system

Abstract

The system according to this embodiment aims to identify skill gaps among veteran employees and provide appropriate reskilling plans. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, an implementation unit, an evaluation unit, and a provision unit. The analysis unit analyzes the skills of each veteran employee. The proposal unit proposes a reskilling plan based on the skills analyzed by the analysis unit. The implementation unit carries out reskilling based on the reskilling plan proposed by the proposal unit. The evaluation unit evaluates the progress of the reskilling carried out by the implementation unit. The provision unit provides rewards and promotion opportunities based on the progress evaluated by the evaluation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently identify the skill gaps of veteran employees and provide an appropriate reskilling plan.

[0005] The system according to the embodiment aims to identify the skill gaps of veteran employees and provide an appropriate reskilling plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, an implementation unit, an evaluation unit, and a provision unit. The analysis unit analyzes the skills of each veteran employee. The proposal unit proposes a reskilling plan based on the skills analyzed by the analysis unit. The implementation unit carries out reskilling based on the reskilling plan proposed by the proposal unit. The evaluation unit evaluates the progress of the reskilling carried out by the implementation unit. The provision unit provides compensation and promotion opportunities based on the progress evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify skill gaps among veteran employees and provide appropriate reskilling plans. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The reskilling platform according to an embodiment of the present invention is an integrated platform for resolving skill gaps among veteran employees and promoting reskilling. This reskilling platform uses AI to automatically analyze each veteran employee's current skills and compare them to skills required within the group and demand in the external market. This clarifies which skills are lacking and presents a reskilling plan to address those deficiencies. Next, the AI ​​automatically suggests the optimal reskilling method (online courses, in-house training, external seminars, etc.) based on the individual's career aspirations and interests. This enables veteran employees to efficiently acquire new skills and improve their market value. Furthermore, to enhance veteran employee motivation and optimize their redeployment, a knowledge transfer platform is constructed using AI. By digitizing the knowledge and experience of veteran employees and providing a "knowledge sharing platform" with younger employees, veteran employees can take on mentoring roles and reaffirm their self-worth through guiding junior colleagues. The AI ​​also analyzes the skills, experience, and interests of veteran employees and suggests redeployment to appropriate projects and leadership positions. This creates a path for veteran employees to leverage their existing skills and advance to new stages in their careers. Redesigning incentives and performance evaluation are also crucial elements. A system will be introduced where AI objectively evaluates newly acquired skills and reskilling progress, providing rewards and promotion opportunities. This will not only motivate employees to continue learning but also clarify career advancement paths. Furthermore, the traditional seniority-based evaluation system will shift to AI-driven performance-based evaluation, implementing evaluations based on reskilling and work contributions regardless of age. This will prevent salary reductions for veteran employees and maintain their motivation. Finally, to restructure and diversify career paths within the group, AI will analyze recruitment trends and project needs within group companies, creating a system that allows veteran employees to transfer to other departments or companies, or take on side jobs. This will maximize the utilization of veteran employees' skills across the entire group.Furthermore, the platform utilizes AI to provide a career coaching system that suggests which direction veteran employees should reskill, or which projects or roles are suitable for them. This provides guidance that allows veteran employees to confidently advance their careers. In this way, the reskilling platform can bridge the skill gaps among veteran employees and promote reskilling.

[0029] The reskilling platform according to this embodiment comprises an analysis unit, a proposal unit, an implementation unit, an evaluation unit, and a provision unit. The analysis unit analyzes the skills of each veteran employee. The analysis unit automatically analyzes the current skills of each veteran employee, for example, using AI, and compares them with the skills required within the group and the demand in the external market. The proposal unit proposes a reskilling plan based on the skills analyzed by the analysis unit. The proposal unit proposes the optimal reskilling method based on the individual's career aspirations and interests, for example, using AI. The implementation unit carries out reskilling based on the reskilling plan proposed by the proposal unit. The implementation unit carries out reskilling methods, for example, online courses, in-house training, and external seminars. The evaluation unit evaluates the progress of reskilling carried out by the implementation unit. The evaluation unit objectively evaluates newly acquired skills and reskilling progress, for example, using AI. The provision unit provides rewards and promotion opportunities based on the progress evaluated by the evaluation unit. The provision unit provides rewards and promotion opportunities based on the progress evaluated using AI, for example. As a result, the reskilling platform according to this embodiment can eliminate the skill gap among veteran employees and promote reskilling.

[0030] The analysis department analyzes the skills of each veteran employee. For example, it uses AI to automatically analyze each veteran employee's current skills and compares them to the skills required within the group and the demand in the external market. Specifically, the AI ​​uses natural language processing technology to analyze document data such as employee resumes, work reports, and project deliverables to extract each employee's skill set. Furthermore, the AI ​​uses machine learning algorithms to analyze past work performance and evaluation data to assess the proficiency and frequency of use of each skill. This allows the analysis department to gain a detailed understanding of each employee's strengths and weaknesses and identify skill gaps. The analysis department also connects with external market databases to obtain real-time information on current market trends and high-demand skills. This allows it to compare the skills required within the group with the demand in the external market, clearly indicating which skills employees should strengthen. Based on this data, the analysis department generates individual skill reports for each employee and provides them in a visually easy-to-understand format. For example, skill matrices and radar charts are used to allow employees to grasp the current status and goals of each skill at a glance. This allows the analysis unit to efficiently and accurately analyze employee skills and provide a foundation for reskilling.

[0031] The proposal department proposes reskilling plans based on the skills analyzed by the analysis department. For example, the proposal department uses AI to propose the optimal reskilling method based on an individual's career aspirations and interests. Specifically, the AI ​​analyzes data from questionnaires and interviews to understand each employee's career goals and interests. Using natural language processing technology, it extracts trends in career aspirations and interests from the free-response answers and comments written by employees. The AI ​​also learns from past successful and unsuccessful reskilling plans and builds a model to propose the optimal reskilling method. This allows the proposal department to generate individual reskilling plans for each employee and propose specific learning content and schedules. For example, it may propose a plan that combines multiple reskilling methods, such as taking online courses, participating in in-house training, and attending external seminars. The proposal department also selects the optimal learning resources and materials according to the employee's skill level and learning style. This allows the proposal department to support employees in reskilling efficiently and effectively. Furthermore, the proposal department monitors the progress of the reskilling plan and proposes revisions or additions to the plan as needed. This allows the proposal department to continuously support employees' career growth and maximize the effectiveness of reskilling.

[0032] The implementation department will carry out reskilling based on the reskilling plan proposed by the proposal department. The implementation department will implement reskilling methods such as online courses, in-house training, and external seminars. Specifically, the implementation department will provide appropriate learning resources to each employee based on the proposed reskilling plan. In the case of online courses, the implementation department will prepare a learning platform that employees can access and provide the necessary materials and assignments. In the case of in-house training, the implementation department will invite professional trainers and instructors to conduct practical skills training. In the case of external seminars, the implementation department will support the procedures for employees to participate and coordinate the necessary costs and time. Furthermore, the implementation department will monitor the progress of reskilling in real time to ensure that employees are learning according to plan. For example, they will regularly check the progress of online courses and attendance at in-house training and follow up as needed. The implementation department will also provide appropriate support for any problems or challenges that employees may face during their learning. In this way, the implementation department can create an environment in which employees can effectively carry out reskilling and maximize learning outcomes.

