System

Abstract

An object of a system according to an embodiment is to analyze a progress status of a project and optimally distribute resources.SOLUTION: A system according to an embodiment includes a progress status analysis unit, a resource distribution unit, and a task priority setting unit. The progress status analysis unit analyzes the progress status of the project using the generated AI. The resource distribution unit optimally distributes the resources based on the data analyzed by the progress analysis unit. The task priority setting unit sets the priority of the task based on the resource distributed by the resource distribution 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 techniques have had the problem of making it difficult to properly analyze the progress of a project and optimally allocate resources.

[0005] The system according to the embodiment aims to analyze the progress of a project and optimally allocate resources. [Means for solving the problem]

[0006] The system according to the embodiment includes a progress analysis unit, a resource allocation unit, and a task priority setting unit. The progress analysis unit analyzes the progress of a project using a generation AI. The resource allocation unit optimally allocates resources based on the data analyzed by the progress analysis unit. The task priority setting unit sets task priorities based on the resources allocated by the resource allocation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the progress of a project and optimally allocate resources. [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 project management system according to an embodiment of the present invention uses generative AI to analyze project progress, resource allocation, and task priorities, and makes optimal adjustments. This allows the project management system to improve project efficiency and success rates.

[0029] A project management system according to an embodiment includes a progress analysis unit, a resource allocation unit, and a task priority setting unit. The progress analysis unit analyzes the progress of a project using a generative AI. For example, the progress analysis unit analyzes the progress, completion status, and presence or absence of delays of each task. The progress analysis unit can also analyze the productivity data of project members and propose task reallocation based on the performance of each member. Furthermore, the progress analysis unit can refer to past project data and apply successful patterns of similar projects. The resource allocation unit optimally allocates resources based on the data analyzed by the progress analysis unit. For example, the resource allocation unit analyzes the human resources, time, and budget required for a project and optimally allocates them. The resource allocation unit can also analyze the skill sets and experience of each member in detail and propose optimal personnel allocation. Furthermore, the resource allocation unit can incorporate an algorithm that dynamically adjusts resource allocation according to the progress of the project. The task priority setting unit sets task priorities based on the resources allocated by the resource allocation unit. For example, the task priority setting unit analyzes the importance and urgency of tasks and sets priorities. The task priority setting unit can also perform a detailed analysis of task dependencies and propose an optimal order. Furthermore, the task priority setting unit can refer to past project data and apply success patterns of similar tasks. This allows the project management system according to the embodiment to improve project efficiency and success rates. For example, delays can be prevented by understanding the progress of a project in real time and making appropriate adjustments. Furthermore, waste can be reduced by optimally allocating resources. Furthermore, by appropriately setting task priorities, important tasks can be completed reliably.

[0030] The progress analysis unit analyzes the productivity data of project members and can propose task reallocation based on the performance of each member. In the progress analysis unit, for example, the generation AI collects productivity data of project members and analyzes each member's work efficiency and task completion speed. For example, optimal task allocation is performed based on past task completion times and quality evaluations. In addition, the generation AI in the progress analysis unit proposes task reallocation based on the members' productivity data. For example, tasks in areas in which a particular member excels are prioritized. In addition, the generation AI in the progress analysis unit analyzes members' productivity data in real time and dynamically reallocates tasks according to the task progress. For example, if a delay occurs, the task is reallocated to another member. This makes it possible to improve project efficiency by optimally reallocating tasks based on the productivity of project members.

[0031] When analyzing the progress of a project, the progress analysis unit can refer to past project data and apply success patterns from similar projects. In this case, for example, the generation AI analyzes past project data and extracts success patterns from similar projects. For example, it refers to the progress schedules and resource allocation of successful projects. In addition, the generation AI in the progress analysis unit proposes success patterns that can be applied to the current project based on past project data. For example, it proposes methods for prioritizing specific tasks and managing risks. In addition, the generation AI in the progress analysis unit refers to past project data in real time and dynamically applies success patterns according to the progress. For example, if a delay occurs, it proposes countermeasures based on past success cases. This makes it possible to improve the project's success rate by optimizing the current project based on past success cases.

[0032] The progress analysis unit can generate visual data when analyzing the progress of a project, allowing the progress to be visually grasped. In the progress analysis unit, for example, the generation AI analyzes the progress of the project and automatically generates progress graphs and Gantt charts. For example, the progress and completion status of each task are visually displayed. The progress analysis unit also uses the visual data to build a system that monitors the progress of the project in real time. For example, the progress graph is displayed on a dashboard so that stakeholders can understand the situation at a glance. In addition, the generation AI analyzes the progress and proposes project adjustments based on the visual data. For example, tasks that have experienced delays are highlighted and countermeasures are proposed. In this way, the project progress can be visually grasped using visual data, facilitating smooth communication between stakeholders.

[0033] When analyzing the progress of a project, the progress analysis unit can collect feedback from project members using voice input and make adjustments in real time. The progress analysis unit, for example, builds a system in which a generation AI uses voice input to collect feedback from project members in real time. For example, it analyzes comments made during meetings using voice recognition technology and collects feedback. The progress analysis unit also uses the generation AI to analyze the progress of the project based on the feedback collected using voice input and proposes necessary adjustments. For example, it reallocates tasks to reflect the opinions of the members. The progress analysis unit also uses the generation AI to analyze feedback from project members in real time using voice input and makes adjustments according to the progress. For example, if a delay occurs, it proposes countermeasures based on the opinions of the members. In this way, it is possible to collect feedback from project members in real time and make appropriate adjustments, thereby improving the efficiency of the project.

[0034] When optimally allocating resources, the resource allocation unit can analyze each member's skill set and experience in detail and propose optimal personnel allocation. For example, the generation AI in the resource allocation unit analyzes each member's skill set and experience in detail and proposes optimal personnel allocation. For example, it assigns members with specific skills to appropriate tasks. The resource allocation unit also optimizes resource allocation for the entire project using the generation AI based on the members' skill sets and experience. For example, it assigns experienced members to important tasks. The resource allocation unit also analyzes members' skill sets and experience in real time and dynamically adjusts resource allocation. For example, when a new task is added, it assigns the most suitable member. In this way, by analyzing members' skill sets and experience in detail and making optimal personnel allocation, it is possible to improve project efficiency.

