System

Abstract

An object of a system according to an embodiment is for a superior to identify a management problem and implement an appropriate follow-up measure.SOLUTION: A system includes a hearing analysis part, a problem specification part, a training proposal part, a feedback generation part, and a follow-up mail generation part. A hearing analysis part analyzes hearing contents from the superior. The problem specifying unit specifies a management problem based on the contents analyzed by the hearing analysis unit. The training proposal section generates a training proposal based on the problem specified by the problem specification section. The feedback generation unit generates feedback to the subordinate based on the problem specified by the problem specifying unit. A follow-up mail generation part generates follow-up mail on the basis of the problem specified by the problem specification part.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 of making it difficult for superiors to identify management issues and implement appropriate follow-up measures.

[0005] The system according to the embodiment aims to enable superiors to identify management issues and implement appropriate follow-up measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a hearing analysis unit, an issue identification unit, a training proposal unit, a feedback generation unit, and a follow-up email generation unit. The hearing analysis unit analyzes the content of the hearing from a superior. The issue identification unit identifies management issues based on the content analyzed by the hearing analysis unit. The training proposal unit generates training proposals based on the issues identified by the issue identification unit. The feedback generation unit generates feedback for subordinates based on the issues identified by the issue identification unit. The follow-up email generation unit generates follow-up emails based on the issues identified by the issue identification unit. [Effects of the Invention]

[0007] The system according to the embodiment allows superiors to identify management issues and implement appropriate follow-up measures. [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) A feedback tool according to an embodiment of the present invention is a system that utilizes generative AI to support management by superiors, and automates the analysis of interview content, identification of issues, training proposals, feedback generation, and creation of follow-up emails. As a result, the feedback tool can make management support for superiors efficient and consistent.

[0029] A feedback tool according to an embodiment includes a hearing analysis unit, an issue identification unit, a training proposal unit, a feedback generation unit, and a follow-up email generation unit. The hearing analysis unit analyzes the content of a hearing from a superior. For example, the hearing analysis unit analyzes the tone of voice and speaking patterns of the superior to estimate the superior's emotional state. The hearing analysis unit can also compare the content with past hearing data to track changes in the superior's management style. The hearing analysis unit can also analyze the superior's non-verbal communication (gestures and facial expressions) to identify detailed issues. For example, the hearing analysis unit analyzes the pitch, strength, and speed of the superior's voice to evaluate the level of tension and stress. The hearing analysis unit analyzes changes in the superior's management style from past data. The hearing analysis unit analyzes hand movements and posture to estimate the superior's emotional state. The issue identification unit identifies management issues based on the content analyzed by the hearing analysis unit. For example, the issue identification unit refers to the superior's past management performance to identify similar issues. The problem identification unit can also collect anonymous feedback from subordinates and identify problems from the perspective of not only the superior but also the subordinate. The problem identification unit can also refer to management problems in different industries and apply success stories from other industries. For example, it extracts similar problems from past performance data. It saves the feedback content as text data. It extracts similar problems from success stories from other industries. The training proposal unit generates training proposals based on the problems identified by the problem identification unit. For example, it refers to the superior's past training history and proposes an effective training plan. The training proposal unit can also analyze the subordinate's skill set and propose a training plan that contributes to the subordinate's growth. The training proposal unit can also refer to training plans in different industries and apply success stories from other industries. For example, it extracts effective plans from past training data. It extracts plans that contribute to growth from the subordinate's skill data. It extracts effective plans from success stories from other industries. The feedback generation unit generates feedback for the subordinate based on the problems identified by the problem identification unit. For example, the feedback generation unit refers to the subordinate's past performance data and suggests specific areas for improvement.The feedback generation unit can also analyze the personality traits of the subordinate and generate individually optimized feedback. The feedback generation unit can also refer to feedback examples from different industries and apply success stories from other industries. For example, the feedback generation unit extracts specific improvement points from past performance data. The feedback generation unit extracts individually optimized feedback from personality data. The feedback generation unit extracts effective feedback from success stories from other industries. The follow-up email generation unit generates a follow-up email based on the issues identified by the issue identification unit. For example, the follow-up email generation unit references past email history to generate effective email content. The follow-up email generation unit can also analyze the subordinate's performance data and propose a specific action plan. The follow-up email generation unit can also refer to email examples from different industries and apply success stories from other industries. For example, the follow-up email generation unit extracts effective content from past email data. The follow-up email generation unit extracts specific action plans from performance data. The feedback generation unit extracts effective content from success stories from other industries. This enables the feedback tool according to the embodiment to provide efficient and consistent management support to superiors. For example, the output unit provides the generated feedback and follow-up email to the superior. The output unit displays the generated feedback and follow-up email through a web application or a mobile application. Emailing provides immediate feedback by sending results directly to a supervisor.

[0030] The hearing analysis unit can compare with past hearing data to track changes in the superior's management style. For example, the hearing analysis unit stores past hearing data in a database and compares it with the content of the current hearing. For example, it analyzes changes in the superior's management style from the past data. The hearing analysis unit also analyzes past hearing data to track changes in the superior's management style. For example, it tracks changes over time, changes in behavior patterns, changes in management style, etc. The hearing analysis unit also develops an algorithm for tracking changes in the superior's management style. For example, it analyzes changes based on past data to identify changes in the superior's management style. This makes it possible to track changes in the superior's management style.

[0031] The hearing analysis unit can integrate feedback from other superiors and identify common issues. The hearing analysis unit, for example, collects feedback from other superiors and stores it in a database. For example, the feedback content is stored as text data. The hearing analysis unit also analyzes feedback from other superiors and identifies common issues. For example, it identifies problems that multiple superiors have in common. The hearing analysis unit also builds a system for integrating feedback from other superiors and identifying common issues. For example, it analyzes feedback data and extracts common issues. This makes it possible to integrate feedback from other superiors and identify common issues.

