system

Abstract

The system according to this embodiment aims to draw out the potential of employees and propose the optimal career path. [Solution] The system according to the embodiment comprises a career analysis unit, a learning proposal unit, a mentoring unit, a job matching unit, and a growth tracking unit. The career analysis unit analyzes each employee's strengths, interests, and skills. The learning proposal unit identifies skill gaps based on the results analyzed by the career analysis unit and proposes a learning plan. The mentoring unit provides mentoring based on the learning plan proposed by the learning proposal unit. The job matching unit provides appropriate job matching to employees supported by the mentoring unit. The growth tracking unit continuously tracks the growth of employees engaged in jobs matched by the job matching unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 conventional technology, the potential ability of employees has not been fully exploited, and an appropriate career path has not been sufficiently proposed, leaving room for improvement.

[0005] The system according to the embodiment aims to draw out the potential ability of employees and propose an optimal career path.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a career analysis unit, a learning proposal unit, a mentoring unit, a job matching unit, and a growth tracking unit. The career analysis unit analyzes each employee's strengths, interests, and skills. The learning proposal unit identifies skill gaps based on the results of the analysis by the career analysis unit and proposes a learning plan. The mentoring unit provides mentoring based on the learning plan proposed by the learning proposal unit. The job matching unit provides appropriate job matching to employees supported by the mentoring unit. The growth tracking unit continuously tracks the growth of employees engaged in jobs matched by the job matching unit. [Effects of the Invention]

[0007] The system according to this embodiment can draw out the potential of employees and propose the optimal career path. [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 controls 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 reception 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 reception 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 career support system according to an embodiment of the present invention is a system that uses AI to draw out the potential of employees and proposes appropriate career paths. This career support system analyzes employees' strengths, interests, and skills and performs individualized career analysis. Next, the career support system identifies skill gaps and proposes an optimal learning plan. Furthermore, the career support system provides 24-hour mentoring using an AI chatbot to support employee motivation. The career support system also performs appropriate job matching so that employees can engage in work that matches their skills and interests. Finally, the career support system performs continuous growth tracking, regularly evaluates employee growth, and supports goal setting. For example, the career support system uses natural language processing technology to analyze employees' strengths, interests, and skills. For example, the career support system uses machine learning to identify skill gaps and propose an optimal learning plan. For example, the career support system uses reinforcement learning with an AI chatbot to generate an optimal learning plan and provides 24-hour mentoring. For example, the career support system uses collaborative filtering to perform appropriate job matching. For example, the career support system regularly evaluates employee growth and supports goal setting. This allows the career support system to maximize employees' potential and improve overall organizational productivity and creativity.

[0029] The career support system according to this embodiment comprises a career analysis unit, a learning proposal unit, a mentoring unit, a job matching unit, and a growth tracking unit. The career analysis unit analyzes employees' strengths, interests, and skills. The career analysis unit analyzes each employee's individual characteristics using, for example, natural language processing technology. The career analysis unit can accurately analyze employees' strengths, interests, and skills using, for example, morphological analysis, grammatical analysis, and semantic analysis technologies. The learning proposal unit identifies skill gaps and proposes learning plans based on the results analyzed by the career analysis unit. The learning proposal unit identifies skill gaps and proposes optimal learning plans using, for example, machine learning technology. The learning proposal unit can accurately identify skill gaps and propose optimal learning plans using, for example, supervised learning, unsupervised learning, and reinforcement learning technologies. The mentoring unit provides mentoring based on the learning plans proposed by the learning proposal unit. The mentoring unit generates optimal learning plans using, for example, reinforcement learning technology and provides 24-hour mentoring via an AI chatbot. The mentoring department can generate optimal learning plans using technologies such as Q-learning, SARSA, and deep reinforcement learning, and provide 24-hour mentoring using an AI chatbot. The job matching department performs appropriate job matching for employees supported by the mentoring department. The job matching department performs appropriate job matching using technologies such as collaborative filtering and user-based collaborative filtering. The growth tracking department continuously tracks the growth of employees engaged in jobs matched by the job matching department. The growth tracking department periodically evaluates employee growth and supports goal setting. The growth tracking department can periodically evaluate employee growth and support goal setting using methods such as monthly, quarterly, and annual evaluations. As a result, the career support system according to this embodiment can maximize the potential of employees and improve the productivity of the entire organization.

[0030] The Career Analysis Department analyzes employees' strengths, interests, and skills. For example, it uses natural language processing technology to analyze each employee's individual characteristics. Specifically, it collects text data such as self-introductions, past work reports, and performance evaluation reports submitted by employees, and performs analysis using technologies such as morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis divides the text data into words and identifies the part of speech of each word. Grammatical analysis analyzes the structure of sentences and clarifies the relationships between subjects, predicates, objects, etc. Semantic analysis understands the meaning of sentences and extracts information related to the employee's strengths, interests, and skills. This allows the Career Analysis Department to accurately grasp the characteristics of employees and clarify their individual strengths and interests. Furthermore, the Career Analysis Department can also integrate employees' past work history and evaluation data to analyze the changes and growth trends in skills. This allows it to predict employees' career paths and indicate the direction of their future growth.

[0031] The Learning Proposal Department identifies skill gaps and proposes learning plans based on the results of analysis by the Career Analysis Department. For example, the Learning Proposal Department uses machine learning to identify skill gaps and propose optimal learning plans. Specifically, it uses techniques such as supervised learning, unsupervised learning, and reinforcement learning to compare employees' current skill sets with their target skill sets and identify gaps. In supervised learning, it trains a model based on past data to predict skill gaps for new data. In unsupervised learning, it uses clustering techniques to group employees' skill sets and identify common skill gaps. In reinforcement learning, it generates optimal learning plans based on employees' learning history. This allows the Learning Proposal Department to provide each employee with an optimal learning plan and support their skill development. Furthermore, the Learning Proposal Department can also utilize learning resources such as online courses and in-house training programs to propose specific learning methods and materials. This enables employees to efficiently acquire skills and advance their careers.

[0032] The Mentoring Department provides mentoring based on learning plans proposed by the Learning Proposal Department. For example, the Mentoring Department generates optimal learning plans using reinforcement learning and provides 24 / 7 mentoring via an AI chatbot. Specifically, it uses technologies such as Q-learning, SARSA, and deep reinforcement learning to dynamically adjust the optimal learning plan based on the employee's learning progress and feedback. The AI ​​chatbot responds to employee questions in real time, supporting their understanding of the learning content. The chatbot can also analyze the employee's learning history and performance data to provide appropriate advice and additional learning resources. This allows the Mentoring Department to support employees in efficiently progressing with their learning and maintain their motivation. Furthermore, the Mentoring Department can conduct regular interviews with employees to check their learning progress and challenges, and revise the learning plan as needed. This allows employees to continuously improve their skills and receive support in achieving their career goals.

[0033] The Job Matching Department provides appropriate job matching for employees supported by the Mentoring Department. The Job Matching Department uses collaborative filtering, for example, to perform appropriate job matching. Specifically, it uses technologies such as user-based collaborative filtering and item-based collaborative filtering to recommend the most suitable jobs based on the employee's skill set and interests. User-based collaborative filtering recommends appropriate jobs by referencing the work history of other employees with similar skill sets and interests. Item-based collaborative filtering recommends jobs suitable for employees based on the characteristics of the job and the skills required. This allows the Job Matching Department to provide employees with jobs that are best suited to their skills and interests, thereby improving employee motivation and performance. Furthermore, the Job Matching Department can continuously monitor employees' work history and performance data to evaluate job suitability. This allows it to support employees in engaging in optimal jobs and achieving career growth.

[0034] The Growth Tracking Department continuously tracks the growth of employees engaged in tasks matched by the Task Matching Department. For example, the Growth Tracking Department regularly evaluates employee growth and supports goal setting. Specifically, it uses methods such as monthly, quarterly, and annual evaluations to assess employee performance and skill improvement. The evaluation combines quantitative indicators (e.g., task completion, skill acquisition) and qualitative indicators (e.g., feedback from supervisors and colleagues). This allows the Growth Tracking Department to comprehensively evaluate employee growth and provide appropriate feedback. Furthermore, the Growth Tracking Department supports employee goal setting and monitors their progress. It regularly checks progress toward employee-set goals and provides goal revisions or additional support as needed. This allows employees to work with clear goals and experience a sense of growth. The Growth Tracking Department can also accumulate employee growth data and utilize it for designing long-term career paths and formulating organizational-wide talent development strategies. This enables the Growth Tracking Department to continuously support employee growth and contribute to improving overall organizational productivity.

[0035] The Career Analysis Department can analyze the individual characteristics of each employee using natural language processing technology. For example, the Career Analysis Department can analyze an employee's writing using morphological analysis and extract individual characteristics. For example, the Career Analysis Department can also analyze the structure of an employee's writing using grammatical analysis and identify individual characteristics. For example, the Career Analysis Department can analyze the meaning of an employee's writing using semantic analysis and grasp individual characteristics. In this way, the individual characteristics of each employee can be accurately analyzed by using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above processing in the Career Analysis Department may be performed using, for example, generative AI, or without generative AI. For example, the Career Analysis Department can input employee text data into a generative AI and have the generative AI perform the extraction of individual characteristics.

