system

Abstract

The system according to this embodiment aims to enable even those without project management experience to perform appropriate tasks and personnel allocations, thereby smoothly advancing projects. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a monitoring unit. The reception unit receives basic information. The generation unit learns from past PM documents based on the information received by the reception unit and proposes necessary tasks or personnel assignments. The monitoring unit monitors the progress of the tasks proposed by the generation unit, detects delays or issues, and provides guidance.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, when a person without project management experience promotes a project, it is difficult to perform appropriate tasks and personnel arrangements, and there is a risk of delays and problems in the progress of the project.

[0005] The system according to the embodiment aims to enable a person without project management experience to perform appropriate tasks and personnel arrangements and smoothly promote the project.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a monitoring unit. The reception unit receives basic information. The generation unit learns from past PM documents based on the information entered by the reception unit and proposes necessary tasks or personnel assignments. The monitoring unit monitors the progress of the tasks proposed by the generation unit, detects delays or issues, and provides guidance. [Effects of the Invention]

[0007] The system according to this embodiment allows even those without project management experience to assign appropriate tasks and personnel, and to smoothly advance a project. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. [[ID=​​​​​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The project management assistant system according to an embodiment of the present invention is a system that enables users without project management experience to advance a project. This system learns from past project manager (PM) documents by inputting basic information such as service categories and estimated man-hours, and then presents necessary tasks (WBS generation) and required personnel (number of people, roles). Furthermore, it supports the start of the project and provides timely guidance on issues and task delays during project advancement. For example, if a specific task is delayed, it analyzes the cause and presents solutions. It also suggests reallocating or adding necessary resources according to the project's progress. This system enables users without project management experience to efficiently advance a project. For example, even new employees or those changing careers from other industries can successfully lead a project by following the system's guidance. Thus, the project management assistant system enables users without project management experience to efficiently advance a project.

[0029] The project management assistant system according to this embodiment comprises a reception unit, a generation unit, and a monitoring unit. The reception unit receives basic information from the user, such as the service category and estimated man-hours. This basic information includes, but is not limited to, the project name, start date, end date, and budget. Based on the information entered by the reception unit, the generation unit learns from past PM documents and proposes necessary tasks and personnel allocation. For example, the generation unit uses a generation AI to learn from past project plans, progress reports, risk management plans, and other documents to generate a Work Breakdown Structure (WBS). The generation unit can also propose the necessary personnel (number of people, roles). For example, the generation AI extracts tasks for each project phase and tasks of high importance, and generates a WBS based on them. Furthermore, the generation unit proposes the optimal personnel allocation, taking into account the number of people and skill sets for each role. The monitoring unit monitors the progress of the tasks proposed by the generation unit, detects delays and issues, and provides guidance. For example, the monitoring unit uses AI to compare the completion rate of tasks and planned versus actual results, and analyzes the causes of delays. Furthermore, the monitoring unit can also suggest solutions to delays and issues. For example, the monitoring unit can propose resource reallocation and task rescheduling. In addition, the monitoring unit can suggest necessary resource reallocation or addition depending on the project's progress. As a result, the project management assistant system according to this embodiment allows even users without project management experience to efficiently advance projects.

[0030] The reception section allows users to input basic information such as the service category and estimated effort. This basic information includes, but is not limited to, project name, start date, end date, and budget. Specifically, users can input detailed project information through a dedicated interface. This interface is designed to be intuitive and easy to use, allowing users to input the necessary information without confusion. For example, the project name input field displays guidelines for concisely describing the project's purpose and overview. The start and end dates can be easily selected using a calendar function, and the budget input field includes auxiliary functions for entering currency units and budget breakdowns. Furthermore, the reception section automatically validates the entered information and provides appropriate feedback to the user if there is missing information or inconsistencies. For example, if the start date is later than the end date, or if the budget is unreasonably low, a warning message is displayed to prompt correction. In this way, the reception section supports users in inputting accurate and complete information, providing a foundation for the subsequent generation and monitoring sections to function efficiently.

[0031] The generation unit learns from past PM documents based on information entered by the reception unit and proposes necessary tasks and personnel allocation. For example, the generation unit uses generation AI to learn from past project plans, progress reports, risk management plans, and other documents to generate a Work Breakdown Structure (WBS). Specifically, the generation AI uses natural language processing technology to extract important information from past documents and automatically generates tasks according to the characteristics of the project. For example, it extracts tasks for each project phase and high-priority tasks and generates a WBS based on them. The generation unit also proposes the optimal personnel allocation considering the number of people and skill sets for each role. The generation AI analyzes successful and unsuccessful cases from past projects and learns what kind of personnel allocation was effective in similar projects. This allows the generation unit to propose the optimal personnel allocation according to the characteristics and scale of the project. Furthermore, the generation unit can also identify project risk factors and propose a risk management plan based on the basic information entered by the user. For example, if the budget is limited, it will propose prioritizing tasks for cost reduction and suggest allocating additional resources to high-risk tasks. In this way, the generation unit supports users in efficiently advancing their projects from the planning stage to the execution stage.

