system

Abstract

The system according to this embodiment aims to analyze participants' skill sets and previous projects and suggest the most suitable teammates. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, an inspiration unit, a monitoring unit, and an adjustment unit. The analysis unit analyzes the skill sets and previous projects of the participants. The proposal unit proposes the most suitable teammates based on the analysis results obtained by the analysis unit. The inspiration unit collaborates with the teammates proposed by the proposal unit to generate new ideas. The monitoring unit monitors the progress of the project based on the ideas generated by the inspiration unit. The adjustment unit makes necessary adjustments according to the progress of the project monitored by the monitoring unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to find an optimal teammate considering the skill sets of participants and previous projects, and there is a problem that an efficient team formation cannot be achieved in the progress of a project.

[0005] The system according to the embodiment aims to analyze the skill sets of participants and previous projects and propose an optimal teammate.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, an inspiration unit, a monitoring unit, and an adjustment unit. The analysis unit analyzes the skill sets and previous projects of the participants. The proposal unit proposes the most suitable teammates based on the analysis results obtained by the analysis unit. The inspiration unit collaborates with the teammates proposed by the proposal unit to generate new ideas. The monitoring unit monitors the progress of the project based on the ideas generated by the inspiration unit. The adjustment unit makes necessary adjustments according to the progress of the project monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze participants' skill sets and previous projects and suggest the most suitable teammates. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Creative Matching Agent System according to an embodiment of the present invention is a system that uses AI to analyze participants' skill sets and previous projects and proposes the most suitable teammates. This system enables users to further develop their ideas and collaborate with other creators who possess different expertise. First, the Creative Matching Agent System uses AI to analyze participants' skill sets and previous projects. In this process, machine learning is used for skill matching, and the system learns from past successful projects. For example, it analyzes the profiles of creators with different expertise, such as designers, programmers, and writers, and proposes the most suitable teammates. Next, the AI ​​provides real-time feedback and makes improvement suggestions according to the progress of the project. This improves the project completion rate and increases cross-disciplinary collaboration. For example, the AI ​​provides appropriate feedback during the project's progress, allowing team members to work efficiently. Furthermore, users can gain new ideas and inspiration through this platform. By matching creators with diverse backgrounds, the AI ​​allows users to incorporate different perspectives and expertise. For example, design studios, advertising agencies, startups, and other companies can use this platform to create new value. This platform automates the creative process and provides user-centered design and accessibility. This allows users to manage projects efficiently and improves satisfaction. For example, by having AI monitor project progress in real time and make necessary adjustments, users can work more smoothly. In this way, AI-powered creative matching agents respond to the digitalization of the creative industry and the spread of remote work, providing an environment where creators with diverse backgrounds can collaborate to create new value. This allows users to further develop their ideas and advance projects efficiently.This allows the creative matching agent system to enable users to further develop their ideas and collaborate with other creators who possess different expertise.

[0029] The creative matching agent system according to this embodiment comprises an analysis unit, a proposal unit, an inspiration unit, a monitoring unit, and a coordination unit. The analysis unit analyzes the skill sets and previous projects of participants. The skill sets of participants include, but are not limited to, technical skills, soft skills, and years of experience. Previous projects include, but are not limited to, project size, field, and success rate. The analysis unit performs skill matching using machine learning and learns from past successful projects. For example, the analysis unit can perform skill matching using a neural network. The analysis unit can also perform skill matching using a support vector machine. The analysis unit can also perform skill matching using a decision tree. The proposal unit proposes the most suitable teammates based on the analysis results obtained by the analysis unit. The most suitable teammates include, but are not limited to, complementary skills and past collaboration history. The proposal unit analyzes the profiles of creators with different expertise, such as designers, programmers, and writers, and proposes the most suitable teammates. The creator profiles include, but are not limited to, skill sets, past projects, and evaluations. The Proposal Department can suggest the best teammates based on, for example, a creator's skill set. It can also suggest the best teammates based on a creator's past projects. Furthermore, it can suggest the best teammates based on a creator's evaluation. The Inspiration Department collaborates with the teammates suggested by the Proposal Department to generate new ideas. These new ideas include, but are not limited to, technical or business ideas. The Inspiration Department functions to help users gain new ideas and inspiration. For example, the Inspiration Department can generate new ideas through brainstorming. It can also generate new ideas using idea generation tools.The Inspiration Department can also generate new ideas through creative workshops. The Monitoring Department monitors the project's progress based on the ideas generated by the Inspiration Department. Project progress includes, but is not limited to, progress rate and milestone achievement. The Monitoring Department monitors the project's progress in real time. Real-time monitoring includes, but is not limited to, data update frequency and monitoring tool type. For example, the Monitoring Department can monitor the project's progress rate in real time. The Monitoring Department can also monitor milestone achievement in real time. The Monitoring Department can also monitor the project's resource usage in real time. The Coordination Department makes necessary adjustments based on the project's progress monitored by the Monitoring Department. Necessary adjustments include, but are not limited to, reallocating resources and changing the schedule. The Coordination Department makes necessary adjustments based on the project's progress. For example, the Coordination Department can reallocate project resources. The Coordination Department can also change the project's schedule. The Coordination Department can also reassign project tasks. This allows the creative matching agent system according to the embodiment to enable users to further develop their own ideas and collaborate with other creators who have different areas of expertise.

[0030] The analysis department conducts a detailed analysis of participants' skill sets and previous projects. Participants' skill sets include, but are not limited to, technical skills, soft skills, and years of experience. Technical skills include specific technologies such as programming languages, design tools, and project management tools. Soft skills include communication skills, teamwork, and problem-solving abilities. Years of experience indicate the length of practical experience in a particular field. Previous projects include, but are not limited to, project size, field, and success rate. Project size indicates the size and complexity of projects the participant was involved in, and field indicates the industry or area of ​​expertise to which the project belonged. Success rate indicates the percentage of projects that achieved their goals. The analysis department uses machine learning to perform skill matching, learning from past successful projects. For example, skill matching can be performed using neural networks. Neural networks use multi-layered artificial neurons to learn complex patterns and match participants' skill sets with project requirements. Skill matching can also be performed using support vector machines. Support vector machines classify data in high-dimensional space to achieve optimal skill matching. Furthermore, skill matching can also be performed using decision trees. Decision trees perform conditional branching based on data characteristics to derive the optimal skill matching. This allows the analysis department to analyze participants' skill sets and previous projects in detail to achieve optimal skill matching.

[0031] The proposal team proposes the most suitable teammates based on the analysis results obtained by the analysis team. The optimal teammates include, but are not limited to, skill complementarity and past collaboration experience. Skill complementarity means that the skills of team members complement each other, contributing to the project's success. Past collaboration experience indicates the results and quality of collaboration in projects the participants have previously worked on. The proposal team analyzes the profiles of creators with different expertise, such as designers, programmers, and writers, and proposes the most suitable teammates. Creator profiles include, but are not limited to, skill sets, past projects, and evaluations. Skill sets indicate the specific skills and abilities a creator possesses, and past projects provide details of projects the creator has been involved in. Evaluations are indicators of the creator's performance and the quality of collaboration. The proposal team can propose the most suitable teammates based on the creator's skill set. For example, combining a programmer proficient in a specific programming language with a designer skilled in design tools can increase the project's success rate. The proposal team can also propose the most suitable teammates based on the creator's past projects. For example, combining creators with successful projects in the same field can increase the project's success rate. Furthermore, the proposal department can suggest the most suitable teammates based on creator evaluations. For example, adding highly-rated creators to the team can improve the quality of the project. In this way, the proposal department can suggest the best teammates and contribute to the success of the project.

[0032] The Inspiration Department collaborates with teammates proposed by the Proposal Department to generate new ideas. These new ideas include, but are not limited to, technical and business ideas. Technical ideas demonstrate solutions using new technologies and methods, while business ideas present new business models and market strategies. The Inspiration Department functions to help users gain new ideas and inspiration. For example, new ideas can be generated through brainstorming. Brainstorming is a method where participants freely share ideas and stimulate each other to generate new ones. The Inspiration Department can also generate new ideas using idea generation tools. Idea generation tools generate ideas based on specific themes or keywords, helping users gain new perspectives and insights. Furthermore, the Inspiration Department can generate new ideas through creative workshops. Creative workshops are spaces where participants collaboratively generate ideas and create concrete prototypes and concepts. In this way, the Inspiration Department can provide users with diverse means to gain new ideas and inspiration, supporting the creative process.

[0033] The monitoring department monitors the project's progress based on ideas generated by the inspiration department. Project progress includes, but is not limited to, progress rate and milestone achievement. Progress rate indicates how much of the project's tasks are completed, while milestone achievement indicates whether key project milestones are being reached as planned. The monitoring department monitors project progress in real time. Real-time monitoring includes, but is not limited to, the frequency of data updates and the type of monitoring tools used. For example, project management tools can be used to update task progress in real time, ensuring team members stay informed. The monitoring department can also monitor the project's progress rate in real time. Progress rate indicates how much of the project's tasks are completed, helping to understand the overall project progress. Furthermore, the monitoring department can monitor milestone achievement in real time. Milestones mark key project milestones, and verifying their achievement allows for an assessment of project progress. The monitoring department can also monitor project resource usage in real time. Resource usage shows how resources such as personnel, time, and budget allocated to the project are being used, supporting efficient resource management. This allows the monitoring unit to monitor the project's progress in real time and contribute to its success.

