Task assignment and priority setting enable appropriate task management based on each member's skills and workload. Furthermore, the communication support function facilitates smooth information sharing within the team, and the resource management function ensures appropriate allocation of necessary resources.

[0082] A project management system according to an embodiment includes a visualization unit, an allocation unit, a setting unit, a comprehension unit, a sharing unit, and a management unit. The visualization unit visualizes progress. For example, the visualization unit visually displays the progress of each task. The visualization unit can also automatically make suggestions for tasks that are delayed. The allocation unit assigns tasks based on the progress visualized by the visualization unit. For example, the allocation unit assigns tasks based on the skills and workload of each member. The allocation unit can also optimize task allocation using AI. The setting unit sets priorities for tasks assigned by the allocation unit. For example, the setting unit sets priorities based on the urgency and importance of the tasks. The setting unit can also optimize the priority settings using AI. The comprehension unit comprehends the status of each member based on the priorities set by the setting unit. For example, the comprehension unit comprehends the work progress and health status of each member. The comprehension unit can also analyze the status of each member using AI. The sharing unit shares information based on the status perceived by the comprehension unit. For example, the sharing unit facilitates information sharing within the team. The sharing unit can also use AI to improve the efficiency of information sharing. The management unit manages resources based on the information shared by the sharing unit. For example, the management unit appropriately allocates necessary resources. The management unit can also optimize resource management using AI. As a result, the project management system according to the embodiment can efficiently visualize progress, assign tasks, set priorities, grasp member situations, share information, and manage resources.

[0083] The visualization unit can visually display the progress status of each task. For example, the visualization unit displays the progress status of each task using a Gantt chart. The visualization unit can also display the progress of a task using a bar graph. The visualization unit can also display the progress status of a task using a dashboard so that the progress status can be grasped at a glance. In this way, by visually displaying the progress status, the progress of a task can be grasped at a glance.

[0084] The allocation unit can allocate tasks based on the skills or workload of each member. For example, the allocation unit can allocate tasks based on the technical skills of each member. The allocation unit can also allocate tasks based on the communication skills of each member. The allocation unit can also allocate tasks based on the workload of each member. This makes it possible to allocate tasks appropriately according to the skills and workload of each member.

[0085] The setting unit can set the priority of the assigned task. For example, the setting unit sets the priority based on the urgency of the task. The setting unit can also set the priority based on the importance of the task. The setting unit can also set the priority based on the deadline of the task. In this way, by setting the priority of the task, important tasks can be processed preferentially.

[0086] The grasping unit can grasp the status of each member. For example, the grasping unit grasps the work progress of each member. The grasping unit can also grasp the health status of each member. The grasping unit can also grasp the skill level of each member. By grasping the status of each member, appropriate support can be provided.

[0087] The sharing section allows smooth information sharing within the team. For example, the sharing section shares the progress status of a project within the team. The sharing section can also share the task assignment status within the team. The sharing section can also share the resource allocation status within the team. This allows smooth information sharing, improving communication within the team.

[0088] The management department can appropriately allocate the necessary resources. For example, the management department appropriately allocates the human resources required for a project. The management department can also appropriately allocate the equipment required for a project. The management department can also appropriately allocate the budget required for a project. This allows for appropriate allocation of resources, which improves the efficiency of the project.

[0089] The visualization unit can estimate the user's emotions and adjust the progress display method based on the estimated user emotions. For example, if the user is feeling stressed, the visualization unit can provide a simple, highly visible display method. If the user is relaxed, the visualization unit can also provide a display method that includes detailed information. If the user is in a hurry, the visualization unit can also provide a display method that focuses on the main points. This can reduce the user's stress by providing a display method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] When visualizing progress, the visualization unit can adjust the level of detail displayed based on the importance of the task. For example, the visualization unit displays detailed information for tasks with high importance and simplified information for tasks with low importance. The visualization unit can also highlight tasks with high importance by changing the color. The visualization unit can also display progress bars for tasks with high importance by dividing them into smaller sections. This allows the display to be tailored to the importance of the task, allowing users to focus on important tasks.

