System

Abstract

An object of a system according to an embodiment is to analyze communication patterns between team members and propose optimal team organization.SOLUTION: A system includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects communication patterns between team members as data. The analysis unit analyzes the data collected by the data collection unit and clarifies a communication pattern between team members. The proposal unit proposes an optimal team organization based on the communication pattern clarified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is difficult to fully understand the communication patterns between team members and to form an optimal team.

[0005] The system according to the embodiment aims to analyze communication patterns between team members and propose an optimal team composition. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects communication patterns between team members as data. The analysis unit analyzes the data collected by the data collection unit to identify communication patterns between team members. The proposal unit proposes an optimal team composition based on the communication patterns identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze communication patterns between team members and propose optimal team composition. [Brief explanation of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The team formation suggestion system according to an embodiment of the present invention is a system that analyzes communication patterns between team members and suggests optimal team formation. As a result, the team formation suggestion system makes it easier for managers, organizational managers, and human resources personnel to understand interpersonal relationships within a team, enabling optimal team formation.

[0029] A team formation proposal system according to an embodiment includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects communication patterns between team members as data. For example, the data collection unit collects email and chat histories. The data collection unit can also collect meeting content. The data collection unit can also collect project management tool usage status. For example, the data collection unit identifies who frequently communicates with whom based on email sending and receiving history. The data collection unit also analyzes meeting content to identify who is demonstrating leadership and who is in a supporting role. The analysis unit analyzes the data collected by the data collection unit to clarify communication patterns between team members. For example, the analysis unit uses a generation AI to evaluate the roles and relationships of each member based on the data. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the frequency of email sending and receiving and the number of times each member speaks in meetings to identify each member's communication style. The generation AI receives a prompt, for example, "Please analyze this member's communication style," and performs the analysis. The proposal unit proposes an optimal team composition based on the communication patterns revealed by the analysis unit. For example, the proposal unit balances leadership members with support members. The proposal unit also places members who get along well with each other on the same team to ensure smooth communication. The generation AI receives a prompt, for example, "Please propose an optimal team composition," and makes a proposal. This enables the team composition proposal system according to the embodiment to analyze communication patterns between team members and propose an optimal team composition. For example, the proposal unit actually uses the proposed team composition and collects the results as feedback. Based on this feedback, the generation AI proposes a more optimal team composition. For example, the proposal unit evaluates how effective the proposed team composition was and reorganizes it as necessary.

[0030] The data collection unit can analyze the non-verbal communication of team members and evaluate the quality of communication. For example, the data collection unit captures the facial expressions of team members during a meeting with a camera and analyzes their emotions using facial expression recognition technology. For example, it detects smiling and surprised expressions and evaluates the quality of communication. The data collection unit also records gestures during the meeting with a motion sensor and evaluates the quality of communication using gesture analysis technology. For example, it analyzes the frequency of hand-raising and nodding. The data collection unit also collects data on non-verbal communication, and the generation AI analyzes the data to evaluate the quality of communication. For example, it analyzes eye movements and changes in posture. In this way, the quality of communication can be evaluated by analyzing non-verbal communication.

[0031] The data collection unit can collect biometric information from members and evaluate the impact of communication. For example, the data collection unit can monitor members' heart rates using a wearable device to evaluate stress levels during communication. For example, it can analyze heart rate fluctuations during a meeting. The data collection unit can also use an electrodermal activity (EDA) sensor to measure stress levels and evaluate the impact of communication. For example, it can analyze EDA data to identify stress peaks. The data collection unit can also collect biometric information, and the generation AI can analyze the data to evaluate the impact of communication. For example, it can analyze heart rate variability and breathing patterns. In this way, the impact of communication can be evaluated by collecting biometric information.

[0032] The data collection unit can analyze the social media activities of team members to understand communication patterns outside of the workplace. For example, the data collection unit analyzes the social media posts of team members to understand communication patterns outside of the workplace. For example, it analyzes the content of posts and the frequency of comments. The data collection unit also analyzes interactions between members on social media to evaluate relationships outside of the workplace. For example, it analyzes the number of likes and shares. The data collection unit also collects social media data, and the generation AI analyzes the data to understand communication patterns outside of the workplace. For example, it analyzes the use of hashtags and mentions. In this way, by analyzing social media activities, it is possible to understand communication patterns outside of the workplace.

