System

Abstract

An object of a system according to an embodiment is to analyze a behavior and a thinking pattern of a high learner crew and to use the behavior and the thinking pattern for training a low learner crew.SOLUTION: A system according to an embodiment includes a persona analysis unit, a mentor rhythm element extraction unit, a training program creation unit, and an evaluation unit. The persona analyzer uses the generated AI to analyze the behavior and thought patterns of the high earner crew and extract their characteristics. A mentor rhythm element extraction part extracts a mentor rhythm element from the feature extracted by the persona analysis part. The training program creation unit creates a training program for the low learner crew based on the elements extracted by the mentor rhythm element extraction unit. The evaluation part executes the raising program prepared by the raising program preparation part and evaluates its effect.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 effectively utilize the behavior and thought patterns of high-achieving crew members in training low-achieving crew members.

[0005] The system according to the embodiment aims to analyze the behavior and thought patterns of high-achieving crew members and use this information to help develop low-achieving crew members. [Means for solving the problem]

[0006] The system according to the embodiment includes a persona analysis unit, a mentalism element extraction unit, a training program creation unit, and an evaluation unit. The persona analysis unit uses a generation AI to analyze the behavior and thought patterns of high-achieving crew members and extract their characteristics. The mentalism element extraction unit extracts mentalism elements from the characteristics extracted by the persona analysis unit. The training program creation unit creates a training program for low-achieving crew members based on the elements extracted by the mentalism element extraction unit. The evaluation unit implements the training program created by the training program creation unit and evaluates its effectiveness. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the behavior and thought patterns of high-achieving crew members and use this information to help develop low-achieving crew members. [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 training support system according to the embodiment of the present invention is a system that divides the personas of high-achieving crew members into mentalism elements and uses them to train low-achieving crew members. As a result, the training support system can impart the success factors of high-achieving crew members to low-achieving crew members, thereby improving overall performance.

[0029] A training support system according to an embodiment includes a persona analysis unit, a mentalism element extraction unit, a training program creation unit, and an evaluation unit. The persona analysis unit uses a generation AI to analyze the behavior and thought patterns of high-achieving crew members and extract their characteristics. For example, the persona analysis unit collects activity logs and communication records of high-achieving crew members during their work, and the generation AI analyzes the data. The persona analysis unit also collects data on self-evaluation and evaluation by others, and the generation AI extracts characteristics based on the data. Furthermore, the persona analysis unit analyzes goal-setting methods, stress management techniques, communication styles, and the like. The mentalism element extraction unit extracts mentalism elements from the characteristics extracted by the persona analysis unit. For example, the mentalism element extraction unit analyzes the psychological factors, thought patterns, and behavioral characteristics of high-achieving crew members. The mentalism element extraction unit analyzes goal-achieving behaviors, stress management methods, and communication methods with others. Furthermore, the mentalism element extraction unit extracts the success factors of high-achieving crew members, and the generation AI extracts them. The training program creation unit creates a training program for low-achieving crew members based on the elements extracted by the mentalism element extraction unit. For example, the training program creation unit creates a training program for the generation AI to learn goal setting methods used by high-achieving crew members. The training program creation unit also creates a workshop for the generation AI to learn stress management techniques. The training program creation unit also creates a seminar for the generation AI to acquire effective communication skills. The evaluation unit implements the training program created by the training program creation unit and evaluates its effectiveness. For example, the evaluation unit collects performance data, self-evaluation, and evaluation by others after training, and the generation AI analyzes the data to evaluate the effectiveness of the program. The evaluation unit also improves the training program based on the evaluation results. The evaluation unit also provides feedback to the generation AI to enable more effective training. As a result, the training support system according to the embodiment can impart the success factors of high-achieving crew members to low-achieving crew members, thereby improving overall performance.For example, the training support system allows low-achieving crew members to learn the goal-setting methods of high-achieving crew members, enabling them to take concrete action toward achieving their goals. The training support system also allows crew members to learn stress management techniques, reducing stress during work and improving performance. Furthermore, the training support system allows crew members to acquire effective communication skills, strengthening cooperation within the team and improving overall performance.

[0030] The persona analysis unit can analyze not only the behavioral data of high-achieving crew members, but also their nonverbal communication. For example, the persona analysis unit records the facial expressions and gestures of high-achieving crew members while they are working with a camera and analyzes them using generative AI. For example, it analyzes facial expressions and hand movements during meetings to determine the type of nonverbal communication they use and in what situations. The persona analysis unit also analyzes a combination of audio and video data to analyze the nonverbal communication of high-achieving crew members. For example, it synchronizes and analyzes changes in facial expressions with what is being said to gain a detailed understanding of emotional changes. The persona analysis unit also uses gesture recognition technology to analyze hand movements and posture to analyze the nonverbal communication of high-achieving crew members. For example, it analyzes changes in hand movements and posture during presentations to identify effective communication methods. This allows for a more detailed understanding of the behavioral patterns of high-achieving crew members.

[0031] The persona analysis unit can analyze the high-achieving crew's past failure experiences or recovery process from setbacks. For example, the persona analysis unit collects the high-achieving crew's past failure experiences and analyzes the data using generative AI. For example, it analyzes the causes of failure and subsequent countermeasures to clarify the process leading to success. The persona analysis unit also collects data through interviews and questionnaires about how the high-achieving crew recovered from setbacks and analyzes the data. For example, it analyzes behavior and thought patterns after setbacks and plots a learning curve leading to success. The persona analysis unit also analyzes the high-achieving crew's past project data to clarify the process from failure to success. For example, it analyzes project progress and deliverables and visualizes the learning curve. This makes it possible to clarify the high-achieving crew's learning curve leading to success.

[0032] The persona analysis unit can also take into account factors outside of work, including the lifestyles, hobbies, and interests of high-earning crew members. For example, the persona analysis unit collects information about the lifestyles, hobbies, and interests of high-earning crew members through questionnaires and interviews, and analyzes the data using the generation AI. For example, it clarifies how hobbies and interests affect work. The persona analysis unit also collects data on the high-earning crew members' activities outside of work and analyzes it using the generation AI. For example, it analyzes the impact of sports and volunteer activities on work and reflects this in the persona. The persona analysis unit also collects lifestyle data on high-earning crew members and analyzes it using the generation AI. For example, it analyzes the impact of eating habits and sleep patterns on work and reflects this in the persona. This allows for a more comprehensive understanding of the personas of high-earning crew members.