[0036] The learning proposal department can use machine learning to identify skill gaps and propose optimal learning plans. For example, the learning proposal department can use supervised learning to identify skill gaps and propose optimal learning plans. The learning proposal department can also use unsupervised learning to identify skill gaps and propose optimal learning plans. The learning proposal department can also use reinforcement learning to identify skill gaps and propose optimal learning plans. In this way, by using machine learning, skill gaps can be accurately identified and optimal learning plans can be proposed. Machine learning includes, but is not limited to, supervised learning, unsupervised learning, and reinforcement learning. Some or all of the above processing in the learning proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the learning proposal department can input employee skill data into a generative AI and have the generative AI identify skill gaps and propose learning plans.

[0037] The mentoring department can generate an optimal learning plan using reinforcement learning and provide 24-hour mentoring via an AI chatbot. For example, the mentoring department can generate an optimal learning plan using Q-learning and provide 24-hour mentoring using an AI chatbot. Alternatively, the mentoring department can generate an optimal learning plan using SARSA and provide 24-hour mentoring using an AI chatbot. Furthermore, the mentoring department can generate an optimal learning plan using deep reinforcement learning and provide 24-hour mentoring using an AI chatbot. This allows for the generation of an optimal learning plan and the provision of 24-hour mentoring by leveraging reinforcement learning. Reinforcement learning includes, but is not limited to, Q-learning, SARSA, and deep reinforcement learning. Some or all of the above-described processes in the mentoring department may be performed using, for example, generative AI, or without generative AI. For example, the mentoring department can input employee learning data into a generative AI and have the generative AI generate an optimal learning plan and provide mentoring.

[0038] The business matching unit can perform appropriate business matching using collaborative filtering. For example, the business matching unit can perform appropriate business matching using user-based collaborative filtering. The business matching unit can also perform appropriate business matching using item-based collaborative filtering. The business matching unit can also perform appropriate business matching using hybrid collaborative filtering. In this way, appropriate business matching can be performed by using collaborative filtering. Examples of collaborative filtering include, but are not limited to, user-based collaborative filtering, item-based collaborative filtering, and hybrid collaborative filtering. Some or all of the above processing in the business matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the business matching unit can input employee business data into a generative AI and have the generative AI perform business matching.

[0039] The growth tracking unit can periodically evaluate employee growth and support goal setting. For example, the growth tracking unit can periodically evaluate employee growth and support goal setting using monthly evaluations. It can also periodically evaluate employee growth and support goal setting using quarterly evaluations. Furthermore, it can periodically evaluate employee growth and support goal setting using annual evaluations. This promotes continuous growth by periodically evaluating employee growth and supporting goal setting. Periodic evaluations include, but are not limited to, monthly, quarterly, and annual evaluations. Some or all of the processes described above in the growth tracking unit may be performed, for example, using a generative AI, or without a generative AI. For example, the growth tracking unit can input employee growth data into a generative AI and have the generative AI perform growth evaluation and goal setting support.

[0040] The Career Analysis Department can analyze an employee's past work history and select the most suitable career analysis method. For example, the Career Analysis Department can select a career analysis method suitable for similar tasks based on a project the employee has successfully completed in the past. For example, the Career Analysis Department can also analyze tasks that an employee has struggled with in the past and conduct a career analysis that includes methods for overcoming those difficulties. For example, the Career Analysis Department can conduct a career analysis that focuses on a specific skill set based on an employee's past work history. This allows for the selection of the most suitable career analysis method by analyzing an employee's past work history. Past work history includes, but is not limited to, project history and performance evaluations. Some or all of the above processes in the Career Analysis Department may be performed using, for example, generative AI, or not. For example, the Career Analysis Department can input employee work history data into a generative AI and have the generative AI select a career analysis method.

[0041] The Career Analysis Department can filter career analyses based on employees' current projects and areas of interest. For example, the Career Analysis Department can focus its analysis on skills related to the projects employees are currently working on. The Career Analysis Department can also suggest relevant career paths based on employees' areas of interest. For example, the Career Analysis Department can consider the challenges employees are facing in their current projects and conduct career analyses that include solutions to those challenges. This allows for more relevant career analyses by filtering based on employees' current projects and areas of interest. Current projects include, but are not limited to, project progress and project goals. Areas of interest include, but are not limited to, work-related interest and willingness to learn. Some or all of the above processing in the Career Analysis Department may be performed using, for example, generative AI, or not. For example, the Career Analysis Department can input employee project data and interest data into a generative AI and have the generative AI perform the filtering.

[0042] The Career Analysis Department can prioritize the analysis of highly relevant data by considering employees' geographical location information during career analysis. For example, if an employee works in a specific region, the Career Analysis Department can conduct career analysis considering industry trends in that region. For example, if an employee works remotely, the Career Analysis Department can focus its analysis on skills suitable for remote work. For example, if an employee wishes to work overseas, the Career Analysis Department can propose overseas career paths. This allows for more appropriate career analysis by prioritizing the analysis of highly relevant data by considering employees' geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the Career Analysis Department may be performed using, for example, generative AI, or without generative AI. For example, the Career Analysis Department can input employees' geographical location data into generative AI and have the generative AI perform priority analysis of highly relevant data.

[0043] The Career Analysis Department can analyze employees' social media activities and obtain relevant data during career analysis. For example, the Career Analysis Department can conduct career analysis based on the interests and concerns that employees express on social media. For example, the Career Analysis Department can also propose career paths considering the industry trends that employees follow on social media. For example, the Career Analysis Department can identify networking opportunities from employees' social media activities and reflect them in career analysis. This allows for a more multifaceted career analysis by analyzing employees' social media activities. Social media activities include, but are not limited to, posts, follower count, and likes. Some or all of the above processes in the Career Analysis Department may be performed using, for example, generative AI, or not. For example, the Career Analysis Department can input employee social media data into a generative AI and have the generative AI retrieve the relevant data.

[0044] The learning suggestion department can adjust the level of detail in learning plans based on the importance of skills when making learning suggestions. For example, the learning suggestion department can provide detailed learning plans for important skills. For example, it can also provide concise learning plans for less important skills. The learning suggestion department can also prioritize learning plans according to the importance of skills. This allows for more effective learning suggestions by adjusting the level of detail in learning plans based on the importance of skills. The importance of skills includes, but is not limited to, job necessity and importance in future careers. Some or all of the above processing in the learning suggestion department may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning suggestion department can input employee skill data into a generative AI and have the generative AI adjust the level of detail in learning plans.

[0045] The learning suggestion unit can apply different learning algorithms depending on the skill category when making learning suggestions. For example, the learning suggestion unit can apply a practical learning algorithm to technical skills. For example, the learning suggestion unit can also apply a simulation-based learning algorithm to soft skills. For example, the learning suggestion unit can also apply a case study-based learning algorithm to management skills. By applying different learning algorithms depending on the skill category, more effective learning suggestions can be made. Skill categories include, but are not limited to, technical skills, soft skills, and management skills. Some or all of the above processing in the learning suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning suggestion unit can input employee skill data into a generative AI and have the generative AI execute the application of the learning algorithm.

[0046] The learning proposal department can prioritize learning plans based on the timing of skill acquisition when proposing learning plans. For example, the learning proposal department can prioritize learning plans for skills that need to be acquired quickly. For example, the learning proposal department can also provide step-by-step learning plans for skills that need to be acquired over a long period. The learning proposal department can also adjust the pace of the learning plan according to the timing of acquisition. This allows for more effective learning proposals by prioritizing learning plans based on the timing of skill acquisition. The timing of skill acquisition includes, but is not limited to, the year and month the skill was acquired and the skill acquisition process. Some or all of the above processing in the learning proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning proposal department can input employee skill data into a generative AI and have the generative AI determine the priority of learning plans.

[0047] The learning proposal unit can adjust the order of learning plans based on the relevance of skills when proposing learning plans. For example, the learning proposal unit can prioritize incorporating highly relevant skills into the learning plan. For example, the learning proposal unit can also construct the learning plan by postponing less relevant skills. For example, the learning proposal unit can adjust the order of progress in the learning plan according to the relevance of skills. This allows for more effective learning proposals by adjusting the order of learning plans based on the relevance of skills. Skill relevance includes, but is not limited to, work-related relevance and relevance of learning content. Some or all of the above processing in the learning proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning proposal unit can input employee skill data into a generative AI and have the generative AI adjust the order of learning plans.

[0048] The mentoring department can select the optimal mentoring method by referring to the employee's past mentoring history during mentoring sessions. For example, the mentoring department may reapply mentoring methods that were effective for the employee in the past. The mentoring department may also avoid mentoring methods that the employee found difficult to use in the past. The mentoring department can also select the optimal mentoring method based on the employee's past mentoring history. This allows the mentoring department to select the optimal mentoring method by referring to the employee's past mentoring history. Past mentoring history includes, but is not limited to, the content and results of mentoring sessions. Some or all of the above-described processes in the mentoring department may be performed using, for example, a generative AI, or not. For example, the mentoring department can input the employee's mentoring history data into a generative AI and have the generative AI select the mentoring method.