[0033] The data collection unit can analyze members' past project histories and identify communication patterns in successful projects. The data collection unit, for example, analyzes members' past project histories and identifies communication patterns in successful projects. For example, it analyzes data from project management tools. The data collection unit also analyzes email and chat histories from past projects and identifies communication patterns that contributed to success. For example, it analyzes frequent communication and quick responses. The data collection unit also collects project history data, and the generation AI analyzes that data to identify communication patterns in successful projects. For example, it analyzes the frequency of task assignments and progress reports. In this way, by analyzing past project histories, it is possible to identify communication patterns in successful projects.

[0034] The analysis unit uses the generation AI to evaluate the trust between members and identify pairs or groups with high trust. For example, the analysis unit uses the generation AI to analyze email and chat history between members to evaluate the trust. For example, it analyzes frequent communication and positive feedback. The analysis unit also analyzes the content of statements made in meetings to identify pairs or groups with high trust. For example, it analyzes statements that support each other and cooperative attitudes. The analysis unit also uses the generation AI to evaluate the trust between members and identify pairs or groups with high trust. For example, it analyzes the frequency and success rate of collaborative work. In this way, the generation AI can evaluate the trust between members and identify pairs or groups with high trust.

[0035] The analysis unit can analyze members' communication styles and reveal patterns for each style. For example, the analysis unit can analyze members' email and chat history to identify their communication styles. For example, it can analyze the characteristics of assertive members. The analysis unit can also analyze what is said in meetings to identify members' communication styles. For example, it can analyze the frequency and content of what passive members say. The analysis unit can also use the generation AI to analyze members' communication styles and reveal patterns for each style. For example, it can analyze the behavioral patterns of aggressive members. In this way, by analyzing members' communication styles, it can reveal patterns for each style.

[0036] The analysis unit can compare communication patterns between different projects and teams to identify success factors. For example, the analysis unit collects communication data from different projects, and the generation AI analyzes that data to identify success factors. For example, it analyzes frequent communication and quick responses. The analysis unit can also compare communication patterns between different teams to identify success factors. For example, it can analyze the demonstration of leadership and a cooperative attitude. The analysis unit can also analyze communication patterns between different projects and teams using the generation AI to identify success factors. For example, it can analyze the frequency of task allocation and progress reports. In this way, success factors can be identified by comparing communication patterns between different projects and teams.

[0037] The analysis unit can analyze members' communication patterns by time of day and identify the optimal timing for communication. For example, the analysis unit analyzes members' email and chat history by time of day and identifies the optimal timing for communication. For example, it analyzes the frequency of communication during working hours. The analysis unit also analyzes the content of comments made in meetings by time of day and identifies the optimal timing for communication. For example, it compares the frequency of comments made in the morning and afternoon. The analysis unit also has the generation AI analyze members' communication patterns by time of day and identify the optimal timing for communication. For example, it identifies peak and off-peak times. In this way, by analyzing members' communication patterns by time of day, the optimal timing for communication can be identified.

[0038] The proposal unit uses the generation AI to comprehensively evaluate the skill sets and communication patterns of members and propose a team with the optimal skill balance. For example, the generation AI analyzes the skill sets and communication patterns of members and proposes a team with the optimal skill balance. For example, it allocates technical skills and leadership skills in a balanced manner. The proposal unit also integrates the skill data and communication data of members and the generation AI proposes the optimal team composition. For example, it combines members with specialized knowledge and a cooperative attitude. The proposal unit also evaluates the skill sets and communication patterns of members and proposes a team with the optimal skill balance. For example, it allocates members with different skills in a balanced manner. In this way, the generation AI can comprehensively evaluate the skill sets and communication patterns of members and propose a team with the optimal skill balance.

[0039] The suggestion unit can take into account the personality traits of the members and form a team based on personality compatibility. For example, the suggestion unit analyzes the personality traits of the members and forms a team based on introversion or extroversion. For example, it allocates a balanced mix of introverted and extroverted members. The suggestion unit also forms a team by having the generation AI combine members who are compatible based on the personality traits. For example, it combines members who have a cooperative attitude. The suggestion unit also has the generation AI evaluate the personality traits of the members and propose a team with optimal personality compatibility. For example, it combines a member who demonstrates leadership with a member who plays a supporting role. In this way, by taking into account the personality traits of the members, it is possible to form a team based on personality compatibility.

