system

Abstract

The system according to this embodiment aims to propose and assign the most suitable communication methods and roles based on the behavioral tendencies of each member. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, a simulation unit, and an assignment unit. The analysis unit analyzes the behavioral tendencies of each member. The proposal unit proposes the most suitable communication method and response strategy to the leader based on the behavioral tendencies analyzed by the analysis unit. The simulation unit performs a decision-making simulation based on the response strategy proposed by the proposal unit. The assignment unit assigns the most suitable role to each member based on the results obtained by the simulation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, an optimal communication method and role assignment based on the behavior tendencies of each member have not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to propose and assign an optimal communication method and role based on the behavior tendencies of each member.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, a simulation unit, and an assignment unit. The analysis unit analyzes the behavioral tendencies of each member. The proposal unit proposes the most suitable communication methods and response strategies to the leader based on the behavioral tendencies analyzed by the analysis unit. The simulation unit performs a decision-making simulation based on the response strategies proposed by the proposal unit. The assignment unit assigns the most suitable roles to each member based on the results obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose and assign optimal communication methods and roles based on the behavioral tendencies of each member. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple 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 reception 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 reception 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 system according to an embodiment of the present invention is a system that uses an AI agent to promote cooperation among members of different genders and behavioral characteristics, and to provide optimal role assignments and feedback. This system analyzes the behavioral tendencies of each member (risk-seeking, strong cooperativeness, etc.) and proposes the optimal communication method and response strategy to the leader. It also simulates catastrophic scenarios when decision-making is biased and promotes constructive exchange of opinions. Furthermore, it provides an environment in which each individual can easily demonstrate their strengths, such as assigning a support role to cooperative members and a leadership role to highly competitive members. First, the AI ​​agent analyzes the behavioral tendencies of each member. In this process, it evaluates characteristics such as risk-seeking, strong cooperativeness, tendency to suppress emotions, and tendency to empathize. For example, risk-seeking members prefer new challenges, while risk-averse members value stability. The AI ​​agent analyzes these characteristics and understands the behavioral tendencies of each member. Next, it proposes the optimal communication method and response strategy to the leader. For example, it gives challenging tasks to risk-seeking members and provides a stable environment to risk-averse members. It also assigns roles that emphasize teamwork to highly cooperative members and tasks that allow highly competitive members to demonstrate leadership. In this way, the system provides an environment where each member can easily leverage their strengths. Furthermore, it simulates catastrophic scenarios that may occur when decision-making is biased, promoting constructive exchange of ideas. For example, if there are many risk-seeking members, there is a possibility that high-risk decisions will be made. In such cases, the AI ​​agent performs a simulation and evaluates the impact of the high-risk decision. This allows leaders to make balanced decisions. This mechanism promotes collaboration among members with different genders and behavioral characteristics, and provides optimal role assignments and feedback. For example, members who are good at controlling their emotions are given empathetic feedback to support their mental health. Also, collaborative members are assigned support roles, and highly competitive members are assigned leadership roles on tasks, creating an environment where each individual can easily utilize their strengths.This allows AI-powered systems to facilitate collaboration among members with different genders and behavioral characteristics, providing optimal role assignments and feedback.

[0029] The system according to this embodiment comprises an analysis unit, a proposal unit, a simulation unit, and an assignment unit. The analysis unit analyzes the behavioral tendencies of each member. The analysis unit evaluates characteristics such as risk-seeking, strong cooperativeness, tendency to suppress emotions, and tendency to empathize. For example, the analysis unit evaluates that risk-seeking members prefer new challenges, while risk-averse members value stability. For example, the analysis unit evaluates that highly cooperative members value teamwork, while highly competitive members demonstrate leadership. The proposal unit proposes optimal communication methods and response strategies to leaders based on the behavioral tendencies analyzed by the analysis unit. For example, the proposal unit gives challenging tasks to risk-seeking members and provides a stable environment to risk-averse members. For example, the proposal unit gives highly cooperative members roles that emphasize teamwork and gives highly competitive members tasks that allow them to demonstrate leadership. The simulation unit performs decision-making simulations based on the response strategies proposed by the proposal unit. For example, the simulation unit evaluates the impact of high-risk decisions. The simulation unit evaluates, for example, the likelihood of high-risk decisions being made when there are many risk-seeking members. The assignment unit assigns the most suitable role to each member based on the results obtained by the simulation unit. For example, the assignment unit assigns a support role to a collaborative member and a task leader role to a highly competitive member. In this way, the system according to the embodiment can analyze the behavioral tendencies of each member, propose the optimal communication method and response policy, simulate decision-making, and assign the optimal role.

[0030] The analysis department conducts a detailed analysis of each member's behavioral tendencies. Specifically, it collects past behavioral data, survey results, and performance evaluations of members, and evaluates their behavioral tendencies based on this data. For example, risk-seeking members tend to like new challenges and changes, and this tendency can be identified from their actions and statements in past projects. On the other hand, risk-averse members prefer stable environments and often show a cautious attitude towards change. To assess the strength of cooperativeness, the frequency and quality of communication within the team and the degree of cooperation with other members are evaluated. Members with strong cooperativeness tend to value teamwork and actively cooperate with other members. To assess the tendency to suppress emotions, the behavior and expression of emotions under stressful situations are observed and evaluated. Members with a strong tendency to suppress emotions tend to deal with stressful situations calmly and often do not show their emotions. To assess the tendency to empathize, the degree of understanding and consideration for the emotions and situations of others is evaluated. Members with a strong tendency to empathize are sensitive to the emotions of others and often provide support and consideration. These evaluations are conducted using AI-based data analysis technology, allowing for a quantitative assessment of each member's behavioral tendencies. This enables the analysis department to gain a detailed understanding of each member's characteristics and provide the foundational data necessary to formulate appropriate response strategies.

[0031] The Proposal Department proposes optimal communication methods and response strategies to leaders based on behavioral trends analyzed by the Analysis Department. Specifically, risk-seeking members can be motivated by being given challenging tasks. For example, assigning them tasks aimed at realizing new projects or innovative ideas can maximize their capabilities. On the other hand, risk-averse members can work with a sense of security by being provided with a stable environment and predictable tasks. Cooperative members can leverage their collaborative spirit by being given roles that emphasize teamwork. For example, assigning them roles as project coordinators or supporters can improve the overall team performance. Competitive members can be stimulated and achieve results by being given tasks that allow them to demonstrate leadership. For example, assigning them roles as project leaders or those responsible for important tasks can maximize the capabilities of competitive members. The Proposal Department provides these proposals to leaders and supports them in taking the optimal approach according to the characteristics of each member. In this way, the Proposal Department can play a crucial role in improving the overall team performance.

[0032] The Simulation Department conducts decision-making simulations based on the response strategies proposed by the Proposal Department. Specifically, it simulates various scenarios to evaluate the impact of high-risk decisions. For example, it evaluates the likelihood of high-risk decisions being made when there are many risk-seeking members and predicts the impact of those decisions on the entire team. The Simulation Department utilizes AI-based simulation technology to quickly and accurately evaluate multiple scenarios. This allows leaders to understand in advance what results the proposed response strategies will actually bring. For example, if a challenging task is given to a risk-seeking member, the probability of success and the risk of failure can be evaluated, which can be used as a reference to determine the optimal response strategy. Also, if a highly collaborative member is given a role that emphasizes teamwork, the impact of that role on the overall team performance can be evaluated. The Simulation Department provides these simulation results to leaders, supporting them in making optimal decisions. In this way, the Simulation Department can play a crucial role in enabling leaders to select the optimal response strategy while minimizing risk.

