System

Abstract

To provide a system according to an embodiment, capable of optimizing a communication method in a project.SOLUTION: A system according to an embodiment, comprises a reception part, an analysis part, an execution part, and an adjustment part. The reception part receives basic information of a project. The analysis part analyzes information received by the reception part, and proposes an optimal communication method. The execution part executes the communication method proposed by the analysis part. The adjustment part monitors a result of the communication method executed by the execution part, and performs adjustment as needed.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, communication methods in projects depended on each individual's judgment and experience, making them difficult to optimize.

[0005] The system according to the embodiment aims to optimize communication methods in projects. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an execution unit, and an adjustment unit. The reception unit receives basic information about the project. The analysis unit analyzes the information received by the reception unit and proposes an optimal communication method. The execution unit executes the communication method proposed by the analysis unit. The adjustment unit monitors the results of the communication method executed by the execution unit and adjusts as necessary. Effect of the Invention

[0007] The system according to the embodiment can optimize communication methods in projects. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) A project management platform according to an embodiment of the present invention is a system for optimizing communication methods in project management. This system improves project performance by accepting and analyzing basic information about a project, proposing, executing, and adjusting the optimal communication method. For example, basic information such as the project's purpose, scope, team members, and schedule is input. Next, the generation AI analyzes this information and proposes the optimal communication method. For example, it specifically suggests which tool should be used for a specific task, when communication should be performed, and how to explain it effectively. The generation AI learns the optimal communication method based on past project data and knowledge of PMBOK. This enables customized suggestions according to the progress of the project and the characteristics of the team members. For example, for a team with a lot of remote work, it recommends the use of an online conference tool, and if face-to-face communication is important, it suggests a regular meeting schedule. In addition, the generation AI monitors the progress of the project in real time and adjusts the communication method as necessary. For example, if the project is delayed, it suggests an emergency meeting and automatically generates a report to share the progress. In addition, if communication between team members is lacking, it suggests regular feedback sessions. In this way, the generation AI optimizes the communication method in project management, thereby improving project performance. As a specific example, in the early stages of the project, we suggest a kickoff meeting to share the overall scope and goals, and during the project, we suggest regular status meetings to share the progress of tasks. In addition, at the end of the project, we suggest a meeting to review deliverables and conduct a retrospective. By using this platform, project managers can leave the decision on how to communicate to generative AI and focus on more important tasks.Also, by following the best communication practices, team members can share information efficiently and contribute to the success of the project, which is why a project management platform can optimize project performance.

[0029] The project management platform according to the embodiment includes a reception unit, an analysis unit, an execution unit, and an adjustment unit. The reception unit receives basic information of the project. The basic information of the project includes, for example, the purpose, scope, team members, schedule, etc. of the project, but is not limited to such examples. The reception unit provides, for example, an interface for inputting the purpose of the project. The reception unit can also provide a field for inputting the scope of the project. Furthermore, the reception unit can also provide an option for inputting information of the team members. For example, the reception unit provides a calendar function for inputting the project schedule. The analysis unit uses a generation AI to analyze the information received by the reception unit and proposes an optimal communication method. The analysis unit proposes an optimal communication method based on, for example, project knowledge based on PMBOK or project knowledge accumulated in the AI. For example, the analysis unit proposes which tool should be used for a specific task. The analysis unit can also propose when communication should be performed. Furthermore, the analysis unit can also propose how to explain effectively. For example, the analysis unit uses a generation AI to analyze past project data and learn the optimal communication method. The execution unit executes the communication method proposed by the analysis unit. The execution unit, for example, uses the proposed tool for a specific task. The execution unit can also communicate at the proposed timing. Furthermore, the execution unit can provide an explanation in the proposed manner. For example, the execution unit executes the proposed communication method using a generative AI. The adjustment unit monitors the results of the communication method executed by the execution unit and adjusts it as necessary. The adjustment unit, for example, monitors the progress of the project in real time. Furthermore, the adjustment unit can propose an emergency meeting if the project is delayed. Furthermore, the adjustment unit can propose regular feedback sessions if there is a lack of communication between team members.For example, the adjustment unit uses the generation AI to monitor the progress of the project and adjust the communication method as necessary. This allows the project management platform according to the embodiment to optimize project performance.

[0030] The reception unit receives basic information of the project. The basic information of the project includes, but is not limited to, for example, the purpose, scope, team members, and schedule of the project. The reception unit provides, for example, an interface for inputting the purpose of the project. Specifically, the reception unit includes a text field in which the user can describe the purpose of the project in detail and a drop-down menu from which the user can select from options. The reception unit can also provide a field for inputting the scope of the project. The input field for the scope includes a multi-line text area and a function for uploading related documents so that the scope and constraints of the project can be described in detail. The reception unit can also provide options for inputting information of team members. For example, the reception unit includes fields for inputting the names, roles, and contact information of team members and a function for selecting members from an existing contact list. This allows the team composition of the project to be set quickly and accurately. For example, the reception unit provides a calendar function for inputting the schedule of the project. The calendar function provides an interface that allows the user to visually manage the start date, end date, and important milestones of the project, allowing the user to intuitively operate the schedule. This allows the reception unit to efficiently collect basic information of the project and smoothly progress the planning stage of the project. In addition, the reception department can provide APIs and database connections to centrally manage collected information and link with other departments and systems, allowing basic project information to be seamlessly integrated with other systems and tools, improving the efficiency of overall project management.

[0031] The analysis part uses the generation AI to analyze the information received by the reception part and proposes the optimal communication method. The analysis part proposes the optimal communication method based on, for example, project knowledge based on PMBOK and project knowledge accumulated in the AI. Specifically, the generation AI analyzes information such as the project's purpose and scope, the composition of team members, and schedule, and derives the communication strategy that is most suitable for the characteristics of the project. For example, the analysis part suggests which tool should be used for a specific task. Based on the nature of the task and the skill sets of the team members, it selects the optimal communication tool, such as email, chat, or video conference. The analysis part can also suggest when communication should be performed. It recommends meetings and reports at appropriate times depending on the progress of the project and important milestones. Furthermore, the analysis part can also suggest how to explain effectively. For example, the generation AI analyzes past project data and learns successful communication patterns to propose effective explanation methods and presentation styles. As a result, the analysis part provides the optimal communication method according to the characteristics of the project and supports the smooth progress of the project. In addition, the Analytics Department can analyze data in real time and continuously improve communication methods based on project progress and team member feedback, allowing the Analytics Department to provide effective communication strategies for project success and optimize project performance.

[0032] The execution unit executes the communication method proposed by the analysis unit. For example, the execution unit uses the tools proposed for a specific task. Specifically, the analysis unit uses tools such as email, chat, and video conferencing proposed by the analysis unit to facilitate communication between team members. The execution unit can also communicate at the proposed timing. For example, a regular meeting is set before an important milestone to check the progress. Furthermore, the execution unit can also provide an explanation in the proposed manner. For example, the execution unit executes the proposed communication method using a generation AI. Specifically, an effective explanation is provided using presentation materials and reports created by the generation AI. This allows the execution unit to execute the optimal communication method based on the proposal of the analysis unit and facilitate the progress of the project. Furthermore, the execution unit can record the results of the executed communication and use them for subsequent analysis and improvement. For example, the minutes of the meeting and communication logs are saved to grasp the progress and problems of the project. The execution unit can also collect feedback from team members and use them to improve the communication method. This allows the execution unit to facilitate the progress of the project and promote cooperation between team members.

[0033] The coordination unit monitors the results of the communication method executed by the execution unit and adjusts it as necessary. For example, the coordination unit monitors the progress of the project in real time. Specifically, the generation AI is used to automatically track the progress of the project and the completion status of the task, and visualize the progress. The coordination unit can also propose an emergency meeting if the project is delayed. For example, if an important milestone is delayed, the generation AI automatically issues an alert and proposes holding an emergency meeting. Furthermore, the coordination unit can also propose regular feedback sessions if communication between team members is lacking. For example, if the generation AI analyzes the communication logs of team members and determines that communication is lacking, it proposes holding regular feedback sessions. This allows the coordination unit to grasp the progress of the project in real time and respond quickly as necessary. Furthermore, the coordination unit can continuously improve the communication method based on the progress of the project and the feedback of the team members. For example, by analyzing past project data and learning successful communication patterns, it proposes a more effective communication method. This allows the coordination unit to smooth the progress of the project and promote cooperation between team members.