[0035] When optimally allocating resources, the resource allocation unit can introduce an algorithm that dynamically adjusts resource allocation according to the progress of the project. For example, the resource allocation unit introduces an algorithm in which the generation AI analyzes the progress of the project in real time and dynamically adjusts resource allocation. For example, additional resources are allocated to tasks that have been delayed. The resource allocation unit also builds a system in which the generation AI dynamically adjusts resource allocation according to the progress of the project. For example, resources are reallocated according to the progress of tasks. The resource allocation unit also develops an algorithm in which the generation AI dynamically adjusts resource allocation based on the progress of the project. For example, resources are preferentially allocated to tasks with high importance. In this way, the efficiency of the project can be improved by dynamically adjusting resource allocation according to the progress of the project.

[0036] The resource allocation unit can utilize a resource pool that includes external freelancers or consultants when optimally allocating resources. For example, the generation AI in the resource allocation unit utilizes a resource pool that includes external freelancers and consultants to propose optimal resource allocation. For example, it adds external resources with specific skills to a project. In addition, the generation AI in the resource allocation unit analyzes a database of external resources to identify freelancers or consultants with the skills required for a project. For example, it assigns external resources with specialized knowledge to important tasks. In addition, the generation AI in the resource allocation unit utilizes external resources and dynamically adjusts resource allocation according to the progress of the project. For example, if a delay occurs, it can respond by adding external resources. In this way, by utilizing external resources, it is possible to optimize resource allocation for a project and improve efficiency.

[0037] When optimally allocating resources, the resource allocation unit can analyze resource usage history and propose optimal allocation based on past success stories. For example, the resource allocation unit uses the generation AI to analyze resource usage history and propose optimal resource allocation based on past success stories. For example, it refers to the resource allocation patterns of successful projects. The resource allocation unit also uses the generation AI to propose optimal resource allocation that can be applied to the current project based on the resource usage history. For example, it applies resource allocation that has been successful in the past to a specific task. The resource allocation unit also analyzes the resource usage history in real time and proposes optimal resource allocation according to the progress. For example, if a delay occurs, it proposes countermeasures based on past success stories. In this way, project efficiency can be improved by optimizing resource allocation based on past success stories.

[0038] When setting task priorities, the task priority setting unit can analyze task dependencies in detail and propose the optimal order. For example, the generation AI in the task priority setting unit analyzes task dependencies in detail and proposes the optimal order. For example, it may prioritize tasks with strong dependencies. The task priority setting unit also optimizes the priority of tasks for the entire project based on task dependencies. For example, it may process dependent tasks consecutively. The task priority setting unit also analyzes task dependencies in real time and dynamically adjusts task priorities. For example, when a new task is added, it may reset the priority taking the dependencies into consideration. In this way, by analyzing task dependencies in detail and proposing the optimal order, it is possible to improve project efficiency.

[0039] The task priority setting unit can refer to past project data and apply success patterns of similar tasks when setting task priorities. For example, the generation AI analyzes past project data and extracts success patterns of similar tasks. For example, it uses the task priorities of successful projects as reference. The task priority setting unit also suggests success patterns that can be applied to the current project based on past project data. For example, it sets the priority of a specific task based on past success cases. The generation AI also dynamically adjusts task priorities by referring to past project data in real time. For example, if a delay occurs, it suggests countermeasures based on past success cases. In this way, setting task priorities based on past success cases can improve project efficiency.

[0040] When setting task priorities, the task priority setting unit can provide a dashboard that visually displays the importance and urgency of tasks. For example, the generation AI in the task priority setting unit analyzes the importance and urgency of tasks and provides a dashboard that visually displays them. For example, the importance and urgency are displayed using different colors. The task priority setting unit also uses the dashboard to build a system that allows users to visually grasp task priorities. For example, the task priority setting unit sorts tasks according to their importance and urgency. The generation AI in the task priority setting unit also analyzes the importance and urgency of tasks in real time and reflects the results in the dashboard. For example, when a new task is added, the dashboard is automatically updated. This visual display of task importance and urgency makes it easier to grasp the progress of a project at a glance.

[0041] The task priority setting unit can collect feedback from project members using voice input when setting task priorities and make adjustments in real time. The task priority setting unit, for example, builds a system in which a generation AI uses voice input to collect feedback from project members in real time. For example, the task priority setting unit analyzes comments made during meetings using voice recognition technology to collect feedback. The task priority setting unit also adjusts task priorities in real time based on the feedback collected using voice input. For example, it reassigns tasks to reflect the opinions of the members. The task priority setting unit also analyzes feedback from project members in real time using voice input and dynamically adjusts task priorities. For example, if a delay occurs, it proposes countermeasures based on the opinions of the members. In this way, project efficiency can be improved by collecting feedback from project members in real time and making appropriate adjustments.

[0042] When performing risk management, the risk management department can analyze past project data and apply risk patterns from similar projects. In the risk management department, for example, the generation AI analyzes past project data and extracts risk patterns from similar projects. For example, it refers to risk management methods from successful projects. In addition, the risk management department uses the generation AI to propose risk patterns that can be applied to the current project based on past project data. For example, it sets countermeasures for specific risks based on past success stories. In addition, the risk management department uses the generation AI to refer to past project data in real time and dynamically adjust risk management. For example, if a delay occurs, it proposes countermeasures based on past success stories. In this way, the accuracy of risk management can be improved by applying risk patterns based on past project data.

[0043] When managing risks, the risk management department can perform a detailed analysis of the probability of risk occurrence and its impact, and propose optimal countermeasures. For example, the generation AI in the risk management department analyzes the probability of risk occurrence and its impact in detail, and proposes optimal countermeasures. For example, additional resources are allocated to tasks with a high probability of risk occurrence. The risk management department also optimizes risk management for the entire project using the generation AI based on the probability of risk occurrence and its impact. For example, it prioritizes countermeasures for risks with high impact. The risk management department also dynamically adjusts risk management using the generation AI, analyzing the probability of risk occurrence and its impact in real time. For example, it proposes optimal countermeasures when a new risk occurs. In this way, the accuracy of risk management can be improved by performing a detailed analysis of the probability of risk occurrence and its impact, and proposing optimal countermeasures.