[0032] The monitoring unit monitors the progress of tasks proposed by the generation unit, detects delays and issues, and provides guidance. For example, the monitoring unit uses AI to compare task completion rates and planned vs. actual completion times, and analyzes the causes of delays. Specifically, the monitoring unit integrates with project management tools and task management systems to collect task progress in real time. This automatically compares task completion rates and planned vs. actual completion times, and identifies the causes of delays. For example, if a particular task is behind schedule, it analyzes whether the allocated resources are insufficient or whether the task's difficulty is higher than expected. The monitoring unit can also propose solutions to delays and issues. For example, it may suggest reallocating resources or rescheduling tasks. Specifically, it may suggest moving resources from other tasks to resolve delays. It can also optimize overall project progress by reviewing task priorities and prioritizing high-priority tasks. In this way, the monitoring unit can also propose necessary resource reallocation or additions according to the project's progress. Furthermore, the monitoring unit provides a dashboard to visualize project progress, allowing users to understand the project status at a glance. As a result, the project management assistant system according to this embodiment allows even users without project management experience to efficiently advance projects.

[0033] The generation unit can learn from past PM documents and generate a Work Breakdown Structure (WBS). For example, it learns from past project plans, progress reports, risk management plans, and other documents to generate a WBS. The WBS generation considers, for example, the hierarchical structure and decomposition levels of tasks. The generation unit can efficiently generate a WBS based on past project data using a generation AI. For example, the generation AI takes past project data as input and uses a model that outputs a WBS to select the optimal task decomposition method. This allows for efficient WBS generation based on past PM documents.

[0034] The generation unit can propose the necessary personnel. For example, it can propose the optimal staffing arrangement by considering the number of people and skill sets for each role. Using generation AI, the generation unit can extract tasks for each phase of a project and high-priority tasks, and propose the necessary personnel based on that. For example, the generation AI uses a model that takes past project data as input and outputs the necessary personnel to propose the optimal staffing arrangement. This allows for the efficient proposal of the personnel needed for a project.

[0035] The monitoring unit can monitor the progress of tasks and analyze the causes of delays. For example, the monitoring unit can analyze the causes of delays by comparing task completion rates and planned versus actual progress. The monitoring unit can use AI to efficiently monitor task progress and analyze the causes of delays. For example, the monitoring unit can identify the causes of delays using a model that takes past project data as input and outputs the causes of delays. This allows for efficient analysis of the causes of task delays.

[0036] The monitoring unit can propose solutions to delays and issues. For example, it can suggest resource reallocation or task rescheduling. The monitoring unit can efficiently propose solutions to delays and issues using AI. For example, the monitoring unit can propose the optimal solution using a model that takes the cause of the delay as input and outputs a solution. This allows for the efficient presentation of solutions to delays and issues.

[0037] The monitoring unit can propose the reallocation or addition of necessary resources according to the project's progress. For example, the monitoring unit can propose the reallocation or addition of resources according to the project's progress. The monitoring unit can use AI to propose the optimal reallocation or addition of resources according to the project's progress. For example, the monitoring unit uses a model that takes the project's progress as input and outputs resource reallocation or addition to propose the optimal resource allocation. This allows the system to propose the optimal reallocation or addition of resources according to the project's progress.

[0038] The reception desk can analyze the user's past project history and suggest methods for entering basic information. For example, the reception desk can automatically display as suggestions basic information that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest basic information related to specific categories from the user's past project history. This allows the reception desk to suggest the optimal method for entering basic information based on the user's past project history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0039] The reception desk can customize input fields based on the user's current project status and areas of interest when basic information is entered. For example, the reception desk can prioritize the input of basic information related to the user's current ongoing project. The reception desk can also automatically suggest relevant basic information based on the user's areas of interest. Furthermore, the reception desk can analyze the user's current project status and customize the input of necessary basic information. This allows for the optimization of input fields according to the user's current project status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0040] The reception desk can prioritize the input of highly relevant information based on the user's geographical location when basic information is entered. For example, if the user is in a specific region, the reception desk can prioritize the input of basic information related to that region. The reception desk can also automatically suggest relevant project information based on the user's geographical location. Furthermore, if the user is on the move, the reception desk can customize the input of necessary basic information based on their current location. This allows the reception desk to prioritize the input of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without the use of AI.