[0034] The coordination department makes necessary adjustments based on the project's progress, which is monitored by the monitoring department. These adjustments include, but are not limited to, reallocating resources and changing the schedule. Resource reallocation means reallocating personnel and budget according to the project's progress, while schedule changes mean adjusting deadlines for project tasks and milestones. For example, if the project is behind schedule, the coordination department can accelerate progress by allocating additional resources. If the project schedule is tight, the coordination department can adjust the schedule by reviewing task priorities and focusing on critical tasks. Furthermore, the coordination department can reassign project tasks. For example, if a particular task is behind schedule, it can be reassigned to other team members to ensure smooth progress. This allows the coordination department to respond flexibly to the project's progress and contribute to its success. Additionally, the coordination department can minimize project risks by regularly reviewing the project's progress and making adjustments as needed. This allows the coordination department to make necessary adjustments based on the project's progress and contribute to its success.

[0035] The analysis unit can perform skill matching using machine learning and learn from past successful projects. For example, the analysis unit can perform skill matching using a neural network. A neural network has the ability to learn from large amounts of data and recognize patterns. For example, a neural network takes participants' skill sets and past project data as input and outputs the results of skill matching. The analysis unit can also perform skill matching using a support vector machine. A support vector machine is a machine learning algorithm used for data classification and regression analysis. For example, a support vector machine takes participants' skill sets and past project data as input and outputs the results of skill matching. The analysis unit can also perform skill matching using a decision tree. A decision tree is an algorithm that classifies data hierarchically. For example, a decision tree takes participants' skill sets and past project data as input and outputs the results of skill matching. This improves the accuracy of skill matching by using machine learning. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis department can input participants' skill sets and past project data into a generating AI, and then have the AI ​​perform a skill matching analysis.

[0036] The proposal department can analyze the profiles of creators with different expertise, such as designers, programmers, and writers, and propose the most suitable teammates. For example, the proposal department can propose the most suitable teammates based on the creator's skill set. The creator's skill set includes, for example, technical skills, soft skills, and years of experience. The proposal department can also propose the most suitable teammates based on, for example, the creator's past projects. The creator's past projects include, for example, project size, field, and success rate. The proposal department can also propose the most suitable teammates based on, for example, the creator's evaluation. The creator's evaluation includes, for example, feedback from other team members and project results. In this way, by analyzing the profiles of creators with different expertise, the most suitable teammates can be proposed. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can input creator profile data into generative AI and have the generative AI propose the most suitable teammates.

[0037] The Inspiration Unit can function to help users gain new ideas and inspiration. For example, the Inspiration Unit can generate new ideas through brainstorming. Brainstorming is a method in which multiple participants freely share ideas to generate new concepts. The Inspiration Unit can also generate new ideas using idea generation tools. Idea generation tools are software or applications that help users generate new ideas. The Inspiration Unit can also generate new ideas through creative workshops. Creative workshops are places where participants collaboratively share ideas and work on specific projects. This allows users to gain new ideas and inspiration. Some or all of the processes described above in the Inspiration Unit may be performed using, for example, generative AI, or not. For example, the Inspiration Unit can input the user's idea data into a generative AI and have the generative AI generate new ideas.

[0038] The monitoring unit can monitor the project's progress in real time. For example, the monitoring unit can monitor the project's progress rate in real time. The progress rate is an indicator of the project's progress and shows the percentage of tasks that have been completed. The monitoring unit can also monitor the achievement of milestones in real time. Milestones are indicators that show important milestones in the project and indicate the achievement of specific tasks or goals. The monitoring unit can also monitor the project's resource usage in real time. Resource usage is an indicator that shows the usage of resources allocated to the project. By monitoring the project's progress in real time, the project's completion rate can be improved. Some or all of the above processes in the monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the monitoring unit can input project progress data into a generative AI and have the generative AI perform real-time monitoring.

[0039] The adjustment unit can make necessary adjustments according to the progress of the project. For example, the adjustment unit can reallocate project resources. Resource reallocation is the process of optimally rearranging the resources allocated to the project. The adjustment unit can also change the project schedule. Changing the schedule is the process of adjusting the deadlines for project tasks and milestones. The adjustment unit can also reassign project tasks. Reassigning tasks is the process of reassigning project tasks to different members. By making necessary adjustments according to the progress of the project, the project completion rate can be improved. Some or all of the above processes in the adjustment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the adjustment unit can input project progress data into a generative AI and have the generative AI perform the necessary adjustments.

[0040] The analysis unit can improve the accuracy of skill matching by considering the success rate of the user's past projects during analysis. For example, the analysis unit can prioritize analyzing skill sets with a high success rate based on the user's past project success rate. Skill sets with a high success rate are likely to contribute to project success. The analysis unit can also, for example, consider the user's past project success rate and suggest skills to complement skill sets with a low success rate. By complementing skill sets with a low success rate, the project success rate is improved. The analysis unit can also, for example, refer to the user's past project success rate and prioritize suggesting teammates with a high success rate. Teammates with a high success rate are likely to contribute to project success. In this way, considering the success rate of the user's past projects improves the accuracy of skill matching. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the user's past project data into a generative AI and have the generative AI perform the skill matching accuracy improvement.

[0041] The analysis unit can determine the priority of skill sets by considering the user's current project progress during the analysis. For example, the analysis unit can prioritize analyzing high-urgency skill sets based on the user's current project progress. High-urgency skill sets are important skills that directly impact the progress of the project. The analysis unit can also prioritize suggesting skill sets necessary for ongoing tasks, considering the user's current project progress. Prioritizing skill sets necessary for ongoing tasks facilitates smooth project progress. The analysis unit can also prioritize analyzing skill sets necessary for project completion, referencing the user's current project progress. Prioritizing skill sets necessary for project completion improves the project's success rate. This allows for the appropriate determination of skill set priorities by considering the user's current project progress. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input the user's current project data into a generative AI and have the generative AI perform the skill set priority determination.

[0042] The analysis unit can prioritize the analysis of highly relevant skill sets by considering the user's geographical location information during the analysis process. For example, the analysis unit can prioritize the analysis of region-specific skill sets based on the user's geographical location information. Region-specific skill sets are skills that are in particularly high demand in that region. The analysis unit can also prioritize the suggestion of skill sets required for nearby projects by considering the user's geographical location information. Prioritizing skill sets required for nearby projects improves the success rate of projects. The analysis unit can also prioritize the analysis of skill sets that meet regional demand by referring to the user's geographical location information. Prioritizing skill sets that meet regional demand improves the success rate of projects. This allows for the prioritization of analysis of highly relevant skill sets by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the user's geographical location information data into a generative AI and have the generative AI perform the analysis of highly relevant skill sets.

[0043] The analysis unit can analyze a user's social media activity and extract relevant skill sets during the analysis process. For example, the analysis unit can extract relevant skill sets based on the user's social media activity. Social media activity includes, for example, analysis of posts and followers. The analysis unit can also analyze a user's social media activity and suggest skill sets based on their interests. Skill sets based on interests are skills in areas that the user is particularly interested in. The analysis unit can also extract trend-based skill sets based on the user's social media activity. Trend-based skill sets are skills that are in high demand in the current market. In this way, relevant skill sets can be extracted by analyzing a user's social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the user's social media data into a generative AI and have the generative AI perform the extraction of relevant skill sets.

[0044] The proposal department can adjust the level of detail of a proposal based on the importance of the teammates' skill sets. For example, the proposal department can make detailed proposals regarding important skill sets based on the importance of the teammates' skill sets. Important skill sets are those that directly contribute to the success of the project. The proposal department can also make concise proposals regarding less important skill sets, taking into account the importance of the teammates' skill sets. Less important skill sets are those that have little impact on the success of the project. The proposal department can also make proposals regarding the skill sets that have the greatest impact on the project, taking into account the importance of the teammates' skill sets. Skill sets that have the greatest impact on the project are those that are essential for the success of the project. By adjusting the level of detail of a proposal based on the importance of the teammates' skill sets, more appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the teammates' skill set data into a generative AI and have the generative AI adjust the level of detail of the proposal.

[0045] The proposal unit can apply different proposal algorithms depending on the success rate of each teammate's past projects. For example, the proposal unit can prioritize proposing teammates with high success rates based on their past project success rates. Teammates with high success rates are more likely to contribute to the success of the project. The proposal unit can also consider the success rate of each teammate's past projects and propose complementary skill sets to teammates with low success rates. By proposing complementary skill sets to teammates with low success rates, the success rate of the project can be improved. The proposal unit can also apply a proposal algorithm suitable for projects with high success rates, based on the success rate of each teammate's past projects. A proposal algorithm suitable for projects with high success rates is more likely to contribute to the success of the project. This allows for more appropriate proposals by applying different proposal algorithms depending on the success rate of each teammate's past projects. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the teammate's past project data into a generative AI and have the generative AI apply the proposal algorithm.

[0046] The proposal department can make optimal proposals by considering the geographical location information of teammates. For example, the proposal department can prioritize proposing nearby teammates based on their geographical location information. Nearby teammates are easier to collaborate with due to their close physical proximity. The proposal department can also consider the geographical location information of teammates and propose teammates with region-specific skill sets. Region-specific skill sets are skills that are in particularly high demand in that region. The proposal department can also use the geographical location information of teammates as a reference to make proposals that meet regional needs. Proposals that meet regional needs improve the success rate of projects in that region. Thus, considering the geographical location information of teammates makes it possible to make optimal proposals. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the geographical location information data of teammates into a generative AI and have the generative AI execute the optimal proposal.