[0033] The persona analysis unit can analyze the personas of high-achieving crew members from different industries or occupations and extract common success factors. For example, the persona analysis unit collects data on high-achieving crew members from different industries and analyzes it using generative AI. For example, it compares the personas of high-achieving crew members from the IT industry and manufacturing industry and extracts common success factors. The persona analysis unit also collects data on high-achieving crew members from different occupations and analyzes it using generative AI. For example, it compares the personas of high-achieving crew members in sales and technical occupations and extracts common success factors. The persona analysis unit also integrates data on high-achieving crew members from different industries and occupations and analyzes it using generative AI. For example, it extracts common behavioral patterns and thinking patterns to identify success factors. This makes it possible to identify success factors in different industries and occupations.

[0034] When analyzing the thought patterns of high-achieving crew members, the mentalism element extraction unit can perform a detailed analysis of their decision-making processes or risk assessment methods. For example, the mentalism element extraction unit collects information about the decision-making processes of high-achieving crew members through interviews or questionnaires and analyzes the data using a generation AI. For example, it clarifies the factors considered when making decisions and the risk assessment methods. The mentalism element extraction unit also analyzes the high-achieving crew members' past project data to clarify their decision-making processes and risk assessment methods. For example, it analyzes project progress and deliverables to extract decision-making patterns. The mentalism element extraction unit also simulates the decision-making processes of high-achieving crew members and analyzes the data using a generation AI. For example, it recreates the decision-making processes using hypothetical scenarios to clarify the risk assessment methods. This makes it possible to clarify the decision-making processes and risk assessment methods of high-achieving crew members.

[0035] The mentalism element extraction unit can analyze specific language patterns or phrases used by high-achieving crew members. The mentalism element extraction unit, for example, collects communication data from high-achieving crew members and analyzes it with a generation AI. For example, it analyzes email and chat logs to extract specific language patterns and phrases. The mentalism element extraction unit also collects conversation data from high-achieving crew members and analyzes it with a generation AI. For example, it analyzes audio data from meetings and presentations to extract specific language patterns and phrases. The mentalism element extraction unit also analyzes the language patterns and phrases of high-achieving crew members and clarifies how they affect mentalism elements. For example, it analyzes how positive language patterns contribute to success. This makes it possible to clarify how the language patterns and phrases of high-achieving crew members affect mentalism elements.

[0036] The mentalism element extraction unit can take cultural background or social influence into account when extracting mentalism elements. For example, the mentalism element extraction unit collects data on high-achieving crew members from different cultural backgrounds and analyzes it using a generation AI. For example, it compares the personas of high-achieving crew members from Asia and Europe to extract success factors that take cultural background and social influence into account. The mentalism element extraction unit also collects behavioral data on high-achieving crew members from different cultural backgrounds and analyzes it using a generation AI. For example, it clarifies how cultural background affects work. The mentalism element extraction unit also compares the mentalism elements of high-achieving crew members from different cultural backgrounds and extracts common success factors. For example, it clarifies success factors that take cultural background and social influence into account. This makes it possible to compare success factors across different cultural backgrounds.

[0037] The mentalism element extraction unit can compare the mentalism elements of high-achieving crew members across different age groups or genders. For example, the mentalism element extraction unit collects data on high-achieving crew members across different age groups and analyzes it using a generation AI. For example, it compares the personas of young and middle-aged high-achieving crew members to identify similarities and differences. The mentalism element extraction unit also collects data on high-achieving crew members across different genders and analyzes it using a generation AI. For example, it compares the personas of male and female high-achieving crew members to identify similarities and differences. The mentalism element extraction unit also compares the mentalism elements of high-achieving crew members across different age groups and genders to identify similarities and differences. For example, it analyzes how age and gender affect work. This makes it possible to identify success factors for different age groups and genders.

[0038] The training program creation unit can analyze the individual weaknesses of low-achieving crew members in detail and create a customized training program that is specialized for them. The training program creation unit, for example, collects work data from low-achieving crew members and analyzes it using a generation AI. For example, it analyzes data on behavioral logs during work, self-assessments, and evaluations by others to identify individual weaknesses. The training program creation unit also analyzes the weaknesses of low-achieving crew members in detail and creates a customized training program that is specialized for them. For example, it designs a training program to strengthen specific skills and knowledge. The training program creation unit also uses a generation AI to identify the weaknesses of low-achieving crew members and create a training program that is specialized for them. For example, it provides customized training that is tailored to each individual weakness. This makes it possible to provide an effective training program that is tailored to each individual weakness of low-achieving crew members.

[0039] The training program creation unit can simulate success stories of high-achieving crew members and introduce virtual reality training that allows low-achieving crew members to experience those scenarios. The training program creation unit, for example, simulates success stories of high-achieving crew members and develops virtual reality training that allows low-achieving crew members to experience those scenarios. For example, it creates virtual reality scenarios based on the success stories. The training program creation unit also introduces virtual reality training that allows low-achieving crew members to experience the success stories of high-achieving crew members. For example, it recreates the success stories using a virtual reality headset. The training program creation unit also develops virtual reality training based on the success stories of high-achieving crew members and allows low-achieving crew members to experience those scenarios. For example, it provides a training program in a virtual reality environment. This allows low-achieving crew members to experience and learn from the success stories of high-achieving crew members.

[0040] The training program creation department can incorporate success stories from different industries into the training program. For example, the training program creation department collects success stories from different industries and incorporates them into the training program. For example, it creates a training program based on success stories from the IT industry or the manufacturing industry. The training program creation department also develops training programs that incorporate success stories from different industries, allowing low-scoring crew members to learn from a wide range of perspectives. For example, it holds workshops to learn the success factors of different industries. The training program creation department also creates training programs based on success stories from different industries, allowing low-scoring crew members to learn from multiple perspectives. For example, it provides training that simulates success stories from different industries. This allows low-scoring crew members to learn from a wide range of perspectives.