[0033] The evaluation department assesses the progress of reskilling implemented by the implementation department. For example, the evaluation department uses AI to objectively evaluate newly acquired skills and reskilling progress. Specifically, the AI ​​analyzes the results of assignments and tests submitted by employees to assess the level of skill acquisition. Using natural language processing technology, it analyzes the content of reports and presentations created by employees to assess their ability to apply and understand skills. The AI ​​also analyzes employees' work performance data to assess the extent to which reskilling contributes to their work. This allows the evaluation department to quantitatively grasp the effectiveness of reskilling and conduct objective evaluations. Furthermore, the evaluation department incorporates employee feedback and supervisor evaluations to conduct a comprehensive evaluation. This allows the evaluation department to accurately grasp the progress of reskilling and propose improvements as needed. For example, if the acquisition of a particular skill is lagging, the evaluation department can identify the cause and provide additional learning resources and support. The evaluation department also provides feedback on the results of reskilling to employees and implements measures to improve motivation. This allows the evaluation department to effectively assess the progress of reskilling and support employee growth.

[0034] The Service Department provides rewards and promotion opportunities based on progress evaluated by the Evaluation Department. For example, the Service Department provides rewards and promotion opportunities based on progress evaluated using AI. Specifically, the AI ​​analyzes evaluation data provided by the Evaluation Department and quantitatively evaluates the results of each employee's reskilling. This allows the Service Department to set reward and promotion criteria according to each employee's contribution and growth. For example, employees who acquire specific skills can receive skill allowances, and employees whose reskilling results significantly contribute to the business can be offered promotion opportunities. The Service Department also widely shares the results of reskilling within the company and takes measures to improve the motivation of other employees. For example, successful reskilling cases are introduced in company newsletters and on the intranet to raise awareness of the importance and effectiveness of reskilling. Furthermore, the Service Department designs employee career paths based on the results of reskilling and supports long-term growth. This allows the Service Department to promote employee reskilling and improve the overall skill level of the organization.

[0035] The Platform Department will build a platform for knowledge transfer. For example, the Platform Department will use AI to digitize the knowledge and experience of veteran employees and facilitate knowledge sharing with younger employees. The Platform Department will include a database, search function, and sharing function. This will enable the digitization of veteran employees' knowledge and experience and facilitate knowledge sharing with younger employees. Some or all of the above-described processes in the Platform Department may be performed using AI, or not. For example, the Platform Department can use generative AI to generate text data for digitizing veteran employees' knowledge and experience and store it in a database.

[0036] The matching unit performs project suitability matching. For example, the matching unit uses AI to analyze the skills, experience, and interests of veteran employees and proposes appropriate reassignment to projects and leadership positions. The matching unit uses criteria such as skill matching, experience matching, and interest matching. This allows it to analyze the skills, experience, and interests of veteran employees and propose appropriate reassignment to projects and leadership positions. Some or all of the above-described processes in the matching unit may be performed using AI, or not. For example, the matching unit can perform project suitability matching using an AI model that takes the skills, experience, and interests of veteran employees as input and outputs appropriate projects and leadership positions.

[0037] The Coaching Department provides career coaching. For example, the Coaching Department uses AI to suggest which direction veteran employees should reskill, or which projects or positions are suitable for them. The Coaching Department sets the frequency, content, and evaluation methods of coaching. This allows them to suggest which direction veteran employees should reskill, or which projects or positions are suitable for them. Some or all of the above processes in the Coaching Department may be performed using AI, or not. For example, the Coaching Department can conduct career coaching using an AI model that takes a veteran employee's career aspirations and interests as input and outputs the optimal reskilling plan, project, or position.

[0038] The analysis unit can analyze the past project history of each veteran employee to improve the accuracy of skill analysis. For example, the analysis unit can retrieve each veteran employee's past project history from a database and reflect it in the skill analysis. The analysis unit can also improve the accuracy of skill analysis by considering the success rate and failure rate of projects. Furthermore, the analysis unit can adjust the weighting of the skill analysis based on the type and scale of the project. This improves the accuracy of skill analysis by analyzing past project history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past project history data into a generating AI and have the generating AI perform the skill analysis accuracy improvement.

[0039] The analysis unit can customize the analysis method based on the current job duties and position of veteran employees during skill analysis. For example, the analysis unit can identify the necessary skill sets based on current job duties and adjust the analysis method accordingly. The analysis unit can also focus on analyzing leadership skills or specialized technical skills depending on the position. Furthermore, the analysis unit can adjust the frequency and timing of skill analysis in response to changes in job duties. By customizing the analysis method based on current job duties and position, more accurate skill analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input current job duties and position data into a generating AI and have the generating AI perform the customization of the analysis method.

[0040] The proposal department can select the optimal reskilling plan by referring to the past learning history of veteran employees when proposing a reskilling plan. For example, the proposal department can propose the most effective reskilling plan based on past learning history. The proposal department can also select a plan that is expected to improve skills by considering past learning outcomes. Furthermore, the proposal department can propose a reskilling plan that suits the learning style based on past learning history. In this way, the optimal reskilling plan can be proposed by referring to past learning history. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input past learning history data into a generating AI and have the generating AI select the optimal reskilling plan.

[0041] The proposal department can customize the proposed reskilling plan based on the veteran employee's current career stage. For example, the proposal department can identify the necessary skill sets according to the current career stage and propose a reskilling plan. The proposal department can also provide reskilling plans for leadership skills and specialized technical skills based on the career stage. Furthermore, the proposal department can adjust the content of the reskilling plan in accordance with changes in the career stage. This allows for the provision of more appropriate reskilling plans by customizing the proposal based on the current career stage. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input current career stage data into a generating AI and have the generating AI perform the customization of the proposal content.

[0042] The implementation department can select the optimal implementation method by referring to the past learning outcomes of veteran employees when conducting reskilling. For example, the implementation department can select the most effective reskilling method based on past learning outcomes. The implementation department can also provide implementation methods that are expected to improve skills, taking past learning outcomes into consideration. Furthermore, the implementation department can select a reskilling method that suits the learning style based on past learning outcomes. In this way, the optimal reskilling implementation method can be provided by referring to past learning outcomes. Some or all of the above processes in the implementation department may be performed using AI, for example, or not using AI. For example, the implementation department can input past learning outcome data into a generating AI and have the generating AI select the optimal reskilling implementation method.

[0043] The implementation department can customize the reskilling process based on the current work content of veteran employees. For example, the implementation department can identify the necessary skill sets based on current work content and customize the reskilling content accordingly. The implementation department can also provide reskilling content for leadership skills and specialized technical skills depending on the work content. Furthermore, the implementation department can adjust the reskilling content in response to changes in work content. This allows for more effective reskilling by customizing the implementation content based on current work content. Some or all of the above processes in the implementation department may be performed using AI, for example, or not. For example, the implementation department can input current work content data into a generating AI and have the generating AI perform the customization of the implementation content.

[0044] The evaluation department can select the optimal evaluation method by referring to the past evaluation history of veteran employees when evaluating reskilling progress. For example, the evaluation department can select the most effective evaluation method based on past evaluation history. The evaluation department can also provide evaluation methods that are expected to improve skills, taking past evaluation history into consideration. Furthermore, the evaluation department can select an evaluation method that suits the evaluation style from past evaluation history. In this way, the optimal evaluation method can be provided by referring to past evaluation history. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI. For example, the evaluation department can input past evaluation history data into a generating AI and have the generating AI select the optimal evaluation method.