[0044] When performing risk management, the risk management department can refer to external risk databases and incorporate the latest risk information. For example, the risk management department may build a system in which the generation AI refers to external risk databases and incorporates the latest risk information. For example, the risk management department may periodically update the industry risk database. The risk management department may also have the generation AI optimize project risk management based on the external risk database. For example, it may take measures based on the latest risk information. The risk management department may also have the generation AI refer to external risk databases in real time and dynamically adjust risk management. For example, when new risk information is added, it may propose the optimal measures. In this way, the accuracy of risk management can be improved by referring to external risk databases and incorporating the latest risk information.

[0045] When performing risk management, the risk management department can provide a dashboard that visually displays the risk occurrence status. For example, the risk management department provides a dashboard that uses generation AI to visually display the risk occurrence status. For example, the risk occurrence probability and impact are displayed color-coded. The risk management department also builds a system that uses the dashboard to visually grasp the risk occurrence status. For example, the dashboard is automatically updated according to the risk occurrence status. The risk management department also has generation AI analyze the risk occurrence status in real time and reflect this in the dashboard. For example, if a new risk occurs, the dashboard is automatically updated. This makes it easier to manage project risks by visually displaying the risk occurrence status.

[0046] When making a progress report, the progress reporting unit can refer to past project data and apply progress patterns from similar projects. In the progress reporting unit, for example, the generation AI analyzes past project data and extracts progress patterns from similar projects. For example, it refers to the progress reporting methods of successful projects. In addition, the generation AI in the progress reporting unit proposes progress patterns that can be applied to the current project based on past project data. For example, it sets the progress reporting method for a specific task based on past success stories. In addition, the generation AI in the progress reporting unit refers to past project data in real time and dynamically adjusts the progress report. For example, if a delay occurs, it proposes countermeasures based on past success stories. In this way, the accuracy of progress reports can be improved by applying progress patterns based on past project data.

[0047] When making a progress report, the progress reporting unit can analyze the report contents in detail and emphasize important points. For example, the progress reporting unit constructs a system in which a generation AI analyzes the contents of a progress report in detail and emphasizes important points. For example, the progress status and the occurrence of risks are highlighted. Furthermore, the progress reporting unit automatically generates a report that emphasizes important points based on the contents of the progress report using the generation AI. For example, it highlights the progress status of important tasks and risk countermeasures. Furthermore, the progress reporting unit analyzes the contents of the progress report in real time using the generation AI and emphasizes important points. For example, if a new risk occurs, it is highlighted in the report. In this way, the accuracy of the progress report can be improved by analyzing the report contents in detail and emphasizing important points.

[0048] The progress reporting unit can generate visual data when making progress reports, allowing progress to be visually grasped. In the progress reporting unit, for example, the generation AI analyzes the progress of the project and automatically generates progress graphs and Gantt charts. For example, the progress and completion status of each task are visually displayed. The progress reporting unit also uses the visual data to build a system that monitors the progress of the project in real time. For example, the progress graph is displayed on a dashboard so that stakeholders can understand the situation at a glance. In addition, the generation AI in the progress reporting unit analyzes the progress and proposes project adjustments based on the visual data. For example, tasks that have been delayed are highlighted and countermeasures are proposed. This makes it easier to manage project progress by visually grasping the progress using visual data.

[0049] When making progress reports, the progress reporting unit can collect feedback from project members using voice input and make adjustments in real time. The progress reporting unit, for example, builds a system in which a generation AI uses voice input to collect feedback from project members in real time. For example, the progress reporting unit analyzes comments made during meetings using voice recognition technology and collects feedback. The progress reporting unit also adjusts the progress report in real time based on the feedback collected using voice input. For example, it reallocates tasks to reflect the opinions of the members. The progress reporting unit also analyzes feedback from project members in real time using voice input and dynamically adjusts the progress report. For example, if a delay occurs, it proposes countermeasures based on the opinions of the members. In this way, the accuracy of progress reports can be improved by collecting feedback from project members in real time and making appropriate adjustments.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The project management system can also refer to external market data when analyzing project progress to evaluate the project's market fit. For example, in the progress analysis section, the generative AI analyzes market data and compares the project's progress with market trends. The progress analysis section then makes suggestions to adjust the project's progress based on the market data. For example, it could focus resources on areas with increasing market demand. The progress analysis section also refers to market data in real time and dynamically evaluates market fit according to the project's progress. For example, it could adjust the project's direction if market trends change. In this way, linking project progress with market data can improve the project's success rate.

[0052] The project management system can also monitor the health status of project members and suggest task reassignment based on health data. For example, in the progress analysis section, the generation AI collects members' health data and analyzes their fatigue levels and health risks. The progress analysis section also suggests assigning less burdensome tasks to members with high health risks based on the health data. For example, it suggests rest for members who have been working long hours. The progress analysis section also analyzes health data in real time and dynamically reassigns tasks. For example, if a member's health condition worsens, it reassigns tasks to another member. This allows for optimal task reassignment based on the project member's health status, improving project efficiency and maintaining the health of members.

[0053] The project management system can also refer to environmental data when analyzing the project's progress and evaluate the project's environmental impact. For example, in the progress analysis section, the generation AI analyzes the environmental data and compares the project's progress with its environmental impact. The progress analysis section then makes suggestions to adjust the project's progress based on the environmental data. For example, it could reduce tasks that have a high environmental impact. The progress analysis section also refers to the environmental data in real time and dynamically evaluates the environmental impact according to the project's progress. For example, it could adjust the project's direction if environmental regulations change. In this way, linking the project's progress with environmental data can improve the project's sustainability.

[0054] The project management system can further identify project risk factors and propose risk management measures when analyzing the project progress. For example, in the progress analysis section, the generative AI analyzes the project progress and identifies risk factors. The progress analysis section then proposes risk management measures based on the risk factors. For example, additional resources may be allocated to high-risk tasks. The progress analysis section also analyzes risk factors in real time and dynamically adjusts risk management measures. For example, if a new risk emerges, it may quickly propose a measure. In this way, linking project progress with risk management can improve the stability and success rate of projects.

[0055] The project management system can also manage project costs when analyzing project progress. For example, in the progress analysis section, the generative AI analyzes the project progress and identifies excessive cost consumption. The progress analysis section also makes cost reduction proposals based on cost data. For example, it proposes alternatives for high-cost tasks. The progress analysis section also analyzes cost data in real time and dynamically adjusts cost management measures. For example, it quickly proposes measures if a budget is exceeded. In this way, by linking project progress with cost management, the economic efficiency of the project can be improved.

