System

Abstract

An object of the system according to the embodiment is to realize equal corporate planning by learning the work and experience value of the manager.SOLUTION: A system according to an embodiment includes a manager work learning unit, an objective setting unit, a task management unit, an evaluation unit, a feedback unit, a compliance monitoring unit, an indication unit, and a consultation handling unit. A manager work learning part learns the work and experience value of a manager. The target setting unit sets a target. The task management unit performs task management. The evaluation unit performs business evaluation. The feedback unit provides feedback. The compliance monitor monitors compliance compliance. The pointing out unit points out to a subordinate. The consultation handling unit acts for the consultation with the superior.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] With conventional technology, there was a problem that the duties and experience levels of managers varied, making it difficult to manage an organization equally.

[0005] The system according to the embodiment aims to learn about the work and experience of managers and to achieve equal organizational management. [Means for solving the problem]

[0006] The system according to the embodiment comprises a managerial work learning unit, a goal setting unit, a task management unit, an evaluation unit, a feedback unit, a compliance monitoring unit, a comment unit, and a consultation response unit. The managerial work learning unit learns about the work and experience of managers. The goal setting unit sets goals. The task management unit manages tasks. The evaluation unit evaluates work. The feedback unit provides feedback. The compliance monitoring unit monitors compliance. The comment unit comments to subordinates. The consultation response unit consults with superiors on behalf of employees. [Effects of the Invention]

[0007] The system according to the embodiment can learn about the work and experience of managers and realize fair organizational management. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The digital leader system according to an embodiment of the present invention is a system that learns the work and experience of managers, sets goals, manages tasks, provides accurate evaluations, provides feedback, promotes compliance, and resolves complaints to subordinates and issues with superiors. This enables the digital leader system to achieve equal organizational management, reduce personnel costs, and improve the results of employee surveys.

[0029] A digital reader system according to an embodiment includes a managerial job learning unit, a goal setting unit, a task management unit, an evaluation unit, a feedback unit, a compliance monitoring unit, a complaints unit, and a consultation response unit. The managerial job learning unit learns the job duties and experience of managers. For example, past job data and evaluation data are input into the generation AI, which analyzes and learns from them. The managerial job learning unit also learns goal setting methods, task management techniques, evaluation criteria, etc. For example, the generation AI learns based on prompts containing the job content and evaluation criteria of managers. The goal setting unit sets goals. For example, the generation AI understands the organization's goals and each member's role and sets specific goals based on them. The task management unit manages tasks. For example, the generation AI manages each member's tasks and monitors their progress. The evaluation unit evaluates job performance. For example, the generation AI analyzes each member's job data and provides an objective evaluation. The feedback unit provides feedback. For example, the generation AI provides feedback based on the evaluation results. The compliance monitoring unit monitors compliance. For example, the generation AI learns the organization's rules and laws and monitors each member's behavior based on that. The warning unit makes warnings to subordinates. For example, the generation AI analyzes the subordinate's behavior and work content and makes necessary warnings. The consultation response unit acts as a proxy for consultations with superiors. For example, the generation AI will respond on behalf of subordinates when it is difficult to consult with a superior. As a result, the digital leader system according to the embodiment can achieve equal organizational management, reduce personnel costs, and improve the survey results of working members.

[0030] The Managerial Work Learning Department collects managerial work data in real time, and the generation AI dynamically updates its learning content, allowing it to respond to the latest work trends. For example, the Managerial Work Learning Department builds a system in which managerial work data is collected in real time and the generation AI dynamically updates its learning content. For example, it automatically collects daily work reports and meeting minutes and reflects them in the generation AI. The Managerial Work Learning Department also allows the generation AI to learn and adapt to the latest work trends based on the work data collected in real time. For example, it learns to respond to the progress of new projects and market fluctuations. The Managerial Work Learning Department also develops a system in which the generation AI can respond to the latest work trends by collecting managerial work data in real time and the generation AI dynamically updates its learning content. For example, it analyzes work progress and results in real time and reflects them in the generation AI. This allows it to respond to the latest work trends and always operate based on the latest information.

[0031] In addition to managerial data, the Managerial Work Learning Department can also learn data on managers from other industries, allowing it to incorporate best practices across industries. For example, the Managerial Work Learning Department can incorporate best practices across industries by collecting managerial data from different industries and training the generation AI to do so. For example, it can compare managerial data from the manufacturing and service industries to extract common success factors. To incorporate best practices across industries, the Managerial Work Learning Department can also analyze managerial data from different industries and train the generation AI to do so. For example, it can integrate managerial data from the IT and medical industries to derive effective management methods. The Managerial Work Learning Department can also learn managerial data from different industries and build a system to incorporate best practices across industries. For example, it can compare managerial data from the financial and education industries and reflect excellent management methods in the generation AI. This allows for more effective business operations by incorporating best practices across industries.

[0032] The Managerial Work Learning Department can apply AI learning for managerial work to other positions, making it possible to handle a wide range of jobs. For example, the Managerial Work Learning Department can apply AI learning for managerial work to team leaders and project managers, building a system that can handle a wide range of jobs. For example, it can collect work data from team leaders and have the generation AI learn it. The Managerial Work Learning Department can also collect work data from team leaders and project managers and have the generation AI learn it in order to apply it to other positions. For example, it can analyze project progress management and team member evaluation data. The Managerial Work Learning Department can also apply AI learning for managerial work to other positions, developing a system that can handle a wide range of jobs. For example, it can have the generation AI learn the work content of team leaders and project managers and adapt it. This makes it possible to handle a wide range of jobs, making more diverse business operations possible.

[0033] The Managerial Work Learning Department can learn not only the work data of managers but also the feedback data of subordinates, thereby achieving a comprehensive understanding of work. For example, the Managerial Work Learning Department collects subordinate feedback data along with the work data of managers and has the generation AI learn from it. For example, it analyzes evaluations and opinions from subordinates to deepen the manager's understanding of work. The Managerial Work Learning Department also builds a system that gives managers a comprehensive understanding of work by learning from subordinate feedback data. For example, it identifies areas for improvement in managers' work based on subordinate feedback. The Managerial Work Learning Department also learns subordinate feedback data in addition to the work data of managers, developing a system that achieves a more comprehensive understanding of work. For example, it reflects subordinate feedback in the generation AI to improve the work performance of managers. In this way, a more comprehensive understanding of work is possible by learning subordinate feedback data.

[0034] The goal setting unit can set realistic and achievable goals in the goal setting and task management processes by learning from past success and failure cases. For example, the goal setting unit has the generation AI learn from past success and failure cases and reflect them in the goal setting and task management processes. For example, it sets realistic goals based on past data. The goal setting unit also analyzes past success and failure cases to build a system in which the generation AI sets more realistic and achievable goals. For example, it analyzes the factors for success and failure and reflects them in goal setting. The goal setting unit also has the generation AI learn from past success and failure cases and reflects them in the goal setting and task management processes. For example, it sets realistic goals based on similar cases. In this way, learning from past success and failure cases makes it possible to set more realistic and achievable goals.

[0035] The task management unit evaluates each member's skill set and workload in real time when the generation AI manages tasks, allowing it to optimally assign tasks. For example, the task management unit builds a system in which the generation AI evaluates each member's skill set and workload in real time and optimally assigns tasks. For example, tasks are assigned based on a skill matrix. The task management unit also evaluates each member's skill set and workload in real time, allowing the generation AI to optimally assign tasks. For example, tasks are adjusted so that the workload is even. The task management unit also develops a system in which the generation AI evaluates each member's skill set and workload in real time when the generation AI manages tasks, allowing it to optimally assign tasks. For example, tasks are automatically assigned taking skills and workload into consideration. This makes it possible to optimally assign tasks by evaluating each member's skill set and workload in real time.