[0033] The assignment department assigns the most suitable role to each member based on the results obtained by the simulation department. Specifically, it assigns support roles to collaborative members and task leader roles to highly competitive members. For example, collaborative members can be given roles as coordinators or supporters within the team, leveraging their cooperative nature. On the other hand, highly competitive members can be given roles as project leaders or responsible for important tasks, stimulating their competitive spirit and leading to better results. The assignment department aims to maximize the overall team performance by considering each member's characteristics and behavioral tendencies and assigning the most suitable role. Furthermore, even after role assignment, the assignment department continuously monitors each member's performance and reviews or reassigns roles as needed. This allows the assignment department to maintain optimal role allocation at all times, improving the overall efficiency and results of the team. In addition, the assignment department can improve the accuracy of role allocation by collecting feedback from each member and understanding their satisfaction with their role assignment and areas for improvement. This allows the assignment department to maximize the strengths of each member and play a crucial role in improving the overall team performance.

[0034] The analysis department can evaluate characteristics such as risk-seeking tendencies, strong cooperativeness, emotional suppression tendencies, and empathy tendencies. For example, the analysis department can evaluate that risk-seeking members prefer new challenges, while risk-averse members prioritize stability. For example, the analysis department can evaluate that highly cooperative members value teamwork, while highly competitive members exhibit leadership qualities. For example, the analysis department can evaluate that members who are good at emotional suppression may experience mental health problems if they do not receive empathetic feedback. By evaluating the characteristics of each member, the accuracy of behavioral tendency analysis is improved. Some or all of the above processing in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input each member's behavioral data into a generating AI and have the generating AI perform the evaluation of behavioral tendencies.

[0035] The proposal department can provide challenging tasks to risk-seeking members and a stable environment to risk-averse members. For example, the proposal department can propose highly difficult projects or the launch of new businesses to risk-seeking members. For example, the proposal department can propose routine tasks or low-risk tasks to risk-averse members. For example, the proposal department can set challenging goals for risk-seeking members and provide a stable work environment for risk-averse members. In this way, by making proposals tailored to the characteristics of each member, an environment can be provided in which members can easily demonstrate their strengths. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input each member's behavioral data into a generating AI and have the generating AI execute the generation of proposal content.

[0036] The proposal department can assign teamwork-focused roles to highly cooperative members and leadership-oriented tasks to highly competitive members. For example, the proposal department could suggest team leader or coordinating roles to highly cooperative members. For example, it could suggest project management or new business development to highly competitive members. For example, it could suggest team-building activities to highly cooperative members and leadership training to highly competitive members. By assigning roles tailored to each member's characteristics, the department can provide an environment where members can easily demonstrate their strengths. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department could input each member's behavioral data into a generating AI and have the generating AI generate role suggestions.

[0037] The simulation unit can evaluate the impact of high-risk decisions. For example, the simulation unit can evaluate the impact of high-risk investment decisions or the launch of new businesses. For example, the simulation unit can evaluate the likelihood of high-risk decisions being made when there are many risk-seeking members. For example, the simulation unit can simulate catastrophic scenarios resulting from high-risk decisions. This allows for balanced decision-making by evaluating the impact of high-risk decisions. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input high-risk decision scenarios into a generating AI and have the generating AI perform the impact evaluation.

[0038] The assignment unit can assign support roles to collaborative members and leadership roles to highly competitive members. For example, the assignment unit can assign support or assistant tasks to collaborative members. For example, the assignment unit can assign project leader or team leader roles to highly competitive members. For example, the assignment unit can assign the role of coordinator for follow-up meetings to collaborative members and the leadership role for new projects to highly competitive members. By assigning roles according to each member's characteristics, an environment can be provided in which members can easily demonstrate their strengths. Some or all of the above processes in the assignment unit may be performed using AI, for example, or not using AI. For example, the assignment unit can input each member's behavioral data into a generating AI and have the generating AI perform the role assignment.

[0039] The analysis unit can analyze members' past behavioral history and predict changes in their behavioral trends. For example, the analysis unit can analyze members' past behavioral history and build a model to predict changes in their behavioral trends. For example, the analysis unit can analyze members' past behavioral history and extract patterns to predict changes in their behavioral trends. For example, the analysis unit can analyze members' past behavioral history and develop an algorithm to predict changes in their behavioral trends. This makes it possible to predict changes in behavioral trends by analyzing members' past behavioral history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input members' past behavioral data into a generating AI and have the generating AI perform predictions of changes in their behavioral trends.

[0040] The analysis unit can monitor members' behavioral trends in real time and update analysis results immediately. For example, the analysis unit can monitor members' behavior in real time and immediately detect changes in behavioral trends. For example, the analysis unit can monitor members' behavior in real time and immediately update analysis results in response to changes in behavioral trends. For example, the analysis unit can monitor members' behavior in real time and immediately notify of changes in behavioral trends. This allows for immediate updates of analysis results by monitoring members' behavioral trends in real time. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input members' behavioral data into a generating AI and have the generating AI perform real-time monitoring and update of analysis results.

[0041] The analysis unit can analyze regional differences in behavioral trends by considering the geographical location information of the members. For example, the analysis unit can acquire the geographical location information of the members and analyze the differences in behavioral trends by region. For example, the analysis unit can acquire the geographical location information of the members and predict changes in behavioral trends by region. For example, the analysis unit can acquire the geographical location information of the members and visualize the differences in behavioral trends by region. In this way, by considering the geographical location information of the members, it is possible to analyze regional differences in behavioral trends. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the geographical location data of the members into a generating AI and have the generating AI perform the analysis of regional differences.

[0042] The analysis unit can analyze members' social media activities and use them as supplementary information for behavioral trends. For example, the analysis unit can build a model for analyzing members' social media activities and using it as supplementary information for behavioral trends. For example, the analysis unit can analyze members' social media activities and extract patterns for use as supplementary information for behavioral trends. For example, the analysis unit can develop an algorithm for analyzing members' social media activities and using it as supplementary information for behavioral trends. This allows the analysis of members' social media activities to be used as supplementary information for behavioral trends. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input members' social media data into a generating AI and have the generating AI perform the analysis of supplementary information for behavioral trends.

[0043] The proposal team can improve the accuracy of proposals by referring to members' past feedback when making proposals. For example, the proposal team can refer to members' past feedback and build a model to improve the accuracy of the proposal. For example, the proposal team can refer to members' past feedback and extract patterns to improve the accuracy of the proposal. For example, the proposal team can refer to members' past feedback and develop an algorithm to improve the accuracy of the proposal. In this way, the accuracy of proposals can be improved by referring to members' past feedback. Some or all of the above processes in the proposal team may be performed using AI, for example, or not using AI. For example, the proposal team can input members' past feedback data into a generating AI and have the generating AI perform the improvement of the accuracy of the proposal.

