system

Abstract

The system according to this embodiment aims to provide personalized training plans tailored to the skills and growth level of individual employees. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a plan generation unit, a progress monitoring unit, and an effectiveness measurement unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The plan generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The progress monitoring unit monitors progress based on the learning plan generated by the plan generation unit. The effectiveness measurement unit measures the effectiveness of learning based on the progress data obtained by the progress monitoring 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 the chatbot's 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, there is a problem that the cultivation of corporate talents cannot meet individual needs and efficient cultivation is difficult.

[0005] The system according to the embodiment aims to provide a personalized training plan according to the skills and growth levels of individual employees.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a plan generation unit, a progress monitoring unit, and an effectiveness measurement unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The plan generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The progress monitoring unit monitors progress based on the learning plan generated by the plan generation unit. The effectiveness measurement unit measures the effectiveness of learning based on the progress data obtained by the progress monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized training plans tailored to the skills and growth level of individual employees. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The human resource development system according to an embodiment of the present invention is a system that maximizes training efficiency in corporate human resource development by utilizing an AI agent to provide personalized training plans tailored to each employee's skills and level of growth. This human resource development system collects past evaluation data of employees, and the AI ​​agent analyzes this data. Next, based on the analysis results, it generates an optimal learning plan for each employee. Furthermore, the AI ​​agent automatically suggests effective learning content and methods, regularly monitors progress, and adjusts the plan in real time. Finally, it measures the effectiveness of learning and forms a feedback loop to achieve sustainable learning effects. For example, past evaluation data of employees is collected. In this process, employee profiles, past learning data, evaluation data, etc., are input into the system. For example, this includes employee performance evaluations and skill test results. This allows the AI ​​agent to understand each employee's skill set and level of growth. Next, the AI ​​agent analyzes the collected data. The AI ​​agent uses machine learning to analyze the data and evaluate each employee's skills and level of growth. For example, it can determine how much a particular skill has improved based on past evaluation data. This provides the basic data for generating an optimal learning plan for each employee. Based on the analysis results, the AI ​​agent generates an optimal learning plan for each employee. Specifically, it creates a personalized learning plan based on the employee's goals. For example, it can suggest courses or training programs to improve specific skills. This allows employees to efficiently progress in their learning according to the learning plan best suited to them. Furthermore, the AI ​​agent automatically suggests effective learning content and methods. For example, it can suggest learning methods tailored to the employee's needs, such as online courses, workshops, or on-the-job training. This allows employees to efficiently improve their skills using the learning method best suited to them. Progress is regularly monitored, and plans are adjusted in real time. The AI ​​agent regularly evaluates the employee's learning progress and adjusts the learning plan as needed. For example, if a particular skill improves more than expected, it can suggest a new plan to move on to the next step.This ensures that employees always progress through their learning according to the optimal learning plan. Finally, the effectiveness of learning is measured, and a feedback loop is formed. The AI ​​agent quantitatively and qualitatively evaluates the effectiveness of learning and provides feedback on the results. For example, by evaluating the effectiveness of a learning plan and reflecting it in the next learning plan, sustained learning effectiveness can be achieved. This allows for continuous support of employee skill improvement and growth. As a result, the talent development system can provide personalized training plans tailored to each employee's skills and level of growth, maximizing training efficiency.