[0041] The training program creation unit can incorporate game elements into the training program. For example, the training program creation unit incorporates game elements into the training program to provide an environment where low-scoring crew members can learn while having fun. For example, a points system or ranking system can be introduced. The training program creation unit also develops a training program that incorporates game elements to enable low-scoring crew members to learn with motivation. For example, it provides quiz-style training or simulation games. The training program creation unit also incorporates game elements into the training program to provide an environment where low-scoring crew members can learn while having fun. For example, it introduces a badge or reward system to visualize learning progress. This makes it possible to provide an environment where low-scoring crew members can learn while having fun.

[0042] The evaluation department can track the long-term performance data of participants when evaluating the effectiveness of the development program. For example, to evaluate the effectiveness of the development program, the evaluation department collects long-term performance data of participants and analyzes it with the generation AI. For example, the effectiveness of the program is quantified based on the performance data. The evaluation department also tracks the long-term performance data of participants to confirm the sustained effectiveness of the development program. For example, performance data is collected regularly even after the program has ended to evaluate the effectiveness. The evaluation department also tracks the performance data of participants and analyzes it with the generation AI to evaluate the effectiveness of the development program over the long term. For example, performance improvement after the program has ended is confirmed. This makes it possible to confirm the sustained effectiveness of the development program.

[0043] The evaluation unit can collect self-assessments and evaluations by others from participants after each session of the development program. For example, the evaluation unit collects self-assessments and evaluations by others from participants after each session of the development program and analyzes them using the generation AI. For example, the evaluation unit quantifies the immediate effectiveness of the program based on the data on self-assessments and evaluations by others. The evaluation unit also collects self-assessments and evaluations by others from participants and evaluates the immediate effectiveness of the development program. For example, the evaluation unit conducts a questionnaire after the end of a session and collects evaluation data. The evaluation unit also collects self-assessments and evaluations by others from participants and analyzes them using the generation AI to immediately evaluate the effectiveness of the development program. For example, the effectiveness of the program is confirmed based on the evaluation data after the end of a session. This makes it possible to evaluate the immediate effectiveness of the development program.

[0044] The evaluation department can compare the effectiveness of the training program across different regions or cultural spheres. For example, the evaluation department collects data on participants from different regions or cultural spheres and compares the effectiveness of the training program. For example, the effectiveness of the program is evaluated based on performance data from participants in Asia and Europe. The evaluation department also analyzes data from participants from different regions or cultural spheres and evaluates the effectiveness of the training program from a global perspective. For example, it clarifies the impact of cultural background on the effectiveness of the program. The evaluation department also integrates data from participants from different regions or cultural spheres and compares the effectiveness of the training program. For example, it extracts success factors in different cultural spheres and uses this information to improve the program. This makes it possible to evaluate the effectiveness of the training program from a global perspective.

[0045] The evaluation department can compare the evaluation results of the training program with other training programs. For example, the evaluation department collects the evaluation results of the training program and compares them with other training programs. For example, the effectiveness is evaluated based on performance data of different programs. The evaluation department also analyzes the evaluation results of the training program and identifies the most effective method. For example, the evaluation department extracts success factors of the program and compares them with other programs. The evaluation department also integrates the evaluation results of the training program and compares them with other training programs. For example, the most effective method is identified based on evaluation data of different programs. This makes it possible to identify the most effective training program.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The training support system can further include a "feedback collection unit." The feedback collection unit collects feedback from participants after each training program session and analyzes it with the generation AI. For example, it may conduct a survey in which participants provide their opinions and thoughts about the program content and progress. The feedback collection unit can also identify areas for improvement in the training program based on the participants' feedback and reflect them in the next session. For example, if a participant lacks understanding of a particular topic, it can provide additional training on that topic. The feedback collection unit can also collect participants' feedback and analyze it with the generation AI to quantify the effectiveness of the program. This allows the effectiveness of the training program to be continuously improved.

[0048] The training support system can further include a "motivation improvement unit." The motivation improvement unit implements measures to improve the motivation of low-performing crew members. For example, the motivation improvement unit periodically holds motivation improvement workshops for low-performing crew members. The motivation improvement unit can also provide incentives to low-performing crew members for improving their performance. For example, it can provide rewards or benefits based on goal achievement. The motivation improvement unit can also provide regular feedback and coaching to improve the motivation of low-performing crew members. This can improve the motivation of low-performing crew members and improve their performance.

[0049] The training support system can further include a "career path suggestion unit." The career path suggestion unit suggests appropriate career paths for low-achieving crew members. For example, the career path suggestion unit suggests future career paths based on the skills and interests of the low-achieving crew members. The career path suggestion unit can also provide specific steps for low-achieving crew members to achieve their goals. For example, it can suggest training programs for acquiring the necessary skills and experience. The career path suggestion unit can also periodically review the career paths of low-achieving crew members and provide appropriate advice. This allows low-achieving crew members to effectively advance their careers.

[0050] The training support system can further include a "stress management support department." The stress management support department implements measures to reduce stress among low-achieving crew members. For example, the stress management support department holds stress management workshops for low-achieving crew members. The stress management support department can also provide resources and support that low-achieving crew members can use when they feel stressed. For example, it can provide counseling services and relaxation programs. The stress management support department can also regularly monitor the stress levels of low-achieving crew members and provide appropriate support as needed. This can reduce stress among low-achieving crew members and improve their performance.