[0044] The proposal department can predict changes in members' behavioral tendencies and make proposals based on future behavior at the time of proposal creation. For example, the proposal department can build a model for predicting changes in members' behavioral tendencies and making proposals based on future behavior. For example, the proposal department can extract patterns for predicting changes in members' behavioral tendencies and making proposals based on future behavior. For example, the proposal department can develop an algorithm for predicting changes in members' behavioral tendencies and making proposals based on future behavior. This allows the proposal department to make proposals based on future behavior by predicting changes in members' behavioral tendencies. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input members' behavioral data into a generating AI and have the generating AI perform behavioral trend change prediction and proposal content generation.

[0045] The proposal department can make region-specific proposals by considering the geographical location information of its members. For example, the proposal department can acquire the geographical location information of its members and build a model for making region-specific proposals. For example, the proposal department can acquire the geographical location information of its members and extract patterns for making region-specific proposals. For example, the proposal department can acquire the geographical location information of its members and develop an algorithm for making region-specific proposals. This allows the proposal department to make region-specific proposals by considering the geographical location information of its members. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the geographical location data of its members into a generating AI and have the generating AI generate region-specific proposal content.

[0046] The proposal department can analyze members' social media activity and make relevant suggestions when making proposals. For example, the proposal department can build a model for analyzing members' social media activity and making relevant suggestions. For example, the proposal department can analyze members' social media activity and extract patterns for making relevant suggestions. For example, the proposal department can develop an algorithm for analyzing members' social media activity and making relevant suggestions. This allows the proposal department to make relevant suggestions by analyzing members' social media activity. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input members' social media data into a generating AI and have the generating AI generate relevant suggestion content.

[0047] The simulation unit can improve the accuracy of the simulation by referring to past decision-making data during the simulation. For example, the simulation unit can refer to past decision-making data and build a model to improve the accuracy of the simulation. For example, the simulation unit can refer to past decision-making data and extract patterns to improve the accuracy of the simulation. For example, the simulation unit can refer to past decision-making data and develop an algorithm to improve the accuracy of the simulation. In this way, the accuracy of the simulation can be improved by referring to past decision-making data. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input past decision-making data into a generating AI and have the generating AI perform the improvement of the simulation accuracy.

[0048] The simulation unit can predict changes in members' behavioral tendencies and simulate future scenarios during the simulation. For example, the simulation unit can build a model to predict changes in members' behavioral tendencies and simulate future scenarios. For example, the simulation unit can extract patterns to predict changes in members' behavioral tendencies and simulate future scenarios. For example, the simulation unit can develop an algorithm to predict changes in members' behavioral tendencies and simulate future scenarios. This makes it possible to simulate future scenarios by predicting changes in members' behavioral tendencies. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input members' behavioral data into a generating AI and have the generating AI execute a simulation of future scenarios.

[0049] The simulation unit can perform region-specific simulations by considering the geographical location information of the members during the simulation. For example, the simulation unit can acquire the geographical location information of the members and build a model for performing region-specific simulations. For example, the simulation unit can acquire the geographical location information of the members and extract patterns for performing region-specific simulations. For example, the simulation unit can acquire the geographical location information of the members and develop an algorithm for performing region-specific simulations. This allows for region-specific simulations to be performed by considering the geographical location information of the members. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the geographical location data of the members into a generating AI and have the generating AI execute a region-specific simulation.

[0050] The simulation unit can analyze members' social media activities and simulate relevant scenarios during the simulation. For example, the simulation unit can analyze members' social media activities and build a model for simulating relevant scenarios. For example, the simulation unit can analyze members' social media activities and extract patterns for simulating relevant scenarios. For example, the simulation unit can develop algorithms for analyzing members' social media activities and simulating relevant scenarios. This makes it possible to simulate relevant scenarios by analyzing members' social media activities. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input members' social media data into a generating AI and have the generating AI execute simulations of relevant scenarios.

[0051] The assignment unit can select the optimal role by referring to the member's past role history during the assignment process. For example, the assignment unit can refer to the member's past role history and build a model for selecting the optimal role. For example, the assignment unit can refer to the member's past role history and extract patterns for selecting the optimal role. For example, the assignment unit can refer to the member's past role history and develop an algorithm for selecting the optimal role. This allows the optimal role to be selected by referring to the member's past role history. Some or all of the above processes in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input the member's past role history data into a generating AI and have the generating AI perform the selection of the optimal role.

[0052] The assignment unit can predict changes in members' behavioral tendencies and make assignments considering their future roles. For example, the assignment unit can build a model for predicting changes in members' behavioral tendencies and making assignments considering their future roles. For example, the assignment unit can extract patterns for predicting changes in members' behavioral tendencies and making assignments considering their future roles. For example, the assignment unit can develop an algorithm for predicting changes in members' behavioral tendencies and making assignments considering their future roles. This allows the assignment unit to predict changes in members' behavioral tendencies and make assignments considering their future roles. Some or all of the above processing in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input members' behavioral data into a generating AI and have the generating AI perform assignments that consider their future roles.

[0053] The assignment unit can assign region-specific roles by considering the geographical location information of members during the assignment process. For example, the assignment unit can acquire the geographical location information of members and build a model for assigning region-specific roles. For example, the assignment unit can acquire the geographical location information of members and extract patterns for assigning region-specific roles. For example, the assignment unit can acquire the geographical location information of members and develop an algorithm for assigning region-specific roles. This allows for the assignment of region-specific roles by considering the geographical location information of members. Some or all of the above processes in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input the geographical location data of members into a generating AI and have the generating AI perform the assignment of region-specific roles.

[0054] The assignment unit can analyze members' social media activity and assign relevant roles during the assignment process. For example, the assignment unit can build a model for analyzing members' social media activity and assigning relevant roles. For example, the assignment unit can analyze members' social media activity and extract patterns for assigning relevant roles. For example, the assignment unit can develop an algorithm for analyzing members' social media activity and assigning relevant roles. This allows the assignment of relevant roles by analyzing members' social media activity. Some or all of the above processes in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input members' social media data into a generating AI and have the generating AI perform the assignment of relevant roles.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The proposal team can refer to members' past successes and make proposals based on those successes. For example, a member who has previously demonstrated leadership and achieved success can be proposed a role where they can again demonstrate leadership. Similarly, a member who has previously succeeded by emphasizing teamwork can be proposed a team-building activity. Furthermore, a member who has previously succeeded in launching a new business can be proposed the launch of a new project. This allows members to leverage their past successes in their proposals, enabling them to work with confidence.

[0057] The analysis department can monitor the health status of its members and analyze their behavioral tendencies based on that status. For example, it can monitor members' sleep patterns and assign less demanding roles to members who are sleep-deprived. It can also monitor members' stress levels and provide a relaxing environment for members with high stress levels. Furthermore, it can monitor members' exercise habits and suggest exercise to members who are not getting enough exercise. By conducting analyses that take into account the health status of members, it is possible to maximize their performance.

[0058] The proposal team can consider the hobbies and interests of its members and make proposals based on those interests. For example, for a member who enjoys the outdoors, they can propose team-building activities that incorporate outdoor activities. For a member who enjoys reading, they can propose reading groups or book clubs. Furthermore, for a member who enjoys music, they can propose relaxation methods that incorporate music. In this way, by making proposals that utilize the hobbies and interests of the members, their motivation can be increased.