[0040] The proposal unit can develop a dynamic team formation algorithm for flexibly changing team formation in accordance with the requirements of different projects. The proposal unit develops a dynamic team formation algorithm in which the generation AI flexibly changes team formation in accordance with the requirements of different projects. For example, it reallocates members in accordance with the progress of the project. The proposal unit also analyzes the requirements of the project, and the generation AI dynamically proposes an optimal team formation. For example, it takes into account the skill sets required for each phase of the project. The proposal unit also uses the dynamic team formation algorithm to build a system that changes team formation in real time in accordance with the requirements of the project. For example, it adds or removes members in accordance with the progress of the project. This makes it possible to develop a dynamic team formation algorithm for flexibly changing team formation in accordance with the requirements of different projects.

[0041] The proposal unit can propose a team composition that promotes individual growth, taking into account the career goals and growth aspirations of each member. For example, the proposal unit analyzes the career goals and growth aspirations of each member, and the generation AI proposes a team composition that promotes individual growth. For example, the relationship between mentor and mentee is taken into consideration. The proposal unit also proposes a team composition that promotes member growth, based on the member's career goals. For example, it forms a team that provides opportunities for skill development. The proposal unit also evaluates the member's growth aspirations, and the generation AI proposes a team composition that promotes growth based on that data. For example, it has the member participate in a challenging project. In this way, it is possible to propose a team composition that promotes individual growth by taking into account the member's career goals and growth aspirations.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The proposal department can analyze members' performance data in past projects and propose optimal team formations based on the division of roles in successful projects. For example, a member who demonstrated leadership in past projects can be reassigned as leader. The proposal department can also analyze members' collaborative relationships in past projects and place members who get along well together on the same team. For example, reuniting pairs that have achieved great results in past collaborations. The proposal department can also analyze the causes of failure in past projects and propose team formations that will avoid the same failures. For example, in a project that failed due to a lack of communication, a member who is good at communication can be assigned. In this way, past project data can be utilized to propose more effective team formations.

[0044] The data collection unit can collect members' health data and evaluate the impact of their health condition on communication. For example, it can collect members' sleep data and analyze the impact of lack of sleep on the quality of communication. The data collection unit can also collect members' exercise data and evaluate the impact of exercise habits on the quality of communication. For example, it can analyze the communication patterns of members who exercise regularly. The data collection unit can also collect members' dietary data and evaluate the impact of dietary quality on communication. For example, it can analyze the communication style of members who eat a balanced diet. This makes it possible to utilize the health data to make suggestions for improving the quality of communication.

[0045] The suggestion unit can analyze the learning styles of members and propose team formations that provide the optimal learning environment. For example, a team that makes heavy use of visual aids can be formed for members who prefer visual learning. The suggestion unit can also analyze the learning pace of members and pair them with members who can learn at the same pace. For example, a balance can be achieved between members who learn at a fast pace and members who learn at a slow pace. The suggestion unit can also analyze the learning motivation of members and propose team formations that will increase their motivation. For example, a member with a high motivation to learn can be placed as a leader. This makes it possible to form teams that take into account the learning styles and paces of members.

[0046] The proposal department can propose team formations that take into account the cultural backgrounds of members and respect diversity. For example, by assigning members with different cultural backgrounds in a balanced manner and incorporating diverse perspectives. The proposal department can also analyze members' language skills and propose team formations that promote communication across language barriers. For example, by assigning members who can speak multiple languages. The proposal department can also propose team formations that take into account members' cultural customs and avoid cultural friction. For example, by assigning members who respect specific cultural customs. This makes it possible to form diverse teams that take cultural backgrounds into account.