[0051] The training support system can further include a "communication improvement department." The communication improvement department implements measures to improve the communication skills of low-achieving crew members. For example, the communication improvement department provides training to low-achieving crew members to improve their communication skills. The communication improvement department can also provide tools and resources to enable low-achieving crew members to communicate effectively. For example, the communication improvement department can provide online courses and workshops to improve communication skills. The communication improvement department can also regularly evaluate the communication skills of low-achieving crew members and provide additional training as needed. This can improve the communication skills of low-achieving crew members and strengthen cooperative relationships within the team.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The persona analysis unit uses the generation AI to analyze the behavior and thought patterns of high-achieving crew members and extract their characteristics. For example, the persona analysis unit collects activity logs and communication records of high-achieving crew members during work, and the generation AI analyzes this data. The persona analysis unit also collects data on self-evaluation and evaluation by others, and the generation AI extracts characteristics based on this data. Furthermore, the persona analysis unit uses the generation AI to analyze goal-setting methods, stress management techniques, communication styles, etc. Step 2: The mentalism element extraction unit extracts mentalism elements from the characteristics extracted by the persona analysis unit. For example, in the mentalism element extraction unit, the generation AI analyzes the psychological factors, thought patterns, and behavioral characteristics of high-achieving crew members. In addition, in the mentalism element extraction unit, the generation AI analyzes the actions taken to achieve goals, stress management methods, and communication methods with others. Furthermore, in the mentalism element extraction unit, the generation AI extracts the success factors of high-achieving crew members. Step 3: The training program creation unit creates a training program for low-achieving crew members based on the elements extracted by the mentalism element extraction unit. For example, the training program creation unit creates a training program for the generation AI to learn the goal setting methods of high-achieving crew members. The training program creation unit also creates a workshop for the generation AI to learn stress management techniques. Furthermore, the training program creation unit creates a seminar for the generation AI to acquire effective communication skills. Step 4: The evaluation unit implements the training program created by the training program creation unit and evaluates its effectiveness. For example, the evaluation unit collects post-training performance data, self-evaluation, and peer-evaluation data, and the generation AI analyzes the data to evaluate the effectiveness of the program. The evaluation unit also allows the generation AI to improve the training program based on the evaluation results. Furthermore, the evaluation unit provides feedback to the generation AI to enable more effective training.

[0054] (Example 2) The training support system according to the embodiment of the present invention is a system that divides the personas of high-achieving crew members into mentalism elements and uses them to train low-achieving crew members. As a result, the training support system can impart the success factors of high-achieving crew members to low-achieving crew members, thereby improving overall performance.

[0055] A training support system according to an embodiment includes a persona analysis unit, a mentalism element extraction unit, a training program creation unit, and an evaluation unit. The persona analysis unit uses a generation AI to analyze the behavior and thought patterns of high-achieving crew members and extract their characteristics. For example, the persona analysis unit collects activity logs and communication records of high-achieving crew members during their work, and the generation AI analyzes the data. The persona analysis unit also collects data on self-evaluation and evaluation by others, and the generation AI extracts characteristics based on the data. Furthermore, the persona analysis unit analyzes goal-setting methods, stress management techniques, communication styles, and the like. The mentalism element extraction unit extracts mentalism elements from the characteristics extracted by the persona analysis unit. For example, the mentalism element extraction unit analyzes the psychological factors, thought patterns, and behavioral characteristics of high-achieving crew members. The mentalism element extraction unit analyzes goal-achieving behaviors, stress management methods, and communication methods with others. Furthermore, the mentalism element extraction unit extracts the success factors of high-achieving crew members, and the generation AI extracts them. The training program creation unit creates a training program for low-achieving crew members based on the elements extracted by the mentalism element extraction unit. For example, the training program creation unit creates a training program for the generation AI to learn goal setting methods used by high-achieving crew members. The training program creation unit also creates a workshop for the generation AI to learn stress management techniques. The training program creation unit also creates a seminar for the generation AI to acquire effective communication skills. The evaluation unit implements the training program created by the training program creation unit and evaluates its effectiveness. For example, the evaluation unit collects performance data, self-evaluation, and evaluation by others after training, and the generation AI analyzes the data to evaluate the effectiveness of the program. The evaluation unit also improves the training program based on the evaluation results. The evaluation unit also provides feedback to the generation AI to enable more effective training. As a result, the training support system according to the embodiment can impart the success factors of high-achieving crew members to low-achieving crew members, thereby improving overall performance.For example, the training support system allows low-achieving crew members to learn the goal-setting methods of high-achieving crew members, enabling them to take concrete action toward achieving their goals. The training support system also allows crew members to learn stress management techniques, reducing stress during work and improving performance. Furthermore, the training support system allows crew members to acquire effective communication skills, strengthening cooperation within the team and improving overall performance.

[0056] The persona analysis unit can analyze not only the behavioral data of high-achieving crew members, but also their nonverbal communication. For example, the persona analysis unit records the facial expressions and gestures of high-achieving crew members while they are working with a camera and analyzes them using generative AI. For example, it analyzes facial expressions and hand movements during meetings to determine the type of nonverbal communication they use and in what situations. The persona analysis unit also analyzes a combination of audio and video data to analyze the nonverbal communication of high-achieving crew members. For example, it synchronizes and analyzes changes in facial expressions with what is being said to gain a detailed understanding of emotional changes. The persona analysis unit also uses gesture recognition technology to analyze hand movements and posture to analyze the nonverbal communication of high-achieving crew members. For example, it analyzes changes in hand movements and posture during presentations to identify effective communication methods. This allows for a more detailed understanding of the behavioral patterns of high-achieving crew members.

[0057] The persona analysis unit can analyze the high-achieving crew's past failure experiences or recovery process from setbacks. For example, the persona analysis unit collects the high-achieving crew's past failure experiences and analyzes the data using generative AI. For example, it analyzes the causes of failure and subsequent countermeasures to clarify the process leading to success. The persona analysis unit also collects data through interviews and questionnaires about how the high-achieving crew recovered from setbacks and analyzes the data. For example, it analyzes behavior and thought patterns after setbacks and plots a learning curve leading to success. The persona analysis unit also analyzes the high-achieving crew's past project data to clarify the process from failure to success. For example, it analyzes project progress and deliverables and visualizes the learning curve. This makes it possible to clarify the high-achieving crew's learning curve leading to success.