[0036] The goal setting unit and task management unit can apply goal setting and task management not only to individual projects but also to individual career development plans, supporting long-term growth. For example, the goal setting unit and task management unit construct a system in which the generation AI applies goal setting and task management to individual career development plans to support long-term growth. For example, it sets goals based on career goals. The goal setting unit and task management unit also construct a system in which the generation AI sets goals and manages tasks based on individual career development plans. For example, it assigns tasks according to skill development and career paths. The goal setting unit and task management unit also apply goal setting and task management to individual career development plans to develop a system that supports long-term growth. For example, it sets goals and manages tasks in line with career plans. This makes it possible to support long-term growth by applying it to individual career development plans.

[0037] The goal setting unit and task management unit can visualize the goals and tasks set by the generation AI and present them to members, making them easier to understand visually. The goal setting unit and task management unit, for example, build a system that visualizes the goals and tasks set by the generation AI and presents them to members. For example, they may visually display them using a Gantt chart or task board. The goal setting unit and task management unit also visualize the goals and tasks to make them easier for members to understand visually. For example, they may display the progress of tasks using color coding. The goal setting unit and task management unit also develop a system that visualizes the goals and tasks set by the generation AI and presents them to members. For example, they may use a dashboard to display the goal achievement status in real time. In this way, visualizing the goals and tasks makes it easier for members to understand visually.

[0038] The evaluation department can incorporate peer reviews and 360-degree evaluations into the evaluation process to enable evaluation from multiple perspectives. For example, the evaluation department will incorporate peer reviews and 360-degree evaluations into the generation AI to build a system that enables evaluation from a more multifaceted perspective. For example, evaluations from colleagues and superiors will be collected and a comprehensive evaluation will be made. The evaluation department will also incorporate peer reviews and 360-degree evaluations into the generation AI to make the evaluation process more multifaceted. For example, evaluations will be made based on feedback from multiple evaluators. The evaluation department will also develop a system in which the generation AI will incorporate peer reviews and 360-degree evaluations to enable evaluation from a more multifaceted perspective. For example, the opinions of evaluators will be integrated and a comprehensive evaluation will be made. In this way, by incorporating peer reviews and 360-degree evaluations, evaluation from a more multifaceted perspective will be possible.

[0039] The feedback unit can refer to past feedback history when the generation AI provides feedback and provide consistent feedback. For example, the feedback unit builds a system in which the generation AI refers to past feedback history and provides consistent feedback. For example, it adjusts current feedback based on the content of past feedback. The feedback unit also analyzes past feedback history and the generation AI provides consistent feedback. For example, it standardizes the feedback content for the same member. The feedback unit also develops a system in which the generation AI refers to past feedback history and provides consistent feedback. For example, it stores and references feedback history in a database. This makes it possible to provide consistent feedback by referring to past feedback history.

[0040] The evaluation and feedback department can apply the evaluation and feedback process not only to periodic performance reviews but also to daily work evaluations, thereby promoting continuous improvement. For example, the evaluation and feedback department builds a system in which generative AI applies the evaluation and feedback process to daily work evaluations, thereby promoting continuous improvement. For example, evaluations are made based on daily work data. The evaluation and feedback department also applies generative AI to daily work evaluations, not only to periodic performance reviews. For example, daily work performance is evaluated in real time. The evaluation and feedback department also develops a system in which generative AI applies the evaluation and feedback process to daily work evaluations, thereby promoting continuous improvement. For example, daily work data is collected and reflected in evaluations. This makes it possible to apply it to daily work evaluations as well, thereby enabling continuous improvement.

[0041] The feedback unit can deliver the feedback provided by the generation AI as an audio or video message, thereby realizing more personalized communication. The feedback unit, for example, builds a system that delivers the feedback provided by the generation AI as an audio or video message. For example, it converts the feedback content into an audio message using voice synthesis technology. The feedback unit can also deliver the feedback as an audio or video message, thereby realizing more personalized communication. For example, it provides feedback as a video message. The feedback unit can also develop a system that delivers the feedback provided by the generation AI as an audio or video message. For example, it converts the feedback content into a video message and delivers it. This enables more personalized communication by delivering feedback as an audio or video message.

[0042] The compliance monitoring department can learn from past violation cases to predict the risk of compliance violations and issue advance warnings of high-risk behavior. For example, the compliance monitoring department has the generation AI learn from past compliance violation cases and build a system that issues advance warnings of high-risk behavior. For example, it predicts risks based on past data. The compliance monitoring department also analyzes past violation cases and the generation AI issues advance warnings of high-risk behavior. For example, it evaluates risks based on similar cases and issues warnings. The compliance monitoring department also develops a system in which the generation AI learns from past compliance violation cases and issues advance warnings of high-risk behavior. For example, it builds a risk assessment model and issues warnings. In this way, by learning from past violation cases, it becomes possible to issue advance warnings of high-risk behavior.

[0043] The compliance monitoring unit monitors behavior in real time when the generation AI monitors compliance, and can immediately point out or correct any violations. For example, the compliance monitoring unit builds a system where the generation AI monitors compliance in real time and immediately points out or corrects any violations. For example, it monitors behavior during work and immediately points out any violations that are detected. The compliance monitoring unit also monitors behavior in real time, and the generation AI immediately points out compliance violations. For example, it analyzes business data in real time and detects violating behavior. The compliance monitoring unit also develops a system where the generation AI monitors compliance in real time and immediately points out or corrects any violations. For example, it builds a real-time monitoring system and immediately corrects any violating behavior. In this way, real-time behavior monitoring makes it possible to immediately point out or correct any violations.

[0044] The Compliance Monitoring Department can expand compliance promotion beyond just legal compliance to also include corporate ethics and social responsibility, thereby achieving comprehensive compliance. For example, the Compliance Monitoring Department will build a system in which generative AI expands compliance promotion to include corporate ethics and social responsibility, thereby achieving comprehensive compliance. For example, it will have it learn data related to corporate ethics. The Compliance Monitoring Department will also apply generative AI to not only legal compliance, but also corporate ethics and social responsibility. For example, it will have it learn code of conduct related to social responsibility and reflect this in guidance. The Compliance Monitoring Department will also develop a system in which generative AI expands compliance promotion to include corporate ethics and social responsibility, thereby achieving comprehensive compliance. For example, it will provide guidance based on corporate ethics. This will enable comprehensive compliance to be achieved by expanding compliance beyond just legal compliance to include corporate ethics and social responsibility.

[0045] The Compliance Monitoring Department can implement the compliance guidance provided by the generative AI as an interactive training program, thereby deepening members' understanding. For example, the Compliance Monitoring Department will build a system that implements the compliance guidance provided by the generative AI as an interactive training program. For example, it will provide training in the form of simulations or quizzes. The Compliance Monitoring Department will also have the generative AI provide compliance guidance through interactive training programs, thereby deepening members' understanding. For example, it will provide training based on actual cases. The Compliance Monitoring Department will also develop a system that implements the compliance guidance provided by the generative AI as an interactive training program. For example, it will provide guidance using interactive scenarios. This will make it possible to deepen members' understanding by implementing it as an interactive training program.

[0046] The commenting unit compares the content of comments made to subordinates with past comment history and makes consistent comments, thereby improving reliability. The commenting unit, for example, builds a system in which the generation AI refers to past comment history and makes consistent comments. For example, it adjusts current comments based on past comment content. The commenting unit also analyzes past comment history and the generation AI makes consistent comments. For example, it standardizes the content of comments made to the same subordinate. The commenting unit also develops a system in which the generation AI refers to past comment history and makes consistent comments. For example, it stores and references the comment history in a database. This makes it possible to make consistent comments by comparing with past comment history, improving reliability.