[0034] The reception unit can receive basic information including the purpose, scope, team members, and schedule of the project. The basic information includes, for example, the purpose, scope, team members, and schedule of the project, but is not limited to such examples. The reception unit provides, for example, an interface for inputting the purpose of the project. The reception unit can also provide a field for inputting the scope of the project. Furthermore, the reception unit can provide an option for inputting information of team members. For example, the reception unit provides a calendar function for inputting the schedule of the project. In this way, the reception unit can provide basic data for the analysis unit to propose an optimal communication method by receiving the basic information of the project. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input an interface for inputting the basic information of the project to the generation AI, and cause the generation AI to execute generation of the interface.

[0035] The analysis unit can propose an optimal communication method based on project knowledge based on PMBOK or project knowledge accumulated in the AI. Project knowledge based on PMBOK includes, for example, best practices and standard methods for project management, but is not limited to such examples. Project knowledge accumulated in the AI ​​includes, for example, past project data and learning algorithms, but is not limited to such examples. The analysis unit, for example, proposes which tool should be used for a specific task based on project knowledge based on PMBOK. The analysis unit can also propose when communication should be performed based on project knowledge accumulated in the AI. Furthermore, the analysis unit can also propose how to explain effectively. For example, the analysis unit uses the generation AI to analyze past project data and learn the optimal communication method. As a result, the analysis unit can propose the optimal communication method by utilizing the knowledge accumulated in PMBOK and the AI. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input basic information about the project to the generation AI and have the generation AI execute a proposal for the optimal communication method.

[0036] The execution unit can propose which tool should be used for a specific task, when communication should be performed, and how to explain it effectively. The specific task includes, for example, a phase of a project and specific work content, but is not limited to such examples. The tools to be used include, for example, a communication tool and a project management tool, but is not limited to such examples. The timing of communication includes, for example, the progress of the project and important milestones, but is not limited to such examples. The execution unit, for example, uses a tool proposed for a specific task. The execution unit can also communicate at the proposed timing. Furthermore, the execution unit can also explain in the proposed method. For example, the execution unit executes the proposed communication method using a generation AI. As a result, the execution unit can improve the efficiency of the project by proposing an optimal communication method for a specific task. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the execution unit can input a method of using a tool proposed for a specific task to the generation AI and cause the generation AI to execute the proposal of the method of using the tool.

[0037] The adjustment unit can monitor the progress of the project in real time and adjust the communication method according to the progress of the project. The method of monitoring in real time includes, for example, the software used and the frequency of updating data, but is not limited to such examples. The adjustment includes, for example, the frequency of adjustment, the algorithm used, the evaluation criteria, etc., but is not limited to such examples. The adjustment unit, for example, monitors the progress of the project in real time. In addition, the adjustment unit can also propose an emergency meeting if the project is delayed. Furthermore, the adjustment unit can also propose a regular feedback session if communication between team members is lacking. For example, the adjustment unit uses the generation AI to monitor the progress of the project and adjust the communication method as necessary. Thereby, the adjustment unit can optimize the performance of the project by adjusting the communication method according to the progress of the project. Part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the progress of the project to the generation AI and cause the generation AI to execute the adjustment of the communication method.

[0038] The reception unit can automatically complement information on similar projects by referring to past data on the project. The past data includes, for example, past project data and information on similar projects, but is not limited to such examples. The reception unit automatically inputs the scope and purpose of the similar project based on, for example, past project data. The reception unit can also automatically suggest appropriate members by referring to team member information on the past project. Furthermore, the reception unit can automatically set a schedule for the current project by referring to the schedule of the past project. For example, the reception unit uses a generation AI to analyze past project data and automatically complement information on similar projects. In this way, the reception unit can utilize past data to reduce the effort of inputting information and realize efficient project management. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input past project data into the generation AI and cause the generation AI to execute complementing of information on similar projects.

[0039] The reception unit can dynamically change the priority of the input items according to the scope and purpose of the project. The priority of the input items includes, for example, a method of setting a priority based on the scope and purpose of the project, but is not limited to such examples. For example, when the scope of the project is wide, the reception unit urges the user to input important items preferentially. In addition, when the purpose of the project is unclear, the reception unit can also prioritize input items that require detailed explanation. Furthermore, when the schedule of the project is tight, the reception unit can also display schedule-related input items preferentially. For example, the reception unit dynamically changes the priority of the input items according to the scope and purpose of the project using the generation AI. As a result, the reception unit can realize efficient information input by changing the priority of the input items according to the characteristics of the project. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the scope and purpose of the project to the generation AI and cause the generation AI to execute the change of the priority of the input items.

[0040] The reception unit can add a function to receive basic information of the project by voice input or gesture input. Voice input includes, for example, voice recognition technology and a device to be used, but is not limited to such examples. Gesture input includes, for example, gesture recognition technology and a device to be used, but is not limited to such examples. The reception unit, for example, enables a user to input the purpose and scope of the project by voice. The reception unit can also enable a user to select and add team members by gesture. Furthermore, the reception unit can enable a user to set a schedule by voice or gesture. For example, the reception unit adds a function to receive basic information of the project by voice input or gesture input using a generation AI. As a result, the reception unit can improve the user's convenience and realize efficient information input by utilizing voice input or gesture input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input data of the voice input or gesture input to the generation AI and cause the generation AI to execute the addition of an input method.

[0041] The reception unit can simplify input by referring to the user's past project history when receiving basic information of the project. The past project history includes, for example, past successful and failed projects, but is not limited to such examples. The reception unit, for example, automatically inputs information of projects that the user has managed in the past. The reception unit can also automatically complement information of similar projects from the user's past project history. Furthermore, the reception unit can suggest appropriate input items based on the user's past project history. For example, the reception unit simplifies input by referring to the user's past project history when receiving basic information of the project using the generation AI. In this way, the reception unit can utilize the past project history to reduce the effort of inputting information and realize efficient project management. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's past project history into the generation AI and cause the generation AI to execute simplification of input.

[0042] The analysis unit can dynamically change the analysis algorithm based on the progress of the project and the characteristics of the team members. The analysis algorithm includes, for example, a method of selecting an algorithm based on the progress of the project and the characteristics of the team members, but is not limited to such examples. For example, when the progress of the project is smooth, the analysis unit uses a standard analysis algorithm. In addition, when the project is delayed, the analysis unit can also use an analysis algorithm for identifying the cause of the delay. Furthermore, the analysis unit can select an appropriate analysis algorithm according to the characteristics of the team members. For example, the analysis unit dynamically changes the analysis algorithm based on the progress of the project and the characteristics of the team members using the generation AI. As a result, the analysis unit can provide optimal analysis results by changing the analysis algorithm according to the progress of the project and the characteristics of the team members. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the progress of the project and the characteristics of the team members to the generation AI and cause the generation AI to execute the change of the analysis algorithm.

[0043] The analysis unit can automatically identify risk factors of the project based on knowledge accumulated in PMBOK and AI, and make a proposal for risk management. The risk factors include, for example, a method for identifying risk factors based on the progress of the project and past data, but are not limited to such examples. The analysis unit can identify risk factors of the project based on knowledge of PMBOK, for example. The analysis unit can also identify risk factors based on past project data accumulated in AI. Furthermore, the analysis unit can propose appropriate risk management measures for the identified risk factors. For example, the analysis unit can automatically identify risk factors of the project based on knowledge accumulated in PMBOK and AI using the generation AI, and make a proposal for risk management. As a result, the analysis unit can identify risk factors of the project and propose appropriate risk management measures by utilizing knowledge accumulated in PMBOK and AI. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input risk factors of the project to the generation AI, and have the generation AI execute a proposal for risk management.