[0058] The persona analysis unit can use the emotion estimation function to analyze the emotions felt by high-achieving crew members during specific tasks. For example, the persona analysis unit uses a camera or microphone equipped with emotion estimation functionality to analyze the emotions of high-achieving crew members while they are working in real time. For example, it analyzes facial expressions and tone of voice during meetings and records changes in emotion. The persona analysis unit also uses the emotion estimation function to analyze the emotions of high-achieving crew members when they perform specific tasks. For example, it analyzes emotions during presentations or problem-solving to clarify the impact of those emotions on behavior. The persona analysis unit also collects emotional data of high-achieving crew members and analyzes it using generative AI. For example, it analyzes changes in emotions during work and quantifies the impact of emotions on behavior. This makes it possible to clarify the impact of high-achieving crew members' emotions on their behavior.

[0059] The persona analysis unit can also take into account factors outside of work, including the lifestyles, hobbies, and interests of high-earning crew members. For example, the persona analysis unit collects information about the lifestyles, hobbies, and interests of high-earning crew members through questionnaires and interviews, and analyzes the data using the generation AI. For example, it clarifies how hobbies and interests affect work. The persona analysis unit also collects data on the high-earning crew members' activities outside of work and analyzes it using the generation AI. For example, it analyzes the impact of sports and volunteer activities on work and reflects this in the persona. The persona analysis unit also collects lifestyle data on high-earning crew members and analyzes it using the generation AI. For example, it analyzes the impact of eating habits and sleep patterns on work and reflects this in the persona. This allows for a more comprehensive understanding of the personas of high-earning crew members.

[0060] The persona analysis unit can analyze the personas of high-achieving crew members from different industries or occupations and extract common success factors. For example, the persona analysis unit collects data on high-achieving crew members from different industries and analyzes it using generative AI. For example, it compares the personas of high-achieving crew members from the IT industry and manufacturing industry and extracts common success factors. The persona analysis unit also collects data on high-achieving crew members from different occupations and analyzes it using generative AI. For example, it compares the personas of high-achieving crew members in sales and technical occupations and extracts common success factors. The persona analysis unit also integrates data on high-achieving crew members from different industries and occupations and analyzes it using generative AI. For example, it extracts common behavioral patterns and thinking patterns to identify success factors. This makes it possible to identify success factors in different industries and occupations.

[0061] The persona analysis unit can use the emotion estimation function to analyze the emotional reactions of high-achieving crew members when they face a new task. For example, the persona analysis unit uses a camera or microphone equipped with emotion estimation functionality to analyze the emotions of high-achieving crew members in real time when they face a new task. For example, it analyzes facial expressions and tone of voice at the start of the task and records changes in emotion. The persona analysis unit also uses the emotion estimation function to analyze the emotional reactions of high-achieving crew members when they face a new task. For example, it analyzes changes in emotion during the task and evaluates their level of adaptability. The persona analysis unit also collects emotional data of high-achieving crew members and analyzes it using generative AI. For example, it analyzes changes in emotion in response to a new task and quantifies their level of adaptability. This makes it possible to evaluate the level of adaptability of high-achieving crew members.

[0062] When analyzing the thought patterns of high-achieving crew members, the mentalism element extraction unit can perform a detailed analysis of their decision-making processes or risk assessment methods. For example, the mentalism element extraction unit collects information about the decision-making processes of high-achieving crew members through interviews or questionnaires and analyzes the data using a generation AI. For example, it clarifies the factors considered when making decisions and the risk assessment methods. The mentalism element extraction unit also analyzes the high-achieving crew members' past project data to clarify their decision-making processes and risk assessment methods. For example, it analyzes project progress and deliverables to extract decision-making patterns. The mentalism element extraction unit also simulates the decision-making processes of high-achieving crew members and analyzes the data using a generation AI. For example, it recreates the decision-making processes using hypothetical scenarios to clarify the risk assessment methods. This makes it possible to clarify the decision-making processes and risk assessment methods of high-achieving crew members.

[0063] The mentalism element extraction unit can analyze specific language patterns or phrases used by high-achieving crew members. The mentalism element extraction unit, for example, collects communication data from high-achieving crew members and analyzes it with a generation AI. For example, it analyzes email and chat logs to extract specific language patterns and phrases. The mentalism element extraction unit also collects conversation data from high-achieving crew members and analyzes it with a generation AI. For example, it analyzes audio data from meetings and presentations to extract specific language patterns and phrases. The mentalism element extraction unit also analyzes the language patterns and phrases of high-achieving crew members and clarifies how they affect mentalism elements. For example, it analyzes how positive language patterns contribute to success. This makes it possible to clarify how the language patterns and phrases of high-achieving crew members affect mentalism elements.

[0064] The mentalism element extraction unit can use the emotion estimation function to analyze the psychological reactions of high-achieving crew members when they feel stressed. The mentalism element extraction unit, for example, uses a camera or microphone equipped with an emotion estimation function to analyze the emotions of high-achieving crew members when they feel stressed in real time. For example, it analyzes facial expressions and tone of voice under stressful situations and records changes in emotions. The mentalism element extraction unit also uses the emotion estimation function to analyze the psychological reactions of high-achieving crew members when they feel stressed. For example, it analyzes changes in emotions during stress and extracts mentalism elements of stress management. The mentalism element extraction unit also collects emotional data of high-achieving crew members and analyzes it using a generation AI. For example, it analyzes changes in emotions under stressful situations and quantifies mentalism elements of stress management. This makes it possible to extract mentalism elements of stress management for high-achieving crew members.

[0065] The mentalism element extraction unit can take cultural background or social influence into account when extracting mentalism elements. For example, the mentalism element extraction unit collects data on high-achieving crew members from different cultural backgrounds and analyzes it using a generation AI. For example, it compares the personas of high-achieving crew members from Asia and Europe to extract success factors that take cultural background and social influence into account. The mentalism element extraction unit also collects behavioral data on high-achieving crew members from different cultural backgrounds and analyzes it using a generation AI. For example, it clarifies how cultural background affects work. The mentalism element extraction unit also compares the mentalism elements of high-achieving crew members from different cultural backgrounds and extracts common success factors. For example, it clarifies success factors that take cultural background and social influence into account. This makes it possible to compare success factors across different cultural backgrounds.