[0045] The evaluation department can customize the evaluation content based on the current job responsibilities of veteran employees when evaluating reskilling progress. For example, the evaluation department can identify the necessary skill sets based on current job responsibilities and customize the evaluation content accordingly. The evaluation department can also provide evaluation content for leadership skills and specialized technical skills depending on the job responsibilities. Furthermore, the evaluation department can adjust the evaluation content in response to changes in job responsibilities. This allows for more appropriate evaluations by customizing the evaluation content based on current job responsibilities. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can input current job responsibilities data into a generating AI and have the generating AI perform the customization of the evaluation content.

[0046] The compensation department can select the optimal compensation and promotion method by referring to the past performance of veteran employees when providing compensation and promotions. For example, the compensation department can select the most effective compensation and promotion method based on past performance. The compensation department can also consider past performance and provide compensation and promotion methods that are expected to improve skills. Furthermore, the compensation department can select compensation and promotion methods that match the performance style based on past performance. In this way, the optimal compensation and promotion method can be provided by referring to past performance. Some or all of the above processes in the compensation department may be performed using AI, for example, or not using AI. For example, the compensation department can input past performance data into a generating AI and have the generating AI select the optimal compensation and promotion method.

[0047] The service provider can customize the content of compensation and promotions based on the current job responsibilities of veteran employees. For example, the service provider can identify the necessary skill sets based on current job responsibilities and customize the compensation and promotion content accordingly. The service provider can also provide compensation and promotion content that includes leadership skills and specialized technical skills, depending on the job responsibilities. Furthermore, the service provider can adjust the compensation and promotion content in response to changes in job responsibilities. This allows for the provision of more appropriate compensation and promotions by customizing the content based on current job responsibilities. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input current job responsibilities data into a generating AI and have the generating AI perform the customization of the content.

[0048] The platform unit can select the optimal knowledge sharing method by referring to the past knowledge transfer history of veteran employees when sharing knowledge. For example, the platform unit can select the most effective knowledge sharing method based on past knowledge transfer history. The platform unit can also consider past knowledge transfer history and provide knowledge sharing methods that are expected to improve skills. Furthermore, the platform unit can select a method that suits the knowledge sharing style from past knowledge transfer history. In this way, the optimal knowledge sharing method can be provided by referring to past knowledge transfer history. Some or all of the above processing in the platform unit may be performed using AI, for example, or not using AI. For example, the platform unit can input past knowledge transfer history data into a generating AI and have the generating AI select the optimal knowledge sharing method.

[0049] The platform unit can customize the content shared based on the current work content of veteran employees when sharing knowledge. For example, the platform unit can identify the necessary knowledge sets based on current work content and customize the content to be shared. The platform unit can also provide knowledge sharing content on leadership skills and specialized technical skills depending on the work content. Furthermore, the platform unit can adjust the content to be shared in response to changes in work content. This makes it possible to share knowledge more effectively by customizing the content to be shared based on current work content. Some or all of the above processes in the platform unit may be performed using AI, for example, or not using AI. For example, the platform unit can input current work content data into a generating AI and have the generating AI perform the customization of the shared content.

[0050] The matching unit can select the optimal matching method by referring to the past project history of veteran employees when matching project suitability. For example, the matching unit can select the most effective matching method based on past project history. The matching unit can also consider past project history and provide matching methods that are expected to improve skills. Furthermore, the matching unit can select a matching method that suits the project style from past project history. In this way, the optimal matching method can be provided by referring to past project history. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input past project history data into a generating AI and have the generating AI select the optimal matching method.

[0051] The matching unit can customize the matching content based on the current work content of veteran employees when matching them for project suitability. For example, the matching unit can identify the necessary skill sets based on current work content and customize the matching content. The matching unit can also provide matching content for leadership skills and specialized technical skills depending on the work content. Furthermore, the matching unit can adjust the matching content in response to changes in work content. This allows for more appropriate matching by customizing the matching content based on current work content. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input current work content data into a generating AI and have the generating AI perform the customization of the matching content.

[0052] The coaching department can select the most suitable coaching method during career coaching by referring to the past career history of veteran employees. For example, the coaching department can select the most effective career coaching method based on past career history. The coaching department can also provide career coaching methods that are expected to improve skills, taking into account past career history. Furthermore, the coaching department can select a coaching method that suits the career style based on past career history. In this way, the coaching department can provide the most suitable coaching method by referring to past career history. Some or all of the above processes in the coaching department may be performed using AI, for example, or not using AI. For example, the coaching department can input past career history data into a generating AI and have the generating AI select the most suitable coaching method.

[0053] The coaching department can customize the content of career coaching based on the current job responsibilities of veteran employees. For example, the coaching department can identify the necessary skill sets based on current job responsibilities and customize the coaching content accordingly. The coaching department can also provide coaching on leadership skills and specialized technical skills depending on the job responsibilities. Furthermore, the coaching department can adjust the coaching content in response to changes in job responsibilities. This allows for more effective coaching by customizing the coaching content based on current job responsibilities. Some or all of the above processes in the coaching department may be performed using AI, for example, or not. For example, the coaching department can input current job responsibilities data into a generating AI and have the generating AI perform the customization of the coaching content.

[0054] The coaching department can select the optimal coaching method during career coaching by considering the geographical location information of veteran employees. For example, the coaching department can conduct career coaching while considering the region-specific skill needs based on geographical location information. The coaching department can also prioritize career coaching for projects and training opportunities that are geographically close. Furthermore, the coaching department can conduct career coaching while considering the possibility of remote work based on geographical location information. In this way, the coaching department can provide the optimal coaching method by considering geographical location information. Some or all of the above processes in the coaching department may be performed using AI, for example, or not using AI. For example, the coaching department can input geographical location data into a generating AI and have the generating AI select the optimal coaching method.

[0055] The coaching department can analyze the social media activities of veteran employees during career coaching sessions and propose relevant coaching methods. For example, the coaching department can analyze the content of social media posts and propose relevant career coaching methods. Furthermore, the coaching department can propose career coaching methods for improving communication skills based on social media interactions and follower counts. In addition, the coaching department can propose career coaching methods related to the latest trends and technologies based on social media activity history. This allows for the provision of optimal coaching methods by analyzing social media activities. Some or all of the above processes performed by the coaching department may be carried out using AI, for example, or not. For example, the coaching department can input social media activity data into a generating AI and have the AI ​​generate suggestions for relevant coaching methods.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The analysis unit can analyze the past project history of each veteran employee to improve the accuracy of skill analysis. For example, the analysis unit can retrieve each veteran employee's past project history from a database and reflect it in the skill analysis. The analysis unit can also improve the accuracy of skill analysis by considering the success rate and failure rate of projects. Furthermore, the analysis unit can adjust the weighting of the skill analysis based on the type and scale of the project. This improves the accuracy of skill analysis by analyzing past project history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past project history data into a generating AI and have the generating AI perform the skill analysis accuracy improvement.

[0058] The proposal department can select the optimal reskilling plan by referring to the past learning history of veteran employees when proposing a reskilling plan. For example, the proposal department can propose the most effective reskilling plan based on past learning history. The proposal department can also select a plan that is expected to improve skills by considering past learning outcomes. Furthermore, the proposal department can propose a reskilling plan that suits the learning style based on past learning history. In this way, the optimal reskilling plan can be proposed by referring to past learning history. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input past learning history data into a generating AI and have the generating AI select the optimal reskilling plan.