[0044] The analysis unit can add a function of proposing optimal resource allocation based on basic information of the project. Resource allocation includes, for example, a resource optimization method based on basic information of the project, but is not limited to such an example. The analysis unit can propose an appropriate resource allocation based on, for example, the scope of the project. The analysis unit can also determine the priority of resources according to the purpose of the project. Furthermore, the analysis unit can optimize the resource allocation based on the schedule of the project. For example, the analysis unit adds a function of proposing optimal resource allocation based on basic information of the project using a generation AI. As a result, the analysis unit can realize efficient project management by proposing resource allocation based on the basic information of the project. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input basic information of the project to the generation AI and cause the generation AI to execute a resource allocation proposal.

[0045] The analysis unit can propose an appropriate training or education program according to the progress of the project. The training or education program includes, for example, a method of selecting a program according to the progress of the project, but is not limited to such an example. For example, when the progress of the project is smooth, the analysis unit proposes a standard training program. In addition, when the project is delayed, the analysis unit can also propose a training program corresponding to the cause of the delay. Furthermore, the analysis unit can also propose an appropriate education program according to the skill level of the team members. For example, the analysis unit uses the generation AI to propose an appropriate training or education program according to the progress of the project. As a result, the analysis unit can support the improvement of the skills of the team members by proposing an appropriate training or education program according to the progress of the project. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the progress of the project to the generation AI and cause the generation AI to execute the proposal of the training or education program.

[0046] The execution unit can customize the execution method according to the progress of the project and the skill level of the team members at the time of execution. The method of customizing the execution method includes, for example, a customization method based on the progress of the project and the skill level of the team members, but is not limited to such examples. For example, when the progress of the project is smooth, the execution unit uses a standard execution method. In addition, when the project is delayed, the execution unit can also use an execution method corresponding to the cause of the delay. Furthermore, the execution unit can select an appropriate execution method according to the skill level of the team members. For example, the execution unit customizes the execution method according to the progress of the project and the skill level of the team members using the generation AI. As a result, the execution unit can realize effective communication by customizing the execution method according to the progress of the project and the skill level of the team members. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the execution unit can input the progress of the project and the skill level of the team members to the generation AI and cause the generation AI to execute the customization of the execution method.

[0047] The execution unit can automatically select different communication tools according to the characteristics of the project at the time of execution. The communication tools include, for example, but are not limited to, a method of selecting a tool according to the characteristics of the project. For example, when there is a lot of remote work, the execution unit selects an online conference tool. In addition, when face-to-face communication is important, the execution unit can also select regular meetings. Furthermore, the execution unit can select an appropriate communication tool according to the characteristics of the project. For example, the execution unit automatically selects different communication tools according to the characteristics of the project using a generation AI. As a result, the execution unit can realize effective communication by selecting the optimal communication tool according to the characteristics of the project. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the execution unit can input the characteristics of the project to the generation AI and cause the generation AI to select a communication tool.

[0048] The execution unit can select the optimal communication means at the time of execution, taking into account the geographical distribution of the project. The geographical distribution includes, for example, the base of the project and the location of the team members, but is not limited to such examples. For example, the execution unit selects an online conference tool when the team members are in different regions. In addition, the execution unit can select a face-to-face meeting when the team members are in the same office. Furthermore, the execution unit can select an appropriate communication means according to the geographical distribution of the project. For example, the execution unit selects the optimal communication means by using the generation AI, taking into account the geographical distribution of the project. As a result, the execution unit can realize effective communication by selecting the optimal communication means according to the geographical distribution of the project. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the execution unit can input the geographical distribution of the project to the generation AI and cause the generation AI to select the communication means.

[0049] The execution unit can improve the accuracy of the execution method by referring to the related literature of the project at the time of execution. The related literature includes, for example, a method of selecting literature according to the progress of the project, but is not limited to such an example. The execution unit optimizes the execution method, for example, based on the related literature of the project. The execution unit can also adjust the execution method by referring to the related literature according to the progress of the project. Furthermore, the execution unit can improve the execution method by referring to the related literature according to the characteristics of the project. For example, the execution unit improves the accuracy of the execution method by referring to the related literature of the project using the generation AI. As a result, the execution unit can improve the accuracy of the execution method by referring to the related literature of the project. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the execution unit can input the related literature of the project to the generation AI and cause the generation AI to execute the improvement of the execution method.

[0050] The adjustment unit can reflect the progress of the project and the feedback of the team members in real time during adjustment. Methods of reflecting feedback in real time include, for example, the tools used and the timing of reflection, but are not limited to such examples. For example, when the progress of the project is delayed, the adjustment unit identifies the cause of the delay and makes appropriate adjustments. The adjustment unit can also update the adjustment content in real time based on the feedback of the team members. Furthermore, the adjustment unit can collect and reflect feedback for making appropriate adjustments according to the progress of the project. For example, the adjustment unit uses the generation AI to reflect the progress of the project and the feedback of the team members in real time. As a result, the adjustment unit can realize effective adjustments by reflecting the progress of the project and the feedback of the team members in real time. Part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the progress of the project and the feedback of the team members into the generation AI and cause the generation AI to execute reflection in real time.

[0051] The adjustment unit can apply different adjustment algorithms depending on the characteristics of the project during adjustment. The adjustment algorithm includes, for example, a method of selecting an algorithm depending on the characteristics of the project, but is not limited to such an example. For example, when the scope of the project is wide, the adjustment unit applies a detailed adjustment algorithm. In addition, when the purpose of the project is not clear, the adjustment unit can also apply a flexible adjustment algorithm. Furthermore, when the schedule of the project is tight, the adjustment unit can also apply a quick adjustment algorithm. For example, the adjustment unit applies different adjustment algorithms depending on the characteristics of the project using the generation AI. As a result, the adjustment unit can realize effective adjustment by applying different adjustment algorithms depending on the characteristics of the project. Part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the characteristics of the project to the generation AI and cause the generation AI to apply the adjustment algorithm.

[0052] The adjustment unit can select the optimal adjustment method in consideration of the geographical distribution of the project at the time of adjustment. The geographical distribution includes, for example, the base of the project and the location of the team members, but is not limited to such examples. For example, when the team members are in different regions, the adjustment unit selects an online adjustment method. In addition, when the team members are in the same office, the adjustment unit can also select a face-to-face adjustment method. Furthermore, the adjustment unit can select an appropriate adjustment method according to the geographical distribution of the project. For example, the adjustment unit selects the optimal adjustment method by using the generation AI in consideration of the geographical distribution of the project. As a result, the adjustment unit can realize effective adjustment by selecting the optimal adjustment method according to the geographical distribution of the project. Part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the geographical distribution of the project to the generation AI and cause the generation AI to select the adjustment method.

[0053] The adjustment unit can improve the accuracy of the adjustment method by referring to the related literature of the project during adjustment. The related literature includes, for example, a method of selecting literature according to the progress of the project, but is not limited to such an example. The adjustment unit optimizes the adjustment method based on, for example, the related literature of the project. The adjustment unit can also adjust the adjustment method by referring to the related literature according to the progress of the project. Furthermore, the adjustment unit can improve the adjustment method by referring to the related literature according to the characteristics of the project. For example, the adjustment unit improves the accuracy of the adjustment method by referring to the related literature of the project using the generation AI. As a result, the adjustment unit can improve the accuracy of the adjustment method by referring to the related literature of the project. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the related literature of the project to the generation AI and cause the generation AI to execute the improvement of the adjustment method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The project management platform may further include a risk assessment unit. The risk assessment unit may assess potential risks and propose risk management measures based on basic information and progress of the project. For example, the risk assessment unit may issue an early warning and propose appropriate measures when the project schedule is likely to be delayed. The risk assessment unit may also suggest additional training when a task may be delayed due to a lack of skills of team members. In addition, the risk assessment unit may assess risks due to external factors (e.g., weather and market fluctuations) and adjust the project plan. In this way, the project management platform may improve the success rate of the project by assessing risks in advance and taking appropriate measures.

[0056] The reception unit can refer to the user's past project history and automatically complete information on similar projects. For example, it can automatically input the scope and objectives of similar projects based on past project data. It can also refer to team member information from past projects and automatically suggest appropriate members. It can also automatically set the schedule for the current project by referring to the schedules of past projects. In this way, the reception unit can utilize past data to reduce the effort of entering information and achieve efficient project management.