[0041] The reception desk can analyze the user's social media activity when basic information is entered and automatically input relevant information. For example, the reception desk can automatically input relevant project information from the user's social media activity. The reception desk can also automatically complete basic information based on information shared by the user on social media. Furthermore, the reception desk can analyze the user's social media activity and suggest relevant basic information. This allows for the automatic input of relevant information based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0042] The generation unit can select a task decomposition method by referring to past project data when generating a Work Breakdown Structure (WBS). For example, the generation unit can select the optimal task decomposition method based on data from similar past projects. The generation unit can also extract and apply successful task decomposition methods from past project data. Furthermore, the generation unit can analyze past project data and select the most efficient task decomposition method. This allows for the selection of the optimal task decomposition method based on past project data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI.

[0043] The generation unit can apply a generation algorithm according to the project category when generating a WBS. For example, in the case of a software development project, the generation unit can generate a WBS based on agile methodologies. It can also generate a WBS based on waterfall methodologies for construction projects. Furthermore, it can generate a WBS based on phase-gate methodologies for research and development projects. This allows for the generation of the optimal WBS according to the project category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI.

[0044] The generation unit can determine task priorities based on the project start date when generating a Work Breakdown Structure (WBS). For example, if the project start date is approaching, the generation unit will prioritize important tasks. If the project start date is far off, the generation unit can also perform a detailed task breakdown. Furthermore, the generation unit can dynamically adjust task priorities based on the project start date. This allows for the optimization of task priorities based on the project start date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0045] The generation unit can adjust the order of tasks based on project relevance when generating a Work Breakdown Structure (WBS). For example, the generation unit prioritizes tasks that are highly relevant to the project. The generation unit can also dynamically adjust the order of tasks based on project relevance. Furthermore, the generation unit can analyze project relevance and determine the optimal task order. This allows for the optimization of the task order based on project relevance. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI.

[0046] The monitoring unit can analyze the cause of delays by referring to past project data during monitoring. For example, the monitoring unit can analyze the cause of delays based on data from similar past projects. The monitoring unit can also extract and analyze delay patterns from past project data. Furthermore, the monitoring unit can analyze past project data to identify the cause of delays. This allows for efficient analysis of the cause of delays based on past project data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI.

[0047] The monitoring unit can apply monitoring methods according to the project's progress during monitoring. For example, the monitoring unit can apply detailed monitoring methods in the initial stages of the project. It can also apply concise monitoring methods in the middle stages of the project. Furthermore, it can apply simplified monitoring methods in the final stages of the project. This allows for the application of the most optimal monitoring method according to the project's progress. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI.

[0048] The monitoring unit can perform monitoring while considering the geographical distribution of the project. For example, if the project is dispersed across multiple regions, the monitoring unit can individually monitor the progress of each region. The monitoring unit can also prioritize monitoring of important regions based on the geographical distribution. Furthermore, the monitoring unit can apply the most appropriate monitoring method considering the geographical distribution. This enables optimal monitoring based on the geographical distribution of the project. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI.

[0049] The monitoring unit can improve the accuracy of monitoring by referring to relevant project literature during monitoring. For example, the monitoring unit can refer to project-related literature to improve monitoring accuracy. The monitoring unit can also extract and apply the optimal monitoring method from the relevant literature. Furthermore, the monitoring unit can dynamically refer to relevant literature according to the progress of the project to improve monitoring accuracy. In this way, the accuracy of monitoring can be improved by referring to project-related literature. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI.

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

[0051] The project management assistant system can also include a feedback unit. This unit can collect user feedback during project progress and use it to improve the system's suggestions. For example, if a user is dissatisfied with a suggestion for a particular task, the generation unit can revise the suggestion based on that feedback. The feedback unit can also analyze user feedback to identify common problems and use that information to improve the entire system. Furthermore, the feedback unit can reflect user feedback in real time and respond flexibly during project progress. This allows for continuous improvement of the system's suggestions by leveraging user feedback, resulting in more effective project management.

[0052] The generation unit may include a risk assessment unit to enhance project risk management. The risk assessment unit can evaluate risks based on past project data and predict risks that may occur during project progress. For example, the risk assessment unit can analyze risk patterns that occurred in past projects and apply them to the current project. The risk assessment unit can also evaluate the impact and probability of occurrence of risks and propose risk countermeasures. Furthermore, the risk assessment unit can dynamically update the risk assessment according to the project progress and provide the latest risk information. This strengthens project risk management and prevents risks from occurring in the first place.

[0053] The generation unit may include a change management unit to handle change requests that arise during the project. The change management unit can receive change requests from users, assess the impact of the changes, and propose appropriate countermeasures. For example, the change management unit can assess the impact of change requests on the project schedule and resources and make necessary adjustments. It can also manage the approval process for change requests and support procedures for obtaining agreement from stakeholders. Furthermore, the change management unit can record the history of change requests and use this as reference information for future projects. This allows for flexible responses to change requests that arise during the project and supports the success of the project.