[0049] The mentoring department can customize the mentoring methods based on the employee's current work situation. For example, if the employee is busy, the mentoring department can provide effective mentoring in a short amount of time. For example, if the employee has ample time, the mentoring department can also provide detailed mentoring. The mentoring department can customize the mentoring methods according to the employee's work situation. This allows for more effective mentoring by customizing the mentoring methods based on the employee's current work situation. Current work situation includes, but is not limited to, the progress of work and the content of work. Some or all of the above processing in the mentoring department may be performed using, for example, a generative AI, or not using a generative AI. For example, the mentoring department can input employee work situation data into a generative AI and have the generative AI perform the customization of the mentoring methods.

[0050] The mentoring department can select the optimal mentoring method by considering the employee's geographical location information during mentoring. For example, if an employee is working remotely, the mentoring department can provide online mentoring. For example, if an employee is in the office, the mentoring department can also provide face-to-face mentoring. The mentoring department can also select the optimal mentoring method according to the employee's geographical location information. This allows for more effective mentoring by selecting the optimal mentoring method by considering the employee's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the mentoring department may be performed using, for example, generative AI, or without using generative AI. For example, the mentoring department can input the employee's geographical location data into a generative AI and have the generative AI select the mentoring method.

[0051] The mentoring department can analyze employees' social media activity during mentoring sessions and propose mentoring methods. For example, the mentoring department can propose mentoring methods based on the content employees post on social media. The mentoring department can also conduct mentoring considering industry trends that employees follow on social media. For example, the mentoring department can identify networking opportunities from employees' social media activity and incorporate them into mentoring. By analyzing employees' social media activity, the mentoring department can propose more effective mentoring methods. Social media activity includes, but is not limited to, posts, follower count, and likes. Some or all of the above processing by the mentoring department may be performed using, for example, generative AI, or not. For example, the mentoring department can input employee social media data into a generative AI and have the generative AI propose mentoring methods.

[0052] The business matching department can improve the accuracy of matching by considering the interrelationship between employee skills and tasks during the business matching process. For example, the business matching department can perform matching by comparing employee skill sets with the required skills of the tasks. For example, the business matching department can also analyze the interrelationship between employee skills and tasks and propose the most suitable tasks. For example, the business matching department can match tasks that provide opportunities for skill development by considering the interrelationship between employee skills and tasks. This allows for more accurate business matching by considering the interrelationship between employee skills and tasks. The interrelationship between skills and tasks includes, but is not limited to, skill sets and required skills of the tasks. Some or all of the above-described processes in the business matching department may be performed using, for example, a generative AI, or not using a generative AI. For example, the business matching department can input employee skill data and business data into a generative AI and have the generative AI perform the task of improving the accuracy of matching.

[0053] The job matching department can perform job matching while considering employee attribute information. For example, the job matching department can match employees with appropriate jobs by considering their age and years of experience. For example, the job matching department can also match employees with jobs that are easy to work with by considering their gender and lifestyle. For example, the job matching department can perform job matching that takes diversity into account based on employee attribute information. This allows for more appropriate job matching by considering employee attribute information. Attribute information includes, but is not limited to, age, years of experience, gender, and lifestyle. Some or all of the above processing in the job matching department may be performed using, for example, a generation AI, or without a generation AI. For example, the job matching department can input employee attribute data into a generation AI and have the generation AI perform the matching.

[0054] The business matching unit can perform business matching while considering the geographical distribution of employees. For example, if an employee works in a specific region, the business matching unit will prioritize matching them with jobs in that region. For example, if an employee wishes to work remotely, the business matching unit can also match them with jobs suitable for remote work. For example, the business matching unit can propose the most suitable jobs by considering the geographical distribution of employees. This allows for more appropriate business matching by considering the geographical distribution of employees. Geographical distribution includes, but is not limited to, specific regions or a preference for remote work. Some or all of the above processing in the business matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the business matching unit can input employee geographical distribution data into a generative AI and have the generative AI perform the matching.

[0055] The business matching unit can improve the accuracy of matching by referring to relevant literature during business matching. For example, the business matching unit can propose the most suitable business by referring to the latest research papers related to the business. The business matching unit can also improve the accuracy of matching by referring to best practices related to the business. The business matching unit can also propose the most suitable business by referring to industry reports related to the business. This allows for more accurate business matching by referring to relevant literature. Relevant literature includes, but is not limited to, research papers, best practices, and industry reports. Some or all of the above processing in the business matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the business matching unit can input relevant literature data into a generative AI and have the generative AI perform the matching accuracy improvement.

[0056] The growth tracking unit can predict current growth by referring to past growth data during growth tracking. For example, the growth tracking unit predicts current growth based on an employee's past growth data. The growth tracking unit can also predict future growth trends from an employee's past growth data. The growth tracking unit can also set growth goals by referring to an employee's past growth data. This allows for the prediction of current growth by referring to past growth data. Past growth data includes, but is not limited to, growth indicators and evaluation frequencies. Some or all of the above processing in the growth tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the growth tracking unit can input an employee's past growth data into a generative AI and have the generative AI perform a prediction of current growth.

[0057] The growth tracking unit can apply different growth analysis methods to each employee category during growth tracking. For example, the growth tracking unit may use technical indicators to track the growth of technical skills. For example, the growth tracking unit may use behavioral observation to track the growth of soft skills. For example, the growth tracking unit may use leadership assessment to track the growth of management skills. This allows for more effective growth tracking by applying different growth analysis methods to each employee category. Examples of category-specific growth analysis methods include, but are not limited to, technical indicators, behavioral observation, and leadership assessment. Some or all of the above processing in the growth tracking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the growth tracking unit can input employee category data into a generative AI and have the generative AI perform the application of growth analysis methods.

[0058] The growth tracking unit can analyze changes in growth based on the employee's growth stage during growth tracking. For example, the growth tracking unit can analyze changes in growth based on the employee's growth stage. The growth tracking unit can also set growth goals according to the employee's growth stage. The growth tracking unit can also evaluate the progress of growth based on the employee's growth stage. This allows for more effective growth tracking by analyzing changes in growth based on the employee's growth stage. Growth stages include, but are not limited to, growth indicators and evaluation frequency. Some or all of the above processing in the growth tracking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the growth tracking unit can input employee growth stage data into a generative AI and have the generative AI perform an analysis of changes in growth.

[0059] The growth tracking unit can analyze growth by referring to relevant market data during growth tracking. For example, the growth tracking unit can analyze employee growth by referring to industry growth data. The growth tracking unit can also predict employee growth based on market growth data. The growth tracking unit can also set employee growth targets by referring to relevant market data. This allows for a more accurate analysis of employee growth by referring to relevant market data. Relevant market data includes, but is not limited to, industry growth data and market growth data. Some or all of the above processing in the growth tracking unit may be performed using, for example, generative AI, or without generative AI. For example, the growth tracking unit can input relevant market data into a generative AI and have the generative AI perform the growth analysis.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The Career Analysis Department can analyze employees' health data and propose career paths based on their health status. For example, if an employee is in good health, a more challenging career path can be proposed. Conversely, if an employee's health is unstable, a less demanding career path can be proposed. Furthermore, based on health data, the department can provide career plans that help employees maintain their health. In this way, by proposing career paths tailored to each employee's health status, the department can help employees achieve both health and career success.

[0062] The Learning Proposal Department can customize learning plans based on employees' hobbies and lifestyles. For example, if an employee enjoys sports, the department can propose a plan to learn sports-related skills. If an employee is interested in art, the department can provide a learning plan to develop creative skills. Furthermore, it is possible to adjust the timing and method of learning to suit the employee's lifestyle. This allows the department to provide learning plans tailored to employees' hobbies and lifestyles, thereby increasing their motivation to learn.

[0063] The Career Analysis Department can analyze the success rates of employees' past projects and propose career paths based on those success rates. For example, it can suggest career paths related to projects in which employees have shown high success rates in the past. Conversely, for projects with low success rates, it can provide career plans that include areas for improvement. Furthermore, based on the success rate analysis, it is possible to propose career paths that leverage the employee's strengths. In this way, by proposing career paths based on the success rates of employees' past projects, more appropriate career support can be provided.

[0064] The Learning Proposal Department can customize learning plans based on employees' learning styles. For example, if an employee has a visual learning style, a learning plan with a lot of visual content can be provided. If an employee has an auditory learning style, a learning plan with a lot of audio content can be provided. Furthermore, if an employee has a practical learning style, a hands-on learning plan can be provided. In this way, the effectiveness of learning can be maximized by providing learning plans that match each employee's learning style.