[0059] The simulation department can conduct simulations based on members' career goals, taking those goals into consideration. For example, a member who wants to demonstrate leadership can simulate scenarios where they can exercise leadership. Similarly, a member who wants to deepen their expertise can simulate scenarios where they can utilize their expertise. Furthermore, a member who wants to challenge themselves in a different field can simulate scenarios in a new field. In this way, by conducting simulations that take members' career goals into consideration, the department can support the growth of its members.

[0060] The assignment department can consider members' skill sets and assign roles based on those skill sets. For example, members with strong programming skills can be assigned programming-related roles. Members with strong communication skills can be assigned team leader or coordination roles. Furthermore, members with strong creative skills can be assigned design or planning roles. By assigning roles that utilize each member's skill set, the department can maximize each member's strengths.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The analysis team analyzes the behavioral tendencies of each member. For example, they evaluate characteristics such as risk-seeking, strong cooperativeness, tendency to suppress emotions, and empathy. They assess that risk-seeking members prefer new challenges, while risk-averse members value stability. They also assess that highly cooperative members value teamwork, while highly competitive members demonstrate leadership. Step 2: The proposal team proposes the most suitable communication methods and response strategies for leaders based on the behavioral tendencies analyzed by the analysis team. For example, they might give challenging tasks to risk-seeking members and provide a stable environment to risk-averse members. They might assign roles that emphasize teamwork to highly cooperative members and give tasks that allow them to demonstrate leadership to highly competitive members. Step 3: The simulation team conducts decision-making simulations based on the response strategies proposed by the proposal team. For example, they evaluate the impact of high-risk decisions. They assess the likelihood of high-risk decisions being made when there are many risk-seeking members. Step 4: The assignment unit assigns the most suitable role to each member based on the results obtained by the simulation unit. For example, a collaborative member might be assigned a support role, while a highly competitive member might be assigned a task leader role.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a system that uses an AI agent to promote cooperation among members of different genders and behavioral characteristics, and to provide optimal role assignments and feedback. This system analyzes the behavioral tendencies of each member (risk-seeking, strong cooperativeness, etc.) and proposes the optimal communication method and response strategy to the leader. It also simulates catastrophic scenarios when decision-making is biased and promotes constructive exchange of opinions. Furthermore, it provides an environment in which each individual can easily demonstrate their strengths, such as assigning a support role to cooperative members and a leadership role to highly competitive members. First, the AI ​​agent analyzes the behavioral tendencies of each member. In this process, it evaluates characteristics such as risk-seeking, strong cooperativeness, tendency to suppress emotions, and tendency to empathize. For example, risk-seeking members prefer new challenges, while risk-averse members value stability. The AI ​​agent analyzes these characteristics and understands the behavioral tendencies of each member. Next, it proposes the optimal communication method and response strategy to the leader. For example, it gives challenging tasks to risk-seeking members and provides a stable environment to risk-averse members. It also assigns roles that emphasize teamwork to highly cooperative members and tasks that allow highly competitive members to demonstrate leadership. In this way, the system provides an environment where each member can easily leverage their strengths. Furthermore, it simulates catastrophic scenarios that may occur when decision-making is biased, promoting constructive exchange of ideas. For example, if there are many risk-seeking members, there is a possibility that high-risk decisions will be made. In such cases, the AI ​​agent performs a simulation and evaluates the impact of the high-risk decision. This allows leaders to make balanced decisions. This mechanism promotes collaboration among members with different genders and behavioral characteristics, and provides optimal role assignments and feedback. For example, members who are good at controlling their emotions are given empathetic feedback to support their mental health. Also, collaborative members are assigned support roles, and highly competitive members are assigned leadership roles on tasks, creating an environment where each individual can easily utilize their strengths.This allows AI-powered systems to facilitate collaboration among members with different genders and behavioral characteristics, providing optimal role assignments and feedback.

[0064] The system according to this embodiment comprises an analysis unit, a proposal unit, a simulation unit, and an assignment unit. The analysis unit analyzes the behavioral tendencies of each member. The analysis unit evaluates characteristics such as risk-seeking, strong cooperativeness, tendency to suppress emotions, and tendency to empathize. For example, the analysis unit evaluates that risk-seeking members prefer new challenges, while risk-averse members value stability. For example, the analysis unit evaluates that highly cooperative members value teamwork, while highly competitive members demonstrate leadership. The proposal unit proposes optimal communication methods and response strategies to leaders based on the behavioral tendencies analyzed by the analysis unit. For example, the proposal unit gives challenging tasks to risk-seeking members and provides a stable environment to risk-averse members. For example, the proposal unit gives highly cooperative members roles that emphasize teamwork and gives highly competitive members tasks that allow them to demonstrate leadership. The simulation unit performs decision-making simulations based on the response strategies proposed by the proposal unit. For example, the simulation unit evaluates the impact of high-risk decisions. The simulation unit evaluates, for example, the likelihood of high-risk decisions being made when there are many risk-seeking members. The assignment unit assigns the most suitable role to each member based on the results obtained by the simulation unit. For example, the assignment unit assigns a support role to a collaborative member and a task leader role to a highly competitive member. In this way, the system according to the embodiment can analyze the behavioral tendencies of each member, propose the optimal communication method and response policy, simulate decision-making, and assign the optimal role.

[0065] The analysis department conducts a detailed analysis of each member's behavioral tendencies. Specifically, it collects past behavioral data, survey results, and performance evaluations of members, and evaluates their behavioral tendencies based on this data. For example, risk-seeking members tend to like new challenges and changes, and this tendency can be identified from their actions and statements in past projects. On the other hand, risk-averse members prefer stable environments and often show a cautious attitude towards change. To assess the strength of cooperativeness, the frequency and quality of communication within the team and the degree of cooperation with other members are evaluated. Members with strong cooperativeness tend to value teamwork and actively cooperate with other members. To assess the tendency to suppress emotions, the behavior and expression of emotions under stressful situations are observed and evaluated. Members with a strong tendency to suppress emotions tend to deal with stressful situations calmly and often do not show their emotions. To assess the tendency to empathize, the degree of understanding and consideration for the emotions and situations of others is evaluated. Members with a strong tendency to empathize are sensitive to the emotions of others and often provide support and consideration. These evaluations are conducted using AI-based data analysis technology, allowing for a quantitative assessment of each member's behavioral tendencies. This enables the analysis department to gain a detailed understanding of each member's characteristics and provide the foundational data necessary to formulate appropriate response strategies.

[0066] The Proposal Department proposes optimal communication methods and response strategies to leaders based on behavioral trends analyzed by the Analysis Department. Specifically, risk-seeking members can be motivated by being given challenging tasks. For example, assigning them tasks aimed at realizing new projects or innovative ideas can maximize their capabilities. On the other hand, risk-averse members can work with a sense of security by being provided with a stable environment and predictable tasks. Cooperative members can leverage their collaborative spirit by being given roles that emphasize teamwork. For example, assigning them roles as project coordinators or supporters can improve the overall team performance. Competitive members can be stimulated and achieve results by being given tasks that allow them to demonstrate leadership. For example, assigning them roles as project leaders or those responsible for important tasks can maximize the capabilities of competitive members. The Proposal Department provides these proposals to leaders and supports them in taking the optimal approach according to the characteristics of each member. In this way, the Proposal Department can play a crucial role in improving the overall team performance.