[0047] The proposal unit can propose team formations that take into account the work-life balance of members and reduce stress. For example, it can assign members who offer flexible working hours in consideration of family circumstances. The proposal unit can also analyze members' vacation data and propose team formations that reduce the burden on members who are on vacation. For example, it can allocate the tasks of a member who is on vacation to other members. The proposal unit can also analyze members' stress levels and propose team formations that reduce stress. For example, it can prioritize allocating tasks that cause less stress. This makes it possible to form teams that reduce stress and take into account the work-life balance of members.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The data collection team collects data on communication patterns between team members. For example, they collect email and chat history, meeting content, and project management tool usage. This allows them to understand who is frequently communicating with whom, who is in leadership roles, and who is in support roles. Step 2: The analysis unit analyzes the data collected by the data collection unit to identify communication patterns between team members. For example, the analysis unit uses the generation AI to analyze the frequency of email sending and receiving and the number of times each member speaks in meetings, and evaluates the roles and relationships of each member. The generation AI receives a prompt such as, "Analyze this member's communication style," and then performs the analysis. Step 3: The proposal unit proposes the optimal team composition based on the communication patterns revealed by the analysis unit. For example, the proposal unit will balance leadership and support members, placing members who get along well together on the same team. The generation AI receives the prompt, "Please propose the optimal team composition," and makes a proposal. The proposal unit also actually uses the proposed team composition, collects the results as feedback, and proposes further optimal team compositions.

[0050] (Example 2) The team formation suggestion system according to an embodiment of the present invention is a system that analyzes communication patterns between team members and suggests optimal team formation. As a result, the team formation suggestion system makes it easier for managers, organizational managers, and human resources personnel to understand interpersonal relationships within a team, enabling optimal team formation.

[0051] A team formation proposal system according to an embodiment includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects communication patterns between team members as data. For example, the data collection unit collects email and chat histories. The data collection unit can also collect meeting content. The data collection unit can also collect project management tool usage status. For example, the data collection unit identifies who frequently communicates with whom based on email sending and receiving history. The data collection unit also analyzes meeting content to identify who is demonstrating leadership and who is in a supporting role. The analysis unit analyzes the data collected by the data collection unit to clarify communication patterns between team members. For example, the analysis unit uses a generation AI to evaluate the roles and relationships of each member based on the data. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the frequency of email sending and receiving and the number of times each member speaks in meetings to identify each member's communication style. The generation AI receives a prompt, for example, "Please analyze this member's communication style," and performs the analysis. The proposal unit proposes an optimal team composition based on the communication patterns revealed by the analysis unit. For example, the proposal unit balances leadership members with support members. The proposal unit also places members who get along well with each other on the same team to ensure smooth communication. The generation AI receives a prompt, for example, "Please propose an optimal team composition," and makes a proposal. This enables the team composition proposal system according to the embodiment to analyze communication patterns between team members and propose an optimal team composition. For example, the proposal unit actually uses the proposed team composition and collects the results as feedback. Based on this feedback, the generation AI proposes a more optimal team composition. For example, the proposal unit evaluates how effective the proposed team composition was and reorganizes it as necessary.

[0052] The data collection unit can analyze the non-verbal communication of team members and evaluate the quality of communication. For example, the data collection unit captures the facial expressions of team members during a meeting with a camera and analyzes their emotions using facial expression recognition technology. For example, it detects smiling and surprised expressions and evaluates the quality of communication. The data collection unit also records gestures during the meeting with a motion sensor and evaluates the quality of communication using gesture analysis technology. For example, it analyzes the frequency of hand-raising and nodding. The data collection unit also collects data on non-verbal communication, and the generation AI analyzes the data to evaluate the quality of communication. For example, it analyzes eye movements and changes in posture. In this way, the quality of communication can be evaluated by analyzing non-verbal communication.

[0053] The data collection unit can collect biometric information from members and evaluate the impact of communication. For example, the data collection unit can monitor members' heart rates using a wearable device to evaluate stress levels during communication. For example, it can analyze heart rate fluctuations during a meeting. The data collection unit can also use an electrodermal activity (EDA) sensor to measure stress levels and evaluate the impact of communication. For example, it can analyze EDA data to identify stress peaks. The data collection unit can also collect biometric information, and the generation AI can analyze the data to evaluate the impact of communication. For example, it can analyze heart rate variability and breathing patterns. In this way, the impact of communication can be evaluated by collecting biometric information.

[0054] The data collection unit can use the emotion estimation function to monitor the emotional states of members in real time and collect emotional changes as data. For example, the data collection unit uses the emotion estimation function to analyze the facial expressions of members in real time and monitor their emotional states. For example, it uses a camera to detect smiling and angry expressions. The data collection unit also uses voice analysis technology to analyze the tone and pitch of members' voices and monitor their emotional states in real time. For example, it analyzes the pitch and speed of voices. The data collection unit also uses the emotion estimation function to monitor the emotional states of members in real time and collect emotional changes as data. For example, it calculates an emotion score and records changes over time. In this way, the emotion estimation function can be used to monitor the emotional states of members in real time and collect emotional changes as data.