[0056] The project management system can also perform project quality management when analyzing the project's progress. For example, in the progress analysis section, the generative AI analyzes the project's progress and identifies any declines in quality. The progress analysis section also makes suggestions for improving quality based on the quality data. For example, it proposes improvement measures for tasks where quality is declining. The progress analysis section also analyzes the quality data in real time and dynamically adjusts quality control measures. For example, it quickly proposes measures if a quality problem occurs. In this way, project quality can be improved by linking the project's progress with quality management.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The progress analysis unit uses generative AI to analyze the progress of the project. For example, it analyzes the progress and completion status of each task, and whether there are any delays. It can also analyze the productivity data of project members and suggest task reassignment based on the performance of each member. It can also refer to past project data and apply successful patterns from similar projects. Step 2: The resource allocation unit optimally allocates resources based on the data analyzed by the progress analysis unit. For example, it analyzes the human resources, time, and budget required for a project and allocates them optimally. It can also analyze each member's skill set and experience in detail to propose optimal personnel allocation. It can also introduce algorithms that dynamically adjust resource allocation according to the project's progress. Step 3: The task priority setting unit sets task priorities based on the resources allocated by the resource allocation unit. For example, it analyzes the importance and urgency of tasks and sets priorities. It can also analyze task dependencies in detail and propose the optimal order. It can also refer to past project data and apply successful patterns of similar tasks.

[0059] (Example 2) A project management system according to an embodiment of the present invention uses generative AI to analyze project progress, resource allocation, and task priorities, and makes optimal adjustments. This allows the project management system to improve project efficiency and success rates.

[0060] A project management system according to an embodiment includes a progress analysis unit, a resource allocation unit, and a task priority setting unit. The progress analysis unit analyzes the progress of a project using a generative AI. For example, the progress analysis unit analyzes the progress, completion status, and presence or absence of delays of each task. The progress analysis unit can also analyze the productivity data of project members and propose task reallocation based on the performance of each member. Furthermore, the progress analysis unit can refer to past project data and apply successful patterns of similar projects. The resource allocation unit optimally allocates resources based on the data analyzed by the progress analysis unit. For example, the resource allocation unit analyzes the human resources, time, and budget required for a project and optimally allocates them. The resource allocation unit can also analyze the skill sets and experience of each member in detail and propose optimal personnel allocation. Furthermore, the resource allocation unit can incorporate an algorithm that dynamically adjusts resource allocation according to the progress of the project. The task priority setting unit sets task priorities based on the resources allocated by the resource allocation unit. For example, the task priority setting unit analyzes the importance and urgency of tasks and sets priorities. The task priority setting unit can also perform a detailed analysis of task dependencies and propose an optimal order. Furthermore, the task priority setting unit can refer to past project data and apply success patterns of similar tasks. This allows the project management system according to the embodiment to improve project efficiency and success rates. For example, delays can be prevented by understanding the progress of a project in real time and making appropriate adjustments. Furthermore, waste can be reduced by optimally allocating resources. Furthermore, by appropriately setting task priorities, important tasks can be completed reliably.

[0061] The progress analysis unit analyzes the productivity data of project members and can propose task reallocation based on the performance of each member. In the progress analysis unit, for example, the generation AI collects productivity data of project members and analyzes each member's work efficiency and task completion speed. For example, optimal task allocation is performed based on past task completion times and quality evaluations. In addition, the generation AI in the progress analysis unit proposes task reallocation based on the members' productivity data. For example, tasks in areas in which a particular member excels are prioritized. In addition, the generation AI in the progress analysis unit analyzes members' productivity data in real time and dynamically reallocates tasks according to the task progress. For example, if a delay occurs, the task is reallocated to another member. This makes it possible to improve project efficiency by optimally reallocating tasks based on the productivity of project members.

[0062] When analyzing the progress of a project, the progress analysis unit can refer to past project data and apply success patterns from similar projects. In this case, for example, the generation AI analyzes past project data and extracts success patterns from similar projects. For example, it refers to the progress schedules and resource allocation of successful projects. In addition, the generation AI in the progress analysis unit proposes success patterns that can be applied to the current project based on past project data. For example, it proposes methods for prioritizing specific tasks and managing risks. In addition, the generation AI in the progress analysis unit refers to past project data in real time and dynamically applies success patterns according to the progress. For example, if a delay occurs, it proposes countermeasures based on past success cases. This makes it possible to improve the project's success rate by optimizing the current project based on past success cases.

[0063] The progress analysis unit uses the emotion estimation function to analyze the emotional states of project members, detect stress or a decline in motivation, and make appropriate adjustments. The progress analysis unit, for example, uses the emotion estimation function to analyze the emotional states of project members in real time. For example, it analyzes facial expressions and vocal tones to detect stress or a decline in motivation. The progress analysis unit also proposes task reassignment according to the emotional states of project members based on the emotion estimation data. For example, it assigns less burdensome tasks to members who are highly stressed. The progress analysis unit also uses the emotion estimation function to monitor the emotional states of project members and makes adjustments to prevent stress and a decline in motivation. For example, it suggests regular breaks and refreshment times. In this way, by making appropriate adjustments taking into account the emotional states of project members, it is possible to improve project efficiency and member satisfaction.

[0064] The progress analysis unit can generate visual data when analyzing the progress of a project, allowing the progress to be visually grasped. In the progress analysis unit, for example, the generation AI analyzes the progress of the project and automatically generates progress graphs and Gantt charts. For example, the progress and completion status of each task are visually displayed. The progress analysis unit also uses the visual data to build a system that monitors the progress of the project in real time. For example, the progress graph is displayed on a dashboard so that stakeholders can understand the situation at a glance. In addition, the generation AI analyzes the progress and proposes project adjustments based on the visual data. For example, tasks that have experienced delays are highlighted and countermeasures are proposed. In this way, the project progress can be visually grasped using visual data, facilitating smooth communication between stakeholders.

[0065] When analyzing the progress of a project, the progress analysis unit can collect feedback from project members using voice input and make adjustments in real time. The progress analysis unit, for example, builds a system in which a generation AI uses voice input to collect feedback from project members in real time. For example, it analyzes comments made during meetings using voice recognition technology and collects feedback. The progress analysis unit also uses the generation AI to analyze the progress of the project based on the feedback collected using voice input and proposes necessary adjustments. For example, it reallocates tasks to reflect the opinions of the members. The progress analysis unit also uses the generation AI to analyze feedback from project members in real time using voice input and makes adjustments according to the progress. For example, if a delay occurs, it proposes countermeasures based on the opinions of the members. In this way, it is possible to collect feedback from project members in real time and make appropriate adjustments, thereby improving the efficiency of the project.