[0066] The mentalism element extraction unit can compare the mentalism elements of high-achieving crew members across different age groups or genders. For example, the mentalism element extraction unit collects data on high-achieving crew members across different age groups and analyzes it using a generation AI. For example, it compares the personas of young and middle-aged high-achieving crew members to identify similarities and differences. The mentalism element extraction unit also collects data on high-achieving crew members across different genders and analyzes it using a generation AI. For example, it compares the personas of male and female high-achieving crew members to identify similarities and differences. The mentalism element extraction unit also compares the mentalism elements of high-achieving crew members across different age groups and genders to identify similarities and differences. For example, it analyzes how age and gender affect work. This makes it possible to identify success factors for different age groups and genders.

[0067] The mentalism element extraction unit can use the emotion estimation function to analyze the emotional reactions of high-achieving crew members when they cooperate with others. The mentalism element extraction unit, for example, uses a camera or microphone equipped with an emotion estimation function to analyze the emotions of high-achieving crew members when they cooperate with others in real time. For example, it analyzes facial expressions and tone of voice during cooperation and records changes in emotion. The mentalism element extraction unit also uses the emotion estimation function to analyze the emotional reactions of high-achieving crew members when they cooperate with others. For example, it analyzes changes in emotion during cooperation and extracts mentalism elements of teamwork. The mentalism element extraction unit also collects emotional data of high-achieving crew members and analyzes it using a generation AI. For example, it analyzes changes in emotion during cooperation and quantifies the mentalism elements of teamwork. This makes it possible to extract the mentalism elements of teamwork of high-achieving crew members.

[0068] The training program creation unit can analyze the individual weaknesses of low-achieving crew members in detail and create a customized training program that is specialized for them. The training program creation unit, for example, collects work data from low-achieving crew members and analyzes it using a generation AI. For example, it analyzes data on behavioral logs during work, self-assessments, and evaluations by others to identify individual weaknesses. The training program creation unit also analyzes the weaknesses of low-achieving crew members in detail and creates a customized training program that is specialized for them. For example, it designs a training program to strengthen specific skills and knowledge. The training program creation unit also uses a generation AI to identify the weaknesses of low-achieving crew members and create a training program that is specialized for them. For example, it provides customized training that is tailored to each individual weakness. This makes it possible to provide an effective training program that is tailored to each individual weakness of low-achieving crew members.

[0069] The training program creation unit can simulate success stories of high-achieving crew members and introduce virtual reality training that allows low-achieving crew members to experience those scenarios. The training program creation unit, for example, simulates success stories of high-achieving crew members and develops virtual reality training that allows low-achieving crew members to experience those scenarios. For example, it creates virtual reality scenarios based on the success stories. The training program creation unit also introduces virtual reality training that allows low-achieving crew members to experience the success stories of high-achieving crew members. For example, it recreates the success stories using a virtual reality headset. The training program creation unit also develops virtual reality training based on the success stories of high-achieving crew members and allows low-achieving crew members to experience those scenarios. For example, it provides a training program in a virtual reality environment. This allows low-achieving crew members to experience and learn from the success stories of high-achieving crew members.

[0070] The training program creation unit can use the emotion estimation function to monitor the emotions felt by low-achieving crew members during the training program in real time and adjust the progress of the program. The training program creation unit, for example, uses the emotion estimation function to develop a system that monitors the emotions felt by low-achieving crew members during the training program in real time. For example, a camera or microphone is used to record changes in emotions. The training program creation unit also collects emotional data of low-achieving crew members in real time and adjusts the progress of the training program. For example, the training content is adjusted according to changes in emotions. The training program creation unit also uses the emotion estimation function to build a system that monitors the emotions of low-achieving crew members and adjusts the progress of the training program. For example, the speed of progress of the program is adjusted according to changes in emotions. This enables the training program to be progressed flexibly in accordance with the emotions of low-achieving crew members.

[0071] The training program creation department can incorporate success stories from different industries into the training program. For example, the training program creation department collects success stories from different industries and incorporates them into the training program. For example, it creates a training program based on success stories from the IT industry or the manufacturing industry. The training program creation department also develops training programs that incorporate success stories from different industries, allowing low-scoring crew members to learn from a wide range of perspectives. For example, it holds workshops to learn the success factors of different industries. The training program creation department also creates training programs based on success stories from different industries, allowing low-scoring crew members to learn from multiple perspectives. For example, it provides training that simulates success stories from different industries. This allows low-scoring crew members to learn from a wide range of perspectives.

[0072] The training program creation unit can incorporate game elements into the training program. For example, the training program creation unit incorporates game elements into the training program to provide an environment where low-scoring crew members can learn while having fun. For example, a points system or ranking system can be introduced. The training program creation unit also develops a training program that incorporates game elements to enable low-scoring crew members to learn with motivation. For example, it provides quiz-style training or simulation games. The training program creation unit also incorporates game elements into the training program to provide an environment where low-scoring crew members can learn while having fun. For example, it introduces a badge or reward system to visualize learning progress. This makes it possible to provide an environment where low-scoring crew members can learn while having fun.

[0073] The training program creation unit can use the emotion estimation function to collect emotional feedback from participants after each session of the training program and use the collected feedback to improve the program. The training program creation unit, for example, uses the emotion estimation function to develop a system that collects emotional feedback from participants after each session of the training program. For example, a camera or microphone is used to record changes in emotions. The training program creation unit also collects emotional feedback from participants and uses the collected feedback to improve the training program. For example, the training content is adjusted based on changes in emotions. The training program creation unit also uses the emotion estimation function to build a system that monitors the emotions of participants after each session of the training program and uses the collected feedback to improve the program. For example, the speed of the program is adjusted according to changes in emotions. This makes it possible to obtain feedback to enhance the effectiveness of the training program.

[0074] The evaluation department can track the long-term performance data of participants when evaluating the effectiveness of the development program. For example, to evaluate the effectiveness of the development program, the evaluation department collects long-term performance data of participants and analyzes it with the generation AI. For example, the effectiveness of the program is quantified based on the performance data. The evaluation department also tracks the long-term performance data of participants to confirm the sustained effectiveness of the development program. For example, performance data is collected regularly even after the program has ended to evaluate the effectiveness. The evaluation department also tracks the performance data of participants and analyzes it with the generation AI to evaluate the effectiveness of the development program over the long term. For example, performance improvement after the program has ended is confirmed. This makes it possible to confirm the sustained effectiveness of the development program.