[0047] The consultation response unit can automatically summarize the consultation content when the generation AI consults with a superior on behalf of the user and efficiently communicate it to the superior. For example, the consultation response unit builds a system that automatically summarizes the consultation content when the generation AI consults with a superior on behalf of the user. For example, it analyzes the text of the consultation content and extracts the main points. The consultation response unit also automatically summarizes the consultation content, and the generation AI efficiently communicates it to the superior. For example, it sends the summarized content by email or message. The consultation response unit also develops a system that automatically summarizes the consultation content when the generation AI consults with a superior on behalf of the user and efficiently communicates it to the superior. For example, it displays the summary content on a dashboard. In this way, the consultation content can be automatically summarized and efficiently communicated, allowing the user to consult with their superior smoothly.

[0048] The commenting unit and the consultation response unit can provide comments to subordinates and consultations to superiors not only as text but also as audio or video messages, thereby realizing more personalized communication. The commenting unit and the consultation response unit, for example, build a system in which comments are provided to subordinates and consultations are provided to superiors as audio or video messages. For example, the content of comments is converted into an audio message using voice synthesis technology. The commenting unit and the consultation response unit can also provide comments and consultations as audio or video messages, thereby realizing more personalized communication. For example, comments and consultations are provided as video messages. The commenting unit and the consultation response unit can also develop a system in which comments are provided to subordinates and consultations are provided to superiors as audio or video messages. For example, the content of comments is converted into a video message and distributed. As a result, comments and consultations are provided as audio or video messages, thereby enabling more personalized communication.

[0049] The commenting unit is capable of providing individually customized comments when the generation AI provides comments to a subordinate, taking into account the subordinate's skill set and work history. For example, the commenting unit builds a system that, when the generation AI provides comments to a subordinate, takes into account the subordinate's skill set and work history. For example, it adjusts the content of the comments based on a skill matrix. The commenting unit also develops a system that, when the generation AI provides comments to a subordinate, takes into account the subordinate's skill set and work history and provides individually customized comments. For example, it automatically adjusts the content of the comments based on skills and work history. This makes it possible to provide individually customized comments that take into account the subordinate's skill set and work history.

[0050] The opt-in introduction to AI can be implemented in stages by opting in, and feedback is collected at each stage to optimize the process. The opt-in introduction to AI can be implemented in stages by opting in, for example, by building a system to collect feedback at each stage. For example, the process is adjusted based on feedback at the early stages of implementation. The opt-in introduction to AI can also be implemented in stages by opting in, and feedback is collected at each stage to optimize the process. For example, the implementation method is improved based on feedback. The opt-in introduction to AI can also be implemented in stages by opting in, and a system is developed to collect feedback at each stage to optimize the process. For example, the feedback data is analyzed and the implementation process is adjusted. This makes it possible to implement the process in stages and collect feedback at each stage to optimize the process.

[0051] The voluntary AI introduction section can share success stories and best practices when introducing voluntary AI, thereby promoting member understanding and cooperation. For example, the voluntary AI introduction section can build a system for sharing success stories and best practices when introducing voluntary AI. For example, it can register success stories in a database and provide it to members. The voluntary AI introduction section can also share success stories and best practices to promote member understanding and cooperation for the voluntary AI introduction. For example, it can explain the introduction method based on success stories. The voluntary AI introduction section can also develop a system for sharing success stories and best practices when introducing voluntary AI, thereby promoting member understanding and cooperation. For example, it can design an introduction process based on best practices. In this way, sharing success stories and best practices can promote member understanding and cooperation.

[0052] The voluntary AI introduction department can roll out the introduction of AI not only to a specific department but as a company-wide project, thereby improving the efficiency of the entire organization. The voluntary AI introduction department, for example, can roll out the introduction of AI not only to a specific department but as a company-wide project, and build a system that improves the efficiency of the entire organization. For example, it can formulate and implement a company-wide implementation plan. The voluntary AI introduction department can also roll out the introduction of AI not only to a specific department but as a company-wide project, and develop a system that improves the efficiency of the entire organization. For example, it can design and implement a company-wide implementation process. In this way, it can improve the efficiency of the entire organization by rolling out the introduction of AI as a company-wide project.

[0053] The opt-in AI introduction section can apply the opt-in AI introduction process to other organizations and industries to create a wide range of introduction cases. The opt-in AI introduction section, for example, builds a system that applies the opt-in AI introduction process to other organizations and industries to create a wide range of introduction cases. For example, it collects and shares introduction cases from different industries. The opt-in AI introduction section also applies the opt-in AI introduction process to other organizations and industries to create introduction cases. For example, it designs an introduction method based on successful cases from different industries. The opt-in AI introduction section also develops a system that applies the opt-in AI introduction process to other organizations and industries to create a wide range of introduction cases. For example, it analyzes and shares the introduction processes from different industries. This makes it possible to apply it to other organizations and industries to create a wide range of introduction cases.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The digital leader system can further include a health management unit. The health management unit monitors the health status of managers and members and predicts health risks. For example, daily activity data and health checkup results are collected, and the generative AI evaluates health risks. The health management unit also provides appropriate health advice based on the health status. For example, it may suggest relaxation methods when stress levels are high. The health management unit also builds a system that monitors health status and predicts health risks. For example, it may evaluate health risks based on health data and suggest preventive measures. Thus, by including a health management unit, it becomes possible to monitor the health status of managers and members and predict health risks.

[0056] The digital leader system can further include a career development department. The career development department designs career paths for members and provides training programs for skill development. For example, a generative AI suggests the optimal career path based on the member's skill set and career goals. The career development department also provides training programs for skill development based on the career path. For example, it suggests online courses and workshops. The career development department also builds a system that designs career paths for members and provides training programs for skill development. For example, a generative AI suggests training programs based on career goals. Thus, by including a career development department, it becomes possible to design career paths for members and provide training programs for skill development.

[0057] The digital reader system can further include a remote work support unit. The remote work support unit supports the optimization of the remote work environment and facilitates communication. For example, it monitors the progress of remote work, and the generation AI provides appropriate support. The remote work support unit also provides the necessary tools and resources to support the optimization of the remote work environment. For example, it proposes video conferencing systems and collaboration tools. The remote work support unit also builds a system to support the optimization of the remote work environment and facilitate communication. For example, it monitors the progress of remote work in real time, and the generation AI provides appropriate support. Thus, by including a remote work support unit, it becomes possible to support the optimization of the remote work environment and facilitate communication.

[0058] The Digital Leader System can further include an Innovation Promotion Department. The Innovation Promotion Department supports innovation activities within the organization and promotes the creation of new ideas. For example, it may hold idea contests and brainstorming sessions, with the generative AI evaluating the ideas. The Innovation Promotion Department also provides support for realizing new ideas. For example, it may support the creation of prototypes and market research. The Innovation Promotion Department also builds a system that supports innovation activities within the organization and promotes the creation of new ideas. For example, the generative AI provides support for the evaluation and realization of ideas. Thus, by having an Innovation Promotion Department, it is possible to support innovation activities within the organization and promote the creation of new ideas.