[0047] The proposal department can analyze the social media activities of teammates and make relevant suggestions when making a proposal. For example, the proposal department can suggest teammates with relevant skill sets based on the social media activities of teammates. Social media activities include, for example, analysis of posts and followers. The proposal department can also analyze the social media activities of teammates and make suggestions based on their interests. Suggestions based on interests are suggestions based on skill sets in areas in which the teammate is particularly interested. The proposal department can also make trend-based suggestions, for example, by referring to the social media activities of teammates. Trend-based suggestions are suggestions based on skill sets that are in high demand in the current market. This makes it possible to make relevant suggestions by analyzing the social media activities of teammates. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can input the social media data of teammates into a generative AI and have the generative AI execute relevant suggestions.

[0048] The inspiration unit can provide optimal inspiration by referring to the user's past idea submission history when providing inspiration. For example, the inspiration unit provides relevant inspiration based on the user's past idea submission history. Past idea submission history includes, for example, the number of ideas submitted and the evaluation of the ideas. The inspiration unit can also provide inspiration based on successful ideas by referring to the user's past idea submission history. Inspiration based on successful ideas is inspiration related to ideas that the user has succeeded with in the past. The inspiration unit can also provide inspiration that helps develop ideas by referring to the user's past idea submission history. Inspiration that helps develop ideas is inspiration that helps develop the idea the user is currently working on further. This allows the inspiration unit to provide optimal inspiration by referring to the user's past idea submission history. Some or all of the above processing in the inspiration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the inspiration unit can input the user's past idea submission history data into a generative AI and have the generative AI perform the task of providing optimal inspiration.

[0049] The inspiration unit can customize the content of inspiration when providing it, taking into account the user's current project progress. For example, the inspiration unit can provide project-related inspiration based on the user's current project progress. Project-related inspiration consists of ideas and suggestions directly related to the current project. The inspiration unit can also provide inspiration that is helpful for ongoing tasks, taking into account the user's current project progress. Inspiration that is helpful for ongoing tasks consists of ideas and suggestions for efficiently carrying out the current tasks. The inspiration unit can also provide inspiration toward project completion, taking into account the user's current project progress. Inspiration toward project completion consists of ideas and suggestions for successfully completing the project. This allows for appropriate customization of the inspiration content by considering the user's current project progress. Some or all of the above processing in the inspiration unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the inspiration unit can input the user's current project data into a generative AI and have the generative AI customize the content of the inspiration.

[0050] The inspiration unit can provide optimal inspiration by considering the user's geographical location information. For example, the inspiration unit can provide region-specific inspiration based on the user's geographical location information. Region-specific inspiration consists of ideas and suggestions that are particularly in demand in that region. The inspiration unit can also provide inspiration related to nearby projects, considering the user's geographical location information. Inspiration related to nearby projects consists of ideas and suggestions that are easy to collaborate on due to their close physical proximity. The inspiration unit can also provide inspiration that meets regional needs, taking into account the user's geographical location information. Inspiration that meets regional needs consists of ideas and suggestions that improve the success rate of projects in that region. In this way, optimal inspiration can be provided by considering the user's geographical location information. Some or all of the above processing in the inspiration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the inspiration unit can input the user's geographical location information data into a generative AI and have the generative AI perform the task of providing optimal inspiration.

[0051] The inspiration unit can provide relevant inspiration by analyzing the user's social media activity when providing inspiration. For example, the inspiration unit can provide relevant inspiration based on the user's social media activity. Social media activity includes, for example, analysis of posts and followers. The inspiration unit can also provide inspiration based on the user's interests by analyzing their social media activity. Inspiration based on interests is ideas and suggestions in areas that the user is particularly interested in. The inspiration unit can also provide trend-based inspiration by referring to the user's social media activity. Inspiration based on trends is ideas and suggestions that are in high demand in the current market. In this way, relevant inspiration can be provided by analyzing the user's social media activity. Some or all of the above processing in the inspiration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the inspiration unit can input the user's social media data into a generative AI and have the generative AI perform the provision of relevant inspiration.

[0052] The monitoring unit can adjust the frequency of monitoring according to the project's progress. For example, based on the project's progress, the monitoring unit can increase the monitoring frequency if the project is behind schedule. For projects that are behind schedule, frequent monitoring is performed to detect problems early. The monitoring unit can also consider the project's progress and reduce the monitoring frequency if it is progressing smoothly. For projects that are progressing smoothly, the monitoring frequency is reduced to use resources more efficiently. The monitoring unit can also refer to the project's progress and adjust the monitoring frequency to match important milestones. By monitoring according to important milestones, the progress of the project is ensured. This allows for more appropriate monitoring by adjusting the monitoring frequency according to the project's progress. Some or all of the above processes in the monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the monitoring unit can input project progress data into a generative AI and have the generative AI adjust the monitoring frequency.

[0053] The monitoring unit can adjust the level of detail of monitoring based on the importance of the project. For example, the monitoring unit can perform detailed monitoring on important projects based on their importance. Important projects require detailed monitoring because they directly impact the success of the project. The monitoring unit can also perform simple monitoring on less important projects, taking into account their importance. Less important projects have little impact on the success of the project, so simple monitoring is sufficient. The monitoring unit can also perform detailed monitoring on the elements that have the greatest impact on the success of the project, taking into account their importance. Elements that have the greatest impact on the success of the project require detailed monitoring to ensure the progress of the project. By adjusting the level of detail of monitoring based on the importance of the project, more appropriate monitoring becomes possible. Some or all of the above processes in the monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the monitoring unit can input project importance data into a generative AI and have the generative AI adjust the level of detail of monitoring.

[0054] The monitoring unit can perform monitoring while considering the geographical distribution of the project. For example, the monitoring unit can monitor for region-specific issues based on the geographical distribution of the project. Region-specific issues are particularly important in those regions. The monitoring unit can also monitor the progress of the project in each region, taking into account the geographical distribution of the project. By monitoring the progress in each region, region-specific problems can be detected early. The monitoring unit can also perform monitoring that is tailored to the needs of each region, taking into account the geographical distribution of the project. Monitoring tailored to the needs of each region improves the success rate of the project in that region. Thus, considering the geographical distribution of the project enables more appropriate monitoring. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the monitoring unit can input the geographical distribution data of the project into a generative AI and have the generative AI perform the monitoring.

[0055] The monitoring unit can improve the accuracy of its monitoring by referring to relevant project literature during monitoring. For example, the monitoring unit improves the accuracy of its monitoring based on relevant project literature. Relevant literature includes, for example, the use of literature databases and literature evaluation criteria. The monitoring unit can also, for example, refer to relevant project literature and perform monitoring based on the latest information. By performing monitoring based on the latest information, the accuracy of monitoring is improved. The monitoring unit can also, for example, refer to relevant project literature and perform monitoring according to the progress of the project. Monitoring according to the progress of the project improves the success rate of the project. Thus, by referring to relevant project literature, the accuracy of monitoring is improved. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input relevant project literature data into a generative AI and have the generative AI perform the improvement of monitoring accuracy.

[0056] The adjustment unit can adjust the frequency of adjustments according to the project's progress. For example, based on the project's progress, the adjustment unit can increase the frequency of adjustments if the project is behind schedule. For projects that are behind schedule, adjustments are made more frequently to detect problems early. The adjustment unit can also consider the project's progress and reduce the frequency of adjustments if the project is progressing smoothly. For projects that are progressing smoothly, the adjustment unit reduces the frequency of adjustments to use resources more efficiently. The adjustment unit can also refer to the project's progress and adjust the frequency of adjustments to match important milestones. By adjusting to important milestones, the project's progress is ensured. This allows for more appropriate adjustments by adjusting the frequency of adjustments according to the project's progress. Some or all of the above processes in the adjustment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the adjustment unit can input project progress data into a generative AI and have the generative AI adjust the frequency of adjustments.

[0057] The adjustment unit can adjust the level of detail of adjustments based on the importance of the project. For example, the adjustment unit can perform detailed adjustments on important projects based on their importance. Important projects require detailed adjustments because they directly impact the success of the project. The adjustment unit can also perform simple adjustments on less important projects, taking into account their importance. Less important projects have little impact on the success of the project, so simple adjustments are sufficient. The adjustment unit can also perform detailed adjustments on the elements that have the greatest impact on the success of the project, taking into account their importance. Elements that have the greatest impact on the success of the project require detailed adjustments to ensure the progress of the project. By adjusting the level of detail of adjustments based on the importance of the project, more appropriate adjustments can be made. Some or all of the above processing in the adjustment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the adjustment unit can input project importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the adjustments.

[0058] The adjustment unit can perform adjustments while considering the geographical distribution of the project. For example, the adjustment unit can make adjustments to region-specific issues based on the geographical distribution of the project. Region-specific issues are those that are particularly important in that region. The adjustment unit can also adjust the progress in each region, for example, while considering the geographical distribution of the project. By adjusting the progress in each region, region-specific problems can be identified early and appropriate responses can be taken. The adjustment unit can also make adjustments in response to regional needs, for example, by referring to the geographical distribution of the project. Adjustments in response to regional needs improve the success rate of the project in that region. Thus, considering the geographical distribution of the project enables more appropriate adjustments. Some or all of the above processing in the adjustment unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the adjustment unit can input the geographical distribution data of the project into a generative AI and have the generative AI perform the adjustments.

[0059] The adjustment unit can improve the accuracy of its adjustments by referring to relevant project literature during the adjustment process. For example, the adjustment unit improves the accuracy of its adjustments based on relevant project literature. Relevant literature includes, for example, the use of literature databases and literature evaluation criteria. The adjustment unit can also, for example, refer to relevant project literature and perform adjustments based on the latest information. Performing adjustments based on the latest information improves the accuracy of the adjustments. The adjustment unit can also, for example, refer to relevant project literature and perform adjustments according to the progress of the project. Adjustments according to the progress of the project improve the success rate of the project. Thus, referring to relevant project literature improves the accuracy of the adjustments. Some or all of the above processing in the adjustment unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the adjustment unit can input project-related literature data into a generative AI and have the generative AI perform the adjustment accuracy improvement.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analytics department can improve the accuracy of skill matching by considering the user's past project failure rates. For example, it can analyze the user's past project failure rates to avoid high-failure skill sets, thereby improving the project's success rate. It can also suggest skills to complement high-failure skill sets. Furthermore, it can suggest avoiding teammates with high failure rates. In this way, considering the user's past project failure rates improves the accuracy of skill matching.