[0075] The evaluation unit can collect self-assessments and evaluations by others from participants after each session of the development program. For example, the evaluation unit collects self-assessments and evaluations by others from participants after each session of the development program and analyzes them using the generation AI. For example, the evaluation unit quantifies the immediate effectiveness of the program based on the data on self-assessments and evaluations by others. The evaluation unit also collects self-assessments and evaluations by others from participants and evaluates the immediate effectiveness of the development program. For example, the evaluation unit conducts a questionnaire after the end of a session and collects evaluation data. The evaluation unit also collects self-assessments and evaluations by others from participants and analyzes them using the generation AI to immediately evaluate the effectiveness of the development program. For example, the effectiveness of the program is confirmed based on the evaluation data after the end of a session. This makes it possible to evaluate the immediate effectiveness of the development program.

[0076] The evaluation unit can use the emotion estimation function to analyze emotional changes of participants during the training program. The evaluation unit, for example, uses the emotion estimation function to develop a system that analyzes emotional changes of participants during the training program in real time. For example, the emotional changes are recorded using a camera or microphone. The evaluation unit also collects emotional data of participants in real time and evaluates their emotional growth during the training program. For example, the growth is quantified based on the emotional changes. The evaluation unit also uses the emotion estimation function to monitor the emotions of participants during the training program and build a system that evaluates their emotional growth. For example, the program progress speed is adjusted according to the emotional changes. This makes it possible to evaluate the emotional growth of participants during the training program.

[0077] The evaluation department can compare the effectiveness of the training program across different regions or cultural spheres. For example, the evaluation department collects data on participants from different regions or cultural spheres and compares the effectiveness of the training program. For example, the effectiveness of the program is evaluated based on performance data from participants in Asia and Europe. The evaluation department also analyzes data from participants from different regions or cultural spheres and evaluates the effectiveness of the training program from a global perspective. For example, it clarifies the impact of cultural background on the effectiveness of the program. The evaluation department also integrates data from participants from different regions or cultural spheres and compares the effectiveness of the training program. For example, it extracts success factors in different cultural spheres and uses this information to improve the program. This makes it possible to evaluate the effectiveness of the training program from a global perspective.

[0078] The evaluation department can compare the evaluation results of the training program with other training programs. For example, the evaluation department collects the evaluation results of the training program and compares them with other training programs. For example, the effectiveness is evaluated based on performance data of different programs. The evaluation department also analyzes the evaluation results of the training program and identifies the most effective method. For example, the evaluation department extracts success factors of the program and compares them with other programs. The evaluation department also integrates the evaluation results of the training program and compares them with other training programs. For example, the most effective method is identified based on evaluation data of different programs. This makes it possible to identify the most effective training program.

[0079] The evaluation unit can use the emotion estimation function to analyze changes in motivation felt by participants in the training program. The evaluation unit, for example, uses the emotion estimation function to develop a system that analyzes changes in motivation felt by participants in the training program in real time. For example, a camera or microphone is used to record changes in emotion. The evaluation unit also collects participants' emotion data in real time and evaluates changes in motivation during the training program. For example, the evaluation unit quantifies motivation based on changes in emotion. The evaluation unit also uses the emotion estimation function to build a system that monitors the emotions of participants during the training program and evaluates changes in motivation. For example, the speed of the program progress is adjusted according to changes in emotion. This makes it possible to obtain feedback to increase the motivation of participants in the training program.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The training support system can further include a "feedback collection unit." The feedback collection unit collects feedback from participants after each training program session and analyzes it with the generation AI. For example, it may conduct a survey in which participants provide their opinions and thoughts about the program content and progress. The feedback collection unit can also identify areas for improvement in the training program based on the participants' feedback and reflect them in the next session. For example, if a participant lacks understanding of a particular topic, it can provide additional training on that topic. The feedback collection unit can also collect participants' feedback and analyze it with the generation AI to quantify the effectiveness of the program. This allows the effectiveness of the training program to be continuously improved.

[0082] The training support system can further include a "motivation improvement unit." The motivation improvement unit implements measures to improve the motivation of low-performing crew members. For example, the motivation improvement unit periodically holds motivation improvement workshops for low-performing crew members. The motivation improvement unit can also provide incentives to low-performing crew members for improving their performance. For example, it can provide rewards or benefits based on goal achievement. The motivation improvement unit can also provide regular feedback and coaching to improve the motivation of low-performing crew members. This can improve the motivation of low-performing crew members and improve their performance.

[0083] The training support system can further include a "career path suggestion unit." The career path suggestion unit suggests appropriate career paths for low-achieving crew members. For example, the career path suggestion unit suggests future career paths based on the skills and interests of the low-achieving crew members. The career path suggestion unit can also provide specific steps for low-achieving crew members to achieve their goals. For example, it can suggest training programs for acquiring the necessary skills and experience. The career path suggestion unit can also periodically review the career paths of low-achieving crew members and provide appropriate advice. This allows low-achieving crew members to effectively advance their careers.

[0084] The training support system can further include a "stress management support department." The stress management support department implements measures to reduce stress among low-achieving crew members. For example, the stress management support department holds stress management workshops for low-achieving crew members. The stress management support department can also provide resources and support that low-achieving crew members can use when they feel stressed. For example, it can provide counseling services and relaxation programs. The stress management support department can also regularly monitor the stress levels of low-achieving crew members and provide appropriate support as needed. This can reduce stress among low-achieving crew members and improve their performance.

[0085] The training support system can further include a "communication improvement department." The communication improvement department implements measures to improve the communication skills of low-achieving crew members. For example, the communication improvement department provides training to low-achieving crew members to improve their communication skills. The communication improvement department can also provide tools and resources to enable low-achieving crew members to communicate effectively. For example, the communication improvement department can provide online courses and workshops to improve communication skills. The communication improvement department can also regularly evaluate the communication skills of low-achieving crew members and provide additional training as needed. This can improve the communication skills of low-achieving crew members and strengthen cooperative relationships within the team.