[0065] The Task Matching Department can analyze employees' past performance evaluations and match them with tasks based on those evaluations. For example, it can propose new tasks related to tasks in which employees received high evaluations. Conversely, it can also propose tasks that include areas for improvement for tasks in which employees received low evaluations. Furthermore, it is possible to match employees with tasks that leverage their strengths based on past performance evaluations. In this way, by performing task matching based on employees' past performance evaluations, it is possible to propose more appropriate tasks.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The Career Analysis Department analyzes employees' strengths, interests, and skills. For example, it uses natural language processing technology to analyze each employee's individual characteristics and employs techniques such as morphological analysis, grammatical analysis, and semantic analysis to accurately analyze employees' strengths, interests, and skills. Step 2: The Learning Proposal Department identifies skill gaps and proposes learning plans based on the results analyzed by the Career Analysis Department. For example, it may use machine learning to identify skill gaps and propose optimal learning plans. It may use techniques such as supervised learning, unsupervised learning, and reinforcement learning to accurately identify skill gaps and propose optimal learning plans. Step 3: The mentoring department provides mentoring based on the learning plan proposed by the learning proposal department. For example, it generates an optimal learning plan using reinforcement learning and provides 24-hour mentoring via an AI chatbot. It uses technologies such as Q-learning, SARSA, and deep reinforcement learning to generate an optimal learning plan and provides 24-hour mentoring via an AI chatbot. Step 4: The Job Matching Department will perform appropriate job matching for employees supported by the Mentoring Department. For example, they will use collaborative filtering to perform appropriate job matching. They will use technologies such as user-based collaborative filtering and item-based collaborative filtering to perform appropriate job matching. Step 5: The Growth Tracking Department continuously tracks the growth of employees engaged in tasks matched by the Task Matching Department. For example, it regularly evaluates employee growth and supports goal setting. It uses methods such as monthly, quarterly, and annual evaluations to regularly evaluate employee growth and support goal setting.

[0068] (Example of form 2) The career support system according to an embodiment of the present invention is a system that uses AI to draw out the potential of employees and proposes appropriate career paths. This career support system analyzes employees' strengths, interests, and skills and performs individualized career analysis. Next, the career support system identifies skill gaps and proposes an optimal learning plan. Furthermore, the career support system provides 24-hour mentoring using an AI chatbot to support employee motivation. The career support system also performs appropriate job matching so that employees can engage in work that matches their skills and interests. Finally, the career support system performs continuous growth tracking, regularly evaluates employee growth, and supports goal setting. For example, the career support system uses natural language processing technology to analyze employees' strengths, interests, and skills. For example, the career support system uses machine learning to identify skill gaps and propose an optimal learning plan. For example, the career support system uses reinforcement learning with an AI chatbot to generate an optimal learning plan and provides 24-hour mentoring. For example, the career support system uses collaborative filtering to perform appropriate job matching. For example, the career support system regularly evaluates employee growth and supports goal setting. This allows the career support system to maximize employees' potential and improve overall organizational productivity and creativity.

[0069] The career support system according to this embodiment comprises a career analysis unit, a learning proposal unit, a mentoring unit, a job matching unit, and a growth tracking unit. The career analysis unit analyzes employees' strengths, interests, and skills. The career analysis unit analyzes each employee's individual characteristics using, for example, natural language processing technology. The career analysis unit can accurately analyze employees' strengths, interests, and skills using, for example, morphological analysis, grammatical analysis, and semantic analysis technologies. The learning proposal unit identifies skill gaps and proposes learning plans based on the results analyzed by the career analysis unit. The learning proposal unit identifies skill gaps and proposes optimal learning plans using, for example, machine learning technology. The learning proposal unit can accurately identify skill gaps and propose optimal learning plans using, for example, supervised learning, unsupervised learning, and reinforcement learning technologies. The mentoring unit provides mentoring based on the learning plans proposed by the learning proposal unit. The mentoring unit generates optimal learning plans using, for example, reinforcement learning technology and provides 24-hour mentoring via an AI chatbot. The mentoring department can generate optimal learning plans using technologies such as Q-learning, SARSA, and deep reinforcement learning, and provide 24-hour mentoring using an AI chatbot. The job matching department performs appropriate job matching for employees supported by the mentoring department. The job matching department performs appropriate job matching using technologies such as collaborative filtering and user-based collaborative filtering. The growth tracking department continuously tracks the growth of employees engaged in jobs matched by the job matching department. The growth tracking department periodically evaluates employee growth and supports goal setting. The growth tracking department can periodically evaluate employee growth and support goal setting using methods such as monthly, quarterly, and annual evaluations. As a result, the career support system according to this embodiment can maximize the potential of employees and improve the productivity of the entire organization.

[0070] The Career Analysis Department analyzes employees' strengths, interests, and skills. For example, it uses natural language processing technology to analyze each employee's individual characteristics. Specifically, it collects text data such as self-introductions, past work reports, and performance evaluation reports submitted by employees, and performs analysis using technologies such as morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis divides the text data into words and identifies the part of speech of each word. Grammatical analysis analyzes the structure of sentences and clarifies the relationships between subjects, predicates, objects, etc. Semantic analysis understands the meaning of sentences and extracts information related to the employee's strengths, interests, and skills. This allows the Career Analysis Department to accurately grasp the characteristics of employees and clarify their individual strengths and interests. Furthermore, the Career Analysis Department can also integrate employees' past work history and evaluation data to analyze the changes and growth trends in skills. This allows it to predict employees' career paths and indicate the direction of their future growth.

[0071] The Learning Proposal Department identifies skill gaps and proposes learning plans based on the results of analysis by the Career Analysis Department. For example, the Learning Proposal Department uses machine learning to identify skill gaps and propose optimal learning plans. Specifically, it uses techniques such as supervised learning, unsupervised learning, and reinforcement learning to compare employees' current skill sets with their target skill sets and identify gaps. In supervised learning, it trains a model based on past data to predict skill gaps for new data. In unsupervised learning, it uses clustering techniques to group employees' skill sets and identify common skill gaps. In reinforcement learning, it generates optimal learning plans based on employees' learning history. This allows the Learning Proposal Department to provide each employee with an optimal learning plan and support their skill development. Furthermore, the Learning Proposal Department can also utilize learning resources such as online courses and in-house training programs to propose specific learning methods and materials. This enables employees to efficiently acquire skills and advance their careers.

[0072] The Mentoring Department provides mentoring based on learning plans proposed by the Learning Proposal Department. For example, the Mentoring Department generates optimal learning plans using reinforcement learning and provides 24 / 7 mentoring via an AI chatbot. Specifically, it uses technologies such as Q-learning, SARSA, and deep reinforcement learning to dynamically adjust the optimal learning plan based on the employee's learning progress and feedback. The AI ​​chatbot responds to employee questions in real time, supporting their understanding of the learning content. The chatbot can also analyze the employee's learning history and performance data to provide appropriate advice and additional learning resources. This allows the Mentoring Department to support employees in efficiently progressing with their learning and maintain their motivation. Furthermore, the Mentoring Department can conduct regular interviews with employees to check their learning progress and challenges, and revise the learning plan as needed. This allows employees to continuously improve their skills and receive support in achieving their career goals.

[0073] The Job Matching Department provides appropriate job matching for employees supported by the Mentoring Department. The Job Matching Department uses collaborative filtering, for example, to perform appropriate job matching. Specifically, it uses technologies such as user-based collaborative filtering and item-based collaborative filtering to recommend the most suitable jobs based on the employee's skill set and interests. User-based collaborative filtering recommends appropriate jobs by referencing the work history of other employees with similar skill sets and interests. Item-based collaborative filtering recommends jobs suitable for employees based on the characteristics of the job and the skills required. This allows the Job Matching Department to provide employees with jobs that are best suited to their skills and interests, thereby improving employee motivation and performance. Furthermore, the Job Matching Department can continuously monitor employees' work history and performance data to evaluate job suitability. This allows it to support employees in engaging in optimal jobs and achieving career growth.

[0074] The Growth Tracking Department continuously tracks the growth of employees engaged in tasks matched by the Task Matching Department. For example, the Growth Tracking Department regularly evaluates employee growth and supports goal setting. Specifically, it uses methods such as monthly, quarterly, and annual evaluations to assess employee performance and skill improvement. The evaluation combines quantitative indicators (e.g., task completion, skill acquisition) and qualitative indicators (e.g., feedback from supervisors and colleagues). This allows the Growth Tracking Department to comprehensively evaluate employee growth and provide appropriate feedback. Furthermore, the Growth Tracking Department supports employee goal setting and monitors their progress. It regularly checks progress toward employee-set goals and provides goal revisions or additional support as needed. This allows employees to work with clear goals and experience a sense of growth. The Growth Tracking Department can also accumulate employee growth data and utilize it for designing long-term career paths and formulating organizational-wide talent development strategies. This enables the Growth Tracking Department to continuously support employee growth and contribute to improving overall organizational productivity.

[0075] The Career Analysis Department can analyze the individual characteristics of each employee using natural language processing technology. For example, the Career Analysis Department can analyze an employee's writing using morphological analysis and extract individual characteristics. For example, the Career Analysis Department can also analyze the structure of an employee's writing using grammatical analysis and identify individual characteristics. For example, the Career Analysis Department can analyze the meaning of an employee's writing using semantic analysis and grasp individual characteristics. In this way, the individual characteristics of each employee can be accurately analyzed by using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above processing in the Career Analysis Department may be performed using, for example, generative AI, or without generative AI. For example, the Career Analysis Department can input employee text data into a generative AI and have the generative AI perform the extraction of individual characteristics.