[0091] When visualizing progress, the visualization unit can apply different display formats depending on the task category. For example, the visualization unit can display development tasks in Gantt chart format and design tasks in Kanban format. The visualization unit can also display management tasks in list format and creative tasks in card format. The visualization unit can also display urgent tasks in pop-up format and regular tasks in timeline format. This improves visibility by providing display formats according to task category.

[0092] When visualizing the progress, the visualization unit can improve the accuracy of the display by referring to the user's past operation history. For example, the visualization unit can prioritize displaying tasks that the user has frequently checked in the past. The visualization unit can also automatically apply a display format that the user has used in the past. The visualization unit can also suggest an optimal display method based on the user's past operation history. In this way, by referring to the past operation history, the accuracy of the display can be improved.

[0093] The visualization unit can estimate the user's emotions and adjust the display order of the progress status based on the estimated user emotions. For example, if the user is feeling stressed, the visualization unit can display important tasks first. Also, if the user is relaxed, the visualization unit can display all tasks evenly. Also, if the user is in a hurry, the visualization unit can display the most urgent tasks first. This can reduce the user's stress by providing a display order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] When visualizing progress, the visualization unit can determine the display priority based on the submission date of the task. For example, the visualization unit can prioritize displaying tasks with an upcoming submission deadline. The visualization unit can also postpone displaying tasks with a distant submission deadline. The visualization unit can also display a warning for tasks whose submission deadline has passed. This makes it easier to meet submission deadlines by displaying tasks based on the submission date.

[0095] When visualizing progress, the visualization unit can adjust the display order based on the relevance of tasks. For example, the visualization unit can group and display related tasks. The visualization unit can also display highly related tasks adjacent to each other. The visualization unit can also display less related tasks in a separate section. This allows for easier task management by displaying tasks based on their relevance.

[0096] When visualizing the progress status, the visualization unit can customize the display content according to the user's level of expertise. For example, the visualization unit displays detailed technical information to a user with high expertise. The visualization unit can also display simplified information to a user with low expertise. The visualization unit can also adjust the depth of the information to be displayed according to the user's level of expertise. This improves visibility by providing display content according to the user's level of expertise.

[0097] The allocation unit can estimate the user's emotions and adjust the task allocation method based on the estimated user's emotions. For example, if the user is feeling stressed, the allocation unit can prioritize assigning easy tasks. Furthermore, if the user is relaxed, the allocation unit can also assign more difficult tasks. Furthermore, if the user is in a hurry, the allocation unit can also assign tasks that can be completed quickly. This allows tasks to be assigned according to the user's emotions, thereby reducing the user's stress. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0098] When assigning tasks, the allocation unit can adjust the level of detail of the assignment based on the skill level of the member. For example, the allocation unit can assign detailed tasks to members with a high skill level. The allocation unit can also assign simplified tasks to members with a low skill level. The allocation unit can also adjust the difficulty of the task according to the skill level. This allows tasks to be assigned according to the skill level of the member, enabling efficient task management.

[0099] When allocating tasks, the allocation unit can improve the accuracy of allocation by referring to the past performance of members. For example, the allocation unit can assign important tasks to members who have demonstrated high performance in the past. The allocation unit can also assign support tasks to members who have demonstrated poor performance in the past. The allocation unit can also assign optimal tasks based on past performance data. This allows optimal task allocation by referring to past performance.

[0100] The allocation unit can allocate tasks while taking into account the current load status of the members. For example, the allocation unit will not allocate new tasks to members with a high current load. The allocation unit can also allocate additional tasks to members with a low current load. The allocation unit can also adjust task allocation according to the load status. This allows the burden on members to be reduced by taking into account the current load status.

[0101] The allocation unit can estimate the user's emotions and adjust the task allocation order based on the estimated user emotions. For example, if the user is feeling stressed, the allocation unit can allocate easier tasks first. Also, if the user is relaxed, the allocation unit can allocate more difficult tasks first. Also, if the user is in a hurry, the allocation unit can allocate tasks that can be completed quickly first. This can reduce the user's stress by providing a task allocation order that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] The allocation unit can take into consideration the geographical location information of the members when allocating tasks. For example, the allocation unit can assign tasks that require on-site response to nearby members. The allocation unit can also assign tasks that can be handled remotely to members in remote locations. The allocation unit can also assign tasks to the most suitable members based on the geographical location information. In this way, tasks can be allocated to the most suitable members by taking geographical location information into consideration.