[0059] The digital leader system can further include a data analysis unit. The data analysis unit collects and analyzes data within the organization and provides insights for business improvement. For example, business data and market data are collected and analyzed by the generation AI. The data analysis unit also makes proposals for business improvement based on the data. For example, it makes proposals for improving business process efficiency and cost reduction. The data analysis unit also collects and analyzes data within the organization and builds a system that provides insights for business improvement. For example, the generation AI creates reports based on the data. Thus, by including a data analysis unit, it becomes possible to collect and analyze data within the organization and provide insights for business improvement.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The managerial work learning unit learns the manager's work and experience. For example, past work data and evaluation data are input into the generation AI, which then analyzes and learns from them. The managerial work learning unit also learns goal setting methods, task management techniques, evaluation criteria, etc. For example, the generation AI learns based on prompts that include the manager's work content and evaluation criteria. Step 2: The goal setting unit sets goals. For example, the generation AI understands the organization's goals and the roles of each member, and sets specific goals based on that. Step 3: The task management unit manages tasks. For example, the generation AI manages each member's tasks and monitors their progress. Step 4: The evaluation unit performs a work evaluation. For example, the generation AI analyzes each member's work data and provides an objective evaluation. Step 5: The feedback unit provides feedback. For example, the generation AI provides feedback based on the evaluation results. Step 6: The Compliance Monitoring Department monitors compliance. For example, the Generative AI learns the organization's rules and laws and monitors each member's behavior based on those rules and laws. Step 7: The feedback unit provides feedback to the subordinate. For example, the generation AI analyzes the subordinate's behavior and work content and provides necessary feedback. Step 8: The consultation response unit will consult with a superior on behalf of the employee. For example, if it is difficult to consult with a superior, the generation AI will respond instead.

[0062] (Example 2) The digital leader system according to an embodiment of the present invention is a system that learns the work and experience of managers, sets goals, manages tasks, provides accurate evaluations, provides feedback, promotes compliance, and resolves complaints to subordinates and issues with superiors. This enables the digital leader system to achieve equal organizational management, reduce personnel costs, and improve the results of employee surveys.

[0063] A digital reader system according to an embodiment includes a managerial job learning unit, a goal setting unit, a task management unit, an evaluation unit, a feedback unit, a compliance monitoring unit, a complaints unit, and a consultation response unit. The managerial job learning unit learns the job duties and experience of managers. For example, past job data and evaluation data are input into the generation AI, which analyzes and learns from them. The managerial job learning unit also learns goal setting methods, task management techniques, evaluation criteria, etc. For example, the generation AI learns based on prompts containing the job content and evaluation criteria of managers. The goal setting unit sets goals. For example, the generation AI understands the organization's goals and each member's role and sets specific goals based on them. The task management unit manages tasks. For example, the generation AI manages each member's tasks and monitors their progress. The evaluation unit evaluates job performance. For example, the generation AI analyzes each member's job data and provides an objective evaluation. The feedback unit provides feedback. For example, the generation AI provides feedback based on the evaluation results. The compliance monitoring unit monitors compliance. For example, the generation AI learns the organization's rules and laws and monitors each member's behavior based on that. The warning unit makes warnings to subordinates. For example, the generation AI analyzes the subordinate's behavior and work content and makes necessary warnings. The consultation response unit acts as a proxy for consultations with superiors. For example, the generation AI will respond on behalf of subordinates when it is difficult to consult with a superior. As a result, the digital leader system according to the embodiment can achieve equal organizational management, reduce personnel costs, and improve the survey results of working members.

[0064] The managerial work learning unit uses the emotion estimation function to learn the emotional state of managers in addition to their work data, and can reproduce emotion-based decision-making processes. For example, the managerial work learning unit uses the emotion estimation function to collect the emotional state of managers in real time along with their work data, and trains the generative AI. For example, emotions can be estimated from comments made during meetings or the content of emails, and reflected in the decision-making process. The managerial work learning unit also analyzes daily work diaries and feedback to identify patterns of emotional fluctuation. For example, it can distinguish between stressful and relaxed situations and extract factors that influence decision-making. The managerial work learning unit also uses the emotion estimation function to learn the emotional state of managers and build a system that reproduces emotion-based decision-making processes. For example, it can analyze past decision-making processes and their outcomes to evaluate the impact of emotions. This allows for more realistic business operations by reproducing the emotion-based decision-making process of managers.

[0065] The Managerial Work Learning Department collects managerial work data in real time, and the generation AI dynamically updates its learning content, allowing it to respond to the latest work trends. For example, the Managerial Work Learning Department builds a system in which managerial work data is collected in real time and the generation AI dynamically updates its learning content. For example, it automatically collects daily work reports and meeting minutes and reflects them in the generation AI. The Managerial Work Learning Department also allows the generation AI to learn and adapt to the latest work trends based on the work data collected in real time. For example, it learns to respond to the progress of new projects and market fluctuations. The Managerial Work Learning Department also develops a system in which the generation AI can respond to the latest work trends by collecting managerial work data in real time and the generation AI dynamically updates its learning content. For example, it analyzes work progress and results in real time and reflects them in the generation AI. This allows it to respond to the latest work trends and always operate based on the latest information.

[0066] In addition to managerial data, the Managerial Work Learning Department can also learn data on managers from other industries, allowing it to incorporate best practices across industries. For example, the Managerial Work Learning Department can incorporate best practices across industries by collecting managerial data from different industries and training the generation AI to do so. For example, it can compare managerial data from the manufacturing and service industries to extract common success factors. To incorporate best practices across industries, the Managerial Work Learning Department can also analyze managerial data from different industries and train the generation AI to do so. For example, it can integrate managerial data from the IT and medical industries to derive effective management methods. The Managerial Work Learning Department can also learn managerial data from different industries and build a system to incorporate best practices across industries. For example, it can compare managerial data from the financial and education industries and reflect excellent management methods in the generation AI. This allows for more effective business operations by incorporating best practices across industries.

[0067] The Managerial Work Learning Department can apply AI learning for managerial work to other positions, making it possible to handle a wide range of jobs. For example, the Managerial Work Learning Department can apply AI learning for managerial work to team leaders and project managers, building a system that can handle a wide range of jobs. For example, it can collect work data from team leaders and have the generation AI learn it. The Managerial Work Learning Department can also collect work data from team leaders and project managers and have the generation AI learn it in order to apply it to other positions. For example, it can analyze project progress management and team member evaluation data. The Managerial Work Learning Department can also apply AI learning for managerial work to other positions, developing a system that can handle a wide range of jobs. For example, it can have the generation AI learn the work content of team leaders and project managers and adapt it. This makes it possible to handle a wide range of jobs, making more diverse business operations possible.

[0068] The Managerial Work Learning Department can learn not only the work data of managers but also the feedback data of subordinates, thereby achieving a comprehensive understanding of work. For example, the Managerial Work Learning Department collects subordinate feedback data along with the work data of managers and has the generation AI learn from it. For example, it analyzes evaluations and opinions from subordinates to deepen the manager's understanding of work. The Managerial Work Learning Department also builds a system that gives managers a comprehensive understanding of work by learning from subordinate feedback data. For example, it identifies areas for improvement in managers' work based on subordinate feedback. The Managerial Work Learning Department also learns subordinate feedback data in addition to the work data of managers, developing a system that achieves a more comprehensive understanding of work. For example, it reflects subordinate feedback in the generation AI to improve the work performance of managers. In this way, a more comprehensive understanding of work is possible by learning subordinate feedback data.

[0069] The managerial work learning unit uses the emotion estimation function to monitor the emotional state of managers in real time, which can be useful for stress management and mental health care. For example, the managerial work learning unit uses the emotion estimation function to build a system that monitors the emotional state of managers in real time. For example, it analyzes the facial expressions and voice of managers to evaluate their stress levels. The managerial work learning unit also monitors the emotional state of managers in real time, which can be useful for stress management and mental health care. For example, it suggests relaxation methods when stress increases. The managerial work learning unit also uses the emotion estimation function to develop a system that monitors the emotional state of managers in real time, which can be useful for mental health care. For example, it provides counseling based on emotional data. In this way, stress management and mental health care become possible by monitoring emotional states in real time.