[0054] The monitoring unit can be equipped with a visualization unit to gain a more detailed understanding of the project's progress. The visualization unit can visually display the project's progress and task progress, making it intuitively understandable to the user. For example, the visualization unit can display task progress using Gantt charts or burn-down charts. It can also display project resource usage and risk assessment results in graph or dashboard format. Furthermore, the visualization unit can provide users with customizable display options, enabling them to efficiently obtain the necessary information. This allows for a visual understanding of the project's progress and supports rapid decision-making.

[0055] The monitoring department may include a communication support department to resolve communication issues that arise during project progress. The communication support department can provide tools and functions to facilitate communication within the project team. For example, it can provide real-time chat and video conferencing functions to enable team members to share information quickly. It can also provide notification functions regarding project progress and task progress, ensuring that important information is communicated to stakeholders in a timely manner. Furthermore, the communication support department can provide project document management functions, making it easier for team members to access necessary information. This facilitates communication within the project team and supports project success.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives basic information from the user, such as the service category and estimated effort. This basic information may include, but is not limited to, the project name, start date, end date, and budget. Step 2: The generation unit learns from past PM documents based on the information entered by the reception unit and proposes necessary tasks and personnel allocation. For example, the generation unit uses generation AI to learn from past project plans, progress reports, risk management plans, and other documents to generate a Work Breakdown Structure (WBS). The generation unit can also propose the necessary personnel (number of people, roles). For example, the generation AI extracts tasks for each project phase and high-priority tasks and generates a WBS based on them. Furthermore, the generation unit proposes the optimal personnel allocation considering the number of people and skill sets for each role. Step 3: The monitoring unit monitors the progress of tasks proposed by the generation unit, detects delays and issues, and provides guidance. The monitoring unit, for example, uses AI to compare task completion rates and planned vs. actual completion times, and analyzes the causes of delays. The monitoring unit can also suggest solutions to delays and issues. For example, the monitoring unit may suggest reallocating resources or rescheduling tasks. Furthermore, the monitoring unit may suggest reallocating or adding necessary resources depending on the progress of the project.

[0058] (Example of form 2) The project management assistant system according to an embodiment of the present invention is a system that enables users without project management experience to advance a project. This system learns from past project manager (PM) documents by inputting basic information such as service categories and estimated man-hours, and then presents necessary tasks (WBS generation) and required personnel (number of people, roles). Furthermore, it supports the start of the project and provides timely guidance on issues and task delays during project advancement. For example, if a specific task is delayed, it analyzes the cause and presents solutions. It also suggests reallocating or adding necessary resources according to the project's progress. This system enables users without project management experience to efficiently advance a project. For example, even new employees or those changing careers from other industries can successfully lead a project by following the system's guidance. Thus, the project management assistant system enables users without project management experience to efficiently advance a project.

[0059] The project management assistant system according to this embodiment comprises a reception unit, a generation unit, and a monitoring unit. The reception unit receives basic information from the user, such as the service category and estimated man-hours. This basic information includes, but is not limited to, the project name, start date, end date, and budget. Based on the information entered by the reception unit, the generation unit learns from past PM documents and proposes necessary tasks and personnel allocation. For example, the generation unit uses a generation AI to learn from past project plans, progress reports, risk management plans, and other documents to generate a Work Breakdown Structure (WBS). The generation unit can also propose the necessary personnel (number of people, roles). For example, the generation AI extracts tasks for each project phase and tasks of high importance, and generates a WBS based on them. Furthermore, the generation unit proposes the optimal personnel allocation, taking into account the number of people and skill sets for each role. The monitoring unit monitors the progress of the tasks proposed by the generation unit, detects delays and issues, and provides guidance. For example, the monitoring unit uses AI to compare the completion rate of tasks and planned versus actual results, and analyzes the causes of delays. Furthermore, the monitoring unit can also suggest solutions to delays and issues. For example, the monitoring unit can propose resource reallocation and task rescheduling. In addition, the monitoring unit can suggest necessary resource reallocation or addition depending on the project's progress. As a result, the project management assistant system according to this embodiment allows even users without project management experience to efficiently advance projects.

[0060] The reception section allows users to input basic information such as the service category and estimated effort. This basic information includes, but is not limited to, project name, start date, end date, and budget. Specifically, users can input detailed project information through a dedicated interface. This interface is designed to be intuitive and easy to use, allowing users to input the necessary information without confusion. For example, the project name input field displays guidelines for concisely describing the project's purpose and overview. The start and end dates can be easily selected using a calendar function, and the budget input field includes auxiliary functions for entering currency units and budget breakdowns. Furthermore, the reception section automatically validates the entered information and provides appropriate feedback to the user if there is missing information or inconsistencies. For example, if the start date is later than the end date, or if the budget is unreasonably low, a warning message is displayed to prompt correction. In this way, the reception section supports users in inputting accurate and complete information, providing a foundation for the subsequent generation and monitoring sections to function efficiently.