[0076] The learning proposal department can use machine learning to identify skill gaps and propose optimal learning plans. For example, the learning proposal department can use supervised learning to identify skill gaps and propose optimal learning plans. The learning proposal department can also use unsupervised learning to identify skill gaps and propose optimal learning plans. The learning proposal department can also use reinforcement learning to identify skill gaps and propose optimal learning plans. In this way, by using machine learning, skill gaps can be accurately identified and optimal learning plans can be proposed. Machine learning includes, but is not limited to, supervised learning, unsupervised learning, and reinforcement learning. Some or all of the above processing in the learning proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the learning proposal department can input employee skill data into a generative AI and have the generative AI identify skill gaps and propose learning plans.

[0077] The mentoring department can generate an optimal learning plan using reinforcement learning and provide 24-hour mentoring via an AI chatbot. For example, the mentoring department can generate an optimal learning plan using Q-learning and provide 24-hour mentoring using an AI chatbot. Alternatively, the mentoring department can generate an optimal learning plan using SARSA and provide 24-hour mentoring using an AI chatbot. Furthermore, the mentoring department can generate an optimal learning plan using deep reinforcement learning and provide 24-hour mentoring using an AI chatbot. This allows for the generation of an optimal learning plan and the provision of 24-hour mentoring by leveraging reinforcement learning. Reinforcement learning includes, but is not limited to, Q-learning, SARSA, and deep reinforcement learning. Some or all of the above-described processes in the mentoring department may be performed using, for example, generative AI, or without generative AI. For example, the mentoring department can input employee learning data into a generative AI and have the generative AI generate an optimal learning plan and provide mentoring.

[0078] The business matching unit can perform appropriate business matching using collaborative filtering. For example, the business matching unit can perform appropriate business matching using user-based collaborative filtering. The business matching unit can also perform appropriate business matching using item-based collaborative filtering. The business matching unit can also perform appropriate business matching using hybrid collaborative filtering. In this way, appropriate business matching can be performed by using collaborative filtering. Examples of collaborative filtering include, but are not limited to, user-based collaborative filtering, item-based collaborative filtering, and hybrid collaborative filtering. Some or all of the above processing in the business matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the business matching unit can input employee business data into a generative AI and have the generative AI perform business matching.

[0079] The growth tracking unit can periodically evaluate employee growth and support goal setting. For example, the growth tracking unit can periodically evaluate employee growth and support goal setting using monthly evaluations. It can also periodically evaluate employee growth and support goal setting using quarterly evaluations. Furthermore, it can periodically evaluate employee growth and support goal setting using annual evaluations. This promotes continuous growth by periodically evaluating employee growth and supporting goal setting. Periodic evaluations include, but are not limited to, monthly, quarterly, and annual evaluations. Some or all of the processes described above in the growth tracking unit may be performed, for example, using a generative AI, or without a generative AI. For example, the growth tracking unit can input employee growth data into a generative AI and have the generative AI perform growth evaluation and goal setting support.

[0080] The Career Analysis Department can estimate employees' emotions and adjust the timing of career analysis based on those emotions. For example, if an employee is stressed, the Career Analysis Department can delay the career analysis to allow for a more relaxed state. If an employee is highly motivated, the Career Analysis Department can conduct the career analysis immediately and provide rapid feedback. If an employee is tired, the Career Analysis Department can conduct the career analysis in a shorter time to reduce their burden. By adjusting the timing of career analysis according to an employee's emotions, it is possible to conduct career analysis at a more appropriate time. 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 Career Analysis Department may be performed using, for example, generative AI, or not. For example, the Career Analysis Department can input employee emotion data into a generative AI and have the generative AI adjust the timing of career analysis.

[0081] The Career Analysis Department can analyze an employee's past work history and select the most suitable career analysis method. For example, the Career Analysis Department can select a career analysis method suitable for similar tasks based on a project the employee has successfully completed in the past. For example, the Career Analysis Department can also analyze tasks that an employee has struggled with in the past and conduct a career analysis that includes methods for overcoming those difficulties. For example, the Career Analysis Department can conduct a career analysis that focuses on a specific skill set based on an employee's past work history. This allows for the selection of the most suitable career analysis method by analyzing an employee's past work history. Past work history includes, but is not limited to, project history and performance evaluations. Some or all of the above processes in the Career Analysis Department may be performed using, for example, generative AI, or not. For example, the Career Analysis Department can input employee work history data into a generative AI and have the generative AI select a career analysis method.

[0082] The Career Analysis Department can filter career analyses based on employees' current projects and areas of interest. For example, the Career Analysis Department can focus its analysis on skills related to the projects employees are currently working on. The Career Analysis Department can also suggest relevant career paths based on employees' areas of interest. For example, the Career Analysis Department can consider the challenges employees are facing in their current projects and conduct career analyses that include solutions to those challenges. This allows for more relevant career analyses by filtering based on employees' current projects and areas of interest. Current projects include, but are not limited to, project progress and project goals. Areas of interest include, but are not limited to, work-related interest and willingness to learn. Some or all of the above processing in the Career Analysis Department may be performed using, for example, generative AI, or not. For example, the Career Analysis Department can input employee project data and interest data into a generative AI and have the generative AI perform the filtering.

[0083] The Career Analysis Department can estimate employees' emotions and determine the priority of analysis items based on those estimated emotions. For example, if an employee is feeling anxious, the Career Analysis Department may prioritize analyzing items that provide a sense of security. If an employee is excited, the Career Analysis Department may also prioritize analyzing challenging items. If an employee is tired, the Career Analysis Department may also prioritize analyzing items that are easy to achieve. This allows for more effective career analysis by determining the priority of analysis items according to the employee'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 Career Analysis Department may be performed using, for example, generative AI, or not using generative AI. For example, the Career Analysis Department can input employee emotion data into a generative AI and have the generative AI determine the priority of analysis items.

[0084] The Career Analysis Department can prioritize the analysis of highly relevant data by considering employees' geographical location information during career analysis. For example, if an employee works in a specific region, the Career Analysis Department can conduct career analysis considering industry trends in that region. For example, if an employee works remotely, the Career Analysis Department can focus its analysis on skills suitable for remote work. For example, if an employee wishes to work overseas, the Career Analysis Department can propose overseas career paths. This allows for more appropriate career analysis by prioritizing the analysis of highly relevant data by considering employees' geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the Career Analysis Department may be performed using, for example, generative AI, or without generative AI. For example, the Career Analysis Department can input employees' geographical location data into generative AI and have the generative AI perform priority analysis of highly relevant data.

[0085] The Career Analysis Department can analyze employees' social media activities and obtain relevant data during career analysis. For example, the Career Analysis Department can conduct career analysis based on the interests and concerns that employees express on social media. For example, the Career Analysis Department can also propose career paths considering the industry trends that employees follow on social media. For example, the Career Analysis Department can identify networking opportunities from employees' social media activities and reflect them in career analysis. This allows for a more multifaceted career analysis by analyzing employees' social media activities. Social media activities include, but are not limited to, posts, follower count, and likes. Some or all of the above processes in the Career Analysis Department may be performed using, for example, generative AI, or not. For example, the Career Analysis Department can input employee social media data into a generative AI and have the generative AI retrieve the relevant data.

[0086] The learning suggestion department can estimate an employee's emotions and adjust the way the learning plan is presented based on the estimated emotions. For example, if an employee is stressed, the learning suggestion department can provide a simple and easy-to-understand learning plan. If an employee is relaxed, for example, the learning suggestion department can also provide a detailed learning plan. If an employee is in a hurry, for example, the learning suggestion department can also provide a learning plan that can be completed in a short period of time. In this way, by adjusting the way the learning plan is presented according to the employee's emotions, a more effective learning 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 learning suggestion department may be performed using, for example, generative AI, or not using generative AI. For example, the learning suggestion department can input employee emotion data into a generative AI and have the generative AI adjust the way the learning plan is presented.

[0087] The learning suggestion department can adjust the level of detail in learning plans based on the importance of skills when making learning suggestions. For example, the learning suggestion department can provide detailed learning plans for important skills. For example, it can also provide concise learning plans for less important skills. The learning suggestion department can also prioritize learning plans according to the importance of skills. This allows for more effective learning suggestions by adjusting the level of detail in learning plans based on the importance of skills. The importance of skills includes, but is not limited to, job necessity and importance in future careers. Some or all of the above processing in the learning suggestion department may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning suggestion department can input employee skill data into a generative AI and have the generative AI adjust the level of detail in learning plans.