[0103] When assigning tasks, the assignment unit can analyze the social media activities of members and assign relevant tasks. For example, the assignment unit assigns relevant tasks based on the content of social media activities. The assignment unit can also analyze skills and interests on social media and assign optimal tasks. The assignment unit can also assign tasks within a team taking into account social media networks. In this way, the optimal tasks can be assigned to members by analyzing social media activities.

[0104] When assigning tasks, the allocation unit can customize the allocation method by reflecting the member's past feedback. For example, the allocation unit can assign tasks related to the member's areas of expertise based on past feedback. The allocation unit can also assign tasks that avoid the member's areas of weakness based on past feedback. The allocation unit can also analyze feedback data and propose the optimal task allocation method. In this way, by reflecting past feedback, optimal task allocation can be performed for the member.

[0105] The setting unit can estimate the user's emotions and adjust the task priority setting method based on the estimated user's emotions. For example, if the user is feeling stressed, the setting unit can prioritize easy tasks. Also, if the user is relaxed, the setting unit can prioritize difficult tasks. Also, if the user is in a hurry, the setting unit can prioritize tasks that can be completed quickly. This allows prioritization according to the user's emotions, thereby reducing the user's stress. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0106] When setting task priorities, the setting unit can adjust the level of detail of the setting based on the importance of the task. For example, the setting unit sets detailed priorities for tasks with high importance. The setting unit can also set simplified priorities for tasks with low importance. The setting unit can also adjust the depth of priority setting based on the importance. This allows priorities to be set according to the importance of the task, allowing you to focus on important tasks.

[0107] When setting task priorities, the setting unit can apply different setting algorithms depending on the task category. For example, the setting unit can set priorities by applying a specific algorithm to development tasks. The setting unit can also set priorities by applying a different algorithm to design tasks. The setting unit can also set priorities by applying a different algorithm to management tasks. This allows for priority setting according to task category, enabling efficient task management.

[0108] When setting task priorities, the setting unit can improve the accuracy of the setting by referring to the user's past setting results. For example, the setting unit proposes optimal priorities based on the priorities set by the user in the past. The setting unit can also analyze the user's past setting results to improve the accuracy of priority setting. The setting unit can also adjust the priority setting algorithm by referring to the past setting results. In this way, the accuracy of priority setting is improved by referring to the past setting results.

[0109] The setting unit can estimate the user's emotions and adjust the priority setting order of tasks based on the estimated user's emotions. For example, if the user is feeling stressed, the setting unit can set easier tasks first. Also, if the user is relaxed, the setting unit can set more difficult tasks first. Also, if the user is in a hurry, the setting unit can set tasks that can be completed quickly first. This can reduce the user's stress by providing a priority setting order according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0110] When setting the priority of a task, the setting unit can determine the priority of the setting based on the submission time of the task. For example, the setting unit can set a task with an upcoming submission deadline as a priority. The setting unit can also set a task with a distant submission deadline to be postponed. The setting unit can also display a warning for a task whose submission deadline has passed. This makes it easier to meet the submission deadline by setting the priority based on the submission time.

[0111] The setting unit can adjust the setting order based on the relevance of the tasks when setting the priority of the tasks. For example, the setting unit groups related tasks and sets them. The setting unit can also set highly related tasks adjacent to each other. The setting unit can also set less related tasks in a separate section. This allows for priority setting based on the relevance of the tasks, making it easier to manage tasks.

[0112] When setting task priorities, the setting unit can customize the setting contents according to the user's level of expertise. For example, the setting unit sets detailed priorities for users with high levels of expertise. The setting unit can also set simplified priorities for users with low levels of expertise. The setting unit can also adjust the depth of the priorities to be set according to the user's level of expertise. This allows for efficient task management by setting priorities according to the user's level of expertise.

[0113] The grasping unit can estimate the user's emotions and adjust the member's situation grasping method based on the estimated user's emotions. For example, if the user is feeling stressed, the grasping unit can provide a simple situation grasping method. If the user is relaxed, the grasping unit can also provide a detailed situation grasping method. If the user is in a hurry, the grasping unit can also provide a situation grasping method that focuses on the main points. In this way, by providing a situation grasping method that corresponds to the user's emotions, the user's stress can be reduced. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0114] When assessing the status of a member, the assessment unit can improve the accuracy of the assessment by referring to the member's past performance. For example, the assessment unit assesses in detail the status of a member who has demonstrated high performance in the past. The assessment unit can also simplify and assess the status of a member who has demonstrated poor performance in the past. The assessment unit can also propose an optimal method of assessing the status based on past performance data. This allows optimal status assessment to be performed by referring to past performance.