[0032] The issue identification unit can refer to the supervisor's past management performance and identify similar issues. For example, the issue identification unit stores the supervisor's past management performance in a database and compares it with current issues. For example, it extracts similar issues from past performance data. The issue identification unit also analyzes past management performance and identifies similar issues. For example, it identifies similar problems that occurred in the past, issues caused by the same causes, etc. The issue identification unit also builds a system for referencing the supervisor's past management performance and identifying similar issues. For example, it analyzes and identifies similar issues based on past data. This makes it possible to refer to the supervisor's past management performance and identify similar issues.

[0033] The issue identification unit collects anonymous feedback from subordinates and can identify issues from the perspective of not only the superior but also the subordinate. The issue identification unit, for example, collects anonymous feedback from subordinates and stores it in a database. For example, the feedback content is stored as text data. The issue identification unit also analyzes the anonymous feedback from subordinates and identifies issues from the perspective of not only the superior but also the subordinate. For example, it identifies problems with the superior's management from the subordinate's perspective. The issue identification unit also collects anonymous feedback from subordinates and builds a system for identifying issues from the perspective of not only the superior but also the subordinate. For example, it analyzes the feedback data and identifies issues from the perspective of both the superior and the subordinate. This makes it possible to collect anonymous feedback from subordinates and identify issues from the perspective of not only the superior but also the subordinate.

[0034] The problem identification unit can refer to management issues in different industries and apply success stories from those industries. For example, the problem identification unit stores management issues in different industries in a database and compares them with current issues. For example, it extracts similar issues from success stories from other industries. The problem identification unit also analyzes management issues in different industries and applies success stories from those industries. For example, it refers to successful projects and best practices in other industries. The problem identification unit also builds a system for referring to management issues in different industries and applying success stories from those industries. For example, it analyzes data from other industries and applies success stories. This makes it possible to refer to management issues in different industries and apply success stories from those industries.

[0035] The problem identification unit can analyze the management style of a superior and propose a problem identification method that suits the style. For example, the problem identification unit stores the management style of a superior in a database and compares it with current problems. For example, it extracts similar problems from past style data. The problem identification unit also analyzes the management style of a superior and proposes a problem identification method that suits the style. For example, it analyzes the style using data analysis, statistical analysis, text analysis, etc. The problem identification unit also builds a system for analyzing the management style of a superior and proposing a problem identification method that suits the style. For example, it analyzes the style based on past data and proposes an identification method. In this way, it is possible to analyze the management style of a superior and propose a problem identification method that suits the style.

[0036] The training proposal department can refer to the past training history of a superior and propose an effective training plan. For example, the training proposal department stores the past training history of a superior in a database and compares it with the current training proposal. For example, it extracts an effective plan from the past training data. The training proposal department also analyzes the past training history and proposes an effective training plan. For example, it makes proposals based on the content of the training attended, the results of the training, feedback, etc. The training proposal department also builds a system for referencing the past training history of a superior and proposing an effective training plan. For example, it analyzes and proposes an effective plan based on past data. In this way, it is possible to refer to the past training history of a superior and propose an effective training plan.

[0037] The training proposal unit can analyze the skill sets of subordinates and propose training plans that contribute to the growth of the subordinates. For example, the training proposal unit stores the skill sets of subordinates in a database and compares them with current training proposals. For example, it extracts plans that contribute to growth from the subordinates' skill data. The training proposal unit also analyzes the skill sets of subordinates and proposes training plans that contribute to growth. For example, it makes proposals based on technical skills, soft skills, business knowledge, etc. The training proposal unit also builds a system for analyzing the skill sets of subordinates and proposing training plans that contribute to growth. For example, it analyzes skill data and proposes plans that contribute to growth. In this way, it is possible to analyze the skill sets of subordinates and propose training plans that contribute to growth.

[0038] The training proposal department can refer to training plans from different industries and apply success stories from those industries. For example, the training proposal department stores training plans from different industries in a database and compares them with current training proposals. For example, it extracts effective plans from success stories from other industries. The training proposal department also analyzes training plans from different industries and applies success stories from those industries. For example, it refers to successful projects and best practices in those industries. The training proposal department also builds a system for referencing training plans from different industries and applying success stories from those industries. For example, it analyzes data from other industries and applies success stories. This makes it possible to refer to training plans from different industries and apply success stories from those industries.

[0039] The training proposal department can analyze the management style of a superior and propose a training plan that matches the style. For example, the training proposal department stores the management style of a superior in a database and compares it with current training proposals. For example, it extracts effective plans from past style data. The training proposal department also analyzes the management style of a superior and proposes a training plan that matches the style. For example, it analyzes the style using data analysis, statistical analysis, text analysis, etc. The training proposal department also builds a system for analyzing the management style of a superior and proposing a training plan that matches the style. For example, it analyzes the style based on past data and proposes a method for identifying the style. This makes it possible to analyze the management style of a superior and propose a training plan that matches the style.

[0040] The feedback generation unit can refer to the subordinate's past performance data and propose specific areas for improvement. For example, the feedback generation unit stores the subordinate's past performance data in a database and compares it with current feedback. For example, it extracts specific areas for improvement from the past performance data. The feedback generation unit also analyzes the past performance data and proposes specific areas for improvement. For example, it makes proposals based on sales data, evaluation results, project results, etc. The feedback generation unit also builds a system for referencing the subordinate's past performance data and proposing specific areas for improvement. For example, it analyzes and proposes areas for improvement based on past data. This makes it possible to refer to the subordinate's past performance data and propose specific areas for improvement.

[0041] The feedback generation unit can analyze the personality traits of the subordinates and generate individually optimized feedback. For example, the feedback generation unit stores the personality traits of the subordinates in a database and compares them with current feedback. For example, the feedback generation unit extracts individually optimized feedback from the personality data. The feedback generation unit also analyzes the personality traits of the subordinates and generates individually optimized feedback. For example, the feedback generation unit proposes the feedback based on a personality diagnostic test, an analysis of behavioral patterns, etc. The feedback generation unit also builds a system for analyzing the personality traits of the subordinates and generating individually optimized feedback. For example, the feedback generation unit analyzes the personality data and proposes individually optimized feedback. In this way, the personality traits of the subordinates can be analyzed and individually optimized feedback can be generated.