[0070] When the generation AI sets goals, the goal setting unit can use the emotion estimation function to take into account the member's motivation and set optimal goals. For example, when the generation AI sets goals, the goal setting unit uses the emotion estimation function to evaluate the member's motivation and set optimal goals. For example, it adjusts goals based on the member's emotion score. The goal setting unit also builds a system in which the generation AI sets optimal goals by taking into account the member's motivation. For example, it analyzes emotion data and sets goals that will increase motivation. The goal setting unit also uses the emotion estimation function to take into account the member's motivation and the generation AI sets optimal goals. For example, it prioritizes goals that evoke strong positive emotions. This makes it possible to set goals that take into account the member's motivation.

[0071] The goal setting unit can set realistic and achievable goals in the goal setting and task management processes by learning from past success and failure cases. For example, the goal setting unit has the generation AI learn from past success and failure cases and reflect them in the goal setting and task management processes. For example, it sets realistic goals based on past data. The goal setting unit also analyzes past success and failure cases to build a system in which the generation AI sets more realistic and achievable goals. For example, it analyzes the factors for success and failure and reflects them in goal setting. The goal setting unit also has the generation AI learn from past success and failure cases and reflects them in the goal setting and task management processes. For example, it sets realistic goals based on similar cases. In this way, learning from past success and failure cases makes it possible to set more realistic and achievable goals.

[0072] The task management unit evaluates each member's skill set and workload in real time when the generation AI manages tasks, allowing it to optimally assign tasks. For example, the task management unit builds a system in which the generation AI evaluates each member's skill set and workload in real time and optimally assigns tasks. For example, tasks are assigned based on a skill matrix. The task management unit also evaluates each member's skill set and workload in real time, allowing the generation AI to optimally assign tasks. For example, tasks are adjusted so that the workload is even. The task management unit also develops a system in which the generation AI evaluates each member's skill set and workload in real time when the generation AI manages tasks, allowing it to optimally assign tasks. For example, tasks are automatically assigned taking skills and workload into consideration. This makes it possible to optimally assign tasks by evaluating each member's skill set and workload in real time.

[0073] The goal setting unit and task management unit can apply goal setting and task management not only to individual projects but also to individual career development plans, supporting long-term growth. For example, the goal setting unit and task management unit construct a system in which the generation AI applies goal setting and task management to individual career development plans to support long-term growth. For example, it sets goals based on career goals. The goal setting unit and task management unit also construct a system in which the generation AI sets goals and manages tasks based on individual career development plans. For example, it assigns tasks according to skill development and career paths. The goal setting unit and task management unit also apply goal setting and task management to individual career development plans to develop a system that supports long-term growth. For example, it sets goals and manages tasks in line with career plans. This makes it possible to support long-term growth by applying it to individual career development plans.

[0074] The goal setting unit and task management unit can visualize the goals and tasks set by the generation AI and present them to members, making them easier to understand visually. The goal setting unit and task management unit, for example, build a system that visualizes the goals and tasks set by the generation AI and presents them to members. For example, they may visually display them using a Gantt chart or task board. The goal setting unit and task management unit also visualize the goals and tasks to make them easier for members to understand visually. For example, they may display the progress of tasks using color coding. The goal setting unit and task management unit also develop a system that visualizes the goals and tasks set by the generation AI and presents them to members. For example, they may use a dashboard to display the goal achievement status in real time. In this way, visualizing the goals and tasks makes it easier for members to understand visually.

[0075] The goal setting unit and task management unit can use the emotion estimation function to collect members' emotional responses to goal setting and task management and make adjustments to elicit positive responses. The goal setting unit and task management unit, for example, use the emotion estimation function to build a system that collects members' emotional responses to goal setting and task management. For example, they adjust goals and tasks based on the emotion data. The goal setting unit and task management unit also collect members' emotional responses and make adjustments to elicit positive responses. For example, they reset goals when the emotion score is low. The goal setting unit and task management unit also use the emotion estimation function to develop a system that collects members' emotional responses to goal setting and task management and makes adjustments to elicit positive responses. For example, they change task priorities based on the emotion data. This makes it possible to collect members' emotional responses and make adjustments to elicit positive responses.

[0076] The evaluation unit can use the emotion estimation function to consider the emotional state of the member when the generation AI makes an evaluation and provide feedback based on the emotion. For example, when the generation AI makes an evaluation, the evaluation unit uses the emotion estimation function to evaluate the emotional state of the member and provide feedback based on the emotion. For example, feedback that elicits positive emotions is provided. The evaluation unit also builds a system in which the generation AI provides feedback based on the emotion, taking the emotional state of the member into consideration. For example, the feedback content is adjusted based on emotion data. The evaluation unit also uses the emotion estimation function to evaluate the emotional state of the member, and the generation AI provides feedback based on the emotion. For example, positive feedback is provided if the emotion score is high. This makes it possible to provide feedback that takes the emotional state of the member into consideration.

[0077] The evaluation department can incorporate peer reviews and 360-degree evaluations into the evaluation process to enable evaluation from multiple perspectives. For example, the evaluation department will incorporate peer reviews and 360-degree evaluations into the generation AI to build a system that enables evaluation from a more multifaceted perspective. For example, evaluations from colleagues and superiors will be collected and a comprehensive evaluation will be made. The evaluation department will also incorporate peer reviews and 360-degree evaluations into the generation AI to make the evaluation process more multifaceted. For example, evaluations will be made based on feedback from multiple evaluators. The evaluation department will also develop a system in which the generation AI will incorporate peer reviews and 360-degree evaluations to enable evaluation from a more multifaceted perspective. For example, the opinions of evaluators will be integrated and a comprehensive evaluation will be made. In this way, by incorporating peer reviews and 360-degree evaluations, evaluation from a more multifaceted perspective will be possible.

[0078] The feedback unit can refer to past feedback history when the generation AI provides feedback and provide consistent feedback. For example, the feedback unit builds a system in which the generation AI refers to past feedback history and provides consistent feedback. For example, it adjusts current feedback based on the content of past feedback. The feedback unit also analyzes past feedback history and the generation AI provides consistent feedback. For example, it standardizes the feedback content for the same member. The feedback unit also develops a system in which the generation AI refers to past feedback history and provides consistent feedback. For example, it stores and references feedback history in a database. This makes it possible to provide consistent feedback by referring to past feedback history.

[0079] The evaluation and feedback department can apply the evaluation and feedback process not only to periodic performance reviews but also to daily work evaluations, thereby promoting continuous improvement. For example, the evaluation and feedback department builds a system in which generative AI applies the evaluation and feedback process to daily work evaluations, thereby promoting continuous improvement. For example, evaluations are made based on daily work data. The evaluation and feedback department also applies generative AI to daily work evaluations, not only to periodic performance reviews. For example, daily work performance is evaluated in real time. The evaluation and feedback department also develops a system in which generative AI applies the evaluation and feedback process to daily work evaluations, thereby promoting continuous improvement. For example, daily work data is collected and reflected in evaluations. This makes it possible to apply it to daily work evaluations as well, thereby enabling continuous improvement.

[0080] The feedback unit can deliver the feedback provided by the generation AI as an audio or video message, thereby realizing more personalized communication. The feedback unit, for example, builds a system that delivers the feedback provided by the generation AI as an audio or video message. For example, it converts the feedback content into an audio message using voice synthesis technology. The feedback unit can also deliver the feedback as an audio or video message, thereby realizing more personalized communication. For example, it provides feedback as a video message. The feedback unit can also develop a system that delivers the feedback provided by the generation AI as an audio or video message. For example, it converts the feedback content into a video message and delivers it. This enables more personalized communication by delivering feedback as an audio or video message.