[0062] The monitoring unit can adjust the level of detail of monitoring according to the project's progress. For example, if the project is behind schedule, detailed monitoring can be performed to detect problems early, thereby ensuring smooth project progress. Conversely, if the project is progressing smoothly, simpler monitoring can be performed to use resources efficiently. Furthermore, the level of detail of monitoring can be adjusted to match important milestones. This allows for more appropriate monitoring by adjusting the level of detail according to the project's progress.

[0063] The analytics department can adjust the skill set analysis method considering the user's current project resource usage. For example, if resources are scarce, it will prioritize analyzing skill sets that enable efficient resource use, thereby facilitating project progress. If resources are sufficient, it will perform a detailed skill set analysis to provide comprehensive information. Furthermore, it can adjust the priority of skill sets according to resource usage. This allows for appropriate adjustment of the skill set analysis method by considering the user's current project resource usage.

[0064] The inspiration section can adjust the content of inspiration based on the user's past project evaluations. For example, it can provide inspiration based on ideas that received high ratings in past projects, making it easier for the user to succeed. It can also provide inspiration that avoids ideas that received low ratings in past projects. Furthermore, it can adjust the priority of inspiration based on past project evaluations. This allows the content of inspiration to be appropriately adjusted by referring to the user's past project evaluations.

[0065] The monitoring department can adjust its monitoring methods according to the project's progress. For example, if the project is behind schedule, it can conduct detailed monitoring to detect problems early, thereby ensuring smooth project progress. Conversely, if the project is progressing smoothly, it can conduct concise monitoring to efficiently utilize resources. Furthermore, it can adjust its monitoring methods to align with important milestones. By adjusting monitoring methods according to the project's progress, more appropriate monitoring becomes possible.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The analysis team analyzes the participants' skill sets and previous projects. Participants' skill sets include technical skills, soft skills, and years of experience, while previous projects include project size, field, and success rate. The analysis team uses machine learning to perform skill matching, learning from past successful projects. For example, neural networks, support vector machines, and decision trees can be used for skill matching. Step 2: The proposal team proposes the most suitable teammates based on the analysis results obtained by the analysis team. The optimal teammates include complementary skills and past collaboration experience. The proposal team analyzes the profiles of creators with different expertise, such as designers, programmers, and writers, and proposes the most suitable teammates. Creator profiles include skill sets, past projects, and evaluations. Step 3: The Inspiration Department collaborates with teammates proposed by the Proposal Department to generate new ideas. These new ideas can include technical ideas, business ideas, and more. The Inspiration Department functions to help users gain new ideas and inspiration, generating new ideas through brainstorming, idea generation tools, and creative workshops. Step 4: The monitoring department monitors the project's progress based on the ideas generated by the inspiration department. Project progress includes progress rate, milestone achievement status, etc. The monitoring department monitors the project's progress in real time, including data update frequency and the type of monitoring tools used. Step 5: The adjustment unit makes necessary adjustments based on the project progress monitored by the monitoring unit. Necessary adjustments include reallocating resources and changing the schedule. The adjustment unit can reallocate resources, change the schedule, and reassign tasks according to the project progress.

[0068] (Example of form 2) The Creative Matching Agent System according to an embodiment of the present invention is a system that uses AI to analyze participants' skill sets and previous projects and proposes the most suitable teammates. This system enables users to further develop their ideas and collaborate with other creators who possess different expertise. First, the Creative Matching Agent System uses AI to analyze participants' skill sets and previous projects. In this process, machine learning is used for skill matching, and the system learns from past successful projects. For example, it analyzes the profiles of creators with different expertise, such as designers, programmers, and writers, and proposes the most suitable teammates. Next, the AI ​​provides real-time feedback and makes improvement suggestions according to the progress of the project. This improves the project completion rate and increases cross-disciplinary collaboration. For example, the AI ​​provides appropriate feedback during the project's progress, allowing team members to work efficiently. Furthermore, users can gain new ideas and inspiration through this platform. By matching creators with diverse backgrounds, the AI ​​allows users to incorporate different perspectives and expertise. For example, design studios, advertising agencies, startups, and other companies can use this platform to create new value. This platform automates the creative process and provides user-centered design and accessibility. This allows users to manage projects efficiently and improves satisfaction. For example, by having AI monitor project progress in real time and make necessary adjustments, users can work more smoothly. In this way, AI-powered creative matching agents respond to the digitalization of the creative industry and the spread of remote work, providing an environment where creators with diverse backgrounds can collaborate to create new value. This allows users to further develop their ideas and advance projects efficiently.This allows the creative matching agent system to enable users to further develop their ideas and collaborate with other creators who possess different expertise.

[0069] The creative matching agent system according to this embodiment comprises an analysis unit, a proposal unit, an inspiration unit, a monitoring unit, and a coordination unit. The analysis unit analyzes the skill sets and previous projects of participants. The skill sets of participants include, but are not limited to, technical skills, soft skills, and years of experience. Previous projects include, but are not limited to, project size, field, and success rate. The analysis unit performs skill matching using machine learning and learns from past successful projects. For example, the analysis unit can perform skill matching using a neural network. The analysis unit can also perform skill matching using a support vector machine. The analysis unit can also perform skill matching using a decision tree. The proposal unit proposes the most suitable teammates based on the analysis results obtained by the analysis unit. The most suitable teammates include, but are not limited to, complementary skills and past collaboration history. The proposal unit analyzes the profiles of creators with different expertise, such as designers, programmers, and writers, and proposes the most suitable teammates. The creator profiles include, but are not limited to, skill sets, past projects, and evaluations. The Proposal Department can suggest the best teammates based on, for example, a creator's skill set. It can also suggest the best teammates based on a creator's past projects. Furthermore, it can suggest the best teammates based on a creator's evaluation. The Inspiration Department collaborates with the teammates suggested by the Proposal Department to generate new ideas. These new ideas include, but are not limited to, technical or business ideas. The Inspiration Department functions to help users gain new ideas and inspiration. For example, the Inspiration Department can generate new ideas through brainstorming. It can also generate new ideas using idea generation tools.The Inspiration Department can also generate new ideas through creative workshops. The Monitoring Department monitors the project's progress based on the ideas generated by the Inspiration Department. Project progress includes, but is not limited to, progress rate and milestone achievement. The Monitoring Department monitors the project's progress in real time. Real-time monitoring includes, but is not limited to, data update frequency and monitoring tool type. For example, the Monitoring Department can monitor the project's progress rate in real time. The Monitoring Department can also monitor milestone achievement in real time. The Monitoring Department can also monitor the project's resource usage in real time. The Coordination Department makes necessary adjustments based on the project's progress monitored by the Monitoring Department. Necessary adjustments include, but are not limited to, reallocating resources and changing the schedule. The Coordination Department makes necessary adjustments based on the project's progress. For example, the Coordination Department can reallocate project resources. The Coordination Department can also change the project's schedule. The Coordination Department can also reassign project tasks. This allows the creative matching agent system according to the embodiment to enable users to further develop their own ideas and collaborate with other creators who have different areas of expertise.

[0070] The analysis department conducts a detailed analysis of participants' skill sets and previous projects. Participants' skill sets include, but are not limited to, technical skills, soft skills, and years of experience. Technical skills include specific technologies such as programming languages, design tools, and project management tools. Soft skills include communication skills, teamwork, and problem-solving abilities. Years of experience indicate the length of practical experience in a particular field. Previous projects include, but are not limited to, project size, field, and success rate. Project size indicates the size and complexity of projects the participant was involved in, and field indicates the industry or area of ​​expertise to which the project belonged. Success rate indicates the percentage of projects that achieved their goals. The analysis department uses machine learning to perform skill matching, learning from past successful projects. For example, skill matching can be performed using neural networks. Neural networks use multi-layered artificial neurons to learn complex patterns and match participants' skill sets with project requirements. Skill matching can also be performed using support vector machines. Support vector machines classify data in high-dimensional space to achieve optimal skill matching. Furthermore, skill matching can also be performed using decision trees. Decision trees perform conditional branching based on data characteristics to derive the optimal skill matching. This allows the analysis department to analyze participants' skill sets and previous projects in detail to achieve optimal skill matching.

[0071] The proposal team proposes the most suitable teammates based on the analysis results obtained by the analysis team. The optimal teammates include, but are not limited to, skill complementarity and past collaboration experience. Skill complementarity means that the skills of team members complement each other, contributing to the project's success. Past collaboration experience indicates the results and quality of collaboration in projects the participants have previously worked on. The proposal team analyzes the profiles of creators with different expertise, such as designers, programmers, and writers, and proposes the most suitable teammates. Creator profiles include, but are not limited to, skill sets, past projects, and evaluations. Skill sets indicate the specific skills and abilities a creator possesses, and past projects provide details of projects the creator has been involved in. Evaluations are indicators of the creator's performance and the quality of collaboration. The proposal team can propose the most suitable teammates based on the creator's skill set. For example, combining a programmer proficient in a specific programming language with a designer skilled in design tools can increase the project's success rate. The proposal team can also propose the most suitable teammates based on the creator's past projects. For example, combining creators with successful projects in the same field can increase the project's success rate. Furthermore, the proposal department can suggest the most suitable teammates based on creator evaluations. For example, adding highly-rated creators to the team can improve the quality of the project. In this way, the proposal department can suggest the best teammates and contribute to the success of the project.