[0088] The learning suggestion unit can apply different learning algorithms depending on the skill category when making learning suggestions. For example, the learning suggestion unit can apply a practical learning algorithm to technical skills. For example, the learning suggestion unit can also apply a simulation-based learning algorithm to soft skills. For example, the learning suggestion unit can also apply a case study-based learning algorithm to management skills. By applying different learning algorithms depending on the skill category, more effective learning suggestions can be made. Skill categories include, but are not limited to, technical skills, soft skills, and management skills. Some or all of the above processing in the learning suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning suggestion unit can input employee skill data into a generative AI and have the generative AI execute the application of the learning algorithm.

[0089] The learning suggestion department can estimate an employee's emotions and adjust the length of the learning plan based on the estimated emotions. For example, if an employee is tired, the learning suggestion department can provide a learning plan that can be completed in a short period of time. For example, if an employee is highly motivated, the learning suggestion department can also provide a long-term learning plan. For example, if an employee is feeling anxious, the learning suggestion department can also provide a learning plan that can be progressed in stages. In this way, by adjusting the length of the learning plan according to the employee's emotions, a more effective learning 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 learning suggestion department may be performed using a generative AI, or not using a generative AI. For example, the learning suggestion department can input employee emotion data into a generative AI and have the generative AI adjust the length of the learning plan.

[0090] The learning proposal department can prioritize learning plans based on the timing of skill acquisition when proposing learning plans. For example, the learning proposal department can prioritize learning plans for skills that need to be acquired quickly. For example, the learning proposal department can also provide step-by-step learning plans for skills that need to be acquired over a long period. The learning proposal department can also adjust the pace of the learning plan according to the timing of acquisition. This allows for more effective learning proposals by prioritizing learning plans based on the timing of skill acquisition. The timing of skill acquisition includes, but is not limited to, the year and month the skill was acquired and the skill acquisition process. Some or all of the above processing in the learning proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning proposal department can input employee skill data into a generative AI and have the generative AI determine the priority of learning plans.

[0091] The learning proposal unit can adjust the order of learning plans based on the relevance of skills when proposing learning plans. For example, the learning proposal unit can prioritize incorporating highly relevant skills into the learning plan. For example, the learning proposal unit can also construct the learning plan by postponing less relevant skills. For example, the learning proposal unit can adjust the order of progress in the learning plan according to the relevance of skills. This allows for more effective learning proposals by adjusting the order of learning plans based on the relevance of skills. Skill relevance includes, but is not limited to, work-related relevance and relevance of learning content. Some or all of the above processing in the learning proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning proposal unit can input employee skill data into a generative AI and have the generative AI adjust the order of learning plans.

[0092] The mentoring department can estimate an employee's emotions and adjust the content of mentoring based on the estimated emotions. For example, if an employee is feeling stressed, the mentoring department can provide relaxing mentoring content. For example, if an employee is highly motivated, the mentoring department can provide challenging mentoring content. For example, if an employee is feeling anxious, the mentoring department can provide reassuring mentoring content. By adjusting the content of mentoring according to the employee's emotions, more effective mentoring 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 mentoring department may be performed using, for example, generative AI, or not using generative AI. For example, the mentoring department can input employee emotion data into a generative AI and have the generative AI adjust the mentoring content.

[0093] The mentoring department can select the optimal mentoring method by referring to the employee's past mentoring history during mentoring sessions. For example, the mentoring department may reapply mentoring methods that were effective for the employee in the past. The mentoring department may also avoid mentoring methods that the employee found difficult to use in the past. The mentoring department can also select the optimal mentoring method based on the employee's past mentoring history. This allows the mentoring department to select the optimal mentoring method by referring to the employee's past mentoring history. Past mentoring history includes, but is not limited to, the content and results of mentoring sessions. Some or all of the above-described processes in the mentoring department may be performed using, for example, a generative AI, or not. For example, the mentoring department can input the employee's mentoring history data into a generative AI and have the generative AI select the mentoring method.

[0094] The mentoring department can customize the mentoring methods based on the employee's current work situation. For example, if the employee is busy, the mentoring department can provide effective mentoring in a short amount of time. For example, if the employee has ample time, the mentoring department can also provide detailed mentoring. The mentoring department can customize the mentoring methods according to the employee's work situation. This allows for more effective mentoring by customizing the mentoring methods based on the employee's current work situation. Current work situation includes, but is not limited to, the progress of work and the content of work. Some or all of the above processing in the mentoring department may be performed using, for example, a generative AI, or not using a generative AI. For example, the mentoring department can input employee work situation data into a generative AI and have the generative AI perform the customization of the mentoring methods.

[0095] The mentoring department can estimate employees' emotions and determine mentoring priorities based on those estimated emotions. For example, if an employee is feeling stressed, the mentoring department will prioritize mentoring. Conversely, if an employee is highly motivated, the mentoring department may prioritize mentoring that can be postponed. The mentoring department can also determine mentoring priorities based on the employee's emotions. This allows for more effective mentoring by prioritizing mentoring according to the employee'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 mentoring department may be performed using, for example, generative AI, or not. For example, the mentoring department can input employee emotion data into a generative AI and have the generative AI determine mentoring priorities.

[0096] The mentoring department can select the optimal mentoring method by considering the employee's geographical location information during mentoring. For example, if an employee is working remotely, the mentoring department can provide online mentoring. For example, if an employee is in the office, the mentoring department can also provide face-to-face mentoring. The mentoring department can also select the optimal mentoring method according to the employee's geographical location information. This allows for more effective mentoring by selecting the optimal mentoring method by considering the employee's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the mentoring department may be performed using, for example, generative AI, or without using generative AI. For example, the mentoring department can input the employee's geographical location data into a generative AI and have the generative AI select the mentoring method.

[0097] The mentoring department can analyze employees' social media activity during mentoring sessions and propose mentoring methods. For example, the mentoring department can propose mentoring methods based on the content employees post on social media. The mentoring department can also conduct mentoring considering industry trends that employees follow on social media. For example, the mentoring department can identify networking opportunities from employees' social media activity and incorporate them into mentoring. By analyzing employees' social media activity, the mentoring department can propose more effective mentoring methods. Social media activity includes, but is not limited to, posts, follower count, and likes. Some or all of the above processing by the mentoring department may be performed using, for example, generative AI, or not. For example, the mentoring department can input employee social media data into a generative AI and have the generative AI propose mentoring methods.

[0098] The task matching department can estimate employees' emotions and adjust the task matching criteria based on the estimated emotions. For example, if an employee is feeling stressed, the task matching department will prioritize matching them with less burdensome tasks. For example, if an employee is highly motivated, the task matching department can also prioritize matching them with challenging tasks. For example, if an employee is feeling anxious, the task matching department can also prioritize matching them with tasks that provide a sense of security. In this way, by adjusting the task matching criteria according to employees' emotions, more appropriate task matching can be achieved. 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 task matching department may be performed using, for example, generative AI, or not using generative AI. For example, the task matching department can input employee emotion data into a generative AI and have the generative AI perform the adjustment of the task matching criteria.

[0099] The business matching department can improve the accuracy of matching by considering the interrelationship between employee skills and tasks during the business matching process. For example, the business matching department can perform matching by comparing employee skill sets with the required skills of the tasks. For example, the business matching department can also analyze the interrelationship between employee skills and tasks and propose the most suitable tasks. For example, the business matching department can match tasks that provide opportunities for skill development by considering the interrelationship between employee skills and tasks. This allows for more accurate business matching by considering the interrelationship between employee skills and tasks. The interrelationship between skills and tasks includes, but is not limited to, skill sets and required skills of the tasks. Some or all of the above-described processes in the business matching department may be performed using, for example, a generative AI, or not using a generative AI. For example, the business matching department can input employee skill data and business data into a generative AI and have the generative AI perform the task of improving the accuracy of matching.

[0100] The job matching department can perform job matching while considering employee attribute information. For example, the job matching department can match employees with appropriate jobs by considering their age and years of experience. For example, the job matching department can also match employees with jobs that are easy to work with by considering their gender and lifestyle. For example, the job matching department can perform job matching that takes diversity into account based on employee attribute information. This allows for more appropriate job matching by considering employee attribute information. Attribute information includes, but is not limited to, age, years of experience, gender, and lifestyle. Some or all of the above processing in the job matching department may be performed using, for example, a generation AI, or without a generation AI. For example, the job matching department can input employee attribute data into a generation AI and have the generation AI perform the matching.

[0101] The task matching unit can estimate employees' emotions and adjust the order in which task matching results are displayed based on the estimated emotions. For example, if an employee is feeling stressed, the task matching unit can prioritize displaying less burdensome tasks. For example, if an employee is highly motivated, the task matching unit can prioritize displaying challenging tasks. For example, if an employee is feeling anxious, the task matching unit can prioritize displaying tasks that provide a sense of security. By adjusting the order in which task matching results are displayed according to employees' emotions, more appropriate task matching can be achieved. 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 task matching unit may be performed using a generative AI, or not using a generative AI. For example, the task matching unit can input employee emotion data into a generative AI and have the generative AI adjust the display order of task matching results.