[0061] The generation unit learns from past PM documents based on information entered by the reception unit and proposes necessary tasks and personnel allocation. For example, the generation unit uses generation AI to learn from past project plans, progress reports, risk management plans, and other documents to generate a Work Breakdown Structure (WBS). Specifically, the generation AI uses natural language processing technology to extract important information from past documents and automatically generates tasks according to the characteristics of the project. For example, it extracts tasks for each project phase and high-priority tasks and generates a WBS based on them. The generation unit also proposes the optimal personnel allocation considering the number of people and skill sets for each role. The generation AI analyzes successful and unsuccessful cases from past projects and learns what kind of personnel allocation was effective in similar projects. This allows the generation unit to propose the optimal personnel allocation according to the characteristics and scale of the project. Furthermore, the generation unit can also identify project risk factors and propose a risk management plan based on the basic information entered by the user. For example, if the budget is limited, it will propose prioritizing tasks for cost reduction and suggest allocating additional resources to high-risk tasks. In this way, the generation unit supports users in efficiently advancing their projects from the planning stage to the execution stage.

[0062] The monitoring unit monitors the progress of tasks proposed by the generation unit, detects delays and issues, and provides guidance. For example, the monitoring unit uses AI to compare task completion rates and planned vs. actual completion times, and analyzes the causes of delays. Specifically, the monitoring unit integrates with project management tools and task management systems to collect task progress in real time. This automatically compares task completion rates and planned vs. actual completion times, and identifies the causes of delays. For example, if a particular task is behind schedule, it analyzes whether the allocated resources are insufficient or whether the task's difficulty is higher than expected. The monitoring unit can also propose solutions to delays and issues. For example, it may suggest reallocating resources or rescheduling tasks. Specifically, it may suggest moving resources from other tasks to resolve delays. It can also optimize overall project progress by reviewing task priorities and prioritizing high-priority tasks. In this way, the monitoring unit can also propose necessary resource reallocation or additions according to the project's progress. Furthermore, the monitoring unit provides a dashboard to visualize project progress, allowing users to understand the project status at a glance. As a result, the project management assistant system according to this embodiment allows even users without project management experience to efficiently advance projects.

[0063] The generation unit can learn from past PM documents and generate a Work Breakdown Structure (WBS). For example, it learns from past project plans, progress reports, risk management plans, and other documents to generate a WBS. The WBS generation considers, for example, the hierarchical structure and decomposition levels of tasks. The generation unit can efficiently generate a WBS based on past project data using a generation AI. For example, the generation AI takes past project data as input and uses a model that outputs a WBS to select the optimal task decomposition method. This allows for efficient WBS generation based on past PM documents.

[0064] The generation unit can propose the necessary personnel. For example, it can propose the optimal staffing arrangement by considering the number of people and skill sets for each role. Using generation AI, the generation unit can extract tasks for each phase of a project and high-priority tasks, and propose the necessary personnel based on that. For example, the generation AI uses a model that takes past project data as input and outputs the necessary personnel to propose the optimal staffing arrangement. This allows for the efficient proposal of the personnel needed for a project.

[0065] The monitoring unit can monitor the progress of tasks and analyze the causes of delays. For example, the monitoring unit can analyze the causes of delays by comparing task completion rates and planned versus actual progress. The monitoring unit can use AI to efficiently monitor task progress and analyze the causes of delays. For example, the monitoring unit can identify the causes of delays using a model that takes past project data as input and outputs the causes of delays. This allows for efficient analysis of the causes of task delays.

[0066] The monitoring unit can propose solutions to delays and issues. For example, it can suggest resource reallocation or task rescheduling. The monitoring unit can efficiently propose solutions to delays and issues using AI. For example, the monitoring unit can propose the optimal solution using a model that takes the cause of the delay as input and outputs a solution. This allows for the efficient presentation of solutions to delays and issues.

[0067] The monitoring unit can propose the reallocation or addition of necessary resources according to the project's progress. For example, the monitoring unit can propose the reallocation or addition of resources according to the project's progress. The monitoring unit can use AI to propose the optimal reallocation or addition of resources according to the project's progress. For example, the monitoring unit uses a model that takes the project's progress as input and outputs resource reallocation or addition to propose the optimal resource allocation. This allows the system to propose the optimal reallocation or addition of resources according to the project's progress.