[0057] The analysis unit can dynamically change the analysis algorithm based on the progress of the project and the characteristics of the team members. For example, if the project is progressing smoothly, a standard analysis algorithm is used. Also, if the project is delayed, an analysis algorithm for identifying the cause of the delay can be used. Furthermore, an appropriate analysis algorithm can be selected according to the characteristics of the team members. In this way, the analysis unit can provide optimal analysis results by changing the analysis algorithm according to the progress of the project and the characteristics of the team members.

[0058] The executive department can automatically select different communication tools depending on the characteristics of the project. For example, if there is a lot of remote work, an online conferencing tool can be selected. Also, if face-to-face communication is important, regular meetings can be selected. Furthermore, appropriate communication tools can be selected depending on the characteristics of the project. This allows the executive department to achieve effective communication by selecting the optimal communication tool depending on the characteristics of the project.

[0059] The coordination department can reflect the progress of the project and feedback from team members in real time. For example, if the progress of the project is delayed, the cause of the delay is identified and appropriate adjustments are made. The coordination department can also update the adjustment content in real time based on feedback from team members. Furthermore, feedback for making appropriate adjustments according to the progress of the project can be collected and reflected. In this way, the coordination department can realize effective adjustments by reflecting the progress of the project and feedback from team members in real time.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception section receives basic project information, including the project's purpose, scope, team members, schedule, etc. The reception section provides an interface for entering the project's purpose, a field for entering the scope, an option for entering team member information, and a calendar function for entering the schedule. Step 2: The analysis part uses the generation AI to analyze the information received by the reception part and proposes the optimal communication method. Based on project knowledge based on PMBOK and project knowledge accumulated in the AI, the analysis part proposes which tools should be used for specific tasks, when communication should be conducted, and how to explain things effectively. Step 3: The execution unit executes the communication methods proposed by the analysis unit. The execution unit uses the proposed tools for a specific task, communicates at the proposed timing, and provides explanations in the proposed manner. Step 4: The coordination department monitors the results of the communication methods implemented by the execution department and adjusts them if necessary. The coordination department monitors the progress of the project in real time, proposes emergency meetings in case of delays, and proposes regular feedback sessions in case of lack of communication.

[0062] (Example 2) A project management platform according to an embodiment of the present invention is a system for optimizing communication methods in project management. This system improves project performance by accepting and analyzing basic information about a project, proposing, executing, and adjusting the optimal communication method. For example, basic information such as the project's purpose, scope, team members, and schedule is input. Next, the generation AI analyzes this information and proposes the optimal communication method. For example, it specifically suggests which tool should be used for a specific task, when communication should be performed, and how to explain it effectively. The generation AI learns the optimal communication method based on past project data and knowledge of PMBOK. This enables customized suggestions according to the progress of the project and the characteristics of the team members. For example, for a team with a lot of remote work, it recommends the use of an online conference tool, and if face-to-face communication is important, it suggests a regular meeting schedule. In addition, the generation AI monitors the progress of the project in real time and adjusts the communication method as necessary. For example, if the project is delayed, it suggests an emergency meeting and automatically generates a report to share the progress. In addition, if communication between team members is lacking, it suggests regular feedback sessions. In this way, the generation AI optimizes the communication method in project management, thereby improving project performance. As a specific example, in the early stages of the project, we suggest a kickoff meeting to share the overall scope and goals, and during the project, we suggest regular status meetings to share the progress of tasks. In addition, at the end of the project, we suggest a meeting to review deliverables and conduct a retrospective. By using this platform, project managers can leave the decision on how to communicate to generative AI and focus on more important tasks.Also, by following the best communication practices, team members can share information efficiently and contribute to the success of the project, which is why a project management platform can optimize project performance.

[0063] The project management platform according to the embodiment includes a reception unit, an analysis unit, an execution unit, and an adjustment unit. The reception unit receives basic information of the project. The basic information of the project includes, for example, the purpose, scope, team members, schedule, etc. of the project, but is not limited to such examples. The reception unit provides, for example, an interface for inputting the purpose of the project. The reception unit can also provide a field for inputting the scope of the project. Furthermore, the reception unit can also provide an option for inputting information of the team members. For example, the reception unit provides a calendar function for inputting the project schedule. The analysis unit uses a generation AI to analyze the information received by the reception unit and proposes an optimal communication method. The analysis unit proposes an optimal communication method based on, for example, project knowledge based on PMBOK or project knowledge accumulated in the AI. For example, the analysis unit proposes which tool should be used for a specific task. The analysis unit can also propose when communication should be performed. Furthermore, the analysis unit can also propose how to explain effectively. For example, the analysis unit uses a generation AI to analyze past project data and learn the optimal communication method. The execution unit executes the communication method proposed by the analysis unit. The execution unit, for example, uses the proposed tool for a specific task. The execution unit can also communicate at the proposed timing. Furthermore, the execution unit can provide an explanation in the proposed manner. For example, the execution unit executes the proposed communication method using a generative AI. The adjustment unit monitors the results of the communication method executed by the execution unit and adjusts it as necessary. The adjustment unit, for example, monitors the progress of the project in real time. Furthermore, the adjustment unit can propose an emergency meeting if the project is delayed. Furthermore, the adjustment unit can propose regular feedback sessions if there is a lack of communication between team members.For example, the adjustment unit uses the generation AI to monitor the progress of the project and adjust the communication method as necessary. This allows the project management platform according to the embodiment to optimize project performance.

[0064] The reception unit receives basic information of the project. The basic information of the project includes, but is not limited to, for example, the purpose, scope, team members, and schedule of the project. The reception unit provides, for example, an interface for inputting the purpose of the project. Specifically, the reception unit includes a text field in which the user can describe the purpose of the project in detail and a drop-down menu from which the user can select from options. The reception unit can also provide a field for inputting the scope of the project. The input field for the scope includes a multi-line text area and a function for uploading related documents so that the scope and constraints of the project can be described in detail. The reception unit can also provide options for inputting information of team members. For example, the reception unit includes fields for inputting the names, roles, and contact information of team members and a function for selecting members from an existing contact list. This allows the team composition of the project to be set quickly and accurately. For example, the reception unit provides a calendar function for inputting the schedule of the project. The calendar function provides an interface that allows the user to visually manage the start date, end date, and important milestones of the project, allowing the user to intuitively operate the schedule. This allows the reception unit to efficiently collect basic information of the project and smoothly progress the planning stage of the project. In addition, the reception department can provide APIs and database connections to centrally manage collected information and link with other departments and systems, allowing basic project information to be seamlessly integrated with other systems and tools, improving the efficiency of overall project management.

[0065] The analysis part uses the generation AI to analyze the information received by the reception part and proposes the optimal communication method. The analysis part proposes the optimal communication method based on, for example, project knowledge based on PMBOK and project knowledge accumulated in the AI. Specifically, the generation AI analyzes information such as the project's purpose and scope, the composition of team members, and schedule, and derives the communication strategy that is most suitable for the characteristics of the project. For example, the analysis part suggests which tool should be used for a specific task. Based on the nature of the task and the skill sets of the team members, it selects the optimal communication tool, such as email, chat, or video conference. The analysis part can also suggest when communication should be performed. It recommends meetings and reports at appropriate times depending on the progress of the project and important milestones. Furthermore, the analysis part can also suggest how to explain effectively. For example, the generation AI analyzes past project data and learns successful communication patterns to propose effective explanation methods and presentation styles. As a result, the analysis part provides the optimal communication method according to the characteristics of the project and supports the smooth progress of the project. In addition, the Analytics Department can analyze data in real time and continuously improve communication methods based on project progress and team member feedback, allowing the Analytics Department to provide effective communication strategies for project success and optimize project performance.

[0066] The execution unit executes the communication method proposed by the analysis unit. For example, the execution unit uses the tools proposed for a specific task. Specifically, the analysis unit uses tools such as email, chat, and video conferencing proposed by the analysis unit to facilitate communication between team members. The execution unit can also communicate at the proposed timing. For example, a regular meeting is set before an important milestone to check the progress. Furthermore, the execution unit can also provide an explanation in the proposed manner. For example, the execution unit executes the proposed communication method using a generation AI. Specifically, an effective explanation is provided using presentation materials and reports created by the generation AI. This allows the execution unit to execute the optimal communication method based on the proposal of the analysis unit and facilitate the progress of the project. Furthermore, the execution unit can record the results of the executed communication and use them for subsequent analysis and improvement. For example, the minutes of the meeting and communication logs are saved to grasp the progress and problems of the project. The execution unit can also collect feedback from team members and use them to improve the communication method. This allows the execution unit to facilitate the progress of the project and promote cooperation between team members.