[0072] The Inspiration Department collaborates with teammates proposed by the Proposal Department to generate new ideas. These new ideas include, but are not limited to, technical and business ideas. Technical ideas demonstrate solutions using new technologies and methods, while business ideas present new business models and market strategies. The Inspiration Department functions to help users gain new ideas and inspiration. For example, new ideas can be generated through brainstorming. Brainstorming is a method where participants freely share ideas and stimulate each other to generate new ones. The Inspiration Department can also generate new ideas using idea generation tools. Idea generation tools generate ideas based on specific themes or keywords, helping users gain new perspectives and insights. Furthermore, the Inspiration Department can generate new ideas through creative workshops. Creative workshops are spaces where participants collaboratively generate ideas and create concrete prototypes and concepts. In this way, the Inspiration Department can provide users with diverse means to gain new ideas and inspiration, supporting the creative process.

[0073] The monitoring department monitors the project's progress based on ideas generated by the inspiration department. Project progress includes, but is not limited to, progress rate and milestone achievement. Progress rate indicates how much of the project's tasks are completed, while milestone achievement indicates whether key project milestones are being reached as planned. The monitoring department monitors project progress in real time. Real-time monitoring includes, but is not limited to, the frequency of data updates and the type of monitoring tools used. For example, project management tools can be used to update task progress in real time, ensuring team members stay informed. The monitoring department can also monitor the project's progress rate in real time. Progress rate indicates how much of the project's tasks are completed, helping to understand the overall project progress. Furthermore, the monitoring department can monitor milestone achievement in real time. Milestones mark key project milestones, and verifying their achievement allows for an assessment of project progress. The monitoring department can also monitor project resource usage in real time. Resource usage shows how resources such as personnel, time, and budget allocated to the project are being used, supporting efficient resource management. This allows the monitoring unit to monitor the project's progress in real time and contribute to its success.

[0074] The coordination department makes necessary adjustments based on the project's progress, which is monitored by the monitoring department. These adjustments include, but are not limited to, reallocating resources and changing the schedule. Resource reallocation means reallocating personnel and budget according to the project's progress, while schedule changes mean adjusting deadlines for project tasks and milestones. For example, if the project is behind schedule, the coordination department can accelerate progress by allocating additional resources. If the project schedule is tight, the coordination department can adjust the schedule by reviewing task priorities and focusing on critical tasks. Furthermore, the coordination department can reassign project tasks. For example, if a particular task is behind schedule, it can be reassigned to other team members to ensure smooth progress. This allows the coordination department to respond flexibly to the project's progress and contribute to its success. Additionally, the coordination department can minimize project risks by regularly reviewing the project's progress and making adjustments as needed. This allows the coordination department to make necessary adjustments based on the project's progress and contribute to its success.

[0075] The analysis unit can perform skill matching using machine learning and learn from past successful projects. For example, the analysis unit can perform skill matching using a neural network. A neural network has the ability to learn from large amounts of data and recognize patterns. For example, a neural network takes participants' skill sets and past project data as input and outputs the results of skill matching. The analysis unit can also perform skill matching using a support vector machine. A support vector machine is a machine learning algorithm used for data classification and regression analysis. For example, a support vector machine takes participants' skill sets and past project data as input and outputs the results of skill matching. The analysis unit can also perform skill matching using a decision tree. A decision tree is an algorithm that classifies data hierarchically. For example, a decision tree takes participants' skill sets and past project data as input and outputs the results of skill matching. This improves the accuracy of skill matching by using machine learning. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis department can input participants' skill sets and past project data into a generating AI, and then have the AI ​​perform a skill matching analysis.

[0076] The proposal department can analyze the profiles of creators with different expertise, such as designers, programmers, and writers, and propose the most suitable teammates. For example, the proposal department can propose the most suitable teammates based on the creator's skill set. The creator's skill set includes, for example, technical skills, soft skills, and years of experience. The proposal department can also propose the most suitable teammates based on, for example, the creator's past projects. The creator's past projects include, for example, project size, field, and success rate. The proposal department can also propose the most suitable teammates based on, for example, the creator's evaluation. The creator's evaluation includes, for example, feedback from other team members and project results. In this way, by analyzing the profiles of creators with different expertise, the most suitable teammates can be proposed. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can input creator profile data into generative AI and have the generative AI propose the most suitable teammates.

[0077] The Inspiration Unit can function to help users gain new ideas and inspiration. For example, the Inspiration Unit can generate new ideas through brainstorming. Brainstorming is a method in which multiple participants freely share ideas to generate new concepts. The Inspiration Unit can also generate new ideas using idea generation tools. Idea generation tools are software or applications that help users generate new ideas. The Inspiration Unit can also generate new ideas through creative workshops. Creative workshops are places where participants collaboratively share ideas and work on specific projects. This allows users to gain new ideas and inspiration. Some or all of the processes described above in the Inspiration Unit may be performed using, for example, generative AI, or not. For example, the Inspiration Unit can input the user's idea data into a generative AI and have the generative AI generate new ideas.

[0078] The monitoring unit can monitor the project's progress in real time. For example, the monitoring unit can monitor the project's progress rate in real time. The progress rate is an indicator of the project's progress and shows the percentage of tasks that have been completed. The monitoring unit can also monitor the achievement of milestones in real time. Milestones are indicators that show important milestones in the project and indicate the achievement of specific tasks or goals. The monitoring unit can also monitor the project's resource usage in real time. Resource usage is an indicator that shows the usage of resources allocated to the project. By monitoring the project's progress in real time, the project's completion rate can be improved. Some or all of the above processes in the monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the monitoring unit can input project progress data into a generative AI and have the generative AI perform real-time monitoring.

[0079] The adjustment unit can make necessary adjustments according to the progress of the project. For example, the adjustment unit can reallocate project resources. Resource reallocation is the process of optimally rearranging the resources allocated to the project. The adjustment unit can also change the project schedule. Changing the schedule is the process of adjusting the deadlines for project tasks and milestones. The adjustment unit can also reassign project tasks. Reassigning tasks is the process of reassigning project tasks to different members. By making necessary adjustments according to the progress of the project, the project completion rate can be improved. Some or all of the above processes in the adjustment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the adjustment unit can input project progress data into a generative AI and have the generative AI perform the necessary adjustments.

[0080] The analysis unit can estimate the user's emotions and adjust the skill set analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit performs a concise skill set analysis and omits detailed information. For stressed users, the amount of information is reduced and provided in an easy-to-understand format. For example, if the user is relaxed, the analysis unit performs a detailed skill set analysis and provides comprehensive information. For relaxed users, detailed information is provided to promote a deeper understanding. For example, if the user is in a hurry, the analysis unit performs a rapid skill set analysis and provides only the most important information. For hurried users, concise information is provided to support rapid decision-making. In this way, by adjusting the skill set analysis method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the analysis method for the skill set.

[0081] The analysis unit can improve the accuracy of skill matching by considering the success rate of the user's past projects during analysis. For example, the analysis unit can prioritize analyzing skill sets with a high success rate based on the user's past project success rate. Skill sets with a high success rate are likely to contribute to project success. The analysis unit can also, for example, consider the user's past project success rate and suggest skills to complement skill sets with a low success rate. By complementing skill sets with a low success rate, the project success rate is improved. The analysis unit can also, for example, refer to the user's past project success rate and prioritize suggesting teammates with a high success rate. Teammates with a high success rate are likely to contribute to project success. In this way, considering the success rate of the user's past projects improves the accuracy of skill matching. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the user's past project data into a generative AI and have the generative AI perform the skill matching accuracy improvement.

[0082] The analysis unit can determine the priority of skill sets by considering the user's current project progress during the analysis. For example, the analysis unit can prioritize analyzing high-urgency skill sets based on the user's current project progress. High-urgency skill sets are important skills that directly impact the progress of the project. The analysis unit can also prioritize suggesting skill sets necessary for ongoing tasks, considering the user's current project progress. Prioritizing skill sets necessary for ongoing tasks facilitates smooth project progress. The analysis unit can also prioritize analyzing skill sets necessary for project completion, referencing the user's current project progress. Prioritizing skill sets necessary for project completion improves the project's success rate. This allows for the appropriate determination of skill set priorities by considering the user's current project progress. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input the user's current project data into a generative AI and have the generative AI perform the skill set priority determination.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For nervous users, it summarizes information concisely and provides it in an easy-to-understand format. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For relaxed users, it provides detailed information to promote a deeper understanding. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. For hurried users, it highlights important information to support quick decision-making. This allows for a more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust how the analysis results are displayed.

[0084] The analysis unit can prioritize the analysis of highly relevant skill sets by considering the user's geographical location information during the analysis process. For example, the analysis unit can prioritize the analysis of region-specific skill sets based on the user's geographical location information. Region-specific skill sets are skills that are in particularly high demand in that region. The analysis unit can also prioritize the suggestion of skill sets required for nearby projects by considering the user's geographical location information. Prioritizing skill sets required for nearby projects improves the success rate of projects. The analysis unit can also prioritize the analysis of skill sets that meet regional demand by referring to the user's geographical location information. Prioritizing skill sets that meet regional demand improves the success rate of projects. This allows for the prioritization of analysis of highly relevant skill sets by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the user's geographical location information data into a generative AI and have the generative AI perform the analysis of highly relevant skill sets.