[0068] The reception desk can estimate the user's emotions and adjust the basic information input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick basic information entry. This allows for the optimization of the basic information input interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0069] The reception desk can analyze the user's past project history and suggest methods for entering basic information. For example, the reception desk can automatically display as suggestions basic information that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest basic information related to specific categories from the user's past project history. This allows the reception desk to suggest the optimal method for entering basic information based on the user's past project history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0070] The reception desk can customize input fields based on the user's current project status and areas of interest when basic information is entered. For example, the reception desk can prioritize the input of basic information related to the user's current ongoing project. The reception desk can also automatically suggest relevant basic information based on the user's areas of interest. Furthermore, the reception desk can analyze the user's current project status and customize the input of necessary basic information. This allows for the optimization of input fields according to the user's current project status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0071] The reception desk can estimate the user's emotions and determine the priority of basic information to be entered based on the estimated emotions. For example, if the user is nervous, the reception desk may prioritize the input of the most important basic information. If the user is relaxed, the reception desk may also prioritize the input of detailed basic information sequentially. Furthermore, if the user is in a hurry, the reception desk may prioritize the input of minimal basic information. This allows for the optimization of the input priority of basic information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The reception desk can prioritize the input of highly relevant information based on the user's geographical location when basic information is entered. For example, if the user is in a specific region, the reception desk can prioritize the input of basic information related to that region. The reception desk can also automatically suggest relevant project information based on the user's geographical location. Furthermore, if the user is on the move, the reception desk can customize the input of necessary basic information based on their current location. This allows the reception desk to prioritize the input of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without the use of AI.

[0073] The reception desk can analyze the user's social media activity when basic information is entered and automatically input relevant information. For example, the reception desk can automatically input relevant project information from the user's social media activity. The reception desk can also automatically complete basic information based on information shared by the user on social media. Furthermore, the reception desk can analyze the user's social media activity and suggest relevant basic information. This allows for the automatic input of relevant information based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0074] The generation unit can estimate the user's emotions and adjust the WBS generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a WBS with detailed task breakdown. If the user is in a hurry, the generation unit can also generate a simplified WBS. Furthermore, if the user is excited, the generation unit can generate a visually easy-to-understand WBS. This allows the WBS generation method to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The generation unit can select a task decomposition method by referring to past project data when generating a Work Breakdown Structure (WBS). For example, the generation unit can select the optimal task decomposition method based on data from similar past projects. The generation unit can also extract and apply successful task decomposition methods from past project data. Furthermore, the generation unit can analyze past project data and select the most efficient task decomposition method. This allows for the selection of the optimal task decomposition method based on past project data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI.

[0076] The generation unit can apply a generation algorithm according to the project category when generating a WBS. For example, in the case of a software development project, the generation unit can generate a WBS based on agile methodologies. It can also generate a WBS based on waterfall methodologies for construction projects. Furthermore, it can generate a WBS based on phase-gate methodologies for research and development projects. This allows for the generation of the optimal WBS according to the project category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI.

[0077] The generation unit can estimate the user's emotions and adjust the level of detail in the Work Breakdown Structure (WBS) based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a WBS with detailed task breakdown. If the user is in a hurry, the generation unit can also generate a simplified WBS. Furthermore, if the user is excited, the generation unit can generate a visually easy-to-understand WBS. This allows for the optimization of the WBS level of detail according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The generation unit can determine task priorities based on the project start date when generating a Work Breakdown Structure (WBS). For example, if the project start date is approaching, the generation unit will prioritize important tasks. If the project start date is far off, the generation unit can also perform a detailed task breakdown. Furthermore, the generation unit can dynamically adjust task priorities based on the project start date. This allows for the optimization of task priorities based on the project start date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0079] The generation unit can adjust the order of tasks based on project relevance when generating a Work Breakdown Structure (WBS). For example, the generation unit prioritizes tasks that are highly relevant to the project. The generation unit can also dynamically adjust the order of tasks based on project relevance. Furthermore, the generation unit can analyze project relevance and determine the optimal task order. This allows for the optimization of the task order based on project relevance. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI.

[0080] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring based on the estimated emotions. For example, if the user is tense, the monitoring unit can provide a simple and highly visible display method. If the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a concise display method. This allows the display method of the monitoring to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] The monitoring unit can analyze the cause of delays by referring to past project data during monitoring. For example, the monitoring unit can analyze the cause of delays based on data from similar past projects. The monitoring unit can also extract and analyze delay patterns from past project data. Furthermore, the monitoring unit can analyze past project data to identify the cause of delays. This allows for efficient analysis of the cause of delays based on past project data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI.

[0082] The monitoring unit can apply monitoring methods according to the project's progress during monitoring. For example, the monitoring unit can apply detailed monitoring methods in the initial stages of the project. It can also apply concise monitoring methods in the middle stages of the project. Furthermore, it can apply simplified monitoring methods in the final stages of the project. This allows for the application of the most optimal monitoring method according to the project's progress. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI.