[0042] The feedback generation unit can refer to feedback examples from different industries and apply successful practices from other industries. For example, the feedback generation unit stores feedback examples from different industries in a database and compares them with current feedback. For example, it extracts effective feedback from successful practices from other industries. The feedback generation unit also analyzes feedback examples from different industries and applies successful practices from other industries. For example, it refers to successful projects and best practices in other industries. The feedback generation unit also builds a system for referring to feedback examples from different industries and applying successful practices from other industries. For example, it analyzes data from other industries and applies successful practices. This makes it possible to refer to feedback examples from different industries and apply successful practices from other industries.

[0043] The feedback generation unit can analyze the skill sets of subordinates and generate feedback that contributes to skill improvement. For example, the feedback generation unit stores the skill sets of subordinates in a database and compares them with current feedback. For example, it extracts feedback that contributes to skill improvement from the skill data. The feedback generation unit also analyzes the skill sets of subordinates and generates feedback that contributes to skill improvement. For example, it makes suggestions based on technical skills, soft skills, business knowledge, etc. The feedback generation unit also builds a system for analyzing the skill sets of subordinates and generating feedback that contributes to skill improvement. For example, it analyzes the skill data and suggests feedback that contributes to skill improvement. In this way, it is possible to analyze the skill sets of subordinates and generate feedback that contributes to skill improvement.

[0044] The follow-up email generation unit can refer to past email history and generate effective email content. For example, the follow-up email generation unit stores past email history in a database and compares it with the current follow-up email. For example, it extracts effective content from past email data. The follow-up email generation unit also analyzes past email history and generates effective email content. For example, it makes suggestions based on the sending date and time, sent content, and recipient reaction. The follow-up email generation unit also references past email history and builds a system for generating effective email content. For example, it analyzes and suggests effective content based on past data. This makes it possible to generate effective email content by referring to past email history.

[0045] The follow-up email generation unit can analyze the subordinate's performance data and propose a specific action plan. For example, the follow-up email generation unit stores the subordinate's performance data in a database and compares it with the current follow-up email. For example, it extracts a specific action plan from the performance data. The follow-up email generation unit also analyzes the subordinate's performance data and proposes a specific action plan. For example, it makes proposals based on sales data, evaluation results, project results, etc. The follow-up email generation unit also builds a system for analyzing the subordinate's performance data and proposing a specific action plan. For example, it analyzes the performance data and proposes a specific action plan. This makes it possible to analyze the subordinate's performance data and propose a specific action plan.

[0046] The follow-up email generation unit can refer to email examples from different industries and apply success stories from those industries. For example, the follow-up email generation unit stores email examples from different industries in a database and compares them with the current follow-up email. For example, it extracts effective content from success stories from those industries. The follow-up email generation unit also analyzes email examples from different industries and applies success stories from those industries. For example, it refers to successful projects and best practices in those industries. The follow-up email generation unit also builds a system for referencing email examples from different industries and applying success stories from those industries. For example, it analyzes data from those industries and applies success stories. This makes it possible to refer to email examples from different industries and apply success stories from those industries.

[0047] The follow-up email generation unit can analyze the personality traits of subordinates and generate individually optimized email content. The follow-up email generation unit, for example, stores the personality traits of subordinates in a database and compares them with current follow-up emails. For example, it extracts individually optimized content from the personality data. The follow-up email generation unit also analyzes the personality traits of subordinates and generates individually optimized email content. For example, it makes suggestions based on personality diagnostic tests, behavioral pattern analysis, etc. The follow-up email generation unit also builds a system for analyzing the personality traits of subordinates and generating individually optimized email content. For example, it analyzes personality data and suggests individually optimized content. In this way, it is possible to analyze the personality traits of subordinates and generate individually optimized email content.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The feedback tool can also be equipped with a health management section that monitors the health status of superiors. For example, the health management section can monitor the superior's heart rate and blood pressure in real time and evaluate their stress level. The health management section can also analyze the superior's sleep patterns and evaluate their fatigue level. Furthermore, the health management section can record the superior's dietary and exercise habits and provide advice on maintaining health. This allows for comprehensive management of the superior's health status and improves the quality of management.

[0050] The feedback tool can also include a schedule management section that supports the supervisor's schedule management. For example, the schedule management section can analyze the supervisor's calendar and remind the supervisor of important meetings and events. The schedule management section can also evaluate the supervisor's workload and suggest appropriate break times. Furthermore, the schedule management section can support efficient task management based on the supervisor's schedule. This can improve the supervisor's work efficiency.

[0051] The feedback tool can also be equipped with a communication support section for improving the supervisor's communication skills. For example, the communication support section can analyze the supervisor's speaking style and presentation skills and suggest areas for improvement. The communication support section can also analyze the content of the supervisor's emails and chats and suggest effective communication methods. Furthermore, the communication support section can provide training programs for improving the supervisor's interpersonal skills. This can improve the supervisor's communication skills.

[0052] The feedback tool may further include a leadership support section for improving the leadership skills of the superior. For example, the leadership support section may analyze the leadership style of the superior and suggest effective leadership methods. The leadership support section may also provide a training program for improving the team building skills of the superior. Furthermore, the leadership support section may provide a simulation for improving the decision-making skills of the superior. This allows the leadership skills of the superior to be improved.