[0115] The ascertaining unit can take into consideration the current load status of the member when ascertaining the status of the member. For example, the ascertaining unit can ascertain in detail the status of a member with a high current load. The ascertaining unit can also simplify and ascertain the status of a member with a low current load. The ascertaining unit can also adjust the depth of the status ascertainment depending on the load status. This can reduce the burden on the member by taking the current load status into consideration.

[0116] When grasping the situation of a member, the grasping unit can adjust the level of detail of the grasping based on the skill level of the member. For example, the grasping unit grasps the situation of a member with a high skill level in detail. The grasping unit can also grasp the situation of a member with a low skill level in a simplified manner. The grasping unit can also adjust the depth of the situation grasping according to the skill level. This allows for situation grasping according to the skill level of the member, thereby enabling efficient member management.

[0117] The grasping unit can estimate the user's emotions and adjust the order of grasping the members' situations based on the estimated user's emotions. For example, if the user is feeling stressed, the grasping unit can grasp the situations of important members first. Also, if the user is relaxed, the grasping unit can grasp the situations of all members equally. Also, if the user is in a hurry, the grasping unit can grasp the situation of the most urgent member first. This can reduce the user's stress by providing a situation grasping order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0118] The grasping unit can grasp the status of members by taking into consideration the geographical location information of the members. For example, the grasping unit prioritizes grasping the status of nearby members. The grasping unit can also postpone grasping the status of members in remote locations. The grasping unit can also grasp the status of members in the optimal order based on the geographical location information. In this way, by taking the geographical location information into consideration, it is possible to grasp the status of members in the optimal order.

[0119] When assessing the status of a member, the assessment unit can analyze the member's social media activity to assess related information. For example, the assessment unit assesses the status of a member based on the content of social media activity. The assessment unit can also analyze skills and interests on social media to suggest the optimal method for assessing the status. The assessment unit can also assess the status within the team, taking into account social media networks. In this way, by analyzing social media activity, the status of a member can be assessed more accurately.

[0120] When assessing a member's situation, the assessment unit can customize the assessment method by reflecting the member's past feedback. For example, the assessment unit assesses in detail the situation related to the member's areas of strength based on past feedback. The assessment unit can also simplify and assess the member's areas of weakness based on past feedback. The assessment unit can also analyze feedback data and propose the optimal method for assessing the situation. In this way, by reflecting past feedback, the optimal situation assessment can be performed for the member.

[0121] The sharing unit can estimate the user's emotions and adjust the information sharing method based on the estimated user's emotions. For example, if the user is feeling stressed, the sharing unit can provide a simple information sharing method. If the user is relaxed, the sharing unit can also provide a detailed information sharing method. If the user is in a hurry, the sharing unit can also provide an information sharing method that focuses on the main points. This can reduce the user's stress by providing an information sharing method that suits the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0122] When sharing information, the sharing unit can adjust the level of detail of the information based on the importance of the information. For example, the sharing unit shares information of high importance in detail. The sharing unit can also simplify and share information of low importance. The sharing unit can also adjust the depth of the information to be shared based on its importance. This allows sharing based on the importance of the information, so that important information can be shared preferentially.

[0123] When sharing information, the sharing unit can apply different sharing formats depending on the category of information. For example, the sharing unit shares technical information in a document format. The sharing unit can also share design information in a visual format. The sharing unit can also share management information in a list format. This improves visibility by providing a sharing format according to the category of information.

[0124] When sharing information, the sharing unit can improve the accuracy of sharing by referring to the user's past sharing history. For example, the sharing unit can propose an optimal sharing method based on information the user has shared in the past. The sharing unit can also analyze the user's past sharing history to improve the accuracy of sharing. The sharing unit can also customize the sharing method by referring to the past sharing history. In this way, the accuracy of sharing is improved by referring to the past sharing history.