[0066] The progress analysis unit can use the emotion estimation function to monitor the emotional states of project members in real time and make suggestions to elicit positive emotions. The progress analysis unit, for example, uses the emotion estimation function to build a system that monitors the emotional states of project members in real time. For example, it analyzes facial expressions and vocal tone to calculate an emotion score. The progress analysis unit also makes positive suggestions based on the emotional states of project members based on the emotion estimation data. For example, it provides encouraging messages to increase motivation. The progress analysis unit also uses the emotion estimation function to monitor the emotional states of project members and makes adjustments to elicit positive emotions. For example, it suggests regular team building activities. In this way, by monitoring the emotional states of project members in real time and providing appropriate feedback, it is possible to improve project efficiency and member satisfaction.

[0067] When optimally allocating resources, the resource allocation unit can analyze each member's skill set and experience in detail and propose optimal personnel allocation. For example, the generation AI in the resource allocation unit analyzes each member's skill set and experience in detail and proposes optimal personnel allocation. For example, it assigns members with specific skills to appropriate tasks. The resource allocation unit also optimizes resource allocation for the entire project using the generation AI based on the members' skill sets and experience. For example, it assigns experienced members to important tasks. The resource allocation unit also analyzes members' skill sets and experience in real time and dynamically adjusts resource allocation. For example, when a new task is added, it assigns the most suitable member. In this way, by analyzing members' skill sets and experience in detail and making optimal personnel allocation, it is possible to improve project efficiency.

[0068] When optimally allocating resources, the resource allocation unit can introduce an algorithm that dynamically adjusts resource allocation according to the progress of the project. For example, the resource allocation unit introduces an algorithm in which the generation AI analyzes the progress of the project in real time and dynamically adjusts resource allocation. For example, additional resources are allocated to tasks that have been delayed. The resource allocation unit also builds a system in which the generation AI dynamically adjusts resource allocation according to the progress of the project. For example, resources are reallocated according to the progress of tasks. The resource allocation unit also develops an algorithm in which the generation AI dynamically adjusts resource allocation based on the progress of the project. For example, resources are preferentially allocated to tasks with high importance. In this way, the efficiency of the project can be improved by dynamically adjusting resource allocation according to the progress of the project.

[0069] The resource allocation unit can use the emotion estimation function to consider the emotional state of project members and propose resource allocation that reduces stress. The resource allocation unit, for example, uses the emotion estimation function to analyze the emotional state of project members in real time and propose resource allocation that reduces stress. For example, it assigns less burdensome tasks to members who are highly stressed. The resource allocation unit also builds a system that allocates resources according to the emotional state of project members based on the emotion estimation data. For example, it assigns important tasks to members who are highly motivated. The resource allocation unit also uses the emotion estimation function to monitor the emotional state of project members and allocate resources that reduces stress. For example, it suggests regular breaks and refreshment times. In this way, by allocating resources that take the emotional state of project members into consideration, it is possible to reduce member stress and improve project efficiency.

[0070] The resource allocation unit can utilize a resource pool that includes external freelancers or consultants when optimally allocating resources. For example, the generation AI in the resource allocation unit utilizes a resource pool that includes external freelancers and consultants to propose optimal resource allocation. For example, it adds external resources with specific skills to a project. In addition, the generation AI in the resource allocation unit analyzes a database of external resources to identify freelancers or consultants with the skills required for a project. For example, it assigns external resources with specialized knowledge to important tasks. In addition, the generation AI in the resource allocation unit utilizes external resources and dynamically adjusts resource allocation according to the progress of the project. For example, if a delay occurs, it can respond by adding external resources. In this way, by utilizing external resources, it is possible to optimize resource allocation for a project and improve efficiency.

[0071] When optimally allocating resources, the resource allocation unit can analyze resource usage history and propose optimal allocation based on past success stories. For example, the resource allocation unit uses the generation AI to analyze resource usage history and propose optimal resource allocation based on past success stories. For example, it refers to the resource allocation patterns of successful projects. The resource allocation unit also uses the generation AI to propose optimal resource allocation that can be applied to the current project based on the resource usage history. For example, it applies resource allocation that has been successful in the past to a specific task. The resource allocation unit also analyzes the resource usage history in real time and proposes optimal resource allocation according to the progress. For example, if a delay occurs, it proposes countermeasures based on past success stories. In this way, project efficiency can be improved by optimizing resource allocation based on past success stories.

[0072] The resource allocation unit can use the emotion estimation function to monitor the emotional states of project members in real time and allocate resources to elicit positive emotions. The resource allocation unit, for example, uses the emotion estimation function to build a system that monitors the emotional states of project members in real time. For example, it analyzes facial expressions and vocal tones to calculate an emotion score. The resource allocation unit also allocates resources to elicit positive emotions according to the emotional states of project members based on the emotion estimation data. For example, it assigns important tasks to highly motivated members. The resource allocation unit also uses the emotion estimation function to monitor the emotional states of project members and makes adjustments to elicit positive emotions. For example, it suggests regular team building activities. In this way, by monitoring the emotional states of project members in real time and allocating resources to elicit positive emotions, it is possible to improve project efficiency and member satisfaction.

[0073] When setting task priorities, the task priority setting unit can analyze task dependencies in detail and propose the optimal order. For example, the generation AI in the task priority setting unit analyzes task dependencies in detail and proposes the optimal order. For example, it may prioritize tasks with strong dependencies. The task priority setting unit also optimizes the priority of tasks for the entire project based on task dependencies. For example, it may process dependent tasks consecutively. The task priority setting unit also analyzes task dependencies in real time and dynamically adjusts task priorities. For example, when a new task is added, it may reset the priority taking the dependencies into consideration. In this way, by analyzing task dependencies in detail and proposing the optimal order, it is possible to improve project efficiency.