[0067] The Simulation Department conducts decision-making simulations based on the response strategies proposed by the Proposal Department. Specifically, it simulates various scenarios to evaluate the impact of high-risk decisions. For example, it evaluates the likelihood of high-risk decisions being made when there are many risk-seeking members and predicts the impact of those decisions on the entire team. The Simulation Department utilizes AI-based simulation technology to quickly and accurately evaluate multiple scenarios. This allows leaders to understand in advance what results the proposed response strategies will actually bring. For example, if a challenging task is given to a risk-seeking member, the probability of success and the risk of failure can be evaluated, which can be used as a reference to determine the optimal response strategy. Also, if a highly collaborative member is given a role that emphasizes teamwork, the impact of that role on the overall team performance can be evaluated. The Simulation Department provides these simulation results to leaders, supporting them in making optimal decisions. In this way, the Simulation Department can play a crucial role in enabling leaders to select the optimal response strategy while minimizing risk.

[0068] The assignment department assigns the most suitable role to each member based on the results obtained by the simulation department. Specifically, it assigns support roles to collaborative members and task leader roles to highly competitive members. For example, collaborative members can be given roles as coordinators or supporters within the team, leveraging their cooperative nature. On the other hand, highly competitive members can be given roles as project leaders or responsible for important tasks, stimulating their competitive spirit and leading to better results. The assignment department aims to maximize the overall team performance by considering each member's characteristics and behavioral tendencies and assigning the most suitable role. Furthermore, even after role assignment, the assignment department continuously monitors each member's performance and reviews or reassigns roles as needed. This allows the assignment department to maintain optimal role allocation at all times, improving the overall efficiency and results of the team. In addition, the assignment department can improve the accuracy of role allocation by collecting feedback from each member and understanding their satisfaction with their role assignment and areas for improvement. This allows the assignment department to maximize the strengths of each member and play a crucial role in improving the overall team performance.

[0069] The analysis department can evaluate characteristics such as risk-seeking tendencies, strong cooperativeness, emotional suppression tendencies, and empathy tendencies. For example, the analysis department can evaluate that risk-seeking members prefer new challenges, while risk-averse members prioritize stability. For example, the analysis department can evaluate that highly cooperative members value teamwork, while highly competitive members exhibit leadership qualities. For example, the analysis department can evaluate that members who are good at emotional suppression may experience mental health problems if they do not receive empathetic feedback. By evaluating the characteristics of each member, the accuracy of behavioral tendency analysis is improved. Some or all of the above processing in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input each member's behavioral data into a generating AI and have the generating AI perform the evaluation of behavioral tendencies.

[0070] The proposal department can provide challenging tasks to risk-seeking members and a stable environment to risk-averse members. For example, the proposal department can propose highly difficult projects or the launch of new businesses to risk-seeking members. For example, the proposal department can propose routine tasks or low-risk tasks to risk-averse members. For example, the proposal department can set challenging goals for risk-seeking members and provide a stable work environment for risk-averse members. In this way, by making proposals tailored to the characteristics of each member, an environment can be provided in which members can easily demonstrate their strengths. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input each member's behavioral data into a generating AI and have the generating AI execute the generation of proposal content.

[0071] The proposal department can assign teamwork-focused roles to highly cooperative members and leadership-oriented tasks to highly competitive members. For example, the proposal department could suggest team leader or coordinating roles to highly cooperative members. For example, it could suggest project management or new business development to highly competitive members. For example, it could suggest team-building activities to highly cooperative members and leadership training to highly competitive members. By assigning roles tailored to each member's characteristics, the department can provide an environment where members can easily demonstrate their strengths. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department could input each member's behavioral data into a generating AI and have the generating AI generate role suggestions.

[0072] The simulation unit can evaluate the impact of high-risk decisions. For example, the simulation unit can evaluate the impact of high-risk investment decisions or the launch of new businesses. For example, the simulation unit can evaluate the likelihood of high-risk decisions being made when there are many risk-seeking members. For example, the simulation unit can simulate catastrophic scenarios resulting from high-risk decisions. This allows for balanced decision-making by evaluating the impact of high-risk decisions. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input high-risk decision scenarios into a generating AI and have the generating AI perform the impact evaluation.

[0073] The assignment unit can assign support roles to collaborative members and leadership roles to highly competitive members. For example, the assignment unit can assign support or assistant tasks to collaborative members. For example, the assignment unit can assign project leader or team leader roles to highly competitive members. For example, the assignment unit can assign the role of coordinator for follow-up meetings to collaborative members and the leadership role for new projects to highly competitive members. By assigning roles according to each member's characteristics, an environment can be provided in which members can easily demonstrate their strengths. Some or all of the above processes in the assignment unit may be performed using AI, for example, or not using AI. For example, the assignment unit can input each member's behavioral data into a generating AI and have the generating AI perform the role assignment.

[0074] The analysis unit can estimate the emotions of members and improve the accuracy of behavioral trend analysis based on the estimated emotions. For example, the analysis unit can estimate the emotions of members and update the behavioral trend analysis in real time according to changes in emotions. For example, the analysis unit can estimate the emotions of members and weight the behavioral trend analysis results based on the intensity of the emotions. For example, the analysis unit can estimate the emotions of members and adjust the behavioral trend analysis method according to the type of emotion. This improves the accuracy of behavioral trend analysis based on members' emotions, thereby obtaining more accurate analysis results. 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 AI, for example, or without AI. For example, the analysis unit can input member emotion data into a generative AI and have the generative AI perform behavioral trend analysis.

[0075] The analysis unit can analyze members' past behavioral history and predict changes in their behavioral trends. For example, the analysis unit can analyze members' past behavioral history and build a model to predict changes in their behavioral trends. For example, the analysis unit can analyze members' past behavioral history and extract patterns to predict changes in their behavioral trends. For example, the analysis unit can analyze members' past behavioral history and develop an algorithm to predict changes in their behavioral trends. This makes it possible to predict changes in behavioral trends by analyzing members' past behavioral history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input members' past behavioral data into a generating AI and have the generating AI perform predictions of changes in their behavioral trends.

[0076] The analysis unit can monitor members' behavioral trends in real time and update analysis results immediately. For example, the analysis unit can monitor members' behavior in real time and immediately detect changes in behavioral trends. For example, the analysis unit can monitor members' behavior in real time and immediately update analysis results in response to changes in behavioral trends. For example, the analysis unit can monitor members' behavior in real time and immediately notify of changes in behavioral trends. This allows for immediate updates of analysis results by monitoring members' behavioral trends in real time. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input members' behavioral data into a generating AI and have the generating AI perform real-time monitoring and update of analysis results.

[0077] The analysis unit can estimate the emotions of members and adjust the method of displaying the analysis results of behavioral tendencies based on the estimated emotions. For example, the analysis unit can estimate the emotions of members and adjust the method of displaying the analysis results of behavioral tendencies according to the type of emotion. For example, the analysis unit can estimate the emotions of members and adjust the method of displaying the analysis results of behavioral tendencies based on the intensity of the emotion. For example, the analysis unit can estimate the emotions of members and adjust the method of displaying the analysis results of behavioral tendencies according to changes in emotion. By adjusting the method of displaying the analysis results of behavioral tendencies based on the emotions of members, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input member emotion data into a generative AI and have the generative AI perform adjustments to the method of displaying the analysis results.