[0085] The analysis unit can analyze a user's social media activity and extract relevant skill sets during the analysis process. For example, the analysis unit can extract relevant skill sets based on the user's social media activity. Social media activity includes, for example, analysis of posts and followers. The analysis unit can also analyze a user's social media activity and suggest skill sets based on their interests. Skill sets based on interests are skills in areas that the user is particularly interested in. The analysis unit can also extract trend-based skill sets based on the user's social media activity. Trend-based skill sets are skills that are in high demand in the current market. In this way, relevant skill sets can be extracted by analyzing a user's social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the user's social media data into a generative AI and have the generative AI perform the extraction of relevant skill sets.

[0086] The suggestion section can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion section will provide concise and easy-to-understand suggestions. For stressed users, it will summarize information concisely and provide it in an easy-to-understand format. For example, if the user is relaxed, the suggestion section will provide suggestions that include detailed information. For relaxed users, it will provide detailed information to promote a deeper understanding. For example, if the user is in a hurry, the suggestion section will provide quick and to-the-point suggestions. For hurried users, it will emphasize important information to support quick decision-making. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion section may be performed using generative AI, for example, or without generative AI. For example, the proposal department can input user emotion data into a generation AI and have the generation AI adjust the way the proposal is expressed.

[0087] The proposal department can adjust the level of detail of a proposal based on the importance of the teammates' skill sets. For example, the proposal department can make detailed proposals regarding important skill sets based on the importance of the teammates' skill sets. Important skill sets are those that directly contribute to the success of the project. The proposal department can also make concise proposals regarding less important skill sets, taking into account the importance of the teammates' skill sets. Less important skill sets are those that have little impact on the success of the project. The proposal department can also make proposals regarding the skill sets that have the greatest impact on the project, taking into account the importance of the teammates' skill sets. Skill sets that have the greatest impact on the project are those that are essential for the success of the project. By adjusting the level of detail of a proposal based on the importance of the teammates' skill sets, more appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the teammates' skill set data into a generative AI and have the generative AI adjust the level of detail of the proposal.

[0088] The proposal unit can apply different proposal algorithms depending on the success rate of each teammate's past projects. For example, the proposal unit can prioritize proposing teammates with high success rates based on their past project success rates. Teammates with high success rates are more likely to contribute to the success of the project. The proposal unit can also consider the success rate of each teammate's past projects and propose complementary skill sets to teammates with low success rates. By proposing complementary skill sets to teammates with low success rates, the success rate of the project can be improved. The proposal unit can also apply a proposal algorithm suitable for projects with high success rates, based on the success rate of each teammate's past projects. A proposal algorithm suitable for projects with high success rates is more likely to contribute to the success of the project. This allows for more appropriate proposals by applying different proposal algorithms depending on the success rate of each teammate's past projects. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the teammate's past project data into a generative AI and have the generative AI apply the proposal algorithm.

[0089] The suggestion section can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is stressed, the suggestion section will provide a concise and short suggestion. For stressed users, it will summarize the information concisely and provide it in an easy-to-understand format. For example, if the user is relaxed, the suggestion section will provide a long suggestion that includes detailed information. For relaxed users, it will provide detailed information to promote a deeper understanding. For example, if the user is in a hurry, the suggestion section will provide a short, quick suggestion that gets straight to the point. For hurried users, it will highlight important information to support quick decision-making. By adjusting the length of the suggestion according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion section may be performed using, for example, generative AI, or not using generative AI. For example, the suggestion unit can input user emotion data into a generating AI and have the AI ​​adjust the length of the suggestion.

[0090] The proposal department can make optimal proposals by considering the geographical location information of teammates. For example, the proposal department can prioritize proposing nearby teammates based on their geographical location information. Nearby teammates are easier to collaborate with due to their close physical proximity. The proposal department can also consider the geographical location information of teammates and propose teammates with region-specific skill sets. Region-specific skill sets are skills that are in particularly high demand in that region. The proposal department can also use the geographical location information of teammates as a reference to make proposals that meet regional needs. Proposals that meet regional needs improve the success rate of projects in that region. Thus, considering the geographical location information of teammates makes it possible to make optimal proposals. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the geographical location information data of teammates into a generative AI and have the generative AI execute the optimal proposal.

[0091] The proposal department can analyze the social media activities of teammates and make relevant suggestions when making a proposal. For example, the proposal department can suggest teammates with relevant skill sets based on the social media activities of teammates. Social media activities include, for example, analysis of posts and followers. The proposal department can also analyze the social media activities of teammates and make suggestions based on their interests. Suggestions based on interests are suggestions based on skill sets in areas in which the teammate is particularly interested. The proposal department can also make trend-based suggestions, for example, by referring to the social media activities of teammates. Trend-based suggestions are suggestions based on skill sets that are in high demand in the current market. This makes it possible to make relevant suggestions by analyzing the social media activities of teammates. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can input the social media data of teammates into a generative AI and have the generative AI execute relevant suggestions.

[0092] The inspiration unit can estimate the user's emotions and adjust how inspiration is provided based on the estimated emotions. For example, if the user is stressed, the inspiration unit provides concise and easy-to-understand inspiration. For stressed users, it summarizes information concisely and presents it in an easy-to-understand format. For example, if the user is relaxed, the inspiration unit provides inspiration that includes detailed information. For relaxed users, it provides detailed information to promote a deeper understanding. For example, if the user is in a hurry, the inspiration unit provides quick and concise inspiration. For hurried users, it emphasizes important information to support quick decision-making. In this way, by adjusting how inspiration is provided according to the user's emotions, more appropriate inspiration can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inspiration unit may be performed using, for example, generative AI, or not using generative AI. For example, the inspiration unit can input user emotion data into a generating AI and have the AI ​​adjust how inspiration is provided.

[0093] The inspiration unit can provide optimal inspiration by referring to the user's past idea submission history when providing inspiration. For example, the inspiration unit provides relevant inspiration based on the user's past idea submission history. Past idea submission history includes, for example, the number of ideas submitted and the evaluation of the ideas. The inspiration unit can also provide inspiration based on successful ideas by referring to the user's past idea submission history. Inspiration based on successful ideas is inspiration related to ideas that the user has succeeded with in the past. The inspiration unit can also provide inspiration that helps develop ideas by referring to the user's past idea submission history. Inspiration that helps develop ideas is inspiration that helps develop the idea the user is currently working on further. This allows the inspiration unit to provide optimal inspiration by referring to the user's past idea submission history. Some or all of the above processing in the inspiration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the inspiration unit can input the user's past idea submission history data into a generative AI and have the generative AI perform the task of providing optimal inspiration.

[0094] The inspiration unit can customize the content of inspiration when providing it, taking into account the user's current project progress. For example, the inspiration unit can provide project-related inspiration based on the user's current project progress. Project-related inspiration consists of ideas and suggestions directly related to the current project. The inspiration unit can also provide inspiration that is helpful for ongoing tasks, taking into account the user's current project progress. Inspiration that is helpful for ongoing tasks consists of ideas and suggestions for efficiently carrying out the current tasks. The inspiration unit can also provide inspiration toward project completion, taking into account the user's current project progress. Inspiration toward project completion consists of ideas and suggestions for successfully completing the project. This allows for appropriate customization of the inspiration content by considering the user's current project progress. Some or all of the above processing in the inspiration unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the inspiration unit can input the user's current project data into a generative AI and have the generative AI customize the content of the inspiration.

[0095] The inspiration unit can estimate the user's emotions and prioritize inspiration based on those emotions. For example, if the user is stressed, the inspiration unit will prioritize providing concise and easy-to-understand inspiration. For stressed users, it will summarize information concisely and provide it in an easy-to-understand format. For example, if the user is relaxed, the inspiration unit will prioritize providing inspiration that includes detailed information. For relaxed users, it will provide detailed information to promote a deeper understanding. For example, if the user is in a hurry, the inspiration unit will prioritize providing quick and concise inspiration. For hurried users, it will highlight important information to support rapid decision-making. In this way, by prioritizing inspiration according to the user's emotions, more appropriate inspiration can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inspiration unit may be performed using, for example, generative AI, or not using generative AI. For example, the inspiration unit can input user emotion data into a generating AI and have the AI ​​determine the priority of inspirations.

[0096] The inspiration unit can provide optimal inspiration by considering the user's geographical location information. For example, the inspiration unit can provide region-specific inspiration based on the user's geographical location information. Region-specific inspiration consists of ideas and suggestions that are particularly in demand in that region. The inspiration unit can also provide inspiration related to nearby projects, considering the user's geographical location information. Inspiration related to nearby projects consists of ideas and suggestions that are easy to collaborate on due to their close physical proximity. The inspiration unit can also provide inspiration that meets regional needs, taking into account the user's geographical location information. Inspiration that meets regional needs consists of ideas and suggestions that improve the success rate of projects in that region. In this way, optimal inspiration can be provided by considering the user's geographical location information. Some or all of the above processing in the inspiration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the inspiration unit can input the user's geographical location information data into a generative AI and have the generative AI perform the task of providing optimal inspiration.

[0097] The inspiration unit can provide relevant inspiration by analyzing the user's social media activity when providing inspiration. For example, the inspiration unit can provide relevant inspiration based on the user's social media activity. Social media activity includes, for example, analysis of posts and followers. The inspiration unit can also provide inspiration based on the user's interests by analyzing their social media activity. Inspiration based on interests is ideas and suggestions in areas that the user is particularly interested in. The inspiration unit can also provide trend-based inspiration by referring to the user's social media activity. Inspiration based on trends is ideas and suggestions that are in high demand in the current market. In this way, relevant inspiration can be provided by analyzing the user's social media activity. Some or all of the above processing in the inspiration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the inspiration unit can input the user's social media data into a generative AI and have the generative AI perform the provision of relevant inspiration.