[0055] The data collection unit can analyze the social media activities of team members to understand communication patterns outside of the workplace. For example, the data collection unit analyzes the social media posts of team members to understand communication patterns outside of the workplace. For example, it analyzes the content of posts and the frequency of comments. The data collection unit also analyzes interactions between members on social media to evaluate relationships outside of the workplace. For example, it analyzes the number of likes and shares. The data collection unit also collects social media data, and the generation AI analyzes the data to understand communication patterns outside of the workplace. For example, it analyzes the use of hashtags and mentions. In this way, by analyzing social media activities, it is possible to understand communication patterns outside of the workplace.

[0056] The data collection unit can analyze members' past project histories and identify communication patterns in successful projects. The data collection unit, for example, analyzes members' past project histories and identifies communication patterns in successful projects. For example, it analyzes data from project management tools. The data collection unit also analyzes email and chat histories from past projects and identifies communication patterns that contributed to success. For example, it analyzes frequent communication and quick responses. The data collection unit also collects project history data, and the generation AI analyzes that data to identify communication patterns in successful projects. For example, it analyzes the frequency of task assignments and progress reports. In this way, by analyzing past project histories, it is possible to identify communication patterns in successful projects.

[0057] The data collection unit can use the emotion estimation function to collect how members feel about specific tasks and accumulate emotion data for each task. For example, the data collection unit uses the emotion estimation function to collect how members feel about specific tasks. For example, it records the emotional state at the time of task completion. The data collection unit also accumulates emotion data for each task, and the generation AI analyzes the data to identify emotion patterns for each task. For example, it identifies tasks that are high in stress and tasks that are high in satisfaction. The data collection unit also uses the emotion estimation function to monitor in real time how members feel about specific tasks and accumulate emotion data. For example, it records changes in emotion as the task progresses. In this way, the emotion estimation function can be used to collect how members feel about specific tasks and accumulate emotion data for each task.

[0058] The analysis unit uses the generation AI to evaluate the trust between members and identify pairs or groups with high trust. For example, the analysis unit uses the generation AI to analyze email and chat history between members to evaluate the trust. For example, it analyzes frequent communication and positive feedback. The analysis unit also analyzes the content of statements made in meetings to identify pairs or groups with high trust. For example, it analyzes statements that support each other and cooperative attitudes. The analysis unit also uses the generation AI to evaluate the trust between members and identify pairs or groups with high trust. For example, it analyzes the frequency and success rate of collaborative work. In this way, the generation AI can evaluate the trust between members and identify pairs or groups with high trust.

[0059] The analysis unit can analyze members' communication styles and reveal patterns for each style. For example, the analysis unit can analyze members' email and chat history to identify their communication styles. For example, it can analyze the characteristics of assertive members. The analysis unit can also analyze what is said in meetings to identify members' communication styles. For example, it can analyze the frequency and content of what passive members say. The analysis unit can also use the generation AI to analyze members' communication styles and reveal patterns for each style. For example, it can analyze the behavioral patterns of aggressive members. In this way, by analyzing members' communication styles, it can reveal patterns for each style.

[0060] The analysis unit can compare communication patterns between different projects and teams to identify success factors. For example, the analysis unit collects communication data from different projects, and the generation AI analyzes that data to identify success factors. For example, it analyzes frequent communication and quick responses. The analysis unit can also compare communication patterns between different teams to identify success factors. For example, it can analyze the demonstration of leadership and a cooperative attitude. The analysis unit can also analyze communication patterns between different projects and teams using the generation AI to identify success factors. For example, it can analyze the frequency of task allocation and progress reports. In this way, success factors can be identified by comparing communication patterns between different projects and teams.

[0061] The analysis unit can analyze members' communication patterns by time of day and identify the optimal timing for communication. For example, the analysis unit analyzes members' email and chat history by time of day and identifies the optimal timing for communication. For example, it analyzes the frequency of communication during working hours. The analysis unit also analyzes the content of comments made in meetings by time of day and identifies the optimal timing for communication. For example, it compares the frequency of comments made in the morning and afternoon. The analysis unit also has the generation AI analyze members' communication patterns by time of day and identify the optimal timing for communication. For example, it identifies peak and off-peak times. In this way, by analyzing members' communication patterns by time of day, the optimal timing for communication can be identified.