[0053] The feedback tool can also be equipped with a stress management section that supports superiors' stress management. For example, the stress management section can monitor the stress level of superiors and suggest relaxation methods. The stress management section can also identify the causes of stress and suggest countermeasures. Furthermore, the stress management section can provide training programs to improve the stress management skills of superiors. This allows superiors to effectively manage their stress and improve the quality of management.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The hearing analysis unit analyzes the content of the hearing from the superior. For example, it analyzes the superior's tone of voice and speaking patterns to estimate their emotional state. It can also compare this with past hearing data to track changes in the superior's management style. It can also analyze the superior's non-verbal communication (gestures and facial expressions) to identify detailed issues. Step 2: The Issue Identification Department identifies management issues based on the results of the analysis by the Interview Analysis Department. For example, it may refer to the supervisor's past management performance to identify similar issues. It may also collect anonymous feedback from subordinates to identify issues from the subordinate's perspective as well as the supervisor's. It may also refer to management issues in other industries and apply successful examples from those industries. Step 3: The training proposal section generates training proposals based on the issues identified by the issue identification section. For example, it can refer to a superior's past training history and propose an effective training plan. It can also analyze a subordinate's skill set and propose a training plan that will contribute to their growth. It can also refer to training plans from other industries and apply successful examples from other industries. Step 4: The feedback generation unit generates feedback for the subordinate based on the issues identified by the issue identification unit. For example, it can refer to the subordinate's past performance data and suggest specific areas for improvement. It can also analyze the subordinate's personality traits and generate individually optimized feedback. It can also refer to feedback examples from other industries and apply successful examples from other industries. Step 5: The follow-up email generation unit generates follow-up emails based on the issues identified by the issue identification unit. For example, it can refer to past email history to generate effective email content. It can also analyze subordinates' performance data and propose specific action plans. It can also refer to email examples from other industries and apply successful examples from other industries.

[0056] (Example 2) A feedback tool according to an embodiment of the present invention is a system that utilizes generative AI to support management by superiors, and automates the analysis of interview content, identification of issues, training proposals, feedback generation, and creation of follow-up emails. As a result, the feedback tool can make management support for superiors efficient and consistent.

[0057] A feedback tool according to an embodiment includes a hearing analysis unit, an issue identification unit, a training proposal unit, a feedback generation unit, and a follow-up email generation unit. The hearing analysis unit analyzes the content of a hearing from a superior. For example, the hearing analysis unit analyzes the tone of voice and speaking patterns of the superior to estimate the superior's emotional state. The hearing analysis unit can also compare the content with past hearing data to track changes in the superior's management style. The hearing analysis unit can also analyze the superior's non-verbal communication (gestures and facial expressions) to identify detailed issues. For example, the hearing analysis unit analyzes the pitch, strength, and speed of the superior's voice to evaluate the level of tension and stress. The hearing analysis unit analyzes changes in the superior's management style from past data. The hearing analysis unit analyzes hand movements and posture to estimate the superior's emotional state. The issue identification unit identifies management issues based on the content analyzed by the hearing analysis unit. For example, the issue identification unit refers to the superior's past management performance to identify similar issues. The problem identification unit can also collect anonymous feedback from subordinates and identify problems from the perspective of not only the superior but also the subordinate. The problem identification unit can also refer to management problems in different industries and apply success stories from other industries. For example, it extracts similar problems from past performance data. It saves the feedback content as text data. It extracts similar problems from success stories from other industries. The training proposal unit generates training proposals based on the problems identified by the problem identification unit. For example, it refers to the superior's past training history and proposes an effective training plan. The training proposal unit can also analyze the subordinate's skill set and propose a training plan that contributes to the subordinate's growth. The training proposal unit can also refer to training plans in different industries and apply success stories from other industries. For example, it extracts effective plans from past training data. It extracts plans that contribute to growth from the subordinate's skill data. It extracts effective plans from success stories from other industries. The feedback generation unit generates feedback for the subordinate based on the problems identified by the problem identification unit. For example, the feedback generation unit refers to the subordinate's past performance data and suggests specific areas for improvement.The feedback generation unit can also analyze the personality traits of the subordinate and generate individually optimized feedback. The feedback generation unit can also refer to feedback examples from different industries and apply success stories from other industries. For example, the feedback generation unit extracts specific improvement points from past performance data. The feedback generation unit extracts individually optimized feedback from personality data. The feedback generation unit extracts effective feedback from success stories from other industries. The follow-up email generation unit generates a follow-up email based on the issues identified by the issue identification unit. For example, the follow-up email generation unit references past email history to generate effective email content. The follow-up email generation unit can also analyze the subordinate's performance data and propose a specific action plan. The follow-up email generation unit can also refer to email examples from different industries and apply success stories from other industries. For example, the follow-up email generation unit extracts effective content from past email data. The follow-up email generation unit extracts specific action plans from performance data. The feedback generation unit extracts effective content from success stories from other industries. This enables the feedback tool according to the embodiment to provide efficient and consistent management support to superiors. For example, the output unit provides the generated feedback and follow-up email to the superior. The output unit displays the generated feedback and follow-up email through a web application or a mobile application. Emailing provides immediate feedback by sending results directly to a supervisor.

[0058] The hearing analysis unit can analyze the tone of a supervisor's voice and speaking patterns to estimate their emotional state and evaluate the urgency of the issue. The hearing analysis unit, for example, analyzes the tone of a supervisor's voice to estimate their emotional state. For example, it analyzes the pitch, strength, and speed of the voice to evaluate the degree of tension or stress. The hearing analysis unit also analyzes speaking patterns to estimate their emotional state. For example, it analyzes the speaking speed, pauses, and word choice to estimate their emotional state. The hearing analysis unit also evaluates the urgency of the issue based on the emotional state. For example, it evaluates issues that require immediate response, issues that should be resolved in the short term, and issues that should be addressed in the long term. This makes it possible to evaluate the urgency of an issue based on the supervisor's emotional state.

[0059] The hearing analysis unit can compare with past hearing data to track changes in the superior's management style. For example, the hearing analysis unit stores past hearing data in a database and compares it with the content of the current hearing. For example, it analyzes changes in the superior's management style from the past data. The hearing analysis unit also analyzes past hearing data to track changes in the superior's management style. For example, it tracks changes over time, changes in behavior patterns, changes in management style, etc. The hearing analysis unit also develops an algorithm for tracking changes in the superior's management style. For example, it analyzes changes based on past data to identify changes in the superior's management style. This makes it possible to track changes in the superior's management style.