[0098] The monitoring unit can estimate the user's emotions and adjust its monitoring method based on the estimated emotions. For example, if the user is stressed, the monitoring unit provides a concise and easy-to-understand monitoring method. For stressed users, it provides information in a concise and easy-to-understand format. For example, if the user is relaxed, the monitoring unit provides a monitoring method that includes detailed information. For relaxed users, it provides detailed information to promote a deeper understanding. For example, if the user is in a hurry, the monitoring unit provides a quick and concise monitoring method. For hurried users, it highlights important information to support quick decision-making. This allows for more appropriate monitoring by adjusting the monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without generative AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI adjust the monitoring method.

[0099] The monitoring unit can adjust the frequency of monitoring according to the project's progress. For example, based on the project's progress, the monitoring unit can increase the monitoring frequency if the project is behind schedule. For projects that are behind schedule, frequent monitoring is performed to detect problems early. The monitoring unit can also consider the project's progress and reduce the monitoring frequency if it is progressing smoothly. For projects that are progressing smoothly, the monitoring frequency is reduced to use resources more efficiently. The monitoring unit can also refer to the project's progress and adjust the monitoring frequency to match important milestones. By monitoring according to important milestones, the progress of the project is ensured. This allows for more appropriate monitoring by adjusting the monitoring frequency according to the project's progress. Some or all of the above processes in the monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the monitoring unit can input project progress data into a generative AI and have the generative AI adjust the monitoring frequency.

[0100] The monitoring unit can adjust the level of detail of monitoring based on the importance of the project. For example, the monitoring unit can perform detailed monitoring on important projects based on their importance. Important projects require detailed monitoring because they directly impact the success of the project. The monitoring unit can also perform simple monitoring on less important projects, taking into account their importance. Less important projects have little impact on the success of the project, so simple monitoring is sufficient. The monitoring unit can also perform detailed monitoring on the elements that have the greatest impact on the success of the project, taking into account their importance. Elements that have the greatest impact on the success of the project require detailed monitoring to ensure the progress of the project. By adjusting the level of detail of monitoring based on the importance of the project, more appropriate monitoring becomes possible. Some or all of the above processes in the monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the monitoring unit can input project importance data into a generative AI and have the generative AI adjust the level of detail of monitoring.

[0101] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is tense, the monitoring unit provides a simple and highly visible display method. For tense users, it summarizes information concisely and provides it in an easy-to-understand format. For example, if the user is relaxed, the monitoring unit provides a display method that includes detailed information. For relaxed users, it provides detailed information to promote a deeper understanding. For example, if the user is in a hurry, the monitoring unit provides a display method that gets straight to the point. For hurried users, it highlights important information to support quick decision-making. This allows for a more appropriate display by adjusting the display method of the monitoring results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or not using generative AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI adjust how the monitoring results are displayed.

[0102] The monitoring unit can perform monitoring while considering the geographical distribution of the project. For example, the monitoring unit can monitor for region-specific issues based on the geographical distribution of the project. Region-specific issues are particularly important in those regions. The monitoring unit can also monitor the progress of the project in each region, taking into account the geographical distribution of the project. By monitoring the progress in each region, region-specific problems can be detected early. The monitoring unit can also perform monitoring that is tailored to the needs of each region, taking into account the geographical distribution of the project. Monitoring tailored to the needs of each region improves the success rate of the project in that region. Thus, considering the geographical distribution of the project enables more appropriate monitoring. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the monitoring unit can input the geographical distribution data of the project into a generative AI and have the generative AI perform the monitoring.

[0103] The monitoring unit can improve the accuracy of its monitoring by referring to relevant project literature during monitoring. For example, the monitoring unit improves the accuracy of its monitoring based on relevant project literature. Relevant literature includes, for example, the use of literature databases and literature evaluation criteria. The monitoring unit can also, for example, refer to relevant project literature and perform monitoring based on the latest information. By performing monitoring based on the latest information, the accuracy of monitoring is improved. The monitoring unit can also, for example, refer to relevant project literature and perform monitoring according to the progress of the project. Monitoring according to the progress of the project improves the success rate of the project. Thus, by referring to relevant project literature, the accuracy of monitoring is improved. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input relevant project literature data into a generative AI and have the generative AI perform the improvement of monitoring accuracy.

[0104] The adjustment unit can estimate the user's emotions and adjust the adjustment method based on the estimated user emotions. For example, if the user is stressed, the adjustment unit provides a concise and easy-to-understand adjustment method. For stressed users, it summarizes information concisely and provides it in an easy-to-understand format. For example, if the user is relaxed, the adjustment unit provides an adjustment method that includes detailed information. For relaxed users, it provides detailed information to promote a deeper understanding. For example, if the user is in a hurry, the adjustment unit provides a quick and concise adjustment method. For hurried users, it emphasizes important information to support quick decision-making. This allows for more appropriate adjustments by adjusting the adjustment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using, for example, generative AI, or without generative AI. For example, the adjustment unit can input user emotion data into the generating AI and have the generating AI perform adjustments to the adjustment method.

[0105] The adjustment unit can adjust the frequency of adjustments according to the project's progress. For example, based on the project's progress, the adjustment unit can increase the frequency of adjustments if the project is behind schedule. For projects that are behind schedule, adjustments are made more frequently to detect problems early. The adjustment unit can also consider the project's progress and reduce the frequency of adjustments if the project is progressing smoothly. For projects that are progressing smoothly, the adjustment unit reduces the frequency of adjustments to use resources more efficiently. The adjustment unit can also refer to the project's progress and adjust the frequency of adjustments to match important milestones. By adjusting to important milestones, the project's progress is ensured. This allows for more appropriate adjustments by adjusting the frequency of adjustments according to the project's progress. Some or all of the above processes in the adjustment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the adjustment unit can input project progress data into a generative AI and have the generative AI adjust the frequency of adjustments.

[0106] The adjustment unit can adjust the level of detail of adjustments based on the importance of the project. For example, the adjustment unit can perform detailed adjustments on important projects based on their importance. Important projects require detailed adjustments because they directly impact the success of the project. The adjustment unit can also perform simple adjustments on less important projects, taking into account their importance. Less important projects have little impact on the success of the project, so simple adjustments are sufficient. The adjustment unit can also perform detailed adjustments on the elements that have the greatest impact on the success of the project, taking into account their importance. Elements that have the greatest impact on the success of the project require detailed adjustments to ensure the progress of the project. By adjusting the level of detail of adjustments based on the importance of the project, more appropriate adjustments can be made. Some or all of the above processing in the adjustment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the adjustment unit can input project importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the adjustments.

[0107] The adjustment unit can estimate the user's emotions and adjust the display method of the adjustment results based on the estimated user emotions. For example, if the user is nervous, the adjustment unit provides a simple and highly visible display method. For nervous users, it summarizes information concisely and provides it in an easy-to-understand format. For example, if the user is relaxed, the adjustment unit provides a display method that includes detailed information. For relaxed users, it provides detailed information to promote a deeper understanding. For example, if the user is in a hurry, the adjustment unit provides a display method that gets straight to the point. For hurried users, it highlights important information to support quick decision-making. In this way, by adjusting the display method of the adjustment results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the adjustment unit can input user emotion data into the generating AI and have the generating AI perform adjustments to how the adjustment results are displayed.

[0108] The adjustment unit can perform adjustments while considering the geographical distribution of the project. For example, the adjustment unit can make adjustments to region-specific issues based on the geographical distribution of the project. Region-specific issues are those that are particularly important in that region. The adjustment unit can also adjust the progress in each region, for example, while considering the geographical distribution of the project. By adjusting the progress in each region, region-specific problems can be identified early and appropriate responses can be taken. The adjustment unit can also make adjustments in response to regional needs, for example, by referring to the geographical distribution of the project. Adjustments in response to regional needs improve the success rate of the project in that region. Thus, considering the geographical distribution of the project enables more appropriate adjustments. Some or all of the above processing in the adjustment unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the adjustment unit can input the geographical distribution data of the project into a generative AI and have the generative AI perform the adjustments.

[0109] The adjustment unit can improve the accuracy of its adjustments by referring to relevant project literature during the adjustment process. For example, the adjustment unit improves the accuracy of its adjustments based on relevant project literature. Relevant literature includes, for example, the use of literature databases and literature evaluation criteria. The adjustment unit can also, for example, refer to relevant project literature and perform adjustments based on the latest information. Performing adjustments based on the latest information improves the accuracy of the adjustments. The adjustment unit can also, for example, refer to relevant project literature and perform adjustments according to the progress of the project. Adjustments according to the progress of the project improve the success rate of the project. Thus, referring to relevant project literature improves the accuracy of the adjustments. Some or all of the above processing in the adjustment unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the adjustment unit can input project-related literature data into a generative AI and have the generative AI perform the adjustment accuracy improvement.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion can be delayed until the user is relaxed. This makes the user more receptive to the suggestion. If the user is in a hurry, the suggestion can be delivered quickly so that the user can get the necessary information immediately. Furthermore, if the user is relaxed, a detailed suggestion can be delivered to allow the user to understand it more deeply. By adjusting the timing of suggestions according to the user's emotions, more appropriate suggestions can be made.

[0112] The analytics department can improve the accuracy of skill matching by considering the user's past project failure rates. For example, it can analyze the user's past project failure rates to avoid high-failure skill sets, thereby improving the project's success rate. It can also suggest skills to complement high-failure skill sets. Furthermore, it can suggest avoiding teammates with high failure rates. In this way, considering the user's past project failure rates improves the accuracy of skill matching.