[0059] The implementation department can select the optimal implementation method by referring to the past learning outcomes of veteran employees when conducting reskilling. For example, the implementation department can select the most effective reskilling method based on past learning outcomes. The implementation department can also provide implementation methods that are expected to improve skills, taking past learning outcomes into consideration. Furthermore, the implementation department can select a reskilling method that suits the learning style based on past learning outcomes. In this way, the optimal reskilling implementation method can be provided by referring to past learning outcomes. Some or all of the above processes in the implementation department may be performed using AI, for example, or not using AI. For example, the implementation department can input past learning outcome data into a generating AI and have the generating AI select the optimal reskilling implementation method.

[0060] The evaluation department can select the optimal evaluation method by referring to the past evaluation history of veteran employees when evaluating reskilling progress. For example, the evaluation department can select the most effective evaluation method based on past evaluation history. The evaluation department can also provide evaluation methods that are expected to improve skills, taking past evaluation history into consideration. Furthermore, the evaluation department can select an evaluation method that suits the evaluation style from past evaluation history. In this way, the optimal evaluation method can be provided by referring to past evaluation history. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI. For example, the evaluation department can input past evaluation history data into a generating AI and have the generating AI select the optimal evaluation method.

[0061] The compensation department can select the optimal compensation and promotion method by referring to the past performance of veteran employees when providing compensation and promotions. For example, the compensation department can select the most effective compensation and promotion method based on past performance. The compensation department can also consider past performance and provide compensation and promotion methods that are expected to improve skills. Furthermore, the compensation department can select compensation and promotion methods that match the performance style based on past performance. In this way, the optimal compensation and promotion method can be provided by referring to past performance. Some or all of the above processes in the compensation department may be performed using AI, for example, or not using AI. For example, the compensation department can input past performance data into a generating AI and have the generating AI select the optimal compensation and promotion method.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The analysis unit analyzes the skills of each veteran employee. The analysis unit automatically analyzes the current skills of each veteran employee, for example, using AI, and compares them with the skills required within the group and the demand in the external market. Step 2: The proposal department proposes a reskilling plan based on the skills analyzed by the analysis department. For example, the proposal department uses AI to propose the optimal reskilling method based on an individual's career aspirations and interests. Step 3: The implementation department carries out reskilling based on the reskilling plan proposed by the proposal department. The implementation department implements reskilling methods such as online courses, in-house training, and external seminars. Step 4: The evaluation department assesses the progress of the reskilling implemented by the implementation department. The evaluation department objectively evaluates newly acquired skills and the progress of reskilling, for example, using AI. Step 5: The provision department provides rewards and promotion opportunities based on the progress evaluated by the evaluation department. For example, the provision department provides rewards and promotion opportunities based on progress evaluated using AI.

[0064] (Example of form 2) The reskilling platform according to an embodiment of the present invention is an integrated platform for resolving skill gaps among veteran employees and promoting reskilling. This reskilling platform uses AI to automatically analyze each veteran employee's current skills and compare them to skills required within the group and demand in the external market. This clarifies which skills are lacking and presents a reskilling plan to address those deficiencies. Next, the AI ​​automatically suggests the optimal reskilling method (online courses, in-house training, external seminars, etc.) based on the individual's career aspirations and interests. This enables veteran employees to efficiently acquire new skills and improve their market value. Furthermore, to enhance veteran employee motivation and optimize their redeployment, a knowledge transfer platform is constructed using AI. By digitizing the knowledge and experience of veteran employees and providing a "knowledge sharing platform" with younger employees, veteran employees can take on mentoring roles and reaffirm their self-worth through guiding junior colleagues. The AI ​​also analyzes the skills, experience, and interests of veteran employees and suggests redeployment to appropriate projects and leadership positions. This creates a path for veteran employees to leverage their existing skills and advance to new stages in their careers. Redesigning incentives and performance evaluation are also crucial elements. A system will be introduced where AI objectively evaluates newly acquired skills and reskilling progress, providing rewards and promotion opportunities. This will not only motivate employees to continue learning but also clarify career advancement paths. Furthermore, the traditional seniority-based evaluation system will shift to AI-driven performance-based evaluation, implementing evaluations based on reskilling and work contributions regardless of age. This will prevent salary reductions for veteran employees and maintain their motivation. Finally, to restructure and diversify career paths within the group, AI will analyze recruitment trends and project needs within group companies, creating a system that allows veteran employees to transfer to other departments or companies, or take on side jobs. This will maximize the utilization of veteran employees' skills across the entire group.Furthermore, the platform utilizes AI to provide a career coaching system that suggests which direction veteran employees should reskill, or which projects or roles are suitable for them. This provides guidance that allows veteran employees to confidently advance their careers. In this way, the reskilling platform can bridge the skill gaps among veteran employees and promote reskilling.

[0065] The reskilling platform according to this embodiment comprises an analysis unit, a proposal unit, an implementation unit, an evaluation unit, and a provision unit. The analysis unit analyzes the skills of each veteran employee. The analysis unit automatically analyzes the current skills of each veteran employee, for example, using AI, and compares them with the skills required within the group and the demand in the external market. The proposal unit proposes a reskilling plan based on the skills analyzed by the analysis unit. The proposal unit proposes the optimal reskilling method based on the individual's career aspirations and interests, for example, using AI. The implementation unit carries out reskilling based on the reskilling plan proposed by the proposal unit. The implementation unit carries out reskilling methods, for example, online courses, in-house training, and external seminars. The evaluation unit evaluates the progress of reskilling carried out by the implementation unit. The evaluation unit objectively evaluates newly acquired skills and reskilling progress, for example, using AI. The provision unit provides rewards and promotion opportunities based on the progress evaluated by the evaluation unit. The provision unit provides rewards and promotion opportunities based on the progress evaluated using AI, for example. As a result, the reskilling platform according to this embodiment can eliminate the skill gap among veteran employees and promote reskilling.

[0066] The analysis department analyzes the skills of each veteran employee. For example, it uses AI to automatically analyze each veteran employee's current skills and compares them to the skills required within the group and the demand in the external market. Specifically, the AI ​​uses natural language processing technology to analyze document data such as employee resumes, work reports, and project deliverables to extract each employee's skill set. Furthermore, the AI ​​uses machine learning algorithms to analyze past work performance and evaluation data to assess the proficiency and frequency of use of each skill. This allows the analysis department to gain a detailed understanding of each employee's strengths and weaknesses and identify skill gaps. The analysis department also connects with external market databases to obtain real-time information on current market trends and high-demand skills. This allows it to compare the skills required within the group with the demand in the external market, clearly indicating which skills employees should strengthen. Based on this data, the analysis department generates individual skill reports for each employee and provides them in a visually easy-to-understand format. For example, skill matrices and radar charts are used to allow employees to grasp the current status and goals of each skill at a glance. This allows the analysis unit to efficiently and accurately analyze employee skills and provide a foundation for reskilling.

[0067] The proposal department proposes reskilling plans based on the skills analyzed by the analysis department. For example, the proposal department uses AI to propose the optimal reskilling method based on an individual's career aspirations and interests. Specifically, the AI ​​analyzes data from questionnaires and interviews to understand each employee's career goals and interests. Using natural language processing technology, it extracts trends in career aspirations and interests from the free-response answers and comments written by employees. The AI ​​also learns from past successful and unsuccessful reskilling plans and builds a model to propose the optimal reskilling method. This allows the proposal department to generate individual reskilling plans for each employee and propose specific learning content and schedules. For example, it may propose a plan that combines multiple reskilling methods, such as taking online courses, participating in in-house training, and attending external seminars. The proposal department also selects the optimal learning resources and materials according to the employee's skill level and learning style. This allows the proposal department to support employees in reskilling efficiently and effectively. Furthermore, the proposal department monitors the progress of the reskilling plan and proposes revisions or additions to the plan as needed. This allows the proposal department to continuously support employees' career growth and maximize the effectiveness of reskilling.