[0078] The analysis unit can analyze regional differences in behavioral trends by considering the geographical location information of the members. For example, the analysis unit can acquire the geographical location information of the members and analyze the differences in behavioral trends by region. For example, the analysis unit can acquire the geographical location information of the members and predict changes in behavioral trends by region. For example, the analysis unit can acquire the geographical location information of the members and visualize the differences in behavioral trends by region. In this way, by considering the geographical location information of the members, it is possible to analyze regional differences in behavioral trends. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the geographical location data of the members into a generating AI and have the generating AI perform the analysis of regional differences.

[0079] The analysis unit can analyze members' social media activities and use them as supplementary information for behavioral trends. For example, the analysis unit can build a model for analyzing members' social media activities and using it as supplementary information for behavioral trends. For example, the analysis unit can analyze members' social media activities and extract patterns for use as supplementary information for behavioral trends. For example, the analysis unit can develop an algorithm for analyzing members' social media activities and using it as supplementary information for behavioral trends. This allows the analysis of members' social media activities to be used as supplementary information for behavioral trends. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input members' social media data into a generating AI and have the generating AI perform the analysis of supplementary information for behavioral trends.

[0080] The proposal unit can estimate the emotions of members and adjust the proposal content based on the estimated emotions. For example, the proposal unit can estimate the emotions of members and adjust the proposal content according to the type of emotion. For example, the proposal unit can estimate the emotions of members and adjust the proposal content based on the intensity of the emotion. For example, the proposal unit can estimate the emotions of members and adjust the proposal content according to changes in emotion. This makes it possible to make more appropriate proposals by adjusting the proposal content based on the emotions of members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input member emotion data into a generative AI and have the generative AI perform the adjustment of the proposal content.

[0081] The proposal team can improve the accuracy of proposals by referring to members' past feedback when making proposals. For example, the proposal team can refer to members' past feedback and build a model to improve the accuracy of the proposal. For example, the proposal team can refer to members' past feedback and extract patterns to improve the accuracy of the proposal. For example, the proposal team can refer to members' past feedback and develop an algorithm to improve the accuracy of the proposal. In this way, the accuracy of proposals can be improved by referring to members' past feedback. Some or all of the above processes in the proposal team may be performed using AI, for example, or not using AI. For example, the proposal team can input members' past feedback data into a generating AI and have the generating AI perform the improvement of the accuracy of the proposal.

[0082] The proposal department can predict changes in members' behavioral tendencies and make proposals based on future behavior at the time of proposal creation. For example, the proposal department can build a model for predicting changes in members' behavioral tendencies and making proposals based on future behavior. For example, the proposal department can extract patterns for predicting changes in members' behavioral tendencies and making proposals based on future behavior. For example, the proposal department can develop an algorithm for predicting changes in members' behavioral tendencies and making proposals based on future behavior. This allows the proposal department to make proposals based on future behavior by predicting changes in members' behavioral tendencies. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input members' behavioral data into a generating AI and have the generating AI perform behavioral trend change prediction and proposal content generation.

[0083] The proposal unit can estimate the emotions of its members and determine the priority of proposals based on the estimated emotions. For example, the proposal unit can estimate the emotions of its members and determine the priority of proposals according to the type of emotion. For example, the proposal unit can estimate the emotions of its members and determine the priority of proposals based on the intensity of the emotion. For example, the proposal unit can estimate the emotions of its members and determine the priority of proposals according to changes in emotion. This makes it possible to make more appropriate proposals by determining the priority of proposals based on the emotions of its members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input member emotion data into a generative AI and have the generative AI perform the determination of proposal priority.

[0084] The proposal department can make region-specific proposals by considering the geographical location information of its members. For example, the proposal department can acquire the geographical location information of its members and build a model for making region-specific proposals. For example, the proposal department can acquire the geographical location information of its members and extract patterns for making region-specific proposals. For example, the proposal department can acquire the geographical location information of its members and develop an algorithm for making region-specific proposals. This allows the proposal department to make region-specific proposals by considering the geographical location information of its members. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the geographical location data of its members into a generating AI and have the generating AI generate region-specific proposal content.

[0085] The proposal department can analyze members' social media activity and make relevant suggestions when making proposals. For example, the proposal department can build a model for analyzing members' social media activity and making relevant suggestions. For example, the proposal department can analyze members' social media activity and extract patterns for making relevant suggestions. For example, the proposal department can develop an algorithm for analyzing members' social media activity and making relevant suggestions. This allows the proposal department to make relevant suggestions by analyzing members' social media activity. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input members' social media data into a generating AI and have the generating AI generate relevant suggestion content.

[0086] The simulation unit can estimate the emotions of the members and adjust the simulation parameters based on the estimated emotions. For example, the simulation unit can estimate the emotions of the members and adjust the simulation parameters according to the type of emotion. For example, the simulation unit can estimate the emotions of the members and adjust the simulation parameters based on the intensity of the emotion. For example, the simulation unit can estimate the emotions of the members and adjust the simulation parameters according to the change in emotion. By adjusting the simulation parameters based on the emotions of the members, a more accurate simulation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input member emotion data into a generative AI and have the generative AI perform the adjustment of the simulation parameters.

[0087] The simulation unit can improve the accuracy of the simulation by referring to past decision-making data during the simulation. For example, the simulation unit can refer to past decision-making data and build a model to improve the accuracy of the simulation. For example, the simulation unit can refer to past decision-making data and extract patterns to improve the accuracy of the simulation. For example, the simulation unit can refer to past decision-making data and develop an algorithm to improve the accuracy of the simulation. In this way, the accuracy of the simulation can be improved by referring to past decision-making data. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input past decision-making data into a generating AI and have the generating AI perform the improvement of the simulation accuracy.

[0088] The simulation unit can predict changes in members' behavioral tendencies and simulate future scenarios during the simulation. For example, the simulation unit can build a model to predict changes in members' behavioral tendencies and simulate future scenarios. For example, the simulation unit can extract patterns to predict changes in members' behavioral tendencies and simulate future scenarios. For example, the simulation unit can develop an algorithm to predict changes in members' behavioral tendencies and simulate future scenarios. This makes it possible to simulate future scenarios by predicting changes in members' behavioral tendencies. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input members' behavioral data into a generating AI and have the generating AI execute a simulation of future scenarios.

[0089] The simulation unit can estimate the emotions of the members and adjust the display method of the simulation results based on the estimated emotions. For example, the simulation unit can estimate the emotions of the members and adjust the display method of the simulation results according to the type of emotion. For example, the simulation unit can estimate the emotions of the members and adjust the display method of the simulation results based on the intensity of the emotion. For example, the simulation unit can estimate the emotions of the members and adjust the display method of the simulation results according to the change in emotion. By adjusting the display method of the simulation results based on the emotions of the members, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input member emotion data into the generative AI and have the generative AI perform the adjustment of the display method of the simulation results.