[0060] The hearing analysis unit can use the emotion estimation function to analyze the emotional state of the superior in real time and set priority levels for tasks based on the emotions. The hearing analysis unit, for example, uses the emotion estimation function to analyze the emotional state of the superior in real time. For example, it analyzes the superior's facial expressions and tone of voice and calculates an emotion score. The hearing analysis unit also uses the emotion estimation function to build a system for analyzing the emotional state of the superior in real time. For example, it analyzes the emotional state using facial expression recognition technology or voice analysis technology. The hearing analysis unit also sets priority levels for tasks based on emotions. For example, it sets priority levels based on urgency, importance, scope of impact, etc. This makes it possible to set priority levels for tasks based on the superior's emotional state.

[0061] The hearing analysis unit analyzes the nonverbal communication of the superior and can identify issues in more detail. The hearing analysis unit, for example, analyzes the gestures of the superior and evaluates the nonverbal communication. For example, it analyzes hand movements and posture to estimate the emotional state. The hearing analysis unit also analyzes the facial expressions of the superior and evaluates the nonverbal communication. For example, it analyzes smiling, angry, surprised, etc. to estimate the emotional state. The hearing analysis unit also builds a system for analyzing the nonverbal communication of the superior. For example, it analyzes nonverbal communication using gesture recognition technology and facial expression recognition technology. This makes it possible to analyze the nonverbal communication of the superior and identify issues in more detail.

[0062] The hearing analysis unit can integrate feedback from other superiors and identify common issues. The hearing analysis unit, for example, collects feedback from other superiors and stores it in a database. For example, the feedback content is stored as text data. The hearing analysis unit also analyzes feedback from other superiors and identifies common issues. For example, it identifies problems that multiple superiors have in common. The hearing analysis unit also builds a system for integrating feedback from other superiors and identifying common issues. For example, it analyzes feedback data and extracts common issues. This makes it possible to integrate feedback from other superiors and identify common issues.

[0063] The hearing analysis unit can use the emotion estimation function to analyze the emotional state of the superior and propose a hearing method for eliciting positive emotions. The hearing analysis unit, for example, uses the emotion estimation function to analyze the emotional state of the superior. For example, it analyzes the superior's facial expression and tone of voice and calculates an emotion score. The hearing analysis unit also uses the emotion estimation function to build a system for analyzing the emotional state of the superior. For example, it analyzes the emotional state using facial expression recognition technology and voice analysis technology. The hearing analysis unit also proposes a hearing method for eliciting positive emotions. For example, it proposes ways of asking questions, ways of listening, and ways of giving feedback. This makes it possible to propose a hearing method for eliciting positive emotions based on the superior's emotional state.

[0064] The issue identification unit can refer to the supervisor's past management performance and identify similar issues. For example, the issue identification unit stores the supervisor's past management performance in a database and compares it with current issues. For example, it extracts similar issues from past performance data. The issue identification unit also analyzes past management performance and identifies similar issues. For example, it identifies similar problems that occurred in the past, issues caused by the same causes, etc. The issue identification unit also builds a system for referencing the supervisor's past management performance and identifying similar issues. For example, it analyzes and identifies similar issues based on past data. This makes it possible to refer to the supervisor's past management performance and identify similar issues.

[0065] The issue identification unit collects anonymous feedback from subordinates and can identify issues from the perspective of not only the superior but also the subordinate. The issue identification unit, for example, collects anonymous feedback from subordinates and stores it in a database. For example, the feedback content is stored as text data. The issue identification unit also analyzes the anonymous feedback from subordinates and identifies issues from the perspective of not only the superior but also the subordinate. For example, it identifies problems with the superior's management from the subordinate's perspective. The issue identification unit also collects anonymous feedback from subordinates and builds a system for identifying issues from the perspective of not only the superior but also the subordinate. For example, it analyzes the feedback data and identifies issues from the perspective of both the superior and the subordinate. This makes it possible to collect anonymous feedback from subordinates and identify issues from the perspective of not only the superior but also the subordinate.

[0066] The issue identification unit can use the emotion estimation function to analyze the emotional state of the superior in real time and set priority of issues based on the emotion. The issue identification unit, for example, uses the emotion estimation function to analyze the emotional state of the superior in real time. For example, it analyzes the superior's facial expression and tone of voice and calculates an emotion score. The issue identification unit also uses the emotion estimation function to build a system for analyzing the emotional state of the superior in real time. For example, it analyzes the emotional state using facial expression recognition technology or voice analysis technology. The issue identification unit also sets priority of issues based on emotion. For example, it sets priority based on urgency, importance, scope of impact, etc. This makes it possible to set priority of issues based on the emotional state of the superior.

[0067] The problem identification unit can refer to management issues in different industries and apply success stories from those industries. For example, the problem identification unit stores management issues in different industries in a database and compares them with current issues. For example, it extracts similar issues from success stories from other industries. The problem identification unit also analyzes management issues in different industries and applies success stories from those industries. For example, it refers to successful projects and best practices in other industries. The problem identification unit also builds a system for referring to management issues in different industries and applying success stories from those industries. For example, it analyzes data from other industries and applies success stories. This makes it possible to refer to management issues in different industries and apply success stories from those industries.

[0068] The problem identification unit can analyze the management style of a superior and propose a problem identification method that suits the style. For example, the problem identification unit stores the management style of a superior in a database and compares it with current problems. For example, it extracts similar problems from past style data. The problem identification unit also analyzes the management style of a superior and proposes a problem identification method that suits the style. For example, it analyzes the style using data analysis, statistical analysis, text analysis, etc. The problem identification unit also builds a system for analyzing the management style of a superior and proposing a problem identification method that suits the style. For example, it analyzes the style based on past data and proposes an identification method. In this way, it is possible to analyze the management style of a superior and propose a problem identification method that suits the style.

[0069] The training proposal department can refer to the past training history of a superior and propose an effective training plan. For example, the training proposal department stores the past training history of a superior in a database and compares it with the current training proposal. For example, it extracts an effective plan from the past training data. The training proposal department also analyzes the past training history and proposes an effective training plan. For example, it makes proposals based on the content of the training attended, the results of the training, feedback, etc. The training proposal department also builds a system for referencing the past training history of a superior and proposing an effective training plan. For example, it analyzes and proposes an effective plan based on past data. In this way, it is possible to refer to the past training history of a superior and propose an effective training plan.