[0083] The monitoring unit can perform monitoring while considering the geographical distribution of the project. For example, if the project is dispersed across multiple regions, the monitoring unit can individually monitor the progress of each region. The monitoring unit can also prioritize monitoring of important regions based on the geographical distribution. Furthermore, the monitoring unit can apply the most appropriate monitoring method considering the geographical distribution. This enables optimal monitoring based on the geographical distribution of the project. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI.

[0084] The monitoring unit can improve the accuracy of monitoring by referring to relevant project literature during monitoring. For example, the monitoring unit can refer to project-related literature to improve monitoring accuracy. The monitoring unit can also extract and apply the optimal monitoring method from the relevant literature. Furthermore, the monitoring unit can dynamically refer to relevant literature according to the progress of the project to improve monitoring accuracy. In this way, the accuracy of monitoring can be improved by referring to project-related literature. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The project management assistant system can also include a feedback unit. This unit can collect user feedback during project progress and use it to improve the system's suggestions. For example, if a user is dissatisfied with a suggestion for a particular task, the generation unit can revise the suggestion based on that feedback. The feedback unit can also analyze user feedback to identify common problems and use that information to improve the entire system. Furthermore, the feedback unit can reflect user feedback in real time and respond flexibly during project progress. This allows for continuous improvement of the system's suggestions by leveraging user feedback, resulting in more effective project management.

[0087] The generation unit may include a risk assessment unit to enhance project risk management. The risk assessment unit can evaluate risks based on past project data and predict risks that may occur during project progress. For example, the risk assessment unit can analyze risk patterns that occurred in past projects and apply them to the current project. The risk assessment unit can also evaluate the impact and probability of occurrence of risks and propose risk countermeasures. Furthermore, the risk assessment unit can dynamically update the risk assessment according to the project progress and provide the latest risk information. This strengthens project risk management and prevents risks from occurring in the first place.

[0088] The generation unit may include a change management unit to handle change requests that arise during the project. The change management unit can receive change requests from users, assess the impact of the changes, and propose appropriate countermeasures. For example, the change management unit can assess the impact of change requests on the project schedule and resources and make necessary adjustments. It can also manage the approval process for change requests and support procedures for obtaining agreement from stakeholders. Furthermore, the change management unit can record the history of change requests and use this as reference information for future projects. This allows for flexible responses to change requests that arise during the project and supports the success of the project.

[0089] The monitoring unit can be equipped with a visualization unit to gain a more detailed understanding of the project's progress. The visualization unit can visually display the project's progress and task progress, making it intuitively understandable to the user. For example, the visualization unit can display task progress using Gantt charts or burn-down charts. It can also display project resource usage and risk assessment results in graph or dashboard format. Furthermore, the visualization unit can provide users with customizable display options, enabling them to efficiently obtain the necessary information. This allows for a visual understanding of the project's progress and supports rapid decision-making.

[0090] The monitoring department may include a communication support department to resolve communication issues that arise during project progress. The communication support department can provide tools and functions to facilitate communication within the project team. For example, it can provide real-time chat and video conferencing functions to enable team members to share information quickly. It can also provide notification functions regarding project progress and task progress, ensuring that important information is communicated to stakeholders in a timely manner. Furthermore, the communication support department can provide project document management functions, making it easier for team members to access necessary information. This facilitates communication within the project team and supports project success.