[0068] The implementation department will carry out reskilling based on the reskilling plan proposed by the proposal department. The implementation department will implement reskilling methods such as online courses, in-house training, and external seminars. Specifically, the implementation department will provide appropriate learning resources to each employee based on the proposed reskilling plan. In the case of online courses, the implementation department will prepare a learning platform that employees can access and provide the necessary materials and assignments. In the case of in-house training, the implementation department will invite professional trainers and instructors to conduct practical skills training. In the case of external seminars, the implementation department will support the procedures for employees to participate and coordinate the necessary costs and time. Furthermore, the implementation department will monitor the progress of reskilling in real time to ensure that employees are learning according to plan. For example, they will regularly check the progress of online courses and attendance at in-house training and follow up as needed. The implementation department will also provide appropriate support for any problems or challenges that employees may face during their learning. In this way, the implementation department can create an environment in which employees can effectively carry out reskilling and maximize learning outcomes.

[0069] The evaluation department assesses the progress of reskilling implemented by the implementation department. For example, the evaluation department uses AI to objectively evaluate newly acquired skills and reskilling progress. Specifically, the AI ​​analyzes the results of assignments and tests submitted by employees to assess the level of skill acquisition. Using natural language processing technology, it analyzes the content of reports and presentations created by employees to assess their ability to apply and understand skills. The AI ​​also analyzes employees' work performance data to assess the extent to which reskilling contributes to their work. This allows the evaluation department to quantitatively grasp the effectiveness of reskilling and conduct objective evaluations. Furthermore, the evaluation department incorporates employee feedback and supervisor evaluations to conduct a comprehensive evaluation. This allows the evaluation department to accurately grasp the progress of reskilling and propose improvements as needed. For example, if the acquisition of a particular skill is lagging, the evaluation department can identify the cause and provide additional learning resources and support. The evaluation department also provides feedback on the results of reskilling to employees and implements measures to improve motivation. This allows the evaluation department to effectively assess the progress of reskilling and support employee growth.

[0070] The Service Department provides rewards and promotion opportunities based on progress evaluated by the Evaluation Department. For example, the Service Department provides rewards and promotion opportunities based on progress evaluated using AI. Specifically, the AI ​​analyzes evaluation data provided by the Evaluation Department and quantitatively evaluates the results of each employee's reskilling. This allows the Service Department to set reward and promotion criteria according to each employee's contribution and growth. For example, employees who acquire specific skills can receive skill allowances, and employees whose reskilling results significantly contribute to the business can be offered promotion opportunities. The Service Department also widely shares the results of reskilling within the company and takes measures to improve the motivation of other employees. For example, successful reskilling cases are introduced in company newsletters and on the intranet to raise awareness of the importance and effectiveness of reskilling. Furthermore, the Service Department designs employee career paths based on the results of reskilling and supports long-term growth. This allows the Service Department to promote employee reskilling and improve the overall skill level of the organization.

[0071] The Platform Department will build a platform for knowledge transfer. For example, the Platform Department will use AI to digitize the knowledge and experience of veteran employees and facilitate knowledge sharing with younger employees. The Platform Department will include a database, search function, and sharing function. This will enable the digitization of veteran employees' knowledge and experience and facilitate knowledge sharing with younger employees. Some or all of the above-described processes in the Platform Department may be performed using AI, or not. For example, the Platform Department can use generative AI to generate text data for digitizing veteran employees' knowledge and experience and store it in a database.

[0072] The matching unit performs project suitability matching. For example, the matching unit uses AI to analyze the skills, experience, and interests of veteran employees and proposes appropriate reassignment to projects and leadership positions. The matching unit uses criteria such as skill matching, experience matching, and interest matching. This allows it to analyze the skills, experience, and interests of veteran employees and propose appropriate reassignment to projects and leadership positions. Some or all of the above-described processes in the matching unit may be performed using AI, or not. For example, the matching unit can perform project suitability matching using an AI model that takes the skills, experience, and interests of veteran employees as input and outputs appropriate projects and leadership positions.

[0073] The Coaching Department provides career coaching. For example, the Coaching Department uses AI to suggest which direction veteran employees should reskill, or which projects or positions are suitable for them. The Coaching Department sets the frequency, content, and evaluation methods of coaching. This allows them to suggest which direction veteran employees should reskill, or which projects or positions are suitable for them. Some or all of the above processes in the Coaching Department may be performed using AI, or not. For example, the Coaching Department can conduct career coaching using an AI model that takes a veteran employee's career aspirations and interests as input and outputs the optimal reskilling plan, project, or position.

[0074] The analysis unit can estimate the user's emotions and adjust the timing of skill analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can postpone the skill analysis and perform it when the user is relaxed. Alternatively, if the user is relaxed, the analysis unit can perform the skill analysis immediately and provide the results quickly. Furthermore, if the user is in a hurry, the analysis unit can perform the skill analysis quickly and provide the results in a short time. This allows for skill analysis to be performed at a more appropriate time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0075] The analysis unit can analyze the past project history of each veteran employee to improve the accuracy of skill analysis. For example, the analysis unit can retrieve each veteran employee's past project history from a database and reflect it in the skill analysis. The analysis unit can also improve the accuracy of skill analysis by considering the success rate and failure rate of projects. Furthermore, the analysis unit can adjust the weighting of the skill analysis based on the type and scale of the project. This improves the accuracy of skill analysis by analyzing past project history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past project history data into a generating AI and have the generating AI perform the skill analysis accuracy improvement.

[0076] The analysis unit can customize the analysis method based on the current job duties and position of veteran employees during skill analysis. For example, the analysis unit can identify the necessary skill sets based on current job duties and adjust the analysis method accordingly. The analysis unit can also focus on analyzing leadership skills or specialized technical skills depending on the position. Furthermore, the analysis unit can adjust the frequency and timing of skill analysis in response to changes in job duties. By customizing the analysis method based on current job duties and position, more accurate skill analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input current job duties and position data into a generating AI and have the generating AI perform the customization of the analysis method.

[0077] The suggestion unit can estimate the user's emotions and adjust the presentation of the reskilling plan based on the estimated emotions. For example, if the user is stressed, the suggestion unit can propose a simple and easy-to-understand reskilling plan. If the user is relaxed, the suggestion unit can also provide a detailed reskilling plan. Furthermore, if the user is in a hurry, the suggestion unit can provide a concise reskilling plan. By adjusting the presentation of the reskilling plan according to the user's emotions, a more appropriate reskilling plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The proposal department can select the optimal reskilling plan by referring to the past learning history of veteran employees when proposing a reskilling plan. For example, the proposal department can propose the most effective reskilling plan based on past learning history. The proposal department can also select a plan that is expected to improve skills by considering past learning outcomes. Furthermore, the proposal department can propose a reskilling plan that suits the learning style based on past learning history. In this way, the optimal reskilling plan can be proposed by referring to past learning history. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input past learning history data into a generating AI and have the generating AI select the optimal reskilling plan.

[0079] The proposal department can customize the proposed reskilling plan based on the veteran employee's current career stage. For example, the proposal department can identify the necessary skill sets according to the current career stage and propose a reskilling plan. The proposal department can also provide reskilling plans for leadership skills and specialized technical skills based on the career stage. Furthermore, the proposal department can adjust the content of the reskilling plan in accordance with changes in the career stage. This allows for the provision of more appropriate reskilling plans by customizing the proposal based on the current career stage. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input current career stage data into a generating AI and have the generating AI perform the customization of the proposal content.