[0067] The coordination unit monitors the results of the communication method executed by the execution unit and adjusts it as necessary. For example, the coordination unit monitors the progress of the project in real time. Specifically, the generation AI is used to automatically track the progress of the project and the completion status of the task, and visualize the progress. The coordination unit can also propose an emergency meeting if the project is delayed. For example, if an important milestone is delayed, the generation AI automatically issues an alert and proposes holding an emergency meeting. Furthermore, the coordination unit can also propose regular feedback sessions if communication between team members is lacking. For example, if the generation AI analyzes the communication logs of team members and determines that communication is lacking, it proposes holding regular feedback sessions. This allows the coordination unit to grasp the progress of the project in real time and respond quickly as necessary. Furthermore, the coordination unit can continuously improve the communication method based on the progress of the project and the feedback of the team members. For example, by analyzing past project data and learning successful communication patterns, it proposes a more effective communication method. This allows the coordination unit to smooth the progress of the project and promote cooperation between team members.

[0068] The reception unit can receive basic information including the purpose, scope, team members, and schedule of the project. The basic information includes, for example, the purpose, scope, team members, and schedule of the project, but is not limited to such examples. The reception unit provides, for example, an interface for inputting the purpose of the project. The reception unit can also provide a field for inputting the scope of the project. Furthermore, the reception unit can provide an option for inputting information of team members. For example, the reception unit provides a calendar function for inputting the schedule of the project. In this way, the reception unit can provide basic data for the analysis unit to propose an optimal communication method by receiving the basic information of the project. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input an interface for inputting the basic information of the project to the generation AI, and cause the generation AI to execute generation of the interface.

[0069] The analysis unit can propose an optimal communication method based on project knowledge based on PMBOK or project knowledge accumulated in the AI. Project knowledge based on PMBOK includes, for example, best practices and standard methods for project management, but is not limited to such examples. Project knowledge accumulated in the AI ​​includes, for example, past project data and learning algorithms, but is not limited to such examples. The analysis unit, for example, proposes which tool should be used for a specific task based on project knowledge based on PMBOK. The analysis unit can also propose when communication should be performed based on project knowledge accumulated in the AI. Furthermore, the analysis unit can also propose how to explain effectively. For example, the analysis unit uses the generation AI to analyze past project data and learn the optimal communication method. As a result, the analysis unit can propose the optimal communication method by utilizing the knowledge accumulated in PMBOK and the AI. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input basic information about the project to the generation AI and have the generation AI execute a proposal for the optimal communication method.

[0070] The execution unit can propose which tool should be used for a specific task, when communication should be performed, and how to explain it effectively. The specific task includes, for example, a phase of a project and specific work content, but is not limited to such examples. The tools to be used include, for example, a communication tool and a project management tool, but is not limited to such examples. The timing of communication includes, for example, the progress of the project and important milestones, but is not limited to such examples. The execution unit, for example, uses a tool proposed for a specific task. The execution unit can also communicate at the proposed timing. Furthermore, the execution unit can also explain in the proposed method. For example, the execution unit executes the proposed communication method using a generation AI. As a result, the execution unit can improve the efficiency of the project by proposing an optimal communication method for a specific task. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the execution unit can input a method of using a tool proposed for a specific task to the generation AI and cause the generation AI to execute the proposal of the method of using the tool.

[0071] The adjustment unit can monitor the progress of the project in real time and adjust the communication method according to the progress of the project. The method of monitoring in real time includes, for example, the software used and the frequency of updating data, but is not limited to such examples. The adjustment includes, for example, the frequency of adjustment, the algorithm used, the evaluation criteria, etc., but is not limited to such examples. The adjustment unit, for example, monitors the progress of the project in real time. In addition, the adjustment unit can also propose an emergency meeting if the project is delayed. Furthermore, the adjustment unit can also propose a regular feedback session if communication between team members is lacking. For example, the adjustment unit uses the generation AI to monitor the progress of the project and adjust the communication method as necessary. Thereby, the adjustment unit can optimize the performance of the project by adjusting the communication method according to the progress of the project. Part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the progress of the project to the generation AI and cause the generation AI to execute the adjustment of the communication method.

[0072] The reception unit can estimate the user's emotion and adjust the input method of the basic information of the project based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple interface and minimizes the input procedure. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the basic information of the project to be input quickly. For example, the reception unit uses a generation AI to estimate the user's emotion and adjust the input method of the basic information of the project based on the estimated user's emotion. In this way, the reception unit can reduce the burden on the user and realize efficient information input by adjusting the input method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the input method based on the emotion.

[0073] The reception unit can automatically complement information on similar projects by referring to past data on the project. The past data includes, for example, past project data and information on similar projects, but is not limited to such examples. The reception unit automatically inputs the scope and purpose of the similar project based on, for example, past project data. The reception unit can also automatically suggest appropriate members by referring to team member information on the past project. Furthermore, the reception unit can automatically set a schedule for the current project by referring to the schedule of the past project. For example, the reception unit uses a generation AI to analyze past project data and automatically complement information on similar projects. In this way, the reception unit can utilize past data to reduce the effort of inputting information and realize efficient project management. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input past project data into the generation AI and cause the generation AI to execute complementing of information on similar projects.

[0074] The reception unit can dynamically change the priority of the input items according to the scope and purpose of the project. The priority of the input items includes, for example, a method of setting a priority based on the scope and purpose of the project, but is not limited to such examples. For example, when the scope of the project is wide, the reception unit urges the user to input important items preferentially. In addition, when the purpose of the project is unclear, the reception unit can also prioritize input items that require detailed explanation. Furthermore, when the schedule of the project is tight, the reception unit can also display schedule-related input items preferentially. For example, the reception unit dynamically changes the priority of the input items according to the scope and purpose of the project using the generation AI. As a result, the reception unit can realize efficient information input by changing the priority of the input items according to the characteristics of the project. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the scope and purpose of the project to the generation AI and cause the generation AI to execute the change of the priority of the input items.

[0075] The reception unit can estimate the user's emotion and adjust the design of the input interface based on the estimated user's emotion. For example, when the user is nervous, the reception unit provides an interface with a calm color scheme to reduce visual stress. Also, when the user is having fun, the reception unit can provide an interface with a bright color scheme to make the input work enjoyable. Furthermore, when the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input work. For example, the reception unit uses a generation AI to estimate the user's emotion and adjusts the design of the input interface based on the estimated user's emotion. Thereby, the reception unit can reduce the burden on the user and realize efficient information input by adjusting the interface design according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input a user's emotional data into the generation AI and cause the generation AI to adjust the design of the interface based on the emotion.

[0076] The reception unit can add a function to receive basic information of the project by voice input or gesture input. Voice input includes, for example, voice recognition technology and a device to be used, but is not limited to such examples. Gesture input includes, for example, gesture recognition technology and a device to be used, but is not limited to such examples. The reception unit, for example, enables a user to input the purpose and scope of the project by voice. The reception unit can also enable a user to select and add team members by gesture. Furthermore, the reception unit can enable a user to set a schedule by voice or gesture. For example, the reception unit adds a function to receive basic information of the project by voice input or gesture input using a generation AI. As a result, the reception unit can improve the user's convenience and realize efficient information input by utilizing voice input or gesture input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input data of the voice input or gesture input to the generation AI and cause the generation AI to execute the addition of an input method.

[0077] The reception unit can simplify input by referring to the user's past project history when receiving basic information of the project. The past project history includes, for example, past successful and failed projects, but is not limited to such examples. The reception unit, for example, automatically inputs information of projects that the user has managed in the past. The reception unit can also automatically complement information of similar projects from the user's past project history. Furthermore, the reception unit can suggest appropriate input items based on the user's past project history. For example, the reception unit simplifies input by referring to the user's past project history when receiving basic information of the project using the generation AI. In this way, the reception unit can utilize the past project history to reduce the effort of inputting information and realize efficient project management. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's past project history into the generation AI and cause the generation AI to execute simplification of input.