[0062] The analysis unit uses the emotion estimation function to analyze what emotions members feel toward specific communication patterns and can provide emotional feedback. For example, the analysis unit uses the emotion estimation function to analyze what emotions members feel toward specific communication patterns. For example, it identifies positive emotions and negative emotions. The analysis unit also collects emotional data from members, and the generation AI analyzes the data to provide emotional feedback. For example, it provides feedback based on an emotion score. The analysis unit also uses the emotion estimation function to analyze what emotions members feel toward specific communication patterns in real time and provide emotional feedback. For example, it monitors changes in emotions. In this way, the emotion estimation function can be used to analyze what emotions members feel toward specific communication patterns and provide emotional feedback.

[0063] The proposal unit uses the generation AI to comprehensively evaluate the skill sets and communication patterns of members and propose a team with the optimal skill balance. For example, the generation AI analyzes the skill sets and communication patterns of members and proposes a team with the optimal skill balance. For example, it allocates technical skills and leadership skills in a balanced manner. The proposal unit also integrates the skill data and communication data of members and the generation AI proposes the optimal team composition. For example, it combines members with specialized knowledge and a cooperative attitude. The proposal unit also evaluates the skill sets and communication patterns of members and proposes a team with the optimal skill balance. For example, it allocates members with different skills in a balanced manner. In this way, the generation AI can comprehensively evaluate the skill sets and communication patterns of members and propose a team with the optimal skill balance.

[0064] The suggestion unit can take into account the personality traits of the members and form a team based on personality compatibility. For example, the suggestion unit analyzes the personality traits of the members and forms a team based on introversion or extroversion. For example, it allocates a balanced mix of introverted and extroverted members. The suggestion unit also forms a team by having the generation AI combine members who are compatible based on the personality traits. For example, it combines members who have a cooperative attitude. The suggestion unit also has the generation AI evaluate the personality traits of the members and propose a team with optimal personality compatibility. For example, it combines a member who demonstrates leadership with a member who plays a supporting role. In this way, by taking into account the personality traits of the members, it is possible to form a team based on personality compatibility.

[0065] The suggestion unit can use the emotion estimation function to evaluate the emotional compatibility of members and propose an emotionally stable team. For example, the suggestion unit uses the emotion estimation function to evaluate the emotional compatibility of members and propose an emotionally stable team. For example, it combines members with positive emotions. The suggestion unit also collects emotional data of members, and the generation AI analyzes the data to propose an emotionally stable team. For example, it evaluates the degree of emotional agreement and conflict. The suggestion unit also uses the emotion estimation function to evaluate the emotional compatibility of members in real time and propose an emotionally stable team. For example, it forms a team based on the emotion score. In this way, the emotion estimation function can be used to evaluate the emotional compatibility of members and propose an emotionally stable team.

[0066] The proposal unit can develop a dynamic team formation algorithm for flexibly changing team formation in accordance with the requirements of different projects. The proposal unit develops a dynamic team formation algorithm in which the generation AI flexibly changes team formation in accordance with the requirements of different projects. For example, it reallocates members in accordance with the progress of the project. The proposal unit also analyzes the requirements of the project, and the generation AI dynamically proposes an optimal team formation. For example, it takes into account the skill sets required for each phase of the project. The proposal unit also uses the dynamic team formation algorithm to build a system that changes team formation in real time in accordance with the requirements of the project. For example, it adds or removes members in accordance with the progress of the project. This makes it possible to develop a dynamic team formation algorithm for flexibly changing team formation in accordance with the requirements of different projects.

[0067] The proposal unit can propose a team composition that promotes individual growth, taking into account the career goals and growth aspirations of each member. For example, the proposal unit analyzes the career goals and growth aspirations of each member, and the generation AI proposes a team composition that promotes individual growth. For example, the relationship between mentor and mentee is taken into consideration. The proposal unit also proposes a team composition that promotes member growth, based on the member's career goals. For example, it forms a team that provides opportunities for skill development. The proposal unit also evaluates the member's growth aspirations, and the generation AI proposes a team composition that promotes growth based on that data. For example, it has the member participate in a challenging project. In this way, it is possible to propose a team composition that promotes individual growth by taking into account the member's career goals and growth aspirations.