[0113] The inspiration unit can estimate the user's emotions and adjust the frequency of inspiration delivery based on those estimates. For example, if the user is stressed, the frequency of inspiration delivery is reduced, and inspiration is provided when the user is relaxed. This makes the user more receptive to the inspiration. Also, if the user is in a hurry, inspiration is provided quickly so that the user can get the necessary information immediately. Furthermore, if the user is relaxed, detailed inspiration is provided so that the user can understand it more deeply. In this way, by adjusting the frequency of inspiration delivery according to the user's emotions, more appropriate inspiration can be provided.

[0114] The monitoring unit can adjust the level of detail of monitoring according to the project's progress. For example, if the project is behind schedule, detailed monitoring can be performed to detect problems early, thereby ensuring smooth project progress. Conversely, if the project is progressing smoothly, simpler monitoring can be performed to use resources efficiently. Furthermore, the level of detail of monitoring can be adjusted to match important milestones. This allows for more appropriate monitoring by adjusting the level of detail according to the project's progress.

[0115] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on those emotions. For example, if the user is stressed, important adjustments are prioritized, and detailed adjustments are made when the user is relaxed. This makes the user more receptive to the adjustments. Also, if the user is in a hurry, important adjustments are made quickly so that the user can get the necessary information immediately. Furthermore, if the user is relaxed, detailed adjustments are made to allow the user to understand more deeply. In this way, prioritizing adjustments according to the user's emotions enables more appropriate adjustments.

[0116] The analytics department can adjust the skill set analysis method considering the user's current project resource usage. For example, if resources are scarce, it will prioritize analyzing skill sets that enable efficient resource use, thereby facilitating project progress. If resources are sufficient, it will perform a detailed skill set analysis to provide comprehensive information. Furthermore, it can adjust the priority of skill sets according to resource usage. This allows for appropriate adjustment of the skill set analysis method by considering the user's current project resource usage.

[0117] The suggestion function can estimate the user's emotions and customize the content of the suggestions based on those emotions. For example, if the user is stressed, it will provide a concise and easy-to-understand suggestion, making it more likely for the user to accept it. If the user is relaxed, it will provide a suggestion with more detailed information to help the user understand it better. Furthermore, if the user is in a hurry, it will provide a quick and concise suggestion. By customizing the content of suggestions according to the user's emotions, it becomes possible to provide more appropriate suggestions.

[0118] The inspiration section can adjust the content of inspiration based on the user's past project evaluations. For example, it can provide inspiration based on ideas that received high ratings in past projects, making it easier for the user to succeed. It can also provide inspiration that avoids ideas that received low ratings in past projects. Furthermore, it can adjust the priority of inspiration based on past project evaluations. This allows the content of inspiration to be appropriately adjusted by referring to the user's past project evaluations.

[0119] The monitoring department can adjust its monitoring methods according to the project's progress. For example, if the project is behind schedule, it can conduct detailed monitoring to detect problems early, thereby ensuring smooth project progress. Conversely, if the project is progressing smoothly, it can conduct concise monitoring to efficiently utilize resources. Furthermore, it can adjust its monitoring methods to align with important milestones. By adjusting monitoring methods according to the project's progress, more appropriate monitoring becomes possible.

[0120] The adjustment unit can estimate the user's emotions and adjust the timing of adjustments based on those emotions. For example, if the user is stressed, the adjustment timing can be delayed until the user is relaxed. This makes the user more receptive to the adjustment. If the user is in a hurry, the adjustment can be performed quickly so that the user can get the necessary information immediately. Furthermore, if the user is relaxed, detailed adjustments can be performed to allow the user to understand more deeply. In this way, adjusting the timing of adjustments according to the user's emotions enables more appropriate adjustments.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The analysis team analyzes the participants' skill sets and previous projects. Participants' skill sets include technical skills, soft skills, and years of experience, while previous projects include project size, field, and success rate. The analysis team uses machine learning to perform skill matching, learning from past successful projects. For example, neural networks, support vector machines, and decision trees can be used for skill matching. Step 2: The proposal team proposes the most suitable teammates based on the analysis results obtained by the analysis team. The optimal teammates include complementary skills and past collaboration experience. The proposal team analyzes the profiles of creators with different expertise, such as designers, programmers, and writers, and proposes the most suitable teammates. Creator profiles include skill sets, past projects, and evaluations. Step 3: The Inspiration Department collaborates with teammates proposed by the Proposal Department to generate new ideas. These new ideas can include technical ideas, business ideas, and more. The Inspiration Department functions to help users gain new ideas and inspiration, generating new ideas through brainstorming, idea generation tools, and creative workshops. Step 4: The monitoring department monitors the project's progress based on the ideas generated by the inspiration department. Project progress includes progress rate, milestone achievement status, etc. The monitoring department monitors the project's progress in real time, including data update frequency and the type of monitoring tools used. Step 5: The adjustment unit makes necessary adjustments based on the project progress monitored by the monitoring unit. Necessary adjustments include reallocating resources and changing the schedule. The adjustment unit can reallocate resources, change the schedule, and reassign tasks according to the project progress.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the analysis unit, proposal unit, inspiration unit, monitoring unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12. The inspiration unit is implemented by the control unit 46A of the smart device 14. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12. The adjustment unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the analysis unit, proposal unit, inspiration unit, monitoring unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The inspiration unit is implemented, for example, by the control unit 46A of the smart glasses 214. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The adjustment unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the analysis unit, proposal unit, inspiration unit, monitoring unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12. The inspiration unit is implemented by the control unit 46A of the headset terminal 314. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12. The adjustment unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the analysis unit, proposal unit, inspiration unit, monitoring unit, and adjustment unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12. The inspiration unit is implemented by the control unit 46A of the robot 414. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12. The adjustment unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The analysis department analyzes the participants' skill sets and previous projects, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the most suitable teammates, The aforementioned proposal department proposes an inspiration department that collaborates with teammates to generate new ideas, A monitoring unit monitors the progress of the project based on the ideas generated by the aforementioned inspiration unit, The system includes an adjustment unit that performs necessary adjustments according to the progress of the project monitored by the monitoring unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is We use machine learning to perform skill matching and learn from past successful projects. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We analyze the profiles of creators with different expertise, such as designers, programmers, and writers, and suggest the best teammates for them. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned inspiration unit is It functions to help users gain new ideas and inspiration. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned monitoring unit, Monitor project progress in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, Make necessary adjustments as the project progresses. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We estimate user emotions and adjust the skill set analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is During analysis, we improve the accuracy of skill matching by considering the user's past project success rate. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During the analysis, we prioritize skill sets by considering the user's current project progress. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During analysis, the system prioritizes analyzing highly relevant skill sets by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During the analysis, we analyze users' social media activity and extract relevant skill sets. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of your teammates' skill sets. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, a different proposal algorithm is applied depending on the success rate of the teammate's past projects. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, take into account the geographical location of your teammates to make the best possible proposal. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, analyze your teammates' social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned inspiration unit is It estimates the user's emotions and adjusts how inspiration is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned inspiration unit is When providing inspiration, we refer to the user's past idea submission history to provide the most suitable inspiration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned inspiration unit is When providing inspiration, the content of the inspiration will be customized considering the user's current project progress. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned inspiration unit is It estimates the user's emotions and prioritizes inspiration based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned inspiration unit is When providing inspiration, we take the user's geographical location into consideration to provide the most optimal inspiration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned inspiration unit is When providing inspiration, we analyze users' social media activity and provide relevant inspiration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned monitoring unit, It estimates user sentiment and adjusts monitoring methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned monitoring unit, During monitoring, adjust the monitoring frequency according to the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit, During monitoring, the level of detail of monitoring is adjusted based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned monitoring unit, During monitoring, the geographical distribution of the project should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned monitoring unit, During monitoring, refer to relevant project documentation to improve monitoring accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 31) The adjustment unit is, It estimates the user's emotions and adjusts the adjustment method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The adjustment unit is, During adjustments, the frequency of adjustments will be adjusted according to the progress of the project. The system described in Appendix 1, characterized by the features described herein. (Note 33) The adjustment unit is, During the adjustment process, adjust the level of detail based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 34) The adjustment unit is, The system estimates the user's emotions and adjusts how the adjustment results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The adjustment unit is, During the adjustment process, the geographical distribution of the projects will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 36) The adjustment unit is, During the adjustment process, refer to relevant project literature to improve the accuracy of the adjustments. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes the participants' skill sets and previous projects, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the most suitable teammates, The aforementioned proposal department proposes an inspiration department that collaborates with teammates to generate new ideas, A monitoring unit monitors the progress of the project based on the ideas generated by the aforementioned inspiration unit, The system includes an adjustment unit that performs necessary adjustments according to the progress of the project monitored by the monitoring unit. A system characterized by the following features.

2. The aforementioned analysis unit is We use machine learning to perform skill matching and learn from past successful projects. The system according to feature 1.

3. The aforementioned proposal section is, We analyze the profiles of creators with different expertise, such as designers, programmers, and writers, and suggest the best teammates for them. The system according to feature 1.

4. The aforementioned inspiration unit is It functions to help users gain new ideas and inspiration. The system according to feature 1.

5. The aforementioned monitoring unit, Monitor project progress in real time. The system according to feature 1.

6. The adjustment unit is, Make necessary adjustments as the project progresses. The system according to feature 1.

7. The aforementioned analysis unit is We estimate user emotions and adjust the skill set analysis method based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit is During analysis, we improve the accuracy of skill matching by considering the user's past project success rate. The system according to feature 1.

9. The aforementioned analysis unit is During the analysis, we prioritize skill sets by considering the user's current project progress. The system according to feature 1.

10. The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system according to feature 1.

Images