[0074] The task priority setting unit can refer to past project data and apply success patterns of similar tasks when setting task priorities. For example, the generation AI analyzes past project data and extracts success patterns of similar tasks. For example, it uses the task priorities of successful projects as reference. The task priority setting unit also suggests success patterns that can be applied to the current project based on past project data. For example, it sets the priority of a specific task based on past success cases. The generation AI also dynamically adjusts task priorities by referring to past project data in real time. For example, if a delay occurs, it suggests countermeasures based on past success cases. In this way, setting task priorities based on past success cases can improve project efficiency.

[0075] The task priority setting unit can analyze the emotional states of project members using the emotion estimation function and prioritize assigning tasks that motivate them highly. The task priority setting unit, for example, uses the emotion estimation function to analyze the emotional states of project members in real time and prioritize assigning tasks that motivate them highly. For example, important tasks are assigned to members with high emotion scores. The task priority setting unit also sets task priorities according to the emotional states of project members based on the emotion estimation data. For example, tasks are assigned preferentially to members with high motivation. The task priority setting unit also monitors the emotional states of project members using the emotion estimation function and prioritize assigning tasks that motivate them highly. For example, the task priority setting unit periodically checks the emotional states and adjusts task priorities. In this way, by assigning tasks taking into account the emotional states of project members, it is possible to increase the motivation of members and improve project efficiency.

[0076] When setting task priorities, the task priority setting unit can provide a dashboard that visually displays the importance and urgency of tasks. For example, the generation AI in the task priority setting unit analyzes the importance and urgency of tasks and provides a dashboard that visually displays them. For example, the importance and urgency are displayed using different colors. The task priority setting unit also uses the dashboard to build a system that allows users to visually grasp task priorities. For example, the task priority setting unit sorts tasks according to their importance and urgency. The generation AI in the task priority setting unit also analyzes the importance and urgency of tasks in real time and reflects the results in the dashboard. For example, when a new task is added, the dashboard is automatically updated. This visual display of task importance and urgency makes it easier to grasp the progress of a project at a glance.

[0077] The task priority setting unit can collect feedback from project members using voice input when setting task priorities and make adjustments in real time. The task priority setting unit, for example, builds a system in which a generation AI uses voice input to collect feedback from project members in real time. For example, the task priority setting unit analyzes comments made during meetings using voice recognition technology to collect feedback. The task priority setting unit also adjusts task priorities in real time based on the feedback collected using voice input. For example, it reassigns tasks to reflect the opinions of the members. The task priority setting unit also analyzes feedback from project members in real time using voice input and dynamically adjusts task priorities. For example, if a delay occurs, it proposes countermeasures based on the opinions of the members. In this way, project efficiency can be improved by collecting feedback from project members in real time and making appropriate adjustments.

[0078] The task priority setting unit can use the emotion estimation function to monitor the emotional states of project members in real time and assign tasks to elicit positive emotions. The task priority setting unit, for example, uses the emotion estimation function to build a system that monitors the emotional states of project members in real time. For example, it analyzes facial expressions and vocal tones to calculate an emotion score. The task priority setting unit also assigns tasks according to the emotional states of project members based on the emotion estimation data. For example, it assigns important tasks to highly motivated members. The task priority setting unit also uses the emotion estimation function to monitor the emotional states of project members and assigns tasks to elicit positive emotions. For example, it periodically checks their emotional states and adjusts task priorities. In this way, by monitoring the emotional states of project members in real time and assigning tasks to elicit positive emotions, it is possible to improve project efficiency and member satisfaction.

[0079] When performing risk management, the risk management department can analyze past project data and apply risk patterns from similar projects. In the risk management department, for example, the generation AI analyzes past project data and extracts risk patterns from similar projects. For example, it refers to risk management methods from successful projects. In addition, the risk management department uses the generation AI to propose risk patterns that can be applied to the current project based on past project data. For example, it sets countermeasures for specific risks based on past success stories. In addition, the risk management department uses the generation AI to refer to past project data in real time and dynamically adjust risk management. For example, if a delay occurs, it proposes countermeasures based on past success stories. In this way, the accuracy of risk management can be improved by applying risk patterns based on past project data.

[0080] When managing risks, the risk management department can perform a detailed analysis of the probability of risk occurrence and its impact, and propose optimal countermeasures. For example, the generation AI in the risk management department analyzes the probability of risk occurrence and its impact in detail, and proposes optimal countermeasures. For example, additional resources are allocated to tasks with a high probability of risk occurrence. The risk management department also optimizes risk management for the entire project using the generation AI based on the probability of risk occurrence and its impact. For example, it prioritizes countermeasures for risks with high impact. The risk management department also dynamically adjusts risk management using the generation AI, analyzing the probability of risk occurrence and its impact in real time. For example, it proposes optimal countermeasures when a new risk occurs. In this way, the accuracy of risk management can be improved by performing a detailed analysis of the probability of risk occurrence and its impact, and proposing optimal countermeasures.

[0081] The risk management department can use the emotion estimation function to analyze the emotional states of project members and propose risk management measures to reduce stress and anxiety. For example, the risk management department can use the emotion estimation function to analyze the emotional states of project members in real time and propose risk management measures to reduce stress and anxiety. For example, it can assign less burdensome tasks to members with high stress levels. The risk management department also builds a system that implements risk management measures according to the emotional states of project members based on the emotion estimation data. For example, it can assign important tasks to highly motivated members. The risk management department also uses the emotion estimation function to monitor the emotional states of project members and make adjustments to reduce stress and anxiety. For example, it can propose regular team building activities. In this way, the stability of the project can be improved by analyzing the emotional states of project members and proposing risk management measures to reduce stress and anxiety.

[0082] When performing risk management, the risk management department can refer to external risk databases and incorporate the latest risk information. For example, the risk management department may build a system in which the generation AI refers to external risk databases and incorporates the latest risk information. For example, the risk management department may periodically update the industry risk database. The risk management department may also have the generation AI optimize project risk management based on the external risk database. For example, it may take measures based on the latest risk information. The risk management department may also have the generation AI refer to external risk databases in real time and dynamically adjust risk management. For example, when new risk information is added, it may propose the optimal measures. In this way, the accuracy of risk management can be improved by referring to external risk databases and incorporating the latest risk information.