[0102] The business matching unit can perform business matching while considering the geographical distribution of employees. For example, if an employee works in a specific region, the business matching unit will prioritize matching them with jobs in that region. For example, if an employee wishes to work remotely, the business matching unit can also match them with jobs suitable for remote work. For example, the business matching unit can propose the most suitable jobs by considering the geographical distribution of employees. This allows for more appropriate business matching by considering the geographical distribution of employees. Geographical distribution includes, but is not limited to, specific regions or a preference for remote work. Some or all of the above processing in the business matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the business matching unit can input employee geographical distribution data into a generative AI and have the generative AI perform the matching.

[0103] The business matching unit can improve the accuracy of matching by referring to relevant literature during business matching. For example, the business matching unit can propose the most suitable business by referring to the latest research papers related to the business. The business matching unit can also improve the accuracy of matching by referring to best practices related to the business. The business matching unit can also propose the most suitable business by referring to industry reports related to the business. This allows for more accurate business matching by referring to relevant literature. Relevant literature includes, but is not limited to, research papers, best practices, and industry reports. Some or all of the above processing in the business matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the business matching unit can input relevant literature data into a generative AI and have the generative AI perform the matching accuracy improvement.

[0104] The growth tracking unit can estimate an employee's emotions and adjust the growth tracking method based on the estimated emotions. For example, if an employee is feeling stressed, the growth tracking unit can provide a simple growth tracking method. For example, if an employee is highly motivated, the growth tracking unit can also provide a detailed growth tracking method. For example, if an employee is feeling anxious, the growth tracking unit can also provide a reassuring growth tracking method. By adjusting the growth tracking method according to the employee's emotions, more effective growth tracking can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 growth tracking unit may be performed using a generative AI, or not using a generative AI. For example, the growth tracking unit can input employee emotion data into a generative AI and have the generative AI adjust the growth tracking method.

[0105] The growth tracking unit can predict current growth by referring to past growth data during growth tracking. For example, the growth tracking unit predicts current growth based on an employee's past growth data. The growth tracking unit can also predict future growth trends from an employee's past growth data. The growth tracking unit can also set growth goals by referring to an employee's past growth data. This allows for the prediction of current growth by referring to past growth data. Past growth data includes, but is not limited to, growth indicators and evaluation frequencies. Some or all of the above processing in the growth tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the growth tracking unit can input an employee's past growth data into a generative AI and have the generative AI perform a prediction of current growth.

[0106] The growth tracking unit can apply different growth analysis methods to each employee category during growth tracking. For example, the growth tracking unit may use technical indicators to track the growth of technical skills. For example, the growth tracking unit may use behavioral observation to track the growth of soft skills. For example, the growth tracking unit may use leadership assessment to track the growth of management skills. This allows for more effective growth tracking by applying different growth analysis methods to each employee category. Examples of category-specific growth analysis methods include, but are not limited to, technical indicators, behavioral observation, and leadership assessment. Some or all of the above processing in the growth tracking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the growth tracking unit can input employee category data into a generative AI and have the generative AI perform the application of growth analysis methods.

[0107] The growth tracking unit can estimate an employee's emotions and adjust the importance of growth tracking based on the estimated emotions. For example, if an employee is stressed, the growth tracking unit can set the importance of growth tracking to a low level. For example, if an employee is highly motivated, the growth tracking unit can set the importance of growth tracking to a high level. For example, if an employee is anxious, the growth tracking unit can set the importance of growth tracking to a medium level. This allows for more effective growth tracking by adjusting the importance of growth tracking according to the employee'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 growth tracking unit may be performed using a generative AI, or not using a generative AI. For example, the growth tracking unit can input employee emotion data into a generative AI and have the generative AI adjust the importance of growth tracking.

[0108] The growth tracking unit can analyze changes in growth based on the employee's growth stage during growth tracking. For example, the growth tracking unit can analyze changes in growth based on the employee's growth stage. The growth tracking unit can also set growth goals according to the employee's growth stage. The growth tracking unit can also evaluate the progress of growth based on the employee's growth stage. This allows for more effective growth tracking by analyzing changes in growth based on the employee's growth stage. Growth stages include, but are not limited to, growth indicators and evaluation frequency. Some or all of the above processing in the growth tracking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the growth tracking unit can input employee growth stage data into a generative AI and have the generative AI perform an analysis of changes in growth.

[0109] The growth tracking unit can analyze growth by referring to relevant market data during growth tracking. For example, the growth tracking unit can analyze employee growth by referring to industry growth data. The growth tracking unit can also predict employee growth based on market growth data. The growth tracking unit can also set employee growth targets by referring to relevant market data. This allows for a more accurate analysis of employee growth by referring to relevant market data. Relevant market data includes, but is not limited to, industry growth data and market growth data. Some or all of the above processing in the growth tracking unit may be performed using, for example, generative AI, or without generative AI. For example, the growth tracking unit can input relevant market data into a generative AI and have the generative AI perform the growth analysis.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The Career Analysis Department can analyze employees' health data and propose career paths based on their health status. For example, if an employee is in good health, a more challenging career path can be proposed. Conversely, if an employee's health is unstable, a less demanding career path can be proposed. Furthermore, based on health data, the department can provide career plans that help employees maintain their health. In this way, by proposing career paths tailored to each employee's health status, the department can help employees achieve both health and career success.

[0112] The Learning Proposal Department can customize learning plans based on employees' hobbies and lifestyles. For example, if an employee enjoys sports, the department can propose a plan to learn sports-related skills. If an employee is interested in art, the department can provide a learning plan to develop creative skills. Furthermore, it is possible to adjust the timing and method of learning to suit the employee's lifestyle. This allows the department to provide learning plans tailored to employees' hobbies and lifestyles, thereby increasing their motivation to learn.

[0113] The mentoring department can estimate employees' emotions and adjust the frequency of mentoring based on those estimates. For example, if an employee is feeling stressed, mentoring can be increased and support can be strengthened. If an employee is highly motivated, the frequency of mentoring can be reduced to encourage self-growth. Furthermore, if an employee is feeling anxious, mentoring can be provided at the appropriate time to reassure them. In this way, by adjusting the frequency of mentoring according to the employee's emotions, more effective support can be provided.

[0114] The task matching department can estimate employees' emotions and adjust the difficulty level of tasks based on those estimates. For example, if an employee is feeling stressed, it can prioritize matching them with easier tasks. If an employee is highly motivated, it can prioritize matching them with more difficult tasks. Furthermore, if an employee is feeling anxious, it can match them with tasks of an appropriate difficulty level to provide a sense of security. In this way, by adjusting the difficulty level of tasks according to employees' emotions, more appropriate task matching can be achieved.

[0115] The growth tracking unit can estimate employees' emotions and adjust the feedback method for growth tracking based on those emotions. For example, if an employee is feeling stressed, positive feedback can be prioritized. If an employee is highly motivated, feedback including specific areas for improvement can be provided. Furthermore, if an employee is feeling anxious, encouraging feedback can be provided to reassure them. By adjusting the feedback method according to the employee's emotions, more effective growth tracking can be achieved.

[0116] The Career Analysis Department can analyze the success rates of employees' past projects and propose career paths based on those success rates. For example, it can suggest career paths related to projects in which employees have shown high success rates in the past. Conversely, for projects with low success rates, it can provide career plans that include areas for improvement. Furthermore, based on the success rate analysis, it is possible to propose career paths that leverage the employee's strengths. In this way, by proposing career paths based on the success rates of employees' past projects, more appropriate career support can be provided.

[0117] The Learning Proposal Department can customize learning plans based on employees' learning styles. For example, if an employee has a visual learning style, a learning plan with a lot of visual content can be provided. If an employee has an auditory learning style, a learning plan with a lot of audio content can be provided. Furthermore, if an employee has a practical learning style, a hands-on learning plan can be provided. In this way, the effectiveness of learning can be maximized by providing learning plans that match each employee's learning style.

[0118] The mentoring department can estimate employees' emotions and adjust the format of mentoring based on those estimates. For example, if an employee is feeling stressed, in-person mentoring can be prioritized. If an employee is highly motivated, online mentoring can be provided. Furthermore, if an employee is feeling anxious, group mentoring can be offered to provide a sense of security. In this way, by adjusting the format of mentoring according to the employee's emotions, more effective support can be provided.

[0119] The Task Matching Department can analyze employees' past performance evaluations and match them with tasks based on those evaluations. For example, it can propose new tasks related to tasks in which employees received high evaluations. Conversely, it can also propose tasks that include areas for improvement for tasks in which employees received low evaluations. Furthermore, it is possible to match employees with tasks that leverage their strengths based on past performance evaluations. In this way, by performing task matching based on employees' past performance evaluations, it is possible to propose more appropriate tasks.