[0080] The implementation unit can estimate the user's emotions and adjust the reskilling method based on the estimated emotions. For example, if the user is stressed, the implementation unit can conduct reskilling in a relaxing environment. If the user is relaxed, the implementation unit can also conduct reskilling in a focused environment. Furthermore, if the user is in a hurry, the implementation unit can provide a quick and effective reskilling method. By adjusting the reskilling method according to the user's emotions, more effective reskilling becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the implementation unit may be performed using AI or not using AI. For example, the implementation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The implementation department can select the optimal implementation method by referring to the past learning outcomes of veteran employees when conducting reskilling. For example, the implementation department can select the most effective reskilling method based on past learning outcomes. The implementation department can also provide implementation methods that are expected to improve skills, taking past learning outcomes into consideration. Furthermore, the implementation department can select a reskilling method that suits the learning style based on past learning outcomes. In this way, the optimal reskilling implementation method can be provided by referring to past learning outcomes. Some or all of the above processes in the implementation department may be performed using AI, for example, or not using AI. For example, the implementation department can input past learning outcome data into a generating AI and have the generating AI select the optimal reskilling implementation method.

[0082] The implementation department can customize the reskilling process based on the current work content of veteran employees. For example, the implementation department can identify the necessary skill sets based on current work content and customize the reskilling content accordingly. The implementation department can also provide reskilling content for leadership skills and specialized technical skills depending on the work content. Furthermore, the implementation department can adjust the reskilling content in response to changes in work content. This allows for more effective reskilling by customizing the implementation content based on current work content. Some or all of the above processes in the implementation department may be performed using AI, for example, or not. For example, the implementation department can input current work content data into a generating AI and have the generating AI perform the customization of the implementation content.

[0083] The evaluation unit can estimate the user's emotions and adjust the reskilling progress evaluation method based on the estimated user emotions. For example, if the user is stressed, the evaluation unit can provide a concise and easy-to-understand evaluation method. It can also provide a detailed evaluation method if the user is relaxed. Furthermore, if the user is in a hurry, the evaluation unit can provide evaluation results quickly. This allows for more appropriate evaluation by adjusting the reskilling progress evaluation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The evaluation department can select the optimal evaluation method by referring to the past evaluation history of veteran employees when evaluating reskilling progress. For example, the evaluation department can select the most effective evaluation method based on past evaluation history. The evaluation department can also provide evaluation methods that are expected to improve skills, taking past evaluation history into consideration. Furthermore, the evaluation department can select an evaluation method that suits the evaluation style from past evaluation history. In this way, the optimal evaluation method can be provided by referring to past evaluation history. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI. For example, the evaluation department can input past evaluation history data into a generating AI and have the generating AI select the optimal evaluation method.

[0085] The evaluation department can customize the evaluation content based on the current job responsibilities of veteran employees when evaluating reskilling progress. For example, the evaluation department can identify the necessary skill sets based on current job responsibilities and customize the evaluation content accordingly. The evaluation department can also provide evaluation content for leadership skills and specialized technical skills depending on the job responsibilities. Furthermore, the evaluation department can adjust the evaluation content in response to changes in job responsibilities. This allows for more appropriate evaluations by customizing the evaluation content based on current job responsibilities. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can input current job responsibilities data into a generating AI and have the generating AI perform the customization of the evaluation content.

[0086] The service provider can estimate the user's emotions and adjust the method of providing rewards and promotions based on the estimated emotions. For example, if the user is stressed, the service provider can suggest a simple and easy-to-understand method of providing rewards and promotions. If the user is relaxed, the service provider can also suggest a more detailed method of providing rewards and promotions. Furthermore, if the user is in a hurry, the service provider can quickly suggest a method of providing rewards and promotions. This allows for the provision of more appropriate rewards and promotions by adjusting the method of providing rewards and promotions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The compensation department can select the optimal compensation and promotion method by referring to the past performance of veteran employees when providing compensation and promotions. For example, the compensation department can select the most effective compensation and promotion method based on past performance. The compensation department can also consider past performance and provide compensation and promotion methods that are expected to improve skills. Furthermore, the compensation department can select compensation and promotion methods that match the performance style based on past performance. In this way, the optimal compensation and promotion method can be provided by referring to past performance. Some or all of the above processes in the compensation department may be performed using AI, for example, or not using AI. For example, the compensation department can input past performance data into a generating AI and have the generating AI select the optimal compensation and promotion method.

[0088] The service provider can customize the content of compensation and promotions based on the current job responsibilities of veteran employees. For example, the service provider can identify the necessary skill sets based on current job responsibilities and customize the compensation and promotion content accordingly. The service provider can also provide compensation and promotion content that includes leadership skills and specialized technical skills, depending on the job responsibilities. Furthermore, the service provider can adjust the compensation and promotion content in response to changes in job responsibilities. This allows for the provision of more appropriate compensation and promotions by customizing the content based on current job responsibilities. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input current job responsibilities data into a generating AI and have the generating AI perform the customization of the content.

[0089] The platform unit can estimate the user's emotions and adjust the method of knowledge sharing based on the estimated emotions. For example, if the user is stressed, the platform unit can provide a simple and easy-to-understand method of knowledge sharing. If the user is relaxed, the platform unit can also provide a more detailed method of knowledge sharing. Furthermore, if the user is in a hurry, the platform unit can provide a method for rapid knowledge sharing. This allows for more effective knowledge sharing by adjusting the method of knowledge sharing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the platform unit may be performed using AI, or not. For example, the platform unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The platform unit can select the optimal knowledge sharing method by referring to the past knowledge transfer history of veteran employees when sharing knowledge. For example, the platform unit can select the most effective knowledge sharing method based on past knowledge transfer history. The platform unit can also consider past knowledge transfer history and provide knowledge sharing methods that are expected to improve skills. Furthermore, the platform unit can select a method that suits the knowledge sharing style from past knowledge transfer history. In this way, the optimal knowledge sharing method can be provided by referring to past knowledge transfer history. Some or all of the above processing in the platform unit may be performed using AI, for example, or not using AI. For example, the platform unit can input past knowledge transfer history data into a generating AI and have the generating AI select the optimal knowledge sharing method.

[0091] The platform unit can customize the content shared based on the current work content of veteran employees when sharing knowledge. For example, the platform unit can identify the necessary knowledge sets based on current work content and customize the content to be shared. The platform unit can also provide knowledge sharing content on leadership skills and specialized technical skills depending on the work content. Furthermore, the platform unit can adjust the content to be shared in response to changes in work content. This makes it possible to share knowledge more effectively by customizing the content to be shared based on current work content. Some or all of the above processes in the platform unit may be performed using AI, for example, or not using AI. For example, the platform unit can input current work content data into a generating AI and have the generating AI perform the customization of the shared content.

[0092] The matching unit can estimate the user's emotions and adjust the project suitability matching method based on the estimated emotions. For example, if the user is stressed, the matching unit can provide a simple and easy-to-understand matching method. If the user is relaxed, the matching unit can also provide a more detailed matching method. Furthermore, if the user is in a hurry, the matching unit can provide matching results quickly. By adjusting the project suitability matching method according to the user's emotions, more appropriate matching becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0093] The matching unit can select the optimal matching method by referring to the past project history of veteran employees when matching project suitability. For example, the matching unit can select the most effective matching method based on past project history. The matching unit can also consider past project history and provide matching methods that are expected to improve skills. Furthermore, the matching unit can select a matching method that suits the project style from past project history. In this way, the optimal matching method can be provided by referring to past project history. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input past project history data into a generating AI and have the generating AI select the optimal matching method.