[0078] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis result based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit provides a detailed analysis result. In addition, when the user is in a hurry, the analysis unit can also provide a concise analysis result that covers the main points. Furthermore, when the user is stressed, the analysis unit can present the analysis result using a visually easy-to-understand graph or chart. For example, the analysis unit estimates the user's emotion using a generation AI and adjusts the presentation method of the analysis result based on the estimated user's emotion. In this way, the analysis unit can deepen the user's understanding and realize efficient project management by adjusting the presentation method of the analysis result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input a user's emotion data into the generation AI and have the generation AI adjust the way in which the analysis results are presented based on the emotion.

[0079] The analysis unit can dynamically change the analysis algorithm based on the progress of the project and the characteristics of the team members. The analysis algorithm includes, for example, a method of selecting an algorithm based on the progress of the project and the characteristics of the team members, but is not limited to such examples. For example, when the progress of the project is smooth, the analysis unit uses a standard analysis algorithm. In addition, when the project is delayed, the analysis unit can also use an analysis algorithm for identifying the cause of the delay. Furthermore, the analysis unit can select an appropriate analysis algorithm according to the characteristics of the team members. For example, the analysis unit dynamically changes the analysis algorithm based on the progress of the project and the characteristics of the team members using the generation AI. As a result, the analysis unit can provide optimal analysis results by changing the analysis algorithm according to the progress of the project and the characteristics of the team members. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the progress of the project and the characteristics of the team members to the generation AI and cause the generation AI to execute the change of the analysis algorithm.

[0080] The analysis unit can automatically identify risk factors of the project based on knowledge accumulated in PMBOK and AI, and make a proposal for risk management. The risk factors include, for example, a method for identifying risk factors based on the progress of the project and past data, but are not limited to such examples. The analysis unit can identify risk factors of the project based on knowledge of PMBOK, for example. The analysis unit can also identify risk factors based on past project data accumulated in AI. Furthermore, the analysis unit can propose appropriate risk management measures for the identified risk factors. For example, the analysis unit can automatically identify risk factors of the project based on knowledge accumulated in PMBOK and AI using the generation AI, and make a proposal for risk management. As a result, the analysis unit can identify risk factors of the project and propose appropriate risk management measures by utilizing knowledge accumulated in PMBOK and AI. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input risk factors of the project to the generation AI, and have the generation AI execute a proposal for risk management.

[0081] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. The priority of the analysis results includes, for example, a method of setting a priority based on the user's emotion, but is not limited to such an example. For example, when the user is nervous, the analysis unit preferentially presents important analysis results. In addition, when the user is relaxed, the analysis unit can also sequentially present detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can preferentially present analysis results that focus on the main points. For example, the analysis unit uses a generation AI to estimate the user's emotion and determines the priority of the analysis results based on the estimated user's emotion. Thereby, the analysis unit can provide important information preferentially by determining the priority of the analysis results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input a user's emotion data into the generation AI and have the generation AI determine the priority of analysis results based on emotion.

[0082] The analysis unit can add a function of proposing optimal resource allocation based on basic information of the project. Resource allocation includes, for example, a resource optimization method based on basic information of the project, but is not limited to such an example. The analysis unit can propose an appropriate resource allocation based on, for example, the scope of the project. The analysis unit can also determine the priority of resources according to the purpose of the project. Furthermore, the analysis unit can optimize the resource allocation based on the schedule of the project. For example, the analysis unit adds a function of proposing optimal resource allocation based on basic information of the project using a generation AI. As a result, the analysis unit can realize efficient project management by proposing resource allocation based on the basic information of the project. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input basic information of the project to the generation AI and cause the generation AI to execute a resource allocation proposal.

[0083] The analysis unit can propose an appropriate training or education program according to the progress of the project. The training or education program includes, for example, a method of selecting a program according to the progress of the project, but is not limited to such an example. For example, when the progress of the project is smooth, the analysis unit proposes a standard training program. In addition, when the project is delayed, the analysis unit can also propose a training program corresponding to the cause of the delay. Furthermore, the analysis unit can also propose an appropriate education program according to the skill level of the team members. For example, the analysis unit uses the generation AI to propose an appropriate training or education program according to the progress of the project. As a result, the analysis unit can support the improvement of the skills of the team members by proposing an appropriate training or education program according to the progress of the project. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the progress of the project to the generation AI and cause the generation AI to execute the proposal of the training or education program.

[0084] The execution unit can estimate the user's emotion and adjust the execution timing of the communication method based on the estimated user's emotion. The execution timing includes, for example, a method of setting the timing based on the user's emotion, but is not limited to such an example. For example, when the user is relaxed, the execution unit executes communication at a normal timing. Also, when the user is in a hurry, the execution unit can execute communication quickly. Furthermore, when the user is stressed, the execution unit can execute communication at an appropriate timing. For example, the execution unit estimates the user's emotion using a generation AI and adjusts the execution timing of the communication method based on the estimated user's emotion. Thereby, the execution unit can realize effective communication by adjusting the execution timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the execution unit can input a user's emotion data to the generation AI and cause the generation AI to adjust the execution timing based on the emotion.

[0085] The execution unit can customize the execution method according to the progress of the project and the skill level of the team members at the time of execution. The method of customizing the execution method includes, for example, a customization method based on the progress of the project and the skill level of the team members, but is not limited to such examples. For example, when the progress of the project is smooth, the execution unit uses a standard execution method. In addition, when the project is delayed, the execution unit can also use an execution method corresponding to the cause of the delay. Furthermore, the execution unit can select an appropriate execution method according to the skill level of the team members. For example, the execution unit customizes the execution method according to the progress of the project and the skill level of the team members using the generation AI. As a result, the execution unit can realize effective communication by customizing the execution method according to the progress of the project and the skill level of the team members. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the execution unit can input the progress of the project and the skill level of the team members to the generation AI and cause the generation AI to execute the customization of the execution method.

[0086] The execution unit can automatically select different communication tools according to the characteristics of the project at the time of execution. The communication tools include, for example, but are not limited to, a method of selecting a tool according to the characteristics of the project. For example, when there is a lot of remote work, the execution unit selects an online conference tool. In addition, when face-to-face communication is important, the execution unit can also select regular meetings. Furthermore, the execution unit can select an appropriate communication tool according to the characteristics of the project. For example, the execution unit automatically selects different communication tools according to the characteristics of the project using a generation AI. As a result, the execution unit can realize effective communication by selecting the optimal communication tool according to the characteristics of the project. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the execution unit can input the characteristics of the project to the generation AI and cause the generation AI to select a communication tool.

[0087] The execution unit can estimate the user's emotion and adjust the execution order of the communication methods based on the estimated user's emotion. The execution order includes, for example, a method of setting an order based on the user's emotion, but is not limited to such an example. For example, when the user is nervous, the execution unit prioritizes important communication. In addition, when the user is relaxed, the execution unit can also sequentially execute detailed communication. Furthermore, when the user is in a hurry, the execution unit can prioritize communication that covers the main points. For example, the execution unit estimates the user's emotion using a generation AI and adjusts the execution order of the communication methods based on the estimated user's emotion. Thereby, the execution unit can realize effective communication by adjusting the execution order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the execution unit can input a user's emotion data to the generation AI and cause the generation AI to adjust the execution order based on the emotion.

[0088] The execution unit can select the optimal communication means at the time of execution, taking into account the geographical distribution of the project. The geographical distribution includes, for example, the base of the project and the location of the team members, but is not limited to such examples. For example, the execution unit selects an online conference tool when the team members are in different regions. In addition, the execution unit can select a face-to-face meeting when the team members are in the same office. Furthermore, the execution unit can select an appropriate communication means according to the geographical distribution of the project. For example, the execution unit selects the optimal communication means by using the generation AI, taking into account the geographical distribution of the project. As a result, the execution unit can realize effective communication by selecting the optimal communication means according to the geographical distribution of the project. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the execution unit can input the geographical distribution of the project to the generation AI and cause the generation AI to select the communication means.