[0083] When performing risk management, the risk management department can provide a dashboard that visually displays the risk occurrence status. For example, the risk management department provides a dashboard that uses generation AI to visually display the risk occurrence status. For example, the risk occurrence probability and impact are displayed color-coded. The risk management department also builds a system that uses the dashboard to visually grasp the risk occurrence status. For example, the dashboard is automatically updated according to the risk occurrence status. The risk management department also has generation AI analyze the risk occurrence status in real time and reflect this in the dashboard. For example, if a new risk occurs, the dashboard is automatically updated. This makes it easier to manage project risks by visually displaying the risk occurrence status.

[0084] The risk management department can use the emotion estimation function to monitor the emotional states of project members in real time and take risk management measures to elicit positive emotions. The risk management department, for example, builds a system that uses the emotion estimation function to monitor the emotional states of project members in real time. For example, it analyzes facial expressions and voice tones to calculate an emotion score. The risk management department also takes risk management measures according to the emotional states of project members based on the emotion estimation data. For example, it assigns less burdensome tasks to members who are highly stressed. The risk management department also uses the emotion estimation function to monitor the emotional states of project members and take risk management measures to elicit positive emotions. For example, it suggests regular team building activities. In this way, the stability of the project can be improved by monitoring the emotional states of project members in real time and taking risk management measures to elicit positive emotions.

[0085] When making a progress report, the progress reporting unit can refer to past project data and apply progress patterns from similar projects. In the progress reporting unit, for example, the generation AI analyzes past project data and extracts progress patterns from similar projects. For example, it refers to the progress reporting methods of successful projects. In addition, the generation AI in the progress reporting unit proposes progress patterns that can be applied to the current project based on past project data. For example, it sets the progress reporting method for a specific task based on past success stories. In addition, the generation AI in the progress reporting unit refers to past project data in real time and dynamically adjusts the progress report. For example, if a delay occurs, it proposes countermeasures based on past success stories. In this way, the accuracy of progress reports can be improved by applying progress patterns based on past project data.

[0086] When making a progress report, the progress reporting unit can analyze the report contents in detail and emphasize important points. For example, the progress reporting unit constructs a system in which a generation AI analyzes the contents of a progress report in detail and emphasizes important points. For example, the progress status and the occurrence of risks are highlighted. Furthermore, the progress reporting unit automatically generates a report that emphasizes important points based on the contents of the progress report using the generation AI. For example, it highlights the progress status of important tasks and risk countermeasures. Furthermore, the progress reporting unit analyzes the contents of the progress report in real time using the generation AI and emphasizes important points. For example, if a new risk occurs, it is highlighted in the report. In this way, the accuracy of the progress report can be improved by analyzing the report contents in detail and emphasizing important points.

[0087] The progress reporting unit can use the emotion estimation function to analyze the emotional state of project members and provide positive feedback. The progress reporting unit, for example, uses the emotion estimation function to analyze the emotional state of project members in real time and build a system to provide positive feedback. For example, it analyzes facial expressions and voice tones and calculates an emotion score. The progress reporting unit also provides positive feedback according to the emotional state of project members based on the emotion estimation data. For example, it provides encouraging messages to increase motivation. The progress reporting unit also uses the emotion estimation function to monitor the emotional state of project members and provide positive feedback. For example, it periodically checks their emotional state and adjusts the feedback. In this way, the project members' emotional states can be analyzed and positive feedback provided to increase their motivation.

[0088] The progress reporting unit can generate visual data when making progress reports, allowing progress to be visually grasped. In the progress reporting unit, for example, the generation AI analyzes the progress of the project and automatically generates progress graphs and Gantt charts. For example, the progress and completion status of each task are visually displayed. The progress reporting unit also uses the visual data to build a system that monitors the progress of the project in real time. For example, the progress graph is displayed on a dashboard so that stakeholders can understand the situation at a glance. In addition, the generation AI in the progress reporting unit analyzes the progress and proposes project adjustments based on the visual data. For example, tasks that have been delayed are highlighted and countermeasures are proposed. This makes it easier to manage project progress by visually grasping the progress using visual data.

[0089] When making progress reports, the progress reporting unit can collect feedback from project members using voice input and make adjustments in real time. The progress reporting unit, for example, builds a system in which a generation AI uses voice input to collect feedback from project members in real time. For example, the progress reporting unit analyzes comments made during meetings using voice recognition technology and collects feedback. The progress reporting unit also adjusts the progress report in real time based on the feedback collected using voice input. For example, it reallocates tasks to reflect the opinions of the members. The progress reporting unit also analyzes feedback from project members in real time using voice input and dynamically adjusts the progress report. For example, if a delay occurs, it proposes countermeasures based on the opinions of the members. In this way, the accuracy of progress reports can be improved by collecting feedback from project members in real time and making appropriate adjustments.

[0090] The progress reporting unit can use the emotion estimation function to monitor the emotional states of project members in real time and provide feedback to elicit positive emotions. The progress reporting unit, for example, uses the emotion estimation function to build a system that monitors the emotional states of project members in real time. For example, it analyzes facial expressions and vocal tones to calculate an emotion score. The progress reporting unit also provides positive feedback according to the emotional states of project members based on the emotion estimation data. For example, it provides encouraging messages to increase motivation. The progress reporting unit also uses the emotion estimation function to monitor the emotional states of project members and provide positive feedback. For example, it periodically checks the emotional states and adjusts the feedback. In this way, it is possible to monitor the emotional states of project members in real time and provide feedback to elicit positive emotions, thereby increasing the motivation of the members.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The project management system can also refer to external market data when analyzing project progress to evaluate the project's market fit. For example, in the progress analysis section, the generative AI analyzes market data and compares the project's progress with market trends. The progress analysis section then makes suggestions to adjust the project's progress based on the market data. For example, it could focus resources on areas with increasing market demand. The progress analysis section also refers to market data in real time and dynamically evaluates market fit according to the project's progress. For example, it could adjust the project's direction if market trends change. In this way, linking project progress with market data can improve the project's success rate.

[0093] The project management system can also monitor the health status of project members and suggest task reassignment based on health data. For example, in the progress analysis section, the generation AI collects members' health data and analyzes their fatigue levels and health risks. The progress analysis section also suggests assigning less burdensome tasks to members with high health risks based on the health data. For example, it suggests rest for members who have been working long hours. The progress analysis section also analyzes health data in real time and dynamically reassigns tasks. For example, if a member's health condition worsens, it reassigns tasks to another member. This allows for optimal task reassignment based on the project member's health status, improving project efficiency and maintaining the health of members.