[0090] The simulation unit can perform region-specific simulations by considering the geographical location information of the members during the simulation. For example, the simulation unit can acquire the geographical location information of the members and build a model for performing region-specific simulations. For example, the simulation unit can acquire the geographical location information of the members and extract patterns for performing region-specific simulations. For example, the simulation unit can acquire the geographical location information of the members and develop an algorithm for performing region-specific simulations. This allows for region-specific simulations to be performed by considering the geographical location information of the members. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the geographical location data of the members into a generating AI and have the generating AI execute a region-specific simulation.

[0091] The simulation unit can analyze members' social media activities and simulate relevant scenarios during the simulation. For example, the simulation unit can analyze members' social media activities and build a model for simulating relevant scenarios. For example, the simulation unit can analyze members' social media activities and extract patterns for simulating relevant scenarios. For example, the simulation unit can develop algorithms for analyzing members' social media activities and simulating relevant scenarios. This makes it possible to simulate relevant scenarios by analyzing members' social media activities. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input members' social media data into a generating AI and have the generating AI execute simulations of relevant scenarios.

[0092] The assignment unit can estimate the emotions of members and adjust the role assignment method based on the estimated emotions. For example, the assignment unit can estimate the emotions of members and adjust the role assignment method according to the type of emotion. For example, the assignment unit can estimate the emotions of members and adjust the role assignment method based on the intensity of the emotion. For example, the assignment unit can estimate the emotions of members and adjust the role assignment method according to changes in emotion. By adjusting the role assignment method based on the emotions of members, more appropriate roles can be assigned. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input member emotion data into a generative AI and have the generative AI perform the adjustment of the role assignment method.

[0093] The assignment unit can select the optimal role by referring to the member's past role history during the assignment process. For example, the assignment unit can refer to the member's past role history and build a model for selecting the optimal role. For example, the assignment unit can refer to the member's past role history and extract patterns for selecting the optimal role. For example, the assignment unit can refer to the member's past role history and develop an algorithm for selecting the optimal role. This allows the optimal role to be selected by referring to the member's past role history. Some or all of the above processes in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input the member's past role history data into a generating AI and have the generating AI perform the selection of the optimal role.

[0094] The assignment unit can predict changes in members' behavioral tendencies and make assignments considering their future roles. For example, the assignment unit can build a model for predicting changes in members' behavioral tendencies and making assignments considering their future roles. For example, the assignment unit can extract patterns for predicting changes in members' behavioral tendencies and making assignments considering their future roles. For example, the assignment unit can develop an algorithm for predicting changes in members' behavioral tendencies and making assignments considering their future roles. This allows the assignment unit to predict changes in members' behavioral tendencies and make assignments considering their future roles. Some or all of the above processing in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input members' behavioral data into a generating AI and have the generating AI perform assignments that consider their future roles.

[0095] The assignment unit can estimate the emotions of members and determine the priority of roles based on the estimated emotions. For example, the assignment unit can estimate the emotions of members and determine the priority of roles according to the type of emotion. For example, the assignment unit can estimate the emotions of members and determine the priority of roles based on the intensity of the emotion. For example, the assignment unit can estimate the emotions of members and determine the priority of roles according to changes in emotion. This allows for the assignment of more appropriate roles by determining the priority of roles based on the emotions of members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input member emotion data into a generative AI and have the generative AI perform the determination of role priorities.

[0096] The assignment unit can assign region-specific roles by considering the geographical location information of members during the assignment process. For example, the assignment unit can acquire the geographical location information of members and build a model for assigning region-specific roles. For example, the assignment unit can acquire the geographical location information of members and extract patterns for assigning region-specific roles. For example, the assignment unit can acquire the geographical location information of members and develop an algorithm for assigning region-specific roles. This allows for the assignment of region-specific roles by considering the geographical location information of members. Some or all of the above processes in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input the geographical location data of members into a generating AI and have the generating AI perform the assignment of region-specific roles.

[0097] The assignment unit can analyze members' social media activity and assign relevant roles during the assignment process. For example, the assignment unit can build a model for analyzing members' social media activity and assigning relevant roles. For example, the assignment unit can analyze members' social media activity and extract patterns for assigning relevant roles. For example, the assignment unit can develop an algorithm for analyzing members' social media activity and assigning relevant roles. This allows the assignment of relevant roles by analyzing members' social media activity. Some or all of the above processes in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input members' social media data into a generating AI and have the generating AI perform the assignment of relevant roles.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The proposal team can refer to members' past successes and make proposals based on those successes. For example, a member who has previously demonstrated leadership and achieved success can be proposed a role where they can again demonstrate leadership. Similarly, a member who has previously succeeded by emphasizing teamwork can be proposed a team-building activity. Furthermore, a member who has previously succeeded in launching a new business can be proposed the launch of a new project. This allows members to leverage their past successes in their proposals, enabling them to work with confidence.

[0100] The analysis department can monitor the health status of its members and analyze their behavioral tendencies based on that status. For example, it can monitor members' sleep patterns and assign less demanding roles to members who are sleep-deprived. It can also monitor members' stress levels and provide a relaxing environment for members with high stress levels. Furthermore, it can monitor members' exercise habits and suggest exercise to members who are not getting enough exercise. By conducting analyses that take into account the health status of members, it is possible to maximize their performance.

[0101] The proposal team can consider the hobbies and interests of its members and make proposals based on those interests. For example, for a member who enjoys the outdoors, they can propose team-building activities that incorporate outdoor activities. For a member who enjoys reading, they can propose reading groups or book clubs. Furthermore, for a member who enjoys music, they can propose relaxation methods that incorporate music. In this way, by making proposals that utilize the hobbies and interests of the members, their motivation can be increased.

[0102] The simulation department can conduct simulations based on members' career goals, taking those goals into consideration. For example, a member who wants to demonstrate leadership can simulate scenarios where they can exercise leadership. Similarly, a member who wants to deepen their expertise can simulate scenarios where they can utilize their expertise. Furthermore, a member who wants to challenge themselves in a different field can simulate scenarios in a new field. In this way, by conducting simulations that take members' career goals into consideration, the department can support the growth of its members.

[0103] The assignment department can consider members' skill sets and assign roles based on those skill sets. For example, members with strong programming skills can be assigned programming-related roles. Members with strong communication skills can be assigned team leader or coordination roles. Furthermore, members with strong creative skills can be assigned design or planning roles. By assigning roles that utilize each member's skill set, the department can maximize each member's strengths.

[0104] The analysis department can estimate members' emotions and propose stress management strategies based on those estimates. For example, it can suggest relaxation and stress relief methods to members experiencing high stress levels. It can also provide support to members experiencing significant emotional fluctuations to help them stabilize their emotions. Furthermore, it can offer positive feedback and encouraging messages to members who are feeling down. In this way, by proposing stress management strategies based on members' emotions, the department can support the mental health of its members.

[0105] The proposal team can estimate the emotions of team members and adjust communication methods based on those estimates. For example, they can suggest listening calmly to members who are emotionally agitated. They can also suggest actively exchanging opinions to members who are emotionally stable. Furthermore, they can suggest communication methods that provide a sense of security to members who are emotionally unstable. By proposing communication methods based on the emotions of the team members, smoother communication can be achieved.