[0125] The sharing unit can estimate the user's emotions and adjust the information sharing order based on the estimated user's emotions. For example, if the user is feeling stressed, the sharing unit can share important information first. Also, if the user is relaxed, the sharing unit can share all information equally. Also, if the user is in a hurry, the sharing unit can share the most urgent information first. This can reduce the user's stress by providing an information sharing order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0126] When sharing information, the sharing unit can determine the priority of sharing based on the time of submission of the information. For example, the sharing unit prioritizes sharing of information with an upcoming submission deadline. The sharing unit can also postpone sharing of information with a distant submission deadline. The sharing unit can also display a warning for information whose submission deadline has passed. This makes it easier to meet submission deadlines by sharing information based on the time of submission.

[0127] When sharing information, the sharing unit can adjust the sharing order based on the relevance of the information. For example, the sharing unit groups and shares related information. The sharing unit can also share highly related information adjacent to each other. The sharing unit can also share less related information in a separate section. This allows sharing based on the relevance of information, making it easier to manage information.

[0128] When sharing information, the sharing unit can customize the content to be shared according to the user's level of expertise. For example, the sharing unit can share detailed information with a user with a high level of expertise. The sharing unit can also share simplified information with a user with a low level of expertise. The sharing unit can also adjust the depth of the information to be shared according to the user's level of expertise. This allows information to be shared according to the user's level of expertise, improving visibility.

[0129] The management unit can estimate the user's emotions and adjust the resource management method based on the estimated user emotions. For example, if the user is feeling stressed, the management unit can provide a simple resource management method. If the user is relaxed, the management unit can also provide a detailed resource management method. If the user is in a hurry, the management unit can also provide a resource management method that focuses on the main points. In this way, by providing a resource management method according to the user's emotions, the user's stress can be reduced. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0130] The management unit can adjust the level of detail of management based on the importance of the resource when managing resources. For example, the management unit manages highly important resources in detail. The management unit can also manage less important resources in a simplified manner. The management unit can also adjust the depth of resources to be managed based on their importance. This allows management based on the importance of resources, allowing important resources to be managed with priority.

[0131] When managing resources, the management unit can apply different management algorithms depending on the category of the resource. For example, the management unit can apply a specific algorithm to technical resources. The management unit can also apply a different algorithm to design resources. The management unit can also apply a different algorithm to managed resources. This allows management according to resource category, making efficient resource management possible.

[0132] When managing resources, the management unit can improve the accuracy of management by referring to the user's past management results. For example, the management unit proposes an optimal management method based on the resources that the user has managed in the past. The management unit can also analyze the user's past management results to improve the accuracy of management. The management unit can also customize the management method by referring to the past management results. In this way, the accuracy of management is improved by referring to the past management results.

[0133] The management unit can estimate the user's emotions and adjust the resource management order based on the estimated user emotions. For example, if the user is feeling stressed, the management unit manages important resources first. Also, if the user is relaxed, the management unit can manage all resources equally. Also, if the user is in a hurry, the management unit can manage the most urgent resources first. This can reduce the user's stress by providing a resource management order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0134] When managing resources, the management unit can determine management priorities based on the submission dates of the resources. For example, the management unit prioritizes management of resources with upcoming submission deadlines. The management unit can also postpone management of resources with more distant submission deadlines. The management unit can also display a warning for resources whose submission deadlines have passed. This makes it easier to meet submission deadlines by managing resources based on submission dates.

[0135] During resource management, the management unit can adjust the management order based on the relevance of resources. For example, the management unit manages related resources by grouping them together. The management unit can also manage highly related resources adjacent to each other. The management unit can also manage less related resources by dividing them into separate sections. This makes it easier to manage resources by managing them based on the relevance of resources.