[0120] The growth tracking unit can estimate employees' emotions and adjust the reporting method of growth tracking based on those estimated emotions. For example, if an employee is feeling stressed, it can provide a concise and positive report. If an employee is highly motivated, it can provide a detailed report. Furthermore, if an employee is feeling anxious, it can provide an encouraging report to reassure them. By adjusting the reporting method according to the employee's emotions, more effective growth tracking can be achieved.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The Career Analysis Department analyzes employees' strengths, interests, and skills. For example, it uses natural language processing technology to analyze each employee's individual characteristics and employs techniques such as morphological analysis, grammatical analysis, and semantic analysis to accurately analyze employees' strengths, interests, and skills. Step 2: The Learning Proposal Department identifies skill gaps and proposes learning plans based on the results analyzed by the Career Analysis Department. For example, it may use machine learning to identify skill gaps and propose optimal learning plans. It may use techniques such as supervised learning, unsupervised learning, and reinforcement learning to accurately identify skill gaps and propose optimal learning plans. Step 3: The mentoring department provides mentoring based on the learning plan proposed by the learning proposal department. For example, it generates an optimal learning plan using reinforcement learning and provides 24-hour mentoring via an AI chatbot. It uses technologies such as Q-learning, SARSA, and deep reinforcement learning to generate an optimal learning plan and provides 24-hour mentoring via an AI chatbot. Step 4: The Job Matching Department will perform appropriate job matching for employees supported by the Mentoring Department. For example, they will use collaborative filtering to perform appropriate job matching. They will use technologies such as user-based collaborative filtering and item-based collaborative filtering to perform appropriate job matching. Step 5: The Growth Tracking Department continuously tracks the growth of employees engaged in tasks matched by the Task Matching Department. For example, it regularly evaluates employee growth and supports goal setting. It uses methods such as monthly, quarterly, and annual evaluations to regularly evaluate employee growth and support goal setting.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the career analysis unit, learning proposal unit, mentoring unit, job matching unit, and growth tracking unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the career analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes employees' strengths, interests, and skills using natural language processing technology. The learning proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and identifies skill gaps using machine learning and proposes an optimal learning plan. The mentoring unit is implemented by the control unit 46A of the smart device 14 and generates an optimal learning plan using reinforcement learning and provides 24-hour mentoring by an AI chatbot. The job matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs appropriate job matching using collaborative filtering. The growth tracking unit is implemented by the control unit 46A of the smart device 14 and periodically evaluates employee growth and supports goal setting. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements mentioned above, including the career analysis unit, learning proposal unit, mentoring unit, job matching unit, and growth tracking unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the career analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes employees' strengths, interests, and skills using natural language processing technology. The learning proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and identifies skill gaps using machine learning and proposes an optimal learning plan. The mentoring unit is implemented by, for example, the control unit 46A of the smart glasses 214 and generates an optimal learning plan using reinforcement learning and provides 24-hour mentoring by an AI chatbot. The job matching unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and performs appropriate job matching using collaborative filtering. The growth tracking unit is implemented by, for example, the control unit 46A of the smart glasses 214 and periodically evaluates employee growth and supports goal setting. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements mentioned above, including the career analysis unit, learning proposal unit, mentoring unit, job matching unit, and growth tracking unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the career analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes employees' strengths, interests, and skills using natural language processing technology. The learning proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and identifies skill gaps using machine learning and proposes an optimal learning plan. The mentoring unit is implemented by the control unit 46A of the headset terminal 314 and generates an optimal learning plan using reinforcement learning and provides 24-hour mentoring by an AI chatbot. The job matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs appropriate job matching using collaborative filtering. The growth tracking unit is implemented by the control unit 46A of the headset terminal 314 and periodically evaluates employee growth and supports goal setting. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the career analysis unit, learning proposal unit, mentoring unit, job matching unit, and growth tracking unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the career analysis unit is implemented by the control unit 46A of the robot 414 and analyzes employees' strengths, interests, and skills using natural language processing technology. The learning proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and identifies skill gaps using machine learning and proposes an optimal learning plan. The mentoring unit is implemented by, for example, the control unit 46A of the robot 414 and generates an optimal learning plan using reinforcement learning and provides 24-hour mentoring by an AI chatbot. The job matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs appropriate job matching using collaborative filtering. The growth tracking unit is implemented by, for example, the control unit 46A of the robot 414 and periodically evaluates employee growth and supports goal setting. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The Career Analysis Department analyzes each employee's strengths, interests, and skills, Based on the results of the analysis conducted by the aforementioned Career Analysis Department, the Learning Proposal Department identifies skill gaps and proposes learning plans. A mentoring department provides mentoring based on the learning plan proposed by the aforementioned learning proposal department, The Business Matching Department provides appropriate job matching for employees who have been supported by the Mentoring Department, The system includes a growth tracking unit that continuously tracks the growth of employees engaged in tasks matched by the aforementioned business matching unit. A system characterized by the following features. (Note 2) The aforementioned Career Analysis Department Analyzing the individual characteristics of each employee using natural language processing technology The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning suggestion unit, We use machine learning to identify skill gaps and propose optimal learning plans. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned mentoring unit is, We utilize reinforcement learning to generate optimal learning plans and provide 24 / 7 mentoring via an AI chatbot. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned business matching department, Using collaborative filtering to perform appropriate job matching The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned growth tracking unit is We regularly evaluate employee growth and support goal setting. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned Career Analysis Department We estimate employees' emotions and adjust the timing of career analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned Career Analysis Department We analyze employees' past work histories and select the most suitable career analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned Career Analysis Department During career analysis, filtering is performed based on employees' current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned Career Analysis Department Estimate employees' emotions and determine the priority of analysis items based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned Career Analysis Department When conducting career analysis, we prioritize analyzing highly relevant data by considering the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned Career Analysis Department During career analysis, we analyze employees' social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning suggestion unit, We estimate employees' emotions and adjust the way learning plans are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning suggestion unit, When proposing learning opportunities, adjust the level of detail in the learning plan based on the importance of the skills. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning suggestion unit, When suggesting learning opportunities, different learning algorithms are applied depending on the skill category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning suggestion unit, The system estimates employees' emotions and adjusts the length of the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning suggestion unit, When proposing learning plans, prioritize the learning plan based on when the skills will be acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning suggestion unit, When proposing learning plans, adjust the order of the learning plan based on the relevance of the skills. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned mentoring unit is, The system estimates the employee's emotions and adjusts the mentoring content based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned mentoring unit is, During mentoring sessions, the optimal mentoring method is selected by referring to the employee's past mentoring history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned mentoring unit is, During mentoring sessions, customize the mentoring methods based on the employee's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned mentoring unit is, The system estimates employees' emotions and determines mentoring priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned mentoring unit is, When mentoring, the most suitable mentoring method is selected by considering the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned mentoring unit is, During mentoring sessions, we analyze employees' social media activity and propose mentoring strategies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned business matching department, We estimate employees' emotions and adjust the criteria for job matching based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned business matching department, When matching employees with jobs, we improve the accuracy of the matching process by considering the interrelationship between employee skills and the job. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned business matching department, When matching job opportunities, employee attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned business matching department, The system estimates employees' emotions and adjusts the order in which job matching results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned business matching department, When matching job opportunities, the geographical distribution of employees is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned business matching department, When matching businesses, we improve the accuracy of the matching process by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned growth tracking unit is We estimate employee sentiment and adjust growth tracking methods based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned growth tracking unit is When tracking growth, historical growth data is used to predict current growth. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned growth tracking unit is When tracking employee growth, different growth analysis methods are applied to each employee category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned growth tracking unit is We estimate employee sentiment and adjust the importance of growth tracking based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned growth tracking unit is When tracking employee growth, analyze changes in growth based on the employee's growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned growth tracking unit is When tracking growth, analyze growth by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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 Career Analysis Department analyzes each employee's strengths, interests, and skills, Based on the results of the analysis conducted by the aforementioned Career Analysis Department, the Learning Proposal Department identifies skill gaps and proposes learning plans. A mentoring department provides mentoring based on the learning plan proposed by the aforementioned learning proposal department, The Business Matching Department provides appropriate job matching for employees who have been supported by the Mentoring Department, The system includes a growth tracking unit that continuously tracks the growth of employees engaged in tasks matched by the aforementioned business matching unit. A system characterized by the following features.

2. The aforementioned Career Analysis Department Analyzing the individual characteristics of each employee using natural language processing technology The system according to feature 1.

3. The aforementioned learning suggestion unit, We use machine learning to identify skill gaps and propose optimal learning plans. The system according to feature 1.

4. The aforementioned mentoring unit is, We utilize reinforcement learning to generate optimal learning plans and provide 24 / 7 mentoring via an AI chatbot. The system according to feature 1.

5. The aforementioned business matching department, Using collaborative filtering to perform appropriate job matching The system according to feature 1.

6. The aforementioned growth tracking unit is We regularly evaluate employee growth and support goal setting. The system according to feature 1.

7. The aforementioned Career Analysis Department We estimate employees' emotions and adjust the timing of career analysis based on those estimated emotions. The system according to feature 1.

8. The aforementioned Career Analysis Department We analyze employees' past work histories and select the most suitable career analysis method. The system according to feature 1.

9. The aforementioned Career Analysis Department During career analysis, filtering is performed based on employees' current projects and areas of interest. The system according to feature 1.

10. The aforementioned Career Analysis Department Estimate employees' emotions and determine the priority of analysis items based on those estimated emotions. The system according to feature 1.

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