[0070] The training proposal unit can analyze the skill sets of subordinates and propose training plans that contribute to the growth of the subordinates. For example, the training proposal unit stores the skill sets of subordinates in a database and compares them with current training proposals. For example, it extracts plans that contribute to growth from the subordinates' skill data. The training proposal unit also analyzes the skill sets of subordinates and proposes training plans that contribute to growth. For example, it makes proposals based on technical skills, soft skills, business knowledge, etc. The training proposal unit also builds a system for analyzing the skill sets of subordinates and proposing training plans that contribute to growth. For example, it analyzes skill data and proposes plans that contribute to growth. In this way, it is possible to analyze the skill sets of subordinates and propose training plans that contribute to growth.

[0071] The training proposal unit can use the emotion estimation function to analyze the emotional state of the superior in real time and propose a training plan based on the emotion. The training proposal unit, for example, uses the emotion estimation function to analyze the emotional state of the superior in real time. For example, it analyzes the superior's facial expression and tone of voice and calculates an emotion score. The training proposal unit also uses the emotion estimation function to build a system for analyzing the emotional state of the superior in real time. For example, it analyzes the emotional state using facial expression recognition technology or voice analysis technology. The training proposal unit also proposes a training plan based on the emotion. For example, it proposes feedback according to the emotional state, communication that takes emotions into consideration, etc. This makes it possible to propose a training plan based on the superior's emotional state.

[0072] The training proposal department can refer to training plans from different industries and apply success stories from those industries. For example, the training proposal department stores training plans from different industries in a database and compares them with current training proposals. For example, it extracts effective plans from success stories from other industries. The training proposal department also analyzes training plans from different industries and applies success stories from those industries. For example, it refers to successful projects and best practices in those industries. The training proposal department also builds a system for referencing training plans from different industries and applying success stories from those industries. For example, it analyzes data from other industries and applies success stories. This makes it possible to refer to training plans from different industries and apply success stories from those industries.

[0073] The training proposal department can analyze the management style of a superior and propose a training plan that matches the style. For example, the training proposal department stores the management style of a superior in a database and compares it with current training proposals. For example, it extracts effective plans from past style data. The training proposal department also analyzes the management style of a superior and proposes a training plan that matches the style. For example, it analyzes the style using data analysis, statistical analysis, text analysis, etc. The training proposal department also builds a system for analyzing the management style of a superior and proposing a training plan that matches the style. For example, it analyzes the style based on past data and proposes a method for identifying the style. This makes it possible to analyze the management style of a superior and propose a training plan that matches the style.

[0074] The feedback generation unit can refer to the subordinate's past performance data and propose specific areas for improvement. For example, the feedback generation unit stores the subordinate's past performance data in a database and compares it with current feedback. For example, it extracts specific areas for improvement from the past performance data. The feedback generation unit also analyzes the past performance data and proposes specific areas for improvement. For example, it makes proposals based on sales data, evaluation results, project results, etc. The feedback generation unit also builds a system for referencing the subordinate's past performance data and proposing specific areas for improvement. For example, it analyzes and proposes areas for improvement based on past data. This makes it possible to refer to the subordinate's past performance data and propose specific areas for improvement.

[0075] The feedback generation unit can analyze the personality traits of the subordinates and generate individually optimized feedback. For example, the feedback generation unit stores the personality traits of the subordinates in a database and compares them with current feedback. For example, the feedback generation unit extracts individually optimized feedback from the personality data. The feedback generation unit also analyzes the personality traits of the subordinates and generates individually optimized feedback. For example, the feedback generation unit proposes the feedback based on a personality diagnostic test, an analysis of behavioral patterns, etc. The feedback generation unit also builds a system for analyzing the personality traits of the subordinates and generating individually optimized feedback. For example, the feedback generation unit analyzes the personality data and proposes individually optimized feedback. In this way, the personality traits of the subordinates can be analyzed and individually optimized feedback can be generated.

[0076] The feedback generation unit can analyze the emotional state of a subordinate in real time using the emotion estimation function and generate feedback based on the emotion. The feedback generation unit, for example, uses the emotion estimation function to analyze the emotional state of a subordinate in real time. For example, it analyzes the subordinate's facial expression and tone of voice and calculates an emotion score. The feedback generation unit also uses the emotion estimation function to build a system for analyzing the emotional state of a subordinate in real time. For example, it analyzes the emotional state using facial expression recognition technology or voice analysis technology. The feedback generation unit also generates feedback based on the emotion. For example, it suggests feedback according to the emotional state, communication that takes emotions into consideration, etc. This makes it possible to generate feedback based on the emotional state of a subordinate.

[0077] The feedback generation unit can refer to feedback examples from different industries and apply successful practices from other industries. For example, the feedback generation unit stores feedback examples from different industries in a database and compares them with current feedback. For example, it extracts effective feedback from successful practices from other industries. The feedback generation unit also analyzes feedback examples from different industries and applies successful practices from other industries. For example, it refers to successful projects and best practices in other industries. The feedback generation unit also builds a system for referring to feedback examples from different industries and applying successful practices from other industries. For example, it analyzes data from other industries and applies successful practices. This makes it possible to refer to feedback examples from different industries and apply successful practices from other industries.

[0078] The feedback generation unit can analyze the skill sets of subordinates and generate feedback that contributes to skill improvement. For example, the feedback generation unit stores the skill sets of subordinates in a database and compares them with current feedback. For example, it extracts feedback that contributes to skill improvement from the skill data. The feedback generation unit also analyzes the skill sets of subordinates and generates feedback that contributes to skill improvement. For example, it makes suggestions based on technical skills, soft skills, business knowledge, etc. The feedback generation unit also builds a system for analyzing the skill sets of subordinates and generating feedback that contributes to skill improvement. For example, it analyzes the skill data and suggests feedback that contributes to skill improvement. In this way, it is possible to analyze the skill sets of subordinates and generate feedback that contributes to skill improvement.