[0086] The training support system can further include a "performance evaluation unit using emotion estimation function." The performance evaluation unit uses the emotion estimation function to monitor the emotions of low-performing crew members in real time while they are working and evaluate their performance based on that data. For example, it can analyze changes in emotions during work and evaluate the impact of stress and motivation. The performance evaluation unit can also provide specific advice to low-performing crew members to improve their performance based on the emotion data. For example, it can suggest ways to deal with increased stress and ways to maintain motivation. The performance evaluation unit can also evaluate the effectiveness of the training program based on the emotion data and adjust the program as necessary. This makes it possible to provide effective support to improve the performance of low-performing crew members.

[0087] The training support system can further include a "feedback provision unit using emotion estimation function." The feedback provision unit uses the emotion estimation function to monitor the emotions of low-performing crew members during work in real time and provide feedback based on that data. For example, it analyzes changes in emotions during work and evaluates the effects of stress and motivation. The feedback provision unit can also provide specific feedback to low-performing crew members based on the emotion data. For example, it can suggest ways to deal with increased stress and ways to maintain motivation. The feedback provision unit can also evaluate the effectiveness of the training program based on the emotion data and adjust the program as necessary. This makes it possible to provide effective feedback to improve the performance of low-performing crew members.

[0088] The training support system can further include a "stress management unit using emotion estimation function." The stress management unit uses the emotion estimation function to monitor the emotions of low-achieving crew members during work in real time and manage stress based on that data. For example, it can analyze changes in emotions during work and detect signs of stress early. The stress management unit can also provide low-achieving crew members with specific stress management advice based on the emotion data. For example, it can suggest relaxation methods and techniques for reducing stress when stress levels rise. The stress management unit can also evaluate the effectiveness of the training program based on the emotion data and adjust the program as necessary. This makes it possible to effectively manage the stress of low-achieving crew members and improve their performance.

[0089] The training support system can further include a "motivation improvement unit using emotion estimation function." The motivation improvement unit uses the emotion estimation function to monitor the emotions of low-performing crew members while they are working in real time and implement measures to improve their motivation based on that data. For example, it can analyze changes in emotions during work and detect a decline in motivation early. The motivation improvement unit can also provide low-performing crew members with specific advice on improving their motivation based on the emotion data. For example, it can suggest ways to deal with a decline in motivation and ways to maintain motivation. The motivation improvement unit can also evaluate the effectiveness of the training program based on the emotion data and adjust the program as necessary. This makes it possible to effectively improve the motivation of low-performing crew members and improve their performance.

[0090] The training support system can further include a "communication improvement unit using emotion estimation function." The communication improvement unit uses the emotion estimation function to monitor the emotions of low-performing crew members while they are working in real time and implement measures to improve their communication skills based on that data. For example, it can analyze changes in emotions during work and identify communication problems early. The communication improvement unit can also provide low-performing crew members with specific advice on improving communication based on the emotion data. For example, it can suggest ways to deal with communication problems and effective communication methods. The communication improvement unit can also evaluate the effectiveness of the training program based on the emotion data and adjust the program as necessary. This effectively improves the communication skills of low-performing crew members and strengthens cooperative relationships within the team.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The persona analysis unit uses the generation AI to analyze the behavior and thought patterns of high-achieving crew members and extract their characteristics. For example, the persona analysis unit collects activity logs and communication records of high-achieving crew members during work, and the generation AI analyzes this data. The persona analysis unit also collects data on self-evaluation and evaluation by others, and the generation AI extracts characteristics based on this data. Furthermore, the persona analysis unit uses the generation AI to analyze goal-setting methods, stress management techniques, communication styles, etc. Step 2: The mentalism element extraction unit extracts mentalism elements from the characteristics extracted by the persona analysis unit. For example, in the mentalism element extraction unit, the generation AI analyzes the psychological factors, thought patterns, and behavioral characteristics of high-achieving crew members. In addition, in the mentalism element extraction unit, the generation AI analyzes the actions taken to achieve goals, stress management methods, and communication methods with others. Furthermore, in the mentalism element extraction unit, the generation AI extracts the success factors of high-achieving crew members. Step 3: The training program creation unit creates a training program for low-achieving crew members based on the elements extracted by the mentalism element extraction unit. For example, the training program creation unit creates a training program for the generation AI to learn the goal setting methods of high-achieving crew members. The training program creation unit also creates a workshop for the generation AI to learn stress management techniques. Furthermore, the training program creation unit creates a seminar for the generation AI to acquire effective communication skills. Step 4: The evaluation unit implements the training program created by the training program creation unit and evaluates its effectiveness. For example, the evaluation unit collects post-training performance data, self-evaluation, and peer-evaluation data, and the generation AI analyzes the data to evaluate the effectiveness of the program. The evaluation unit also allows the generation AI to improve the training program based on the evaluation results. Furthermore, the evaluation unit provides feedback to the generation AI to enable more effective training.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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 persona analysis unit uses generative AI to analyze the behavior and thought patterns of high-achieving crew members and extract their characteristics. a mentalism element extraction unit that extracts mentalism elements from the features extracted by the persona analysis unit; a training program creation unit that creates a training program for low-achieving crew members based on the elements extracted by the mentalism element extraction unit; an evaluation unit that implements the training program created by the training program creation unit and evaluates its effectiveness. A system characterized by:

2. The persona analysis unit Analyze the personas of the high-achieving crew members from different industries or occupations and extract common success factors.

2. The system of claim 1.

3. The mentalism element extraction unit When analyzing the thought patterns of the high earner crew, analyze in detail the decision-making process or risk assessment method.

2. The system of claim 1.

4. The training program creation unit Introduce virtual reality training that simulates successful scenarios for the high-achieving crew and allows the low-achieving crew to experience those scenarios.

2. The system of claim 1.

5. The evaluation unit Tracking participants' long-term performance data in assessing the effectiveness of the development program 2. The system of claim 1.

6. The persona analysis unit Analyzing the emotions felt by the high earner crew during specific tasks 2. The system of claim 1.

Images