[0136] The management unit can customize the management content according to the user's level of expertise when managing resources. For example, the management unit performs detailed resource management for users with high levels of expertise. The management unit can also perform simplified resource management for users with low levels of expertise. The management unit can also adjust the depth of the resources to be managed according to the user's level of expertise. This allows resource management according to the user's level of expertise, thereby enabling efficient resource management. === Hard Collateral 1-1 === Each of the multiple elements, including the visualization unit, allocation unit, setting unit, understanding unit, sharing unit, and management unit, described above, is realized by, for example, at least one of the smart device 14 and the data processing device 12. For example, the visualization unit visually displays the progress of each task using the display 40A of the smart device 14. The allocation unit is realized by the specific processing unit 290 of the data processing device 12 and assigns tasks based on the skills and workload of each member. The setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets priorities based on the urgency and importance of each task. The understanding unit uses the camera 42 and microphone 38B of the smart device 14 to understand the work progress and health status of each member. The sharing unit shares information within the team via the communication I / F 44 of the smart device 14. The management unit is realized by the specific processing unit 290 of the data processing device 12 and appropriately allocates necessary resources. === Hard Collateral 1-2 === Each of the multiple elements, including the visualization unit, allocation unit, setting unit, understanding unit, sharing unit, and management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the visualization unit visually displays the progress of each task using the display of the smart glasses 214. The allocation unit is realized by the specific processing unit 290 of the data processing device 12 and assigns tasks based on the skills and workload of each member. The setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets priorities based on the urgency and importance of the tasks. The understanding unit understands the work progress and health status of the members using the camera 42 and microphone 238 of the smart glasses 214. The sharing unit shares information within the team via the communication I / F 44 of the smart glasses 214. The management unit is realized by the specific processing unit 290 of the data processing device 12 and appropriately allocates necessary resources. === Hard Collateral 1-3 === Each of the multiple elements, including the visualization unit, allocation unit, setting unit, understanding unit, sharing unit, and management unit, described above, is realized by, for example, at least one of the headset terminal 314 and the data processing device 12. For example, the visualization unit visually displays the progress of each task using the display 343 of the headset terminal 314. The allocation unit is realized by the specific processing unit 290 of the data processing device 12 and assigns tasks based on the skills and workload of each member. The setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets priorities based on the urgency and importance of the tasks. The understanding unit understands the work progress and health status of the members using the camera 42 and microphone 238 of the headset terminal 314. The sharing unit shares information within the team via the communication I / F 44 of the headset terminal 314. The management unit is realized by the specific processing unit 290 of the data processing device 12 and appropriately allocates necessary resources. === Hard Collateral 1-4 === Each of the multiple elements, including the visualization unit, allocation unit, setting unit, understanding unit, sharing unit, and management unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the visualization unit visually displays the progress of each task using the display of the robot 414. The allocation unit is realized by the specific processing unit 290 of the data processing device 12 and assigns tasks based on the skills and workload of each member. The setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets priorities based on the urgency and importance of the tasks. The understanding unit understands the work progress and health status of the members using the camera 42 and microphone 238 of the robot 414. The sharing unit shares information within the team via the communication I / F 44 of the robot 414. The management unit is realized by the specific processing unit 290 of the data processing device 12 and appropriately allocates necessary resources.

[0137] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0138] The project management system may further include a notification section. The notification section sends notifications to members regarding the progress of tasks and important events. For example, the notification section sends reminders when a task deadline is approaching. The notification section can also send warnings when task progress is behind schedule. The notification section can also send reminders before important meetings or events. This allows members to manage tasks efficiently without missing important information.

[0139] The visualization unit can estimate the user's emotions and adjust the progress display method based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by providing a display method that corresponds to the user's emotions, it is possible to reduce the user's stress.

[0140] The allocation unit can take into account the geographical location information of members when allocating tasks. For example, a task that requires on-site response can be assigned to a nearby member. A task that can be handled remotely can also be assigned to a member in a remote location. Furthermore, the allocation unit can assign tasks to the most suitable member based on the geographical location information. This allows tasks to be assigned to the most suitable member by taking geographical location information into consideration.

[0141] The setting unit can estimate the user's emotions and adjust the method for setting task priorities based on the estimated user emotions. For example, if the user is feeling stressed, easy tasks can be set as a priority. Also, if the user is relaxed, difficult tasks can be set as a priority. Also, if the user is in a hurry, tasks that can be completed quickly can be set as a priority. In this way, priority setting according to the user's emotions can reduce the user's stress.

[0142] When assessing a member's situation, the assessment unit can analyze the member's social media activity to ascertain relevant information. For example, it can assess a member's situation based on their social media activity. It can also analyze their social media skills and interests to suggest the optimal method for assessing their situation. It can also assess the situation within the team, taking into account their social media network. This allows for a more accurate assessment of the member's situation by analyzing their social media activity.