[0079] The feedback generation unit can analyze the emotional state of a subordinate in real time using the emotion estimation function and generate feedback based on the emotion. The feedback generation unit, for example, uses the emotion estimation function to analyze the emotional state of a subordinate in real time. For example, it analyzes the subordinate's facial expression and tone of voice and calculates an emotion score. The feedback generation unit also uses the emotion estimation function to build a system for analyzing the emotional state of a subordinate in real time. For example, it analyzes the emotional state using facial expression recognition technology or voice analysis technology. The feedback generation unit also generates feedback based on the emotion. For example, it suggests feedback according to the emotional state, communication that takes emotions into consideration, etc. This makes it possible to generate feedback based on the emotional state of a subordinate.

[0080] The follow-up email generation unit can refer to past email history and generate effective email content. For example, the follow-up email generation unit stores past email history in a database and compares it with the current follow-up email. For example, it extracts effective content from past email data. The follow-up email generation unit also analyzes past email history and generates effective email content. For example, it makes suggestions based on the sending date and time, sent content, and recipient reaction. The follow-up email generation unit also references past email history and builds a system for generating effective email content. For example, it analyzes and suggests effective content based on past data. This makes it possible to generate effective email content by referring to past email history.

[0081] The follow-up email generation unit can analyze the subordinate's performance data and propose a specific action plan. For example, the follow-up email generation unit stores the subordinate's performance data in a database and compares it with the current follow-up email. For example, it extracts a specific action plan from the performance data. The follow-up email generation unit also analyzes the subordinate's performance data and proposes a specific action plan. For example, it makes proposals based on sales data, evaluation results, project results, etc. The follow-up email generation unit also builds a system for analyzing the subordinate's performance data and proposing a specific action plan. For example, it analyzes the performance data and proposes a specific action plan. This makes it possible to analyze the subordinate's performance data and propose a specific action plan.

[0082] The follow-up email generation unit can analyze the emotional state of a subordinate in real time using the emotion estimation function and generate a follow-up email based on the emotion. The follow-up email generation unit, for example, uses the emotion estimation function to analyze the emotional state of a subordinate in real time. For example, it analyzes the subordinate's facial expression and tone of voice and calculates an emotion score. The follow-up email generation unit also uses the emotion estimation function to build a system for analyzing the emotional state of a subordinate in real time. For example, it analyzes the emotional state using facial expression recognition technology or voice analysis technology. The follow-up email generation unit also generates a follow-up email based on the emotion. For example, it suggests feedback according to the emotional state, communication that takes emotions into consideration, etc. This makes it possible to generate a follow-up email based on the emotional state of a subordinate.

[0083] The follow-up email generation unit can refer to email examples from different industries and apply success stories from those industries. For example, the follow-up email generation unit stores email examples from different industries in a database and compares them with the current follow-up email. For example, it extracts effective content from success stories from those industries. The follow-up email generation unit also analyzes email examples from different industries and applies success stories from those industries. For example, it refers to successful projects and best practices in those industries. The follow-up email generation unit also builds a system for referencing email examples from different industries and applying success stories from those industries. For example, it analyzes data from those industries and applies success stories. This makes it possible to refer to email examples from different industries and apply success stories from those industries.

[0084] The follow-up email generation unit can analyze the personality traits of subordinates and generate individually optimized email content. The follow-up email generation unit, for example, stores the personality traits of subordinates in a database and compares them with current follow-up emails. For example, it extracts individually optimized content from the personality data. The follow-up email generation unit also analyzes the personality traits of subordinates and generates individually optimized email content. For example, it makes suggestions based on personality diagnostic tests, behavioral pattern analysis, etc. The follow-up email generation unit also builds a system for analyzing the personality traits of subordinates and generating individually optimized email content. For example, it analyzes personality data and suggests individually optimized content. In this way, it is possible to analyze the personality traits of subordinates and generate individually optimized email content.

[0085] The follow-up email generation unit can analyze the emotional state of a subordinate in real time using the emotion estimation function and generate a follow-up email based on the emotion. The follow-up email generation unit, for example, uses the emotion estimation function to analyze the emotional state of a subordinate in real time. For example, it analyzes the subordinate's facial expression and tone of voice and calculates an emotion score. The follow-up email generation unit also uses the emotion estimation function to build a system for analyzing the emotional state of a subordinate in real time. For example, it analyzes the emotional state using facial expression recognition technology or voice analysis technology. The follow-up email generation unit also generates a follow-up email based on the emotion. For example, it suggests feedback according to the emotional state, communication that takes emotions into consideration, etc. This makes it possible to generate a follow-up email based on the emotional state of a subordinate.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The feedback tool can also be equipped with a health management section that monitors the health status of superiors. For example, the health management section can monitor the superior's heart rate and blood pressure in real time and evaluate their stress level. The health management section can also analyze the superior's sleep patterns and evaluate their fatigue level. Furthermore, the health management section can record the superior's dietary and exercise habits and provide advice on maintaining health. This allows for comprehensive management of the superior's health status and improves the quality of management.

[0088] The feedback tool can also include a schedule management section that supports the supervisor's schedule management. For example, the schedule management section can analyze the supervisor's calendar and remind the supervisor of important meetings and events. The schedule management section can also evaluate the supervisor's workload and suggest appropriate break times. Furthermore, the schedule management section can support efficient task management based on the supervisor's schedule. This can improve the supervisor's work efficiency.

[0089] The feedback tool can also be equipped with a communication support section for improving the supervisor's communication skills. For example, the communication support section can analyze the supervisor's speaking style and presentation skills and suggest areas for improvement. The communication support section can also analyze the content of the supervisor's emails and chats and suggest effective communication methods. Furthermore, the communication support section can provide training programs for improving the supervisor's interpersonal skills. This can improve the supervisor's communication skills.

[0090] The feedback tool may further include a leadership support section for improving the leadership skills of the superior. For example, the leadership support section may analyze the leadership style of the superior and suggest effective leadership methods. The leadership support section may also provide a training program for improving the team building skills of the superior. Furthermore, the leadership support section may provide a simulation for improving the decision-making skills of the superior. This allows the leadership skills of the superior to be improved.