[0089] The execution unit can improve the accuracy of the execution method by referring to the related literature of the project at the time of execution. The related literature includes, for example, a method of selecting literature according to the progress of the project, but is not limited to such an example. The execution unit optimizes the execution method, for example, based on the related literature of the project. The execution unit can also adjust the execution method by referring to the related literature according to the progress of the project. Furthermore, the execution unit can improve the execution method by referring to the related literature according to the characteristics of the project. For example, the execution unit improves the accuracy of the execution method by referring to the related literature of the project using the generation AI. As a result, the execution unit can improve the accuracy of the execution method by referring to the related literature of the project. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the execution unit can input the related literature of the project to the generation AI and cause the generation AI to execute the improvement of the execution method.

[0090] The adjustment unit can estimate the user's emotion and customize the adjustment method based on the estimated user's emotion. The method of customizing the adjustment method includes, for example, a customization method based on the user's emotion, but is not limited to such an example. For example, when the user is stressed, the adjustment unit provides a simple adjustment method to reduce the burden. In addition, when the user is relaxed, the adjustment unit can provide detailed adjustment options and suggest a customizable adjustment method. Furthermore, when the user is in a hurry, the adjustment unit can provide a simplified method for quickly making adjustments. For example, the adjustment unit uses a generation AI to estimate the user's emotion and customizes the adjustment method based on the estimated user's emotion. Thereby, the adjustment unit can realize effective adjustment by customizing the adjustment method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input a user's emotion data into the generation AI and cause the generation AI to customize the adjustment method based on the emotion.

[0091] The adjustment unit can reflect the progress of the project and the feedback of the team members in real time during adjustment. Methods of reflecting feedback in real time include, for example, the tools used and the timing of reflection, but are not limited to such examples. For example, when the progress of the project is delayed, the adjustment unit identifies the cause of the delay and makes appropriate adjustments. The adjustment unit can also update the adjustment content in real time based on the feedback of the team members. Furthermore, the adjustment unit can collect and reflect feedback for making appropriate adjustments according to the progress of the project. For example, the adjustment unit uses the generation AI to reflect the progress of the project and the feedback of the team members in real time. As a result, the adjustment unit can realize effective adjustments by reflecting the progress of the project and the feedback of the team members in real time. Part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the progress of the project and the feedback of the team members into the generation AI and cause the generation AI to execute reflection in real time.

[0092] The adjustment unit can apply different adjustment algorithms depending on the characteristics of the project during adjustment. The adjustment algorithm includes, for example, a method of selecting an algorithm depending on the characteristics of the project, but is not limited to such an example. For example, when the scope of the project is wide, the adjustment unit applies a detailed adjustment algorithm. In addition, when the purpose of the project is not clear, the adjustment unit can also apply a flexible adjustment algorithm. Furthermore, when the schedule of the project is tight, the adjustment unit can also apply a quick adjustment algorithm. For example, the adjustment unit applies different adjustment algorithms depending on the characteristics of the project using the generation AI. As a result, the adjustment unit can realize effective adjustment by applying different adjustment algorithms depending on the characteristics of the project. Part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the characteristics of the project to the generation AI and cause the generation AI to apply the adjustment algorithm.

[0093] The adjustment unit can estimate the user's emotion and determine the priority of adjustment based on the estimated user's emotion. The priority of adjustment includes, for example, a method of setting a priority based on the user's emotion, but is not limited to such an example. For example, when the user is nervous, the adjustment unit prioritizes important adjustments. In addition, when the user is relaxed, the adjustment unit can also sequentially perform detailed adjustments. Furthermore, when the user is in a hurry, the adjustment unit can prioritize adjustments that focus on the main points. For example, the adjustment unit uses a generation AI to estimate the user's emotion and determines the priority of adjustments based on the estimated user's emotion. Thereby, the adjustment unit can prioritize important adjustments by determining the priority of adjustments according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input a user's emotion data to the generation AI and cause the generation AI to determine the priority of adjustments based on emotions.

[0094] The adjustment unit can select the optimal adjustment method in consideration of the geographical distribution of the project at the time of adjustment. The geographical distribution includes, for example, the base of the project and the location of the team members, but is not limited to such examples. For example, when the team members are in different regions, the adjustment unit selects an online adjustment method. In addition, when the team members are in the same office, the adjustment unit can also select a face-to-face adjustment method. Furthermore, the adjustment unit can select an appropriate adjustment method according to the geographical distribution of the project. For example, the adjustment unit selects the optimal adjustment method by using the generation AI in consideration of the geographical distribution of the project. As a result, the adjustment unit can realize effective adjustment by selecting the optimal adjustment method according to the geographical distribution of the project. Part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the geographical distribution of the project to the generation AI and cause the generation AI to select the adjustment method.

[0095] The adjustment unit can improve the accuracy of the adjustment method by referring to the related literature of the project during adjustment. The related literature includes, for example, a method of selecting literature according to the progress of the project, but is not limited to such an example. The adjustment unit optimizes the adjustment method based on, for example, the related literature of the project. The adjustment unit can also adjust the adjustment method by referring to the related literature according to the progress of the project. Furthermore, the adjustment unit can improve the adjustment method by referring to the related literature according to the characteristics of the project. For example, the adjustment unit improves the accuracy of the adjustment method by referring to the related literature of the project using the generation AI. As a result, the adjustment unit can improve the accuracy of the adjustment method by referring to the related literature of the project. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the related literature of the project to the generation AI and cause the generation AI to execute the improvement of the adjustment method.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The project management platform may further include a risk assessment unit. The risk assessment unit may assess potential risks and propose risk management measures based on basic information and progress of the project. For example, the risk assessment unit may issue an early warning and propose appropriate measures when the project schedule is likely to be delayed. The risk assessment unit may also suggest additional training when a task may be delayed due to a lack of skills of team members. In addition, the risk assessment unit may assess risks due to external factors (e.g., weather and market fluctuations) and adjust the project plan. In this way, the project management platform may improve the success rate of the project by assessing risks in advance and taking appropriate measures.

[0098] The reception unit can refer to the user's past project history and automatically complete information on similar projects. For example, it can automatically input the scope and objectives of similar projects based on past project data. It can also refer to team member information from past projects and automatically suggest appropriate members. It can also automatically set the schedule for the current project by referring to the schedules of past projects. In this way, the reception unit can utilize past data to reduce the effort of entering information and achieve efficient project management.

[0099] The analysis unit can dynamically change the analysis algorithm based on the progress of the project and the characteristics of the team members. For example, if the project is progressing smoothly, a standard analysis algorithm is used. Also, if the project is delayed, an analysis algorithm for identifying the cause of the delay can be used. Furthermore, an appropriate analysis algorithm can be selected according to the characteristics of the team members. In this way, the analysis unit can provide optimal analysis results by changing the analysis algorithm according to the progress of the project and the characteristics of the team members.

[0100] The executive department can automatically select different communication tools depending on the characteristics of the project. For example, if there is a lot of remote work, an online conferencing tool can be selected. Also, if face-to-face communication is important, regular meetings can be selected. Furthermore, appropriate communication tools can be selected depending on the characteristics of the project. This allows the executive department to achieve effective communication by selecting the optimal communication tool depending on the characteristics of the project.

[0101] The coordination department can reflect the progress of the project and feedback from team members in real time. For example, if the progress of the project is delayed, the cause of the delay is identified and appropriate adjustments are made. The coordination department can also update the adjustment content in real time based on feedback from team members. Furthermore, feedback for making appropriate adjustments according to the progress of the project can be collected and reflected. In this way, the coordination department can realize effective adjustments by reflecting the progress of the project and feedback from team members in real time.

[0102] The reception unit can estimate the user's emotions and adjust the input method of the basic project information based on the estimated user's emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input procedure. Also, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable the basic project information to be input quickly. In this way, the reception unit can reduce the burden on the user and realize efficient information input by adjusting the input method according to the user's emotions.

[0103] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is feeling stressed, the analysis results can be presented using visually easy-to-understand graphs and charts. In this way, the analysis unit can deepen the user's understanding and realize efficient project management by adjusting the way in which the analysis results are presented according to the user's emotions.