[0068] The suggestion unit can use the emotion estimation function to identify team formations that make members feel most motivated and make suggestions that maximize motivation. The suggestion unit, for example, uses the emotion estimation function to identify team formations that make members feel most motivated. For example, it forms a highly motivated team based on emotion scores. The suggestion unit also collects emotion data from members, and the generation AI analyzes the data to propose team formations that maximize motivation. For example, it combines members with positive emotions. The suggestion unit also uses the emotion estimation function to identify team formations that make members feel most motivated in real time and make suggestions that maximize motivation. For example, it monitors changes in emotions. In this way, the emotion estimation function can be used to identify team formations that make members feel most motivated and make suggestions that maximize motivation.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The proposal department can analyze members' performance data in past projects and propose optimal team formations based on the division of roles in successful projects. For example, a member who demonstrated leadership in past projects can be reassigned as leader. The proposal department can also analyze members' collaborative relationships in past projects and place members who get along well together on the same team. For example, reuniting pairs that have achieved great results in past collaborations. The proposal department can also analyze the causes of failure in past projects and propose team formations that will avoid the same failures. For example, in a project that failed due to a lack of communication, a member who is good at communication can be assigned. In this way, past project data can be utilized to propose more effective team formations.

[0071] The data collection unit can collect members' health data and evaluate the impact of their health condition on communication. For example, it can collect members' sleep data and analyze the impact of lack of sleep on the quality of communication. The data collection unit can also collect members' exercise data and evaluate the impact of exercise habits on the quality of communication. For example, it can analyze the communication patterns of members who exercise regularly. The data collection unit can also collect members' dietary data and evaluate the impact of dietary quality on communication. For example, it can analyze the communication style of members who eat a balanced diet. This makes it possible to utilize the health data to make suggestions for improving the quality of communication.

[0072] The suggestion unit can analyze the learning styles of members and propose team formations that provide the optimal learning environment. For example, a team that makes heavy use of visual aids can be formed for members who prefer visual learning. The suggestion unit can also analyze the learning pace of members and pair them with members who can learn at the same pace. For example, a balance can be achieved between members who learn at a fast pace and members who learn at a slow pace. The suggestion unit can also analyze the learning motivation of members and propose team formations that will increase their motivation. For example, a member with a high motivation to learn can be placed as a leader. This makes it possible to form teams that take into account the learning styles and paces of members.

[0073] The proposal department can propose team formations that take into account the cultural backgrounds of members and respect diversity. For example, by assigning members with different cultural backgrounds in a balanced manner and incorporating diverse perspectives. The proposal department can also analyze members' language skills and propose team formations that promote communication across language barriers. For example, by assigning members who can speak multiple languages. The proposal department can also propose team formations that take into account members' cultural customs and avoid cultural friction. For example, by assigning members who respect specific cultural customs. This makes it possible to form diverse teams that take cultural backgrounds into account.

[0074] The proposal unit can propose team formations that take into account the work-life balance of members and reduce stress. For example, it can assign members who offer flexible working hours in consideration of family circumstances. The proposal unit can also analyze members' vacation data and propose team formations that reduce the burden on members who are on vacation. For example, it can allocate the tasks of a member who is on vacation to other members. The proposal unit can also analyze members' stress levels and propose team formations that reduce stress. For example, it can prioritize allocating tasks that cause less stress. This makes it possible to form teams that reduce stress and take into account the work-life balance of members.

[0075] The suggestion unit can use the emotion estimation function to suggest team formations for providing emotional support to members. For example, it can pair members who need emotional support with emotionally stable members. The suggestion unit can also collect members' emotional data, and the generation AI can analyze the data to suggest team formations for providing emotional support. For example, it can monitor changes in emotions and provide support at appropriate times. The suggestion unit can also use the emotion estimation function to evaluate members' emotional needs in real time and suggest team formations for providing emotional support. For example, it can provide support based on emotion scores. In this way, the emotion estimation function can be used to suggest team formations for providing emotional support to members.

[0076] The suggestion unit can use the emotion estimation function to evaluate the emotional fatigue of members and propose team composition to reduce fatigue. For example, by removing emotionally fatigued members from tasks that require rest. The suggestion unit can also collect members' emotional data, and the generation AI can analyze the data to propose team composition to reduce emotional fatigue. For example, by monitoring changes in emotions and providing rest at appropriate times. The suggestion unit can also use the emotion estimation function to evaluate members' emotional fatigue in real time and propose team composition to reduce fatigue. For example, by providing rest based on the emotion score. In this way, the emotion estimation function can be used to evaluate members' emotional fatigue and propose team composition to reduce fatigue.