[0091] The reception desk can estimate the user's emotions and adjust the basic information input interface based on the estimated emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick basic information entry. This allows for the optimization of the basic information input interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The generation unit can estimate the user's emotions and adjust the WBS generation method based on the estimated user emotions. For example, if the user is relaxed, it can generate a WBS with detailed task breakdown. The generation unit can also generate a simplified WBS if the user is in a hurry. Furthermore, if the user is excited, it can generate a visually easy-to-understand WBS. This allows for the optimization of the WBS generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring based on the estimated emotions. For example, if the user is tense, it can provide a simple and highly visible display method. The monitoring unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. This allows the display method of the monitoring to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The reception desk can estimate the user's emotions and determine the priority of basic information to be entered based on the estimated emotions. For example, if the user is nervous, it can prioritize the input of the most important basic information. If the user is relaxed, the reception desk can also prioritize the input of detailed basic information sequentially. Furthermore, if the user is in a hurry, the reception desk can prioritize the input of minimal basic information. This allows for the optimization of the input priority of basic information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The generation unit can estimate the user's emotions and adjust the level of detail in the Work Breakdown Structure (WBS) based on the estimated emotions. For example, if the user is relaxed, it can generate a WBS with detailed task breakdown. The generation unit can also generate a simplified WBS if the user is in a hurry. Furthermore, if the user is excited, it can generate a visually easy-to-understand WBS. This allows for the optimization of the WBS level of detail according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The reception desk receives basic information from the user, such as the service category and estimated effort. This basic information may include, but is not limited to, the project name, start date, end date, and budget. Step 2: The generation unit learns from past PM documents based on the information entered by the reception unit and proposes necessary tasks and personnel allocation. For example, the generation unit uses generation AI to learn from past project plans, progress reports, risk management plans, and other documents to generate a Work Breakdown Structure (WBS). The generation unit can also propose the necessary personnel (number of people, roles). For example, the generation AI extracts tasks for each project phase and high-priority tasks and generates a WBS based on them. Furthermore, the generation unit proposes the optimal personnel allocation considering the number of people and skill sets for each role. Step 3: The monitoring unit monitors the progress of tasks proposed by the generation unit, detects delays and issues, and provides guidance. The monitoring unit, for example, uses AI to compare task completion rates and planned vs. actual completion times, and analyzes the causes of delays. The monitoring unit can also suggest solutions to delays and issues. For example, the monitoring unit may suggest reallocating resources or rescheduling tasks. Furthermore, the monitoring unit may suggest reallocating or adding necessary resources depending on the progress of the project.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user inputs basic information such as the service category and estimated man-hours. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, where it learns from past PM documents and proposes necessary tasks and personnel allocations. The monitoring unit is implemented by the control unit 46A of the smart device 14, where it monitors the progress of tasks, detects delays and issues, and provides guidance. The generation unit may also be implemented by the control unit 46A of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, where the user inputs basic information such as the service category and estimated man-hours. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, where it learns from past PM documents and proposes necessary tasks and personnel allocations. The monitoring unit is implemented by the control unit 46A of the smart glasses 214, where it monitors the progress of tasks, detects delays and issues, and provides guidance. The generation unit may also be implemented by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user inputs basic information such as the service category and estimated man-hours. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, where it learns from past PM documents and proposes necessary tasks and personnel allocations. The monitoring unit is implemented by the control unit 46A of the headset terminal 314, where it monitors the progress of tasks, detects delays and issues, and provides guidance. The generation unit may also be implemented by the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] For example, the reception unit is implemented by the microphone 238 of robot 414, where the user inputs basic information such as the service category and estimated man-hours. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, which learns from past PM documents and proposes necessary tasks and personnel allocations. The monitoring unit is implemented by the control unit 46A of robot 414, which monitors the progress of tasks, detects delays and issues, and provides guidance. The generation unit may also be implemented by the control unit 46A of robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0151] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) The reception area where you enter basic information, Based on the information entered by the reception unit, a generation unit learns from past PM documents and proposes necessary tasks or personnel assignments. The system includes a monitoring unit that monitors the progress of tasks proposed by the generation unit, detects delays or issues, and provides guidance. A system characterized by the following features. (Note 2) The generating unit is Learn from past PM documents and generate a WBS. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Propose the necessary personnel. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, Monitor task progress and analyze the causes of delays. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, Providing solutions to delays and challenges. The system described in Appendix 1, characterized by the features described herein. (Note 6) The monitoring unit, Depending on the project's progress, we will propose reallocating or adding necessary resources. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the basic information input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past project history and suggest methods for entering basic information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering basic information, the input fields are customized based on the user's current project status or areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of basic information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter basic information, the system prioritizes inputting information that is highly relevant based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter basic information, the system analyzes their social media activity and automatically fills in relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate the user's emotions and adjust the WBS generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a Work Breakdown Structure (WBS), select a task decomposition method by referring to past project data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a Work Breakdown Structure (WBS), apply the generation algorithm according to the project category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is Estimate the user's emotions and adjust the level of detail in the Work Breakdown Structure (WBS) based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a Work Breakdown Structure (WBS), prioritize tasks based on the project's start date. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a Work Breakdown Structure (WBS), adjust the order of tasks based on project relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The monitoring unit, It estimates the user's emotions and adjusts how monitoring is displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The monitoring unit, During monitoring, we analyze the cause of delays by referring to past project data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The monitoring unit, During monitoring, apply monitoring methods according to the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 22) The monitoring unit, When monitoring, the geographical distribution of the project should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The monitoring unit, During monitoring, refer to relevant project literature to improve monitoring accuracy. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area where you enter basic information, Based on the information entered by the reception unit, a generation unit learns from past PM documents and proposes necessary tasks or personnel assignments. The system includes a monitoring unit that monitors the progress of tasks proposed by the generation unit, detects delays or issues, and provides guidance. A system characterized by the following features.

2. The generating unit is Learn from past PM documents and generate a WBS. The system according to feature 1.

3. The generating unit is Propose the necessary personnel. The system according to feature 1.

4. The monitoring unit, Monitor task progress and analyze the causes of delays. The system according to feature 1.

5. The monitoring unit, Providing solutions to delays and challenges. The system according to feature 1.

6. The monitoring unit, Depending on the project's progress, we will propose reallocating or adding necessary resources. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the basic information input interface based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the user's past project history and suggest methods for entering basic information. The system according to feature 1.

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