[0143] The sharing unit can estimate the user's emotions and adjust the information sharing method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple information sharing method can be provided. If the user is relaxed, a detailed information sharing method can be provided. If the user is in a hurry, a basic information sharing method can be provided. In this way, by providing an information sharing method that suits the user's emotions, the user's stress can be reduced.

[0144] When managing resources, the management unit can determine management priorities based on the submission date of the resource. For example, resources with an upcoming submission deadline can be managed preferentially. Resources with a distant submission deadline can also be managed later. A warning can also be displayed for resources whose submission deadline has passed. This makes it easier to meet submission deadlines by managing resources based on submission date.

[0145] When visualizing progress, the visualization unit can apply different display formats depending on the task category. For example, development tasks can be displayed in Gantt chart format, and design tasks in Kanban format. Management tasks can also be displayed in list format, and creative tasks in card format. Urgent tasks can also be displayed in pop-up format, and regular tasks in timeline format. This improves visibility by providing display formats according to task category.

[0146] The allocation unit can estimate the user's emotions and adjust the task allocation method based on the estimated user's emotions. For example, if the user is feeling stressed, easy tasks can be preferentially allocated. Also, if the user is relaxed, more difficult tasks can be allocated. Also, if the user is in a hurry, tasks that can be completed quickly can be allocated. In this way, tasks can be allocated according to the user's emotions, thereby reducing the user's stress.

[0147] When setting task priorities, the setting unit can adjust the setting order based on the relevance of the tasks. For example, related tasks can be grouped and set. Also, highly related tasks can be set adjacent to each other. Furthermore, less related tasks can be set in a separate section. This allows for prioritization based on task relevance, making task management easier.

[0148] The processing flow of the second embodiment will be briefly explained below.

[0149] Step 1: The visualization unit visualizes the progress status. For example, it can visually display the progress status of each task and automatically make suggestions for tasks that are delayed. Step 2: The allocation unit allocates tasks based on the progress visualized by the visualization unit. For example, tasks can be allocated based on the skills and workload of each member, and AI can be used to optimize task allocation. Step 3: The setting unit sets the priority of the tasks assigned by the assignment unit. For example, the priority can be set based on the urgency or importance of the task, and the priority setting can be optimized using AI. Step 4: The understanding unit understands the status of each member based on the priority set by the setting unit. For example, it can understand each member's work progress and health condition and use AI to analyze the member's status. Step 5: The sharing unit shares information based on the situation grasped by the grasping unit. For example, it can facilitate information sharing within a team and improve the efficiency of information sharing using AI. Step 6: The management unit manages resources based on the information shared by the sharing unit. For example, it can appropriately allocate the necessary resources and optimize resource management using AI.

[0150] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0151] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0155] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0156] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0171] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0172] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0187] 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.

[0188] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0189] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0190] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0191] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0192] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0193] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0194] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0195] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0196] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0197] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0198] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0199] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0200] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0201] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0202] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0203] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0204] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0205] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0206] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0207] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0208] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0209] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0210] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0211] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0212] 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.

[0213] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0214] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0215] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0216] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0217] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0218] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0219] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0220] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0221] [Explanation of symbols]

[0222] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a visualization unit that visualizes the progress; an allocation unit that allocates tasks based on the progress visualized by the visualization unit; a setting unit that sets the priority of the task assigned by the assignment unit; a grasping unit that grasps the status of the members based on the priority set by the setting unit; a sharing unit that shares information based on the situation grasped by the grasping unit; a management unit that manages resources based on the information shared by the sharing unit.

2. The visualization unit Visually display the progress of each task 2. The system of claim 1.

3. The allocation unit Assign tasks based on each member's skills or workload, 2. The system of claim 1.

4. The setting unit Set the priority of assigned tasks 2. The system of claim 1.

5. The grasping unit is Understand the status of each member 2. The system of claim 1.

6. The common part is Facilitate information sharing within the team 2. The system of claim 1.

7. The management unit Allocate the necessary resources appropriately 2. The system of claim 1.

8. The visualization unit Inferring user emotions and adjusting progress display based on the inferred user emotions 2. The system of claim 1.

Images