[0081] The feedback unit can use the emotion estimation function to collect members' emotional reactions to feedback and evaluate the effectiveness of the feedback. The feedback unit, for example, uses the emotion estimation function to build a system that collects members' emotional reactions to feedback. For example, the emotion data after feedback is analyzed and the effectiveness is evaluated. The feedback unit also collects members' emotional reactions to feedback and evaluates the effectiveness of the feedback. For example, feedback with a high number of positive emotional reactions is preferentially adopted. The feedback unit also uses the emotion estimation function to collect members' emotional reactions to feedback and develop a system that evaluates the effectiveness of the feedback. For example, the feedback content is adjusted based on the emotion data. This makes it possible to collect members' emotional reactions to feedback and evaluate the effectiveness of the feedback.

[0082] When the generation AI promotes compliance, the compliance monitoring unit can use the emotion estimation function to take into account the emotional state of the member and provide emotion-based guidance. For example, when the generation AI promotes compliance, the compliance monitoring unit uses the emotion estimation function to evaluate the emotional state of the member and provide emotion-based guidance. For example, guidance is provided that brings out positive emotions. The compliance monitoring unit also builds a system in which the generation AI provides emotion-based compliance guidance, taking into account the emotional state of the member. For example, it adjusts the content of guidance based on emotion data. The compliance monitoring unit also uses the emotion estimation function to evaluate the emotional state of the member, and the generation AI provides emotion-based compliance guidance. For example, positive guidance is provided if the emotion score is high. This makes it possible to provide compliance guidance that takes into account the emotional state of the member.

[0083] The compliance monitoring department can learn from past violation cases to predict the risk of compliance violations and issue advance warnings of high-risk behavior. For example, the compliance monitoring department has the generation AI learn from past compliance violation cases and build a system that issues advance warnings of high-risk behavior. For example, it predicts risks based on past data. The compliance monitoring department also analyzes past violation cases and the generation AI issues advance warnings of high-risk behavior. For example, it evaluates risks based on similar cases and issues warnings. The compliance monitoring department also develops a system in which the generation AI learns from past compliance violation cases and issues advance warnings of high-risk behavior. For example, it builds a risk assessment model and issues warnings. In this way, by learning from past violation cases, it becomes possible to issue advance warnings of high-risk behavior.

[0084] The compliance monitoring unit monitors behavior in real time when the generation AI monitors compliance, and can immediately point out or correct any violations. For example, the compliance monitoring unit builds a system where the generation AI monitors compliance in real time and immediately points out or corrects any violations. For example, it monitors behavior during work and immediately points out any violations that are detected. The compliance monitoring unit also monitors behavior in real time, and the generation AI immediately points out compliance violations. For example, it analyzes business data in real time and detects violating behavior. The compliance monitoring unit also develops a system where the generation AI monitors compliance in real time and immediately points out or corrects any violations. For example, it builds a real-time monitoring system and immediately corrects any violating behavior. In this way, real-time behavior monitoring makes it possible to immediately point out or correct any violations.

[0085] The Compliance Monitoring Department can expand compliance promotion beyond just legal compliance to also include corporate ethics and social responsibility, thereby achieving comprehensive compliance. For example, the Compliance Monitoring Department will build a system in which generative AI expands compliance promotion to include corporate ethics and social responsibility, thereby achieving comprehensive compliance. For example, it will have it learn data related to corporate ethics. The Compliance Monitoring Department will also apply generative AI to not only legal compliance, but also corporate ethics and social responsibility. For example, it will have it learn code of conduct related to social responsibility and reflect this in guidance. The Compliance Monitoring Department will also develop a system in which generative AI expands compliance promotion to include corporate ethics and social responsibility, thereby achieving comprehensive compliance. For example, it will provide guidance based on corporate ethics. This will enable comprehensive compliance to be achieved by expanding compliance beyond just legal compliance to include corporate ethics and social responsibility.

[0086] The Compliance Monitoring Department can implement the compliance guidance provided by the generative AI as an interactive training program, thereby deepening members' understanding. For example, the Compliance Monitoring Department will build a system that implements the compliance guidance provided by the generative AI as an interactive training program. For example, it will provide training in the form of simulations or quizzes. The Compliance Monitoring Department will also have the generative AI provide compliance guidance through interactive training programs, thereby deepening members' understanding. For example, it will provide training based on actual cases. The Compliance Monitoring Department will also develop a system that implements the compliance guidance provided by the generative AI as an interactive training program. For example, it will provide guidance using interactive scenarios. This will make it possible to deepen members' understanding by implementing it as an interactive training program.

[0087] The compliance monitoring unit can use the emotion estimation function to collect members' emotional reactions to compliance guidance and use the collected information to improve the guidance method. The compliance monitoring unit, for example, uses the emotion estimation function to build a system that collects members' emotional reactions to compliance guidance. For example, the emotional data after guidance is analyzed to identify areas for improvement in the guidance method. The compliance monitoring unit also collects members' emotional reactions to compliance guidance and uses the collected information to improve the guidance method. For example, the compliance monitoring unit prioritizes the adoption of guidance methods that generate a high number of positive emotional reactions. The compliance monitoring unit also uses the emotion estimation function to develop a system that collects members' emotional reactions to compliance guidance and uses the collected information to improve the guidance method. For example, the content of the guidance is adjusted based on the emotional data. This makes it possible to collect members' emotional reactions and use the information to improve the guidance method.

[0088] The commenting unit can use the emotion estimation function to consider the emotional state of the subordinate when the generation AI gives comments to the subordinate and make comments based on the emotion. For example, when the generation AI gives comments to the subordinate, the commenting unit uses the emotion estimation function to evaluate the emotional state of the subordinate and make comments based on the emotion. For example, comments that elicit positive emotions are made. The commenting unit also builds a system in which the generation AI makes comments based on the emotion, taking the emotional state of the subordinate into consideration. For example, it adjusts the content of the comments based on emotion data. The commenting unit also uses the emotion estimation function to evaluate the emotional state of the subordinate, and the generation AI makes comments based on the emotion. For example, it makes positive comments when the emotion score is high. This makes it possible to make comments that take the emotional state of the subordinate into consideration.

[0089] The commenting unit compares the content of comments made to subordinates with past comment history and makes consistent comments, thereby improving reliability. The commenting unit, for example, builds a system in which the generation AI refers to past comment history and makes consistent comments. For example, it adjusts current comments based on past comment content. The commenting unit also analyzes past comment history and the generation AI makes consistent comments. For example, it standardizes the content of comments made to the same subordinate. The commenting unit also develops a system in which the generation AI refers to past comment history and makes consistent comments. For example, it stores and references the comment history in a database. This makes it possible to make consistent comments by comparing with past comment history, improving reliability.

[0090] The consultation response unit can automatically summarize the consultation content when the generation AI consults with a superior on behalf of the user and efficiently communicate it to the superior. For example, the consultation response unit builds a system that automatically summarizes the consultation content when the generation AI consults with a superior on behalf of the user. For example, it analyzes the text of the consultation content and extracts the main points. The consultation response unit also automatically summarizes the consultation content, and the generation AI efficiently communicates it to the superior. For example, it sends the summarized content by email or message. The consultation response unit also develops a system that automatically summarizes the consultation content when the generation AI consults with a superior on behalf of the user and efficiently communicates it to the superior. For example, it displays the summary content on a dashboard. In this way, the consultation content can be automatically summarized and efficiently communicated, allowing the user to consult with their superior smoothly.