[0104] The execution unit can estimate the user's emotions and adjust the timing of execution of the communication method based on the estimated user's emotions. For example, when the user is relaxed, communication can be executed at a normal timing. When the user is in a hurry, communication can be executed quickly. Furthermore, when the user is feeling stressed, communication can be executed at an appropriate timing. In this way, the execution unit can realize effective communication by adjusting the execution timing according to the user's emotions.

[0105] The adjustment unit can estimate the user's emotion and customize the adjustment method based on the estimated user's emotion. For example, if the user is stressed, a simple adjustment method is provided to reduce the burden. Also, if the user is relaxed, detailed adjustment options can be provided and a customizable adjustment method can be suggested. Furthermore, if the user is in a hurry, a simplified method for making adjustments quickly can be provided. In this way, the adjustment unit can realize effective adjustment by customizing the adjustment method according to the user's emotion.

[0106] The adjustment unit can estimate the user's emotions and determine the priority order of adjustments based on the estimated user's emotions. For example, if the user is nervous, important adjustments can be made with priority. Also, if the user is relaxed, detailed adjustments can be made sequentially. Furthermore, if the user is in a hurry, adjustments that focus on the main points can be made with priority. In this way, the adjustment unit can prioritize important adjustments by determining the priority order of adjustments according to the user's emotions.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The reception section receives basic project information, including the project's purpose, scope, team members, schedule, etc. The reception section provides an interface for entering the project's purpose, a field for entering the scope, an option for entering team member information, and a calendar function for entering the schedule. Step 2: The analysis part uses the generation AI to analyze the information received by the reception part and proposes the optimal communication method. Based on project knowledge based on PMBOK and project knowledge accumulated in the AI, the analysis part proposes which tools should be used for specific tasks, when communication should be conducted, and how to explain things effectively. Step 3: The execution unit executes the communication methods proposed by the analysis unit. The execution unit uses the proposed tools for a specific task, communicates at the proposed timing, and provides explanations in the proposed manner. Step 4: The coordination department monitors the results of the communication methods implemented by the execution department and adjusts them if necessary. The coordination department monitors the progress of the project in real time, proposes emergency meetings in case of delays, and proposes regular feedback sessions in case of lack of communication.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the above-mentioned reception unit, analysis unit, execution unit, and adjustment unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and provides an interface for inputting basic information about the project. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal communication method using a generation AI. The execution unit is realized, for example, by the control unit 46A of the smart device 14, and executes the proposed communication method. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the progress of the project and adjusts the communication method as necessary. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0120] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned reception unit, analysis unit, execution unit, and adjustment unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and provides an interface for inputting basic information of the project. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal communication method using a generation AI. The execution unit is realized, for example, by the control unit 46A of the smart glasses 214, and executes the proposed communication method. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the progress of the project and adjusts the communication method as necessary. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned reception unit, analysis unit, execution unit, and adjustment unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and provides an interface for inputting basic information of the project. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal communication method using a generation AI. The execution unit is realized, for example, by the control unit 46A of the headset type terminal 314, and executes the proposed communication method. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the progress of the project and adjusts the communication method as necessary. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] Each of the multiple elements including the above-mentioned reception unit, analysis unit, execution unit, and adjustment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and provides an interface for inputting basic information about the project. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal communication method using a generation AI. The execution unit is realized, for example, by the control unit 46A of the robot 414, and executes the proposed communication method. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the progress of the project and adjusts the communication method as necessary. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) A reception section that accepts basic information about the project; an analysis unit that analyzes the information received by the reception unit and proposes an optimal communication method; an execution unit that executes the communication method proposed by the analysis unit; and an adjustment unit that monitors the results of the communication method executed by the execution unit and adjusts the results as necessary. A system characterized by: (Appendix 2) The reception unit is Accepts basic information including project purpose, scope, team members, and schedule 2. The system of claim 1, (Appendix 3) The analysis unit is Propose optimal communication methods based on project knowledge based on PMBOK or project knowledge accumulated in AI 2. The system of claim 1, (Appendix 4) The execution unit is Suggest which tools should be used for specific tasks, when to communicate, and how to explain them effectively 2. The system of claim 1, (Appendix 5) The adjustment unit is Monitor project progress in real time and adjust communication methods according to project progress 2. The system of claim 1, (Appendix 6) The reception unit is Infer user sentiment and adjust how basic project information is entered based on the inferred user sentiment 2. The system of claim 1, (Appendix 7) The reception unit is Referencing past project data and automatically completing information on similar projects 2. The system of claim 1, (Appendix 8) The reception unit is Dynamically change the priority of inputs depending on the project scope and objectives 2. The system of claim 1, (Appendix 9) The reception unit is Estimating user emotions and adjusting the design of the input interface based on the estimated user emotions 2. The system of claim 1, (Appendix 10) The reception unit is Add a function to accept basic project information via voice input or gesture input 2. The system of claim 1, (Appendix 11) The reception unit is When accepting basic project information, simplify input by referencing the user's past project history 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user emotions and adjust the presentation of analysis results based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is Dynamically change analysis algorithms based on project progress and team member characteristics 2. The system of claim 1, (Appendix 14) The analysis unit is Based on knowledge accumulated in PMBOK and AI, automatically identify project risk factors and propose risk management 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and prioritize analysis results based on the estimated user emotions. 2. The system of claim 1, (Appendix 16) The analysis unit is Add a function to suggest optimal resource allocation based on basic project information 2. The system of claim 1, (Appendix 17) The analysis unit is Propose appropriate training and education programs according to the progress of the project 2. The system of claim 1, (Appendix 18) The execution unit is To estimate a user's emotion and adjust a timing of execution of a communication method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 19) The execution unit is Customize execution methods according to project progress and team member skill levels during execution 2. The system of claim 1, (Appendix 20) The execution unit is Automatically select different communication tools at runtime depending on project characteristics 2. The system of claim 1, (Appendix 21) The execution unit is To estimate a user's emotion and adjust an execution order of communication methods based on the estimated user's emotion. 2. The system of claim 1, (Appendix 22) The execution unit is During execution, choose the most appropriate means of communication, taking into account the geographical distribution of the project 2. The system of claim 1, (Appendix 23) The execution unit is During execution, refer to the relevant literature of the project to improve the accuracy of the execution method 2. The system of claim 1, (Appendix 24) The adjustment unit is Inferring user emotion and customizing the adjustment method based on the estimated user emotion 2. The system of claim 1, (Appendix 25) The adjustment unit is Adjustments are made in real time to reflect project progress and team member feedback 2. The system of claim 1, (Appendix 26) The adjustment unit is During tuning, different tuning algorithms are applied depending on the project characteristics. 2. The system of claim 1, (Appendix 27) The adjustment unit is To estimate a user's emotion and determine an adjustment priority based on the estimated user's emotion. 2. The system of claim 1, (Appendix 28) The adjustment unit is When making adjustments, consider the geographical distribution of projects and select the most appropriate adjustment method. 2. The system of claim 1, (Appendix 29) The adjustment unit is During the adjustment, refer to the relevant literature of the project to improve the accuracy of the adjustment method 2. The system of claim 1, [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section that accepts basic information about the project; an analysis unit that analyzes the information received by the reception unit and proposes an optimal communication method; an execution unit that executes the communication method proposed by the analysis unit; and an adjustment unit that monitors the results of the communication method executed by the execution unit and adjusts the results as necessary. A system characterized by:

2. The reception unit is Accepts basic information including project purpose, scope, team members, and schedule 2. The system of claim 1 .

3. The analysis unit is Propose optimal communication methods based on project knowledge based on PMBOK or project knowledge accumulated in AI 2. The system of claim 1 .

4. The execution unit is Suggest which tools should be used for specific tasks, when to communicate, and how to explain them effectively 2. The system of claim 1 .

5. The adjustment unit is Monitor project progress in real time and adjust communication methods according to project progress 2. The system of claim 1 .

6. The reception unit is Infer user sentiment and adjust how basic project information is entered based on the inferred user sentiment 2. The system of claim 1 .

7. The reception unit is Referencing past project data and automatically completing information on similar projects 2. The system of claim 1 .

8. The reception unit is Dynamically change the priority of inputs depending on the project scope and objectives 2. The system of claim 1 .

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