[0094] The project management system can also refer to environmental data when analyzing the project's progress and evaluate the project's environmental impact. For example, in the progress analysis section, the generation AI analyzes the environmental data and compares the project's progress with its environmental impact. The progress analysis section then makes suggestions to adjust the project's progress based on the environmental data. For example, it could reduce tasks that have a high environmental impact. The progress analysis section also refers to the environmental data in real time and dynamically evaluates the environmental impact according to the project's progress. For example, it could adjust the project's direction if environmental regulations change. In this way, linking the project's progress with environmental data can improve the project's sustainability.

[0095] The project management system can further analyze the emotional states of project members and suggest team building activities based on the emotional data. For example, the progress analysis unit uses an emotion estimation function to analyze the emotional states of members in real time and suggest activities to boost team morale. The progress analysis unit also suggests events to promote communication between members based on the emotional data. For example, it may hold regular team building workshops. The progress analysis unit also analyzes the emotional data in real time and dynamically adjusts activities according to the team's emotional state. For example, it may suggest refreshing activities when stress is rising. In this way, team building activities can be carried out taking into account the emotional states of project members, thereby improving team morale and cooperation.

[0096] The project management system can further identify project risk factors and propose risk management measures when analyzing the project progress. For example, in the progress analysis section, the generative AI analyzes the project progress and identifies risk factors. The progress analysis section then proposes risk management measures based on the risk factors. For example, additional resources may be allocated to high-risk tasks. The progress analysis section also analyzes risk factors in real time and dynamically adjusts risk management measures. For example, if a new risk emerges, it may quickly propose a measure. In this way, linking project progress with risk management can improve the stability and success rate of projects.

[0097] The project management system can further analyze the emotional states of project members and make suggestions to support their career development based on the emotional data. For example, the progress analysis unit uses an emotion estimation function to analyze the emotional states of members in real time and suggest training programs for career development. The progress analysis unit also provides resources to support members' skill improvement based on the emotional data. For example, it suggests specialized training for highly motivated members. The progress analysis unit also analyzes the emotional data in real time and dynamically adjusts support for career development. For example, it changes the content of a training program depending on the member's emotional state. In this way, supporting career development by taking into account the emotional states of project members can promote member growth and project success.

[0098] The project management system can also manage project costs when analyzing project progress. For example, in the progress analysis section, the generative AI analyzes the project progress and identifies excessive cost consumption. The progress analysis section also makes cost reduction proposals based on cost data. For example, it proposes alternatives for high-cost tasks. The progress analysis section also analyzes cost data in real time and dynamically adjusts cost management measures. For example, it quickly proposes measures if a budget is exceeded. In this way, by linking project progress with cost management, the economic efficiency of the project can be improved.

[0099] The project management system can further analyze the emotional states of project members and make suggestions to improve their work-life balance based on the emotional data. For example, the progress analysis unit uses an emotion estimation function to analyze the emotional states of members in real time and make suggestions to improve their work-life balance. The progress analysis unit also provides resources to reduce members' stress based on the emotional data. For example, it suggests refreshment time for members who are highly stressed. The progress analysis unit also analyzes the emotional data in real time and dynamically adjusts support for improving work-life balance. For example, it suggests adjusting working hours depending on the members' emotional state. In this way, by improving the work-life balance by taking into account the emotional states of project members, it is possible to improve member satisfaction and project efficiency.

[0100] The project management system can also perform project quality management when analyzing the project's progress. For example, in the progress analysis section, the generative AI analyzes the project's progress and identifies any declines in quality. The progress analysis section also makes suggestions for improving quality based on the quality data. For example, it proposes improvement measures for tasks where quality is declining. The progress analysis section also analyzes the quality data in real time and dynamically adjusts quality control measures. For example, it quickly proposes measures if a quality problem occurs. In this way, project quality can be improved by linking the project's progress with quality management.

[0101] The project management system can further analyze the emotional states of project members and make suggestions to optimize their performance based on the emotional data. For example, the progress analysis unit uses an emotion estimation function to analyze the emotional states of members in real time and make suggestions to optimize performance. The progress analysis unit also provides resources to improve members' performance based on the emotional data. For example, it provides an encouraging message to a member whose motivation is declining. The progress analysis unit also analyzes the emotional data in real time and dynamically adjusts support for optimizing performance. For example, it changes task allocation depending on the emotional state of the member. In this way, the efficiency and success rate of the project can be improved by optimizing performance by taking into account the emotional states of project members.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The progress analysis unit uses generative AI to analyze the progress of the project. For example, it analyzes the progress and completion status of each task, and whether there are any delays. It can also analyze the productivity data of project members and suggest task reassignment based on the performance of each member. It can also refer to past project data and apply successful patterns from similar projects. Step 2: The resource allocation unit optimally allocates resources based on the data analyzed by the progress analysis unit. For example, it analyzes the human resources, time, and budget required for a project and allocates them optimally. It can also analyze each member's skill set and experience in detail to propose optimal personnel allocation. It can also introduce algorithms that dynamically adjust resource allocation according to the project's progress. Step 3: The task priority setting unit sets task priorities based on the resources allocated by the resource allocation unit. For example, it analyzes the importance and urgency of tasks and sets priorities. It can also analyze task dependencies in detail and propose the optimal order. It can also refer to past project data and apply successful patterns of similar tasks.

[0104] 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.

[0105] 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.

[0106] 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.

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

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0120] 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 AI 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.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0130] 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.

[0131] 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.

[0132] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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 AI 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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 AI 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] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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, in order to avoid confusion and to 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.

[0170] 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. [Explanation of symbols]

[0171] 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 progress analysis unit that uses generative AI to analyze the progress of the project; a resource allocation unit that performs optimal resource allocation based on the data analyzed by the progress analysis unit; a task priority setting unit that sets the priority of tasks based on the resources allocated by the resource allocation unit. A system characterized by:

2. The progress status analysis unit Analyzes project member productivity data and suggests task reassignment based on individual member performance 2. The system of claim 1.

3. The progress status analysis unit When analyzing the progress of the project, refer to past project data and apply the success patterns of similar projects.

2. The system of claim 1.

4. The progress status analysis unit Analyze the emotional state of project members, detect stress or a decline in motivation, and make appropriate adjustments 2. The system of claim 1.

5. The progress status analysis unit When analyzing the progress of the project, visual data is generated to enable the progress to be visually grasped.

2. The system of claim 1.

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