[0091] The commenting unit and the consultation response unit can provide comments to subordinates and consultations to superiors not only as text but also as audio or video messages, thereby realizing more personalized communication. The commenting unit and the consultation response unit, for example, build a system in which comments are provided to subordinates and consultations are provided to superiors as audio or video messages. For example, the content of comments is converted into an audio message using voice synthesis technology. The commenting unit and the consultation response unit can also provide comments and consultations as audio or video messages, thereby realizing more personalized communication. For example, comments and consultations are provided as video messages. The commenting unit and the consultation response unit can also develop a system in which comments are provided to subordinates and consultations are provided to superiors as audio or video messages. For example, the content of comments is converted into a video message and distributed. As a result, comments and consultations are provided as audio or video messages, thereby enabling more personalized communication.

[0092] The commenting unit is capable of providing individually customized comments when the generation AI provides comments to a subordinate, taking into account the subordinate's skill set and work history. For example, the commenting unit builds a system that, when the generation AI provides comments to a subordinate, takes into account the subordinate's skill set and work history. For example, it adjusts the content of the comments based on a skill matrix. The commenting unit also develops a system that, when the generation AI provides comments to a subordinate, takes into account the subordinate's skill set and work history and provides individually customized comments. For example, it automatically adjusts the content of the comments based on skills and work history. This makes it possible to provide individually customized comments that take into account the subordinate's skill set and work history.

[0093] The commenting unit and the consultation response unit use the emotion estimation function to collect the emotional reactions of subordinates and superiors to comments and consultations, thereby improving the quality of communication. The commenting unit and the consultation response unit, for example, use the emotion estimation function to build a system that collects the emotional reactions of subordinates and superiors to comments and consultations. For example, they analyze the emotional data after comments are made to improve the quality of communication. The commenting unit and the consultation response unit also collect the emotional reactions of subordinates and superiors to comments and consultations, thereby improving the quality of communication. For example, they preferentially adopt a commenting method that generates a high number of positive emotional reactions. The commenting unit and the consultation response unit also use the emotion estimation function to collect the emotional reactions of subordinates and superiors to comments and consultations, thereby developing a system that improves the quality of communication. For example, they adjust the content of comments based on the emotion data. In this way, it is possible to improve the quality of communication by collecting the emotional reactions of subordinates and superiors to comments and consultations.

[0094] The opt-in AI introduction section can use an emotion estimation function to consider the emotional state of members during the opt-in AI introduction process and take measures to reduce resistance. For example, the opt-in AI introduction section can use an emotion estimation function to evaluate the emotional state of members during the opt-in AI introduction process and take measures to reduce resistance. For example, it can adopt an introduction method that elicits positive emotions. The opt-in AI introduction section can also build a system that adjusts the opt-in AI introduction process by taking members' emotional state into consideration. For example, it can adjust the introduction method based on emotional data. The opt-in AI introduction section can also use an emotion estimation function to evaluate members' emotional state during the opt-in AI introduction process and take measures to reduce resistance. For example, it can adopt a positive introduction method when the emotion score is high. This makes it possible to reduce resistance to the introduction of AI by considering members' emotional state.

[0095] The opt-in introduction to AI can be implemented in stages by opting in, and feedback is collected at each stage to optimize the process. The opt-in introduction to AI can be implemented in stages by opting in, for example, by building a system to collect feedback at each stage. For example, the process is adjusted based on feedback at the early stages of implementation. The opt-in introduction to AI can also be implemented in stages by opting in, and feedback is collected at each stage to optimize the process. For example, the implementation method is improved based on feedback. The opt-in introduction to AI can also be implemented in stages by opting in, and a system is developed to collect feedback at each stage to optimize the process. For example, the feedback data is analyzed and the implementation process is adjusted. This makes it possible to implement the process in stages and collect feedback at each stage to optimize the process.

[0096] The voluntary AI introduction section can share success stories and best practices when introducing voluntary AI, thereby promoting member understanding and cooperation. For example, the voluntary AI introduction section can build a system for sharing success stories and best practices when introducing voluntary AI. For example, it can register success stories in a database and provide it to members. The voluntary AI introduction section can also share success stories and best practices to promote member understanding and cooperation for the voluntary AI introduction. For example, it can explain the introduction method based on success stories. The voluntary AI introduction section can also develop a system for sharing success stories and best practices when introducing voluntary AI, thereby promoting member understanding and cooperation. For example, it can design an introduction process based on best practices. In this way, sharing success stories and best practices can promote member understanding and cooperation.

[0097] The voluntary AI introduction department can roll out the introduction of AI not only to a specific department but as a company-wide project, thereby improving the efficiency of the entire organization. The voluntary AI introduction department, for example, can roll out the introduction of AI not only to a specific department but as a company-wide project, and build a system that improves the efficiency of the entire organization. For example, it can formulate and implement a company-wide implementation plan. The voluntary AI introduction department can also roll out the introduction of AI not only to a specific department but as a company-wide project, and develop a system that improves the efficiency of the entire organization. For example, it can design and implement a company-wide implementation process. In this way, it can improve the efficiency of the entire organization by rolling out the introduction of AI as a company-wide project.

[0098] The opt-in AI introduction section can apply the opt-in AI introduction process to other organizations and industries to create a wide range of introduction cases. The opt-in AI introduction section, for example, builds a system that applies the opt-in AI introduction process to other organizations and industries to create a wide range of introduction cases. For example, it collects and shares introduction cases from different industries. The opt-in AI introduction section also applies the opt-in AI introduction process to other organizations and industries to create introduction cases. For example, it designs an introduction method based on successful cases from different industries. The opt-in AI introduction section also develops a system that applies the opt-in AI introduction process to other organizations and industries to create a wide range of introduction cases. For example, it analyzes and shares the introduction processes from different industries. This makes it possible to apply it to other organizations and industries to create a wide range of introduction cases.

[0099] The voluntary introduction of AI can use an emotion estimation function to collect members' emotional reactions to the introduction of AI, which can be used to improve the introduction process. The voluntary introduction of AI can, for example, use the emotion estimation function to build a system that collects members' emotional reactions to the introduction of AI. For example, emotional data from the early stages of introduction can be analyzed to identify areas for improvement in the process. The voluntary introduction of AI can also collect members' emotional reactions to the introduction of AI, which can be used to improve the introduction process. For example, an introduction method that elicits a high number of positive emotional reactions can be prioritized. The voluntary introduction of AI can also use the emotion estimation function to collect members' emotional reactions to the introduction of AI, which can be used to develop a system that can help improve the introduction process. For example, the introduction method can be adjusted based on the emotional data. In this way, collecting members' emotional reactions can be used to improve the introduction process.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The digital leader system can further include a health management unit. The health management unit monitors the health status of managers and members and predicts health risks. For example, daily activity data and health checkup results are collected, and the generative AI evaluates health risks. The health management unit also provides appropriate health advice based on the health status. For example, it may suggest relaxation methods when stress levels are high. The health management unit also builds a system that monitors health status and predicts health risks. For example, it may evaluate health risks based on health data and suggest preventive measures. Thus, by including a health management unit, it becomes possible to monitor the health status of managers and members and predict health risks.

[0102] The digital leader system can further include a career development department. The career development department designs career paths for members and provides training programs for skill development. For example, a generative AI suggests the optimal career path based on the member's skill set and career goals. The career development department also provides training programs for skill development based on the career path. For example, it suggests online courses and workshops. The career development department also builds a system that designs career paths for members and provides training programs for skill development. For example, a generative AI suggests training programs based on career goals. Thus, by including a career development department, it becomes possible to design career paths for members and provide training programs for skill development.