[0077] The suggestion unit can use the emotion estimation function to evaluate the emotional motivation of members and propose team composition to increase their motivation. For example, it can assign a member with high emotional motivation as a leader. The suggestion unit can also collect members' emotional data, and the generation AI can analyze the data to propose team composition to increase their emotional motivation. For example, it can monitor changes in emotions and provide feedback to increase motivation at appropriate times. The suggestion unit can also use the emotion estimation function to evaluate members' emotional motivation in real time and propose team composition to increase their motivation. For example, it can assign tasks to increase motivation based on the emotion score. In this way, the emotion estimation function can be used to evaluate members' emotional motivation and propose team composition to increase their motivation.

[0078] The suggestion unit can use the emotion estimation function to evaluate the emotional stress of members and propose team composition to reduce stress. For example, it assigns members who are emotionally stressed to less stressful tasks. The suggestion unit can also collect members' emotional data, and the generation AI can analyze the data to propose team composition to reduce emotional stress. For example, it can monitor changes in emotions and provide feedback to reduce stress at an appropriate time. The suggestion unit can also use the emotion estimation function to evaluate members' emotional stress in real time and propose team composition to reduce stress. For example, it can assign tasks to reduce stress based on the emotion score. In this way, the emotion estimation function can be used to evaluate members' emotional stress and propose team composition to reduce stress.

[0079] The suggestion unit can use the emotion estimation function to evaluate the emotional satisfaction of members and propose team composition to increase satisfaction. For example, the team can be composed mainly of members with high emotional satisfaction. The suggestion unit can also collect members' emotional data, and the generation AI can analyze the data to propose team composition to increase emotional satisfaction. For example, the suggestion unit can monitor changes in emotions and provide feedback to increase satisfaction at an appropriate time. The suggestion unit can also use the emotion estimation function to evaluate members' emotional satisfaction in real time and propose team composition to increase satisfaction. For example, the suggestion unit can assign tasks to increase satisfaction based on the emotion score. In this way, the emotion estimation function can be used to evaluate members' emotional satisfaction and propose team composition to increase satisfaction.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The data collection team collects data on communication patterns between team members. For example, they collect email and chat history, meeting content, and project management tool usage. This allows them to understand who is frequently communicating with whom, who is in leadership roles, and who is in support roles. Step 2: The analysis unit analyzes the data collected by the data collection unit to identify communication patterns between team members. For example, the analysis unit uses the generation AI to analyze the frequency of email sending and receiving and the number of times each member speaks in meetings, and evaluates the roles and relationships of each member. The generation AI receives a prompt such as, "Analyze this member's communication style," and then performs the analysis. Step 3: The proposal unit proposes the optimal team composition based on the communication patterns revealed by the analysis unit. For example, the proposal unit will balance leadership and support members, placing members who get along well together on the same team. The generation AI receives the prompt, "Please propose the optimal team composition," and makes a proposal. The proposal unit also actually uses the proposed team composition, collects the results as feedback, and proposes further optimal team compositions.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects communication patterns between team members as data; an analysis unit that analyzes the data collected by the data collection unit and identifies communication patterns between team members; a proposal unit that proposes an optimal team composition based on the communication pattern revealed by the analysis unit. A system characterized by:

2. The data collection unit Monitor members' emotional states in real time and collect data on changes in their emotions.

2. The system of claim 1.

3. The analysis unit Generative AI is used to evaluate trust relationships between members and identify highly trustworthy pairs and groups.

2. The system of claim 1.

4. The proposal unit Using generative AI, we comprehensively evaluate team members' skill sets and communication patterns to propose teams with the optimal skill balance.

2. The system of claim 1.

5. The data collection unit Collecting how members feel about specific tasks and accumulating emotion data for each task 2. The system of claim 1.

6. The analysis unit Analyze emotional interactions between members and evaluate the degree of emotional agreement and conflict.

2. The system of claim 1.

7. The proposal unit Evaluate the emotional compatibility of members and propose emotionally stable teams 2. The system of claim 1.

8. The proposal unit Identify the team structure that most motivates members and make suggestions to maximize motivation 2. The system of claim 1.

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