[0094] The matching unit can customize the matching content based on the current work content of veteran employees when matching them for project suitability. For example, the matching unit can identify the necessary skill sets based on current work content and customize the matching content. The matching unit can also provide matching content for leadership skills and specialized technical skills depending on the work content. Furthermore, the matching unit can adjust the matching content in response to changes in work content. This allows for more appropriate matching by customizing the matching content based on current work content. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input current work content data into a generating AI and have the generating AI perform the customization of the matching content.

[0095] The coaching unit can estimate the user's emotions and adjust the career coaching method based on the estimated emotions. For example, if the user is stressed, the coaching unit can provide a simple and easy-to-understand career coaching method. If the user is relaxed, the coaching unit can also provide a detailed career coaching method. Furthermore, if the user is in a hurry, the coaching unit can provide a method for rapid career coaching. By adjusting the career coaching method according to the user's emotions, more effective coaching becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coaching unit may be performed using AI, for example, or not using AI. For example, the coaching unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0096] The coaching department can select the most suitable coaching method during career coaching by referring to the past career history of veteran employees. For example, the coaching department can select the most effective career coaching method based on past career history. The coaching department can also provide career coaching methods that are expected to improve skills, taking into account past career history. Furthermore, the coaching department can select a coaching method that suits the career style based on past career history. In this way, the coaching department can provide the most suitable coaching method by referring to past career history. Some or all of the above processes in the coaching department may be performed using AI, for example, or not using AI. For example, the coaching department can input past career history data into a generating AI and have the generating AI select the most suitable coaching method.

[0097] The coaching department can customize the content of career coaching based on the current job responsibilities of veteran employees. For example, the coaching department can identify the necessary skill sets based on current job responsibilities and customize the coaching content accordingly. The coaching department can also provide coaching on leadership skills and specialized technical skills depending on the job responsibilities. Furthermore, the coaching department can adjust the coaching content in response to changes in job responsibilities. This allows for more effective coaching by customizing the coaching content based on current job responsibilities. Some or all of the above processes in the coaching department may be performed using AI, for example, or not. For example, the coaching department can input current job responsibilities data into a generating AI and have the generating AI perform the customization of the coaching content.

[0098] The coaching unit can estimate the user's emotions and determine the priority of coaching based on those estimated emotions. For example, if the user is stressed, the coaching unit may postpone less important coaching sessions. Conversely, if the user is relaxed, the coaching unit may provide all coaching sessions equally. Furthermore, if the user is in a hurry, the coaching unit may prioritize providing the most important coaching sessions. This allows for more appropriate coaching by prioritizing coaching sessions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the coaching unit may be performed using AI or not. For example, the coaching unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The coaching department can select the optimal coaching method during career coaching by considering the geographical location information of veteran employees. For example, the coaching department can conduct career coaching while considering the region-specific skill needs based on geographical location information. The coaching department can also prioritize career coaching for projects and training opportunities that are geographically close. Furthermore, the coaching department can conduct career coaching while considering the possibility of remote work based on geographical location information. In this way, the coaching department can provide the optimal coaching method by considering geographical location information. Some or all of the above processes in the coaching department may be performed using AI, for example, or not using AI. For example, the coaching department can input geographical location data into a generating AI and have the generating AI select the optimal coaching method.

[0100] The coaching department can analyze the social media activities of veteran employees during career coaching sessions and propose relevant coaching methods. For example, the coaching department can analyze the content of social media posts and propose relevant career coaching methods. Furthermore, the coaching department can propose career coaching methods for improving communication skills based on social media interactions and follower counts. In addition, the coaching department can propose career coaching methods related to the latest trends and technologies based on social media activity history. This allows for the provision of optimal coaching methods by analyzing social media activities. Some or all of the above processes performed by the coaching department may be carried out using AI, for example, or not. For example, the coaching department can input social media activity data into a generating AI and have the AI ​​generate suggestions for relevant coaching methods.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The analysis unit can estimate the user's emotions and adjust the timing of skill analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can postpone the skill analysis and perform it when the user is relaxed. Alternatively, if the user is relaxed, the analysis unit can perform the skill analysis immediately and provide the results quickly. Furthermore, if the user is in a hurry, the analysis unit can perform the skill analysis quickly and provide the results in a short time. This allows for skill analysis to be performed at a more appropriate time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0103] The analysis unit can analyze the past project history of each veteran employee to improve the accuracy of skill analysis. For example, the analysis unit can retrieve each veteran employee's past project history from a database and reflect it in the skill analysis. The analysis unit can also improve the accuracy of skill analysis by considering the success rate and failure rate of projects. Furthermore, the analysis unit can adjust the weighting of the skill analysis based on the type and scale of the project. This improves the accuracy of skill analysis by analyzing past project history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past project history data into a generating AI and have the generating AI perform the skill analysis accuracy improvement.

[0104] The suggestion unit can estimate the user's emotions and adjust the presentation of the reskilling plan based on the estimated emotions. For example, if the user is stressed, the suggestion unit can propose a simple and easy-to-understand reskilling plan. If the user is relaxed, the suggestion unit can also provide a detailed reskilling plan. Furthermore, if the user is in a hurry, the suggestion unit can provide a concise reskilling plan. By adjusting the presentation of the reskilling plan according to the user's emotions, a more appropriate reskilling plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0105] The proposal department can select the optimal reskilling plan by referring to the past learning history of veteran employees when proposing a reskilling plan. For example, the proposal department can propose the most effective reskilling plan based on past learning history. The proposal department can also select a plan that is expected to improve skills by considering past learning outcomes. Furthermore, the proposal department can propose a reskilling plan that suits the learning style based on past learning history. In this way, the optimal reskilling plan can be proposed by referring to past learning history. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input past learning history data into a generating AI and have the generating AI select the optimal reskilling plan.

[0106] The implementation unit can estimate the user's emotions and adjust the reskilling method based on the estimated emotions. For example, if the user is stressed, the implementation unit can conduct reskilling in a relaxing environment. If the user is relaxed, the implementation unit can also conduct reskilling in a focused environment. Furthermore, if the user is in a hurry, the implementation unit can provide a quick and effective reskilling method. By adjusting the reskilling method according to the user's emotions, more effective reskilling becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the implementation unit may be performed using AI or not using AI. For example, the implementation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0107] The implementation department can select the optimal implementation method by referring to the past learning outcomes of veteran employees when conducting reskilling. For example, the implementation department can select the most effective reskilling method based on past learning outcomes. The implementation department can also provide implementation methods that are expected to improve skills, taking past learning outcomes into consideration. Furthermore, the implementation department can select a reskilling method that suits the learning style based on past learning outcomes. In this way, the optimal reskilling implementation method can be provided by referring to past learning outcomes. Some or all of the above processes in the implementation department may be performed using AI, for example, or not using AI. For example, the implementation department can input past learning outcome data into a generating AI and have the generating AI select the optimal reskilling implementation method.