[0103] The digital reader system can further include a remote work support unit. The remote work support unit supports the optimization of the remote work environment and facilitates communication. For example, it monitors the progress of remote work, and the generation AI provides appropriate support. The remote work support unit also provides the necessary tools and resources to support the optimization of the remote work environment. For example, it proposes video conferencing systems and collaboration tools. The remote work support unit also builds a system to support the optimization of the remote work environment and facilitate communication. For example, it monitors the progress of remote work in real time, and the generation AI provides appropriate support. Thus, by including a remote work support unit, it becomes possible to support the optimization of the remote work environment and facilitate communication.

[0104] The Digital Leader System can further include an Innovation Promotion Department. The Innovation Promotion Department supports innovation activities within the organization and promotes the creation of new ideas. For example, it may hold idea contests and brainstorming sessions, with the generative AI evaluating the ideas. The Innovation Promotion Department also provides support for realizing new ideas. For example, it may support the creation of prototypes and market research. The Innovation Promotion Department also builds a system that supports innovation activities within the organization and promotes the creation of new ideas. For example, the generative AI provides support for the evaluation and realization of ideas. Thus, by having an Innovation Promotion Department, it is possible to support innovation activities within the organization and promote the creation of new ideas.

[0105] The digital leader system can further include a data analysis unit. The data analysis unit collects and analyzes data within the organization and provides insights for business improvement. For example, business data and market data are collected and analyzed by the generation AI. The data analysis unit also makes proposals for business improvement based on the data. For example, it makes proposals for improving business process efficiency and cost reduction. The data analysis unit also collects and analyzes data within the organization and builds a system that provides insights for business improvement. For example, the generation AI creates reports based on the data. Thus, by including a data analysis unit, it becomes possible to collect and analyze data within the organization and provide insights for business improvement.

[0106] The digital reader system can also use an emotion estimation function to monitor members' emotional states in real time, which can be useful for stress management and mental health care. For example, it can analyze members' facial expressions and voices to evaluate their stress levels. The emotion estimation function can also be used to monitor members' emotional states in real time, building a system that can be useful for stress management and mental health care. For example, it can suggest ways to relax when stress levels rise. The emotion estimation function can also be used to monitor members' emotional states in real time, which can be useful for mental health care. For example, it can provide counseling based on emotional data. In this way, stress management and mental health care become possible by monitoring emotional states in real time.

[0107] The digital reader system can further use an emotion estimation function to evaluate the emotional state of members and provide feedback based on their emotions. For example, when the generation AI makes an evaluation, it uses the emotion estimation function to evaluate the emotional state of members and provide feedback based on their emotions. A system can also be built using the emotion estimation function to evaluate the emotional state of members and provide feedback based on their emotions. For example, the content of the feedback can be adjusted based on emotion data. The emotion estimation function can also be used to evaluate the emotional state of members, and the generation AI can provide feedback based on their emotions. For example, positive feedback can be provided if the emotion score is high. This makes it possible to provide feedback that takes into account the emotional state of members.

[0108] The digital leader system can further use an emotion estimation function to collect members' emotional responses to goal setting and task management and make adjustments to elicit positive responses. For example, a system is constructed that uses the emotion estimation function to collect members' emotional responses to goal setting and task management. For example, goals and tasks are adjusted based on the emotion data. The emotion estimation function is also used to collect members' emotional responses and make adjustments to elicit positive responses. For example, goals are reset when the emotion score is low. The emotion estimation function is also used to develop a system that collects members' emotional responses to goal setting and task management and makes adjustments to elicit positive responses. For example, task priorities are changed based on the emotion data. This makes it possible to collect members' emotional responses and make adjustments to elicit positive responses.

[0109] The digital reader system can further use an emotion estimation function to collect members' emotional responses to compliance training and use the collected data to improve training methods. For example, a system can be built using the emotion estimation function to collect members' emotional responses to compliance training. For example, emotional data after training can be analyzed to identify areas for improvement in the training method. The emotion estimation function can also be used to collect members' emotional responses to compliance training and use the collected data to improve training methods. For example, training methods that generate a high number of positive emotional responses can be prioritized. The emotion estimation function can also be used to develop a system that collects members' emotional responses to compliance training and use the collected data to improve training methods. For example, the content of the training can be adjusted based on the emotional data. This makes it possible to collect members' emotional responses and use the collected data to improve training methods.

[0110] The digital reader system can further use an emotion estimation function to collect emotional responses to suggestions given to subordinates or consultations with superiors, thereby improving the quality of communication. For example, a system can be constructed using the emotion estimation function to collect emotional responses to suggestions given to subordinates or consultations with superiors. For example, the emotional data after suggestions can be analyzed to improve the quality of communication. The emotion estimation function can also be used to collect emotional responses to suggestions given to subordinates or consultations with superiors, thereby improving the quality of communication. For example, a suggestion method that generates a high number of positive emotional responses can be preferentially adopted. A system can also be developed using the emotion estimation function to collect emotional responses to suggestions given to subordinates or consultations with superiors, thereby improving the quality of communication. For example, the content of suggestions can be adjusted based on the emotional data. In this way, it is possible to improve the quality of communication by collecting emotional responses to suggestions given to subordinates or consultations with superiors.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The managerial work learning unit learns the manager's work and experience. For example, past work data and evaluation data are input into the generation AI, which then analyzes and learns from them. The managerial work learning unit also learns goal setting methods, task management techniques, evaluation criteria, etc. For example, the generation AI learns based on prompts that include the manager's work content and evaluation criteria. Step 2: The goal setting unit sets goals. For example, the generation AI understands the organization's goals and the roles of each member, and sets specific goals based on that. Step 3: The task management unit manages tasks. For example, the generation AI manages each member's tasks and monitors their progress. Step 4: The evaluation unit performs a work evaluation. For example, the generation AI analyzes each member's work data and provides an objective evaluation. Step 5: The feedback unit provides feedback. For example, the generation AI provides feedback based on the evaluation results. Step 6: The Compliance Monitoring Department monitors compliance. For example, the Generative AI learns the organization's rules and laws and monitors each member's behavior based on those rules and laws. Step 7: The feedback unit provides feedback to the subordinate. For example, the generation AI analyzes the subordinate's behavior and work content and provides necessary feedback. Step 8: The consultation response unit will consult with a superior on behalf of the employee. For example, if it is difficult to consult with a superior, the generation AI will respond instead.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

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

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

[0126] In the 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.

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

[0128] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0130] The data processing system 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.

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

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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]

[0180] 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. The Managerial Work Learning Department, where employees learn about managerial work and experience, a goal setting unit that sets goals; a task management section that manages tasks; an evaluation department that conducts business evaluations; a feedback unit for providing feedback; a compliance monitoring department that monitors compliance; A pointing out department that points out problems to subordinates; A consultation department will be established to handle consultations with superiors on behalf of employees. A system characterized by:

2. The managerial work learning department The business data of the managers is collected in real time, and the generation AI dynamically updates its learning content to respond to the latest business trends.

2. The system of claim 1.

3. The goal setting unit When the generation AI sets the goals, it takes into account the motivation of the members and sets the optimal goals.

2. The system of claim 1.

4. The evaluation unit Generative AI takes into account the emotional state of members when making evaluations and provides emotion-based feedback 2. The system of claim 1.

5. The compliance monitoring unit When promoting compliance, the generative AI takes into account the emotional state of the member and provides emotion-based guidance.

2. The system of claim 1.

6. The indicating unit When the generating AI gives instructions to the subordinate, it takes into account the subordinate's emotional state and gives instructions based on emotions.

2. The system of claim 1.

7. The introduction of AI through a voluntary system is as follows: In the process of introducing AI, which is based on a voluntary system, we will take into consideration the emotional state of members and take measures to reduce resistance.

2. The system of claim 1.

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