[0091] The feedback tool can also be equipped with a stress management section that supports superiors' stress management. For example, the stress management section can monitor the stress level of superiors and suggest relaxation methods. The stress management section can also identify the causes of stress and suggest countermeasures. Furthermore, the stress management section can provide training programs to improve the stress management skills of superiors. This allows superiors to effectively manage their stress and improve the quality of management.

[0092] The feedback tool may further include an emotion feedback generation unit that analyzes the emotional state of the superior and generates feedback based on the emotion. For example, the emotion feedback generation unit may analyze the facial expression and tone of voice of the superior and calculate an emotion score. The emotion feedback generation unit may also generate feedback according to the emotional state and provide it to the superior. Furthermore, the emotion feedback generation unit may monitor the emotional state of the superior in real time and provide feedback at an appropriate time. This allows for providing feedback based on the emotional state of the superior, thereby improving the quality of management.

[0093] The feedback tool can also be equipped with an emotional training proposal unit that analyzes the emotional state of the superior and proposes a training plan based on the emotion. For example, the emotional training proposal unit can analyze the superior's facial expressions and tone of voice to calculate an emotion score. The emotional training proposal unit can also propose a training plan based on the superior's emotional state and provide it to the superior. Furthermore, the emotional training proposal unit can monitor the superior's emotional state in real time and propose a training plan at the appropriate time. This makes it possible to provide a training plan based on the superior's emotional state and improve the quality of management.

[0094] The feedback tool may further include an emotion follow-up email generation unit that analyzes the emotional state of the superior and generates a follow-up email based on the emotion. For example, the emotion follow-up email generation unit may analyze the superior's facial expression and tone of voice to calculate an emotion score. The emotion follow-up email generation unit may also generate a follow-up email according to the superior's emotional state and provide it to the superior. Furthermore, the emotion follow-up email generation unit may monitor the superior's emotional state in real time and provide a follow-up email at an appropriate time. This allows for the provision of a follow-up email based on the superior's emotional state, improving the quality of management.

[0095] The feedback tool may further include an emotional communication suggestion unit that analyzes the emotional state of the superior and suggests a communication method based on the emotion. For example, the emotional communication suggestion unit may analyze the superior's facial expression and tone of voice and calculate an emotion score. The emotional communication suggestion unit may also suggest a communication method according to the superior's emotional state and provide it to the superior. Furthermore, the emotional communication suggestion unit may monitor the superior's emotional state in real time and suggest a communication method at an appropriate time. This allows for a communication method based on the superior's emotional state to be offered, improving the quality of management.

[0096] The feedback tool may further include an emotional stress management suggestion unit that analyzes the emotional state of the supervisor and suggests a stress management method based on the emotion. For example, the emotional stress management suggestion unit may analyze the facial expression and tone of voice of the supervisor and calculate an emotion score. The emotional stress management suggestion unit may also suggest a stress management method according to the emotional state and provide it to the supervisor. Furthermore, the emotional stress management suggestion unit may monitor the emotional state of the supervisor in real time and suggest a stress management method at an appropriate time. This allows for the provision of a stress management method based on the emotional state of the supervisor, improving the quality of management.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The hearing analysis unit analyzes the content of the hearing from the superior. For example, it analyzes the superior's tone of voice and speaking patterns to estimate their emotional state. It can also compare this with past hearing data to track changes in the superior's management style. It can also analyze the superior's non-verbal communication (gestures and facial expressions) to identify detailed issues. Step 2: The Issue Identification Department identifies management issues based on the results of the analysis by the Interview Analysis Department. For example, it may refer to the supervisor's past management performance to identify similar issues. It may also collect anonymous feedback from subordinates to identify issues from the subordinate's perspective as well as the supervisor's. It may also refer to management issues in other industries and apply successful examples from those industries. Step 3: The training proposal section generates training proposals based on the issues identified by the issue identification section. For example, it can refer to a superior's past training history and propose an effective training plan. It can also analyze a subordinate's skill set and propose a training plan that will contribute to their growth. It can also refer to training plans from other industries and apply successful examples from other industries. Step 4: The feedback generation unit generates feedback for the subordinate based on the issues identified by the issue identification unit. For example, it can refer to the subordinate's past performance data and suggest specific areas for improvement. It can also analyze the subordinate's personality traits and generate individually optimized feedback. It can also refer to feedback examples from other industries and apply successful examples from other industries. Step 5: The follow-up email generation unit generates follow-up emails based on the issues identified by the issue identification unit. For example, it can refer to past email history to generate effective email content. It can also analyze subordinates' performance data and propose specific action plans. It can also refer to email examples from other industries and apply successful examples from other industries.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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 hearing analysis department that analyzes the contents of hearings from superiors, a problem identification unit that identifies management problems based on the content analyzed by the hearing analysis unit; a training proposal unit that generates a training proposal based on the task identified by the task identification unit; a feedback generation unit that generates feedback to a subordinate based on the issue identified by the issue identification unit; a follow-up email generation unit that generates a follow-up email based on the issue identified by the issue identification unit. A system characterized by:

2. The hearing analysis unit Analyze the supervisor's tone of voice and speaking patterns to estimate their emotional state and assess the urgency of the issue.

2. The system of claim 1.

3. The hearing analysis unit Integrate feedback from other managers and identify common challenges 2. The system of claim 1.

4. The problem identification unit Analyze the emotional state of the superior in real time and prioritize tasks based on their emotions.

2. The system of claim 1.

5. The training proposal department Analyze the emotional state of the superior in real time and propose a training plan based on that emotion.

2. The system of claim 1.

6. The feedback generation unit Analyzing the subordinate's emotional state in real time and generating the feedback based on the emotion 2. The system of claim 1.

7. The follow-up email generation unit Analyzing the subordinate's emotional state in real time and generating the follow-up email based on the emotion 2. The system of claim 1.

8. The hearing analysis unit Analyze the emotional state of the superior in real time and prioritize tasks based on their emotions.

2. The system of claim 1.

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