[0108] The evaluation unit can estimate the user's emotions and adjust the reskilling progress evaluation method based on the estimated user emotions. For example, if the user is stressed, the evaluation unit can provide a concise and easy-to-understand evaluation method. It can also provide a detailed evaluation method if the user is relaxed. Furthermore, if the user is in a hurry, the evaluation unit can provide evaluation results quickly. This allows for more appropriate evaluation by adjusting the reskilling progress evaluation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0109] The evaluation department can select the optimal evaluation method by referring to the past evaluation history of veteran employees when evaluating reskilling progress. For example, the evaluation department can select the most effective evaluation method based on past evaluation history. The evaluation department can also provide evaluation methods that are expected to improve skills, taking past evaluation history into consideration. Furthermore, the evaluation department can select an evaluation method that suits the evaluation style from past evaluation history. In this way, the optimal evaluation method can be provided by referring to past evaluation history. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not using AI. For example, the evaluation department can input past evaluation history data into a generating AI and have the generating AI select the optimal evaluation method.

[0110] The service provider can estimate the user's emotions and adjust the method of providing rewards and promotions based on the estimated emotions. For example, if the user is stressed, the service provider can suggest a simple and easy-to-understand method of providing rewards and promotions. If the user is relaxed, the service provider can also suggest a more detailed method of providing rewards and promotions. Furthermore, if the user is in a hurry, the service provider can quickly suggest a method of providing rewards and promotions. This allows for the provision of more appropriate rewards and promotions by adjusting the method of providing rewards and promotions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0111] The compensation department can select the optimal compensation and promotion method by referring to the past performance of veteran employees when providing compensation and promotions. For example, the compensation department can select the most effective compensation and promotion method based on past performance. The compensation department can also consider past performance and provide compensation and promotion methods that are expected to improve skills. Furthermore, the compensation department can select compensation and promotion methods that match the performance style based on past performance. In this way, the optimal compensation and promotion method can be provided by referring to past performance. Some or all of the above processes in the compensation department may be performed using AI, for example, or not using AI. For example, the compensation department can input past performance data into a generating AI and have the generating AI select the optimal compensation and promotion method.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The analysis unit analyzes the skills of each veteran employee. The analysis unit automatically analyzes the current skills of each veteran employee, for example, using AI, and compares them with the skills required within the group and the demand in the external market. Step 2: The proposal department proposes a reskilling plan based on the skills analyzed by the analysis department. For example, the proposal department uses AI to propose the optimal reskilling method based on an individual's career aspirations and interests. Step 3: The implementation department carries out reskilling based on the reskilling plan proposed by the proposal department. The implementation department implements reskilling methods such as online courses, in-house training, and external seminars. Step 4: The evaluation department assesses the progress of the reskilling implemented by the implementation department. The evaluation department objectively evaluates newly acquired skills and the progress of reskilling, for example, using AI. Step 5: The provision department provides rewards and promotion opportunities based on the progress evaluated by the evaluation department. For example, the provision department provides rewards and promotion opportunities based on progress evaluated using AI.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the analysis unit, proposal unit, implementation unit, evaluation unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The implementation unit is implemented by, for example, the control unit 46A of the smart device 14. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by, for example, the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the analysis unit, proposal unit, implementation unit, evaluation unit, and provision unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12. The implementation unit is implemented by the control unit 46A of the smart glasses 214. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the analysis unit, proposal unit, implementation unit, evaluation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The implementation unit is implemented by, for example, the control unit 46A of the headset terminal 314. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the analysis unit, proposal unit, implementation unit, evaluation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The implementation unit is implemented by, for example, the control unit 46A of the robot 414. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) The analysis department analyzes the skills of each veteran employee, A proposal unit proposes a reskilling plan based on the skills analyzed by the aforementioned analysis unit, An implementation unit that carries out reskilling based on the reskilling plan proposed by the aforementioned proposal unit, An evaluation unit that evaluates the progress of reskilling carried out by the implementation unit, The system includes a provisioning unit that provides rewards and promotion opportunities based on the progress evaluated by the aforementioned evaluation unit. A system characterized by the following features. (Note 2) It includes a platform division for building a platform for knowledge transfer. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a matching department that performs project suitability matching. The system described in Appendix 1, characterized by the features described herein. (Note 4) We have a coaching department that provides career coaching. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, It estimates the user's emotions and adjusts the timing of skill analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We analyze the past project history of each veteran employee to improve the accuracy of skill analysis. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, During skill analysis, the analysis method is customized based on the current job responsibilities and position of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, We estimate the user's emotions and adjust how the reskilling plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, When proposing a risk-reducing plan, the optimal plan is selected by referring to the past learning history of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, When proposing a risk-reducing plan, customize the proposal based on the current career stage of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned implementation unit is We estimate the user's emotions and adjust the reskilling method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned implementation unit is When implementing risk-skilling, the optimal implementation method will be selected by referring to the past learning achievements of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned implementation unit is When implementing reskilling, customize the implementation plan based on the current job responsibilities of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit described above, We estimate user sentiment and adjust the reskilling progress evaluation method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit described above, When evaluating the progress of risk-killing, the optimal evaluation method is selected by referring to the past evaluation history of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit described above, When evaluating the progress of risk-skilling, customize the evaluation criteria based on the current job responsibilities of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how rewards and promotions are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When offering compensation or promotions, the optimal method of provision is selected by referring to the past performance of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When offering compensation or promotions, customize the offerings based on the current job responsibilities of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned platform unit is It estimates user sentiment and adjusts the knowledge sharing method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned platform unit is When sharing knowledge, the optimal sharing method is selected by referring to the past knowledge transfer history of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned platform unit is When sharing knowledge, customize the shared content based on the current job responsibilities of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is We estimate user emotions and adjust the project suitability matching method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is During project suitability matching, the optimal matching method is selected by referring to the past project history of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 25) The matching unit is During project suitability matching, the matching criteria are customized based on the current job responsibilities of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned coaching department, It estimates the user's emotions and adjusts the career coaching method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned coaching department, During career coaching, we select the most suitable coaching method by referring to the past career history of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned coaching department, During career coaching sessions, the coaching content is customized based on the current job responsibilities of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned coaching department, It estimates the user's emotions and determines the priority of coaching based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned coaching department, When providing career coaching, we select the most suitable coaching method by considering the geographical location of veteran employees. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned coaching department, During career coaching sessions, we analyze the social media activity of veteran employees and propose relevant coaching methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes the skills of each veteran employee, A proposal unit proposes a reskilling plan based on the skills analyzed by the aforementioned analysis unit, An implementation unit that carries out reskilling based on the reskilling plan proposed by the aforementioned proposal unit, An evaluation unit that evaluates the progress of reskilling carried out by the implementation unit, The system includes a provisioning unit that provides rewards and promotion opportunities based on the progress evaluated by the aforementioned evaluation unit. A system characterized by the following features.

2. It includes a platform division for building a platform for knowledge transfer. The system according to feature 1.

3. It has a matching department that performs project suitability matching. The system according to feature 1.

4. We have a coaching department that provides career coaching. The system according to feature 1.

5. The aforementioned analysis unit, It estimates the user's emotions and adjusts the timing of skill analysis based on the estimated user emotions. The system according to feature 1.

6. The aforementioned analysis unit, We analyze the past project history of each veteran employee to improve the accuracy of skill analysis. The system according to feature 1.

7. The aforementioned analysis unit, During skill analysis, the analysis method is customized based on the current job responsibilities and position of veteran employees. The system according to feature 1.

8. The aforementioned proposal section is, We estimate the user's emotions and adjust how the reskilling plan is presented based on those estimated emotions. The system according to feature 1.

9. The aforementioned proposal section is, When proposing a risk-reducing plan, the optimal plan is selected by referring to the past learning history of veteran employees. The system according to feature 1.

10. The aforementioned proposal section is, When proposing a risk-reducing plan, customize the proposal based on the current career stage of veteran employees. The system according to feature 1.

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