[0106] The simulation unit can estimate the emotions of the members and adjust the method of providing feedback on the simulation results based on those estimated emotions. For example, it can provide feedback that helps members who are emotionally agitated to accept the results calmly. It can also provide feedback that points out specific areas for improvement to members who are emotionally stable. Furthermore, it can provide mainly positive feedback to members who are emotionally unstable. By providing feedback based on the members' emotions, it becomes possible to share simulation results more effectively.

[0107] The assignment unit can estimate the emotions of its members and propose role changes based on those estimates. For example, it can suggest a less demanding role to a member who is emotionally agitated. It can also suggest a more challenging role to a member who is emotionally calm. Furthermore, it can suggest a role requiring support to a member who is emotionally unstable. By proposing role changes based on members' emotions, the system can optimize member performance.

[0108] The proposal team can estimate the emotions of team members and adjust the content of feedback based on those estimates. For example, they can provide feedback that is easy for an emotionally agitated member to receive calmly. They can also provide feedback that points out specific areas for improvement for an emotionally calm member. Furthermore, they can focus on providing positive feedback for an emotionally unstable member. This allows for more effective communication by providing feedback based on the emotions of the members.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The analysis team analyzes the behavioral tendencies of each member. For example, they evaluate characteristics such as risk-seeking, strong cooperativeness, tendency to suppress emotions, and empathy. They assess that risk-seeking members prefer new challenges, while risk-averse members value stability. They also assess that highly cooperative members value teamwork, while highly competitive members demonstrate leadership. Step 2: The proposal team proposes the most suitable communication methods and response strategies for leaders based on the behavioral tendencies analyzed by the analysis team. For example, they might give challenging tasks to risk-seeking members and provide a stable environment to risk-averse members. They might assign roles that emphasize teamwork to highly cooperative members and give tasks that allow them to demonstrate leadership to highly competitive members. Step 3: The simulation team conducts decision-making simulations based on the response strategies proposed by the proposal team. For example, they evaluate the impact of high-risk decisions. They assess the likelihood of high-risk decisions being made when there are many risk-seeking members. Step 4: The assignment unit assigns the most suitable role to each member based on the results obtained by the simulation unit. For example, a collaborative member might be assigned a support role, while a highly competitive member might be assigned a task leader role.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the analysis unit, proposal unit, simulation unit, and assignment 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 and evaluates the behavioral tendencies of each member. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal communication method and response policy for the leader. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a simulation of decision-making. The assignment unit is implemented by the control unit 46A of the smart device 14 and assigns the optimal role to each member. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the analysis unit, proposal unit, simulation unit, and assignment unit, is implemented 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 and evaluates the behavioral tendencies of each member. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal communication method and response policy for the leader. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a simulation of decision-making. The assignment unit is implemented by the control unit 46A of the smart glasses 214 and assigns the optimal role to each member. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the analysis unit, proposal unit, simulation unit, and assignment 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 and evaluates the behavioral tendencies of each member. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal communication method and response policy for the leader. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a simulation of decision-making. The assignment unit is implemented by the control unit 46A of the headset terminal 314 and assigns the optimal role to each member. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the analysis unit, proposal unit, simulation unit, and assignment 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 and evaluates the behavioral tendencies of each member. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal communication method and response policy for the leader. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a simulation of decision-making. The assignment unit is implemented by the control unit 46A of the robot 414 and assigns the optimal role to each member. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) The analysis department analyzes the behavioral tendencies of each member, Based on the behavioral trends analyzed by the aforementioned analysis department, the proposal department proposes the most suitable communication methods and response strategies for leaders. A simulation unit that performs a decision-making simulation based on the response policy proposed by the aforementioned proposal unit, An assignment unit that assigns the optimal role to each member based on the results obtained from the simulation unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned analysis unit is We evaluate characteristics such as risk-seeking, strong cooperativeness, tendency to suppress emotions, and empathy. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We offer challenging tasks to risk-seeking members and a stable environment to risk-averse members. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We assign roles that emphasize teamwork to members with a strong cooperative spirit, and tasks that allow members with a strong competitive spirit to demonstrate their leadership skills. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned simulation unit, Evaluate the impact of high-risk decisions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned allocation unit is, Assign support roles to collaborative members and leadership roles to highly competitive members. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We estimate the emotions of our members and improve the accuracy of our behavioral trend analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is Analyze the members' past behavioral history and predict changes in their behavioral tendencies. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is We monitor members' behavioral trends in real time and update the analysis results immediately. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is Adjust the method for estimating members' emotions and displaying analysis results of behavioral tendencies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is We will analyze regional differences in behavioral trends, taking into account the geographical location information of the members. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We analyze members' social media activity and use it as supplementary information to understand their behavioral trends. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, Estimate the emotions of the members and adjust the proposal 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, we refer to past feedback from team members to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, predict changes in the behavioral tendencies of team members and make proposals based on their future actions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, Estimate the emotions of the members and determine the priority of proposals based on those 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 the members and make a proposal tailored to the region. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, analyze the members' social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned simulation unit, The system estimates the emotions of the members and adjusts the simulation parameters based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned simulation unit, During simulations, past decision-making data is referenced to improve the accuracy of the simulation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, During the simulation, we predict changes in the behavioral tendencies of the members and simulate future scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned simulation unit, The system estimates the emotions of the members and adjusts how the simulation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned simulation unit, During the simulation, the geographical location information of the members will be taken into consideration to perform region-specific simulations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, During the simulation, the social media activity of the members is analyzed, and relevant scenarios are simulated. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned allocation unit is, Estimate the emotions of the team members and adjust the role assignment method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned allocation unit is, When assigning roles, the most suitable role is selected by referring to the member's past role history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned allocation unit is, When assigning roles, we predict changes in members' behavioral tendencies and make assignments considering their future roles. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned allocation unit is, Estimate the emotions of the team members and determine the priority of roles based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned allocation unit is, When assigning roles, consider the geographical location of the members and assign region-specific roles. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned allocation unit is, During the assignment process, we analyze members' social media activity and assign them to relevant roles. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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 behavioral tendencies of each member, Based on the behavioral trends analyzed by the aforementioned analysis department, the proposal department proposes the most suitable communication methods and response strategies for leaders. A simulation unit that performs a decision-making simulation based on the response policy proposed by the aforementioned proposal unit, An assignment unit that assigns the optimal role to each member based on the results obtained from the simulation unit, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit is We evaluate characteristics such as risk-seeking, strong cooperativeness, tendency to suppress emotions, and empathy. The system according to feature 1.

3. The aforementioned proposal section is, We offer challenging tasks to risk-seeking members and provide a stable environment to risk-averse members. The system according to feature 1.

4. The aforementioned proposal section is, We assign roles that emphasize teamwork to members with a strong cooperative spirit, and tasks that allow members with a strong competitive spirit to demonstrate their leadership skills. The system according to feature 1.

5. The aforementioned simulation unit, Evaluate the impact of high-risk decisions. The system according to feature 1.

6. The aforementioned allocation unit is, Assign support roles to collaborative members and leadership roles to highly competitive members. The system according to feature 1.

7. The aforementioned analysis unit is We estimate the emotions of our members and improve the accuracy of our behavioral trend analysis based on those estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit is Analyze the members' past behavioral history and predict changes in their behavioral trends. The system according to feature 1.

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