[0029] The human resource development system according to this embodiment comprises a data collection unit, an analysis unit, a plan generation unit, a progress monitoring unit, and an effectiveness measurement unit. The data collection unit collects data. The data collection unit can collect, for example, employee profiles, past learning data, and evaluation data. For example, the data collection unit collects employee performance evaluations and skill test results. The data collection unit can also collect employee learning history and feedback data. For example, the data collection unit retrieves employee learning history from a database and collects feedback data. Furthermore, the data collection unit can also collect employee self-evaluation data. For example, the data collection unit collects data from self-evaluations conducted by employees. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses machine learning to analyze the data and evaluate each employee's skills and growth. For example, the analysis unit determines how much a particular skill has improved based on past evaluation data. The analysis unit can also use data mining techniques to analyze employees' skill sets. For example, the analysis unit uses data mining techniques to analyze employees' skill sets and identify skill gaps. Furthermore, the analysis unit can also analyze employee feedback data using natural language processing technology. For example, the analysis unit uses natural language processing technology to analyze employee feedback data and perform sentiment analysis. The plan generation unit generates learning plans based on the analysis results obtained by the analysis unit. The plan generation unit can, for example, create personalized learning plans based on employee goals. For example, the plan generation unit can suggest courses or training programs to improve specific skills. The plan generation unit can also create learning plans tailored to employees' learning styles. For example, the plan generation unit can suggest online courses, workshops, or in-person training based on employees' learning styles. Furthermore, the plan generation unit can also create learning plans tailored to employees' learning pace. For example, the plan generation unit adjusts the learning schedule based on employees' learning pace. The progress monitoring unit monitors progress based on the learning plans generated by the plan generation unit. The progress monitoring unit can, for example, periodically evaluate employees' learning progress.For example, the progress monitoring unit evaluates employees' learning progress weekly or monthly. The progress monitoring unit can also monitor learning progress in real time. For example, it evaluates employees' learning progress in real time and adjusts learning plans as needed. Furthermore, the progress monitoring unit can visualize employees' learning progress. For example, it displays learning progress in graphs or charts. The effectiveness measurement unit measures the effectiveness of learning based on the progress data obtained by the progress monitoring unit. The effectiveness measurement unit evaluates the effectiveness of learning quantitatively and qualitatively, for example. For example, it evaluates the effectiveness of learning using numerical or statistical data. The effectiveness measurement unit can also evaluate the effectiveness of learning through interviews or questionnaires. For example, it conducts interviews with employees to evaluate the effectiveness of learning. Furthermore, the effectiveness measurement unit can provide feedback on the effectiveness of learning. For example, it evaluates the effectiveness of learning and reflects the results in the next learning plan. This allows the human resource development system according to this embodiment to efficiently carry out a series of processes from data collection to effectiveness measurement.

[0030] The data collection department collects data. For example, the data collection department can collect employee profiles, past learning data, and evaluation data. Specifically, it collects basic employee information such as name, age, department, position, and date of joining the company. It also collects past learning data such as the history of training and courses taken, acquired qualifications and skills, and learning progress. As evaluation data, it can collect performance evaluations from supervisors and colleagues, skill test results, and project results. For example, the data collection department collects employee performance evaluations and skill test results. This allows for an understanding of employee performance and skill levels. The data collection department can also collect employee learning history and feedback data. For example, the data collection department retrieves employee learning history from a database and collects feedback data. Learning history includes the content and progress of courses taken, study time, and test scores. Feedback data includes comments and evaluations from supervisors and colleagues, as well as self-evaluations. Furthermore, the data collection department can also collect employee self-evaluation data. For example, the data collection department collects data from self-evaluations conducted by employees. Self-assessment data includes employees' perceived strengths and weaknesses in skills, as well as their future goals and challenges. This allows the data collection unit to gather multifaceted data on employees and gain a detailed understanding of each employee's situation. The collected data is stored in a central database and made accessible to the analysis and plan generation units. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department uses machine learning to analyze the data and evaluate each employee's skills and growth. Specifically, it determines how much a particular skill has improved based on past evaluation data. For instance, the analysis department uses past evaluation data to analyze the degree of skill improvement and growth patterns of employees. The analysis department can also analyze employees' skill sets using data mining techniques. For example, the analysis department uses data mining techniques to analyze employees' skill sets and identify skill gaps. This makes it clear which skills employees need to strengthen. Furthermore, the analysis department can analyze employee feedback data using natural language processing techniques. For example, the analysis department uses natural language processing techniques to analyze employee feedback data and perform sentiment analysis. Sentiment analysis allows for understanding employee motivation, satisfaction, and stress levels. This enables the analysis department to analyze the collected data from multiple perspectives and evaluate employees' skills and growth in detail. Additionally, the analysis department can utilize past data and statistical information to predict long-term growth trends and performance fluctuations. For example, based on past data, it can analyze how specific skills have improved and predict future growth. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early problem identification. This allows the analysis unit to not only monitor the situation in real time but also handle long-term growth management and anomaly detection, improving the overall reliability and security of the system.

[0032] The plan generation unit generates learning plans based on the analysis results obtained by the analysis unit. For example, the plan generation unit creates personalized learning plans based on employees' goals. Specifically, it proposes the optimal learning plan according to the employee's career goals and skill development goals. For example, the plan generation unit proposes courses and training programs to improve specific skills. The plan generation unit can also create learning plans tailored to the employee's learning style. For example, it proposes online courses, workshops, and in-person training based on the employee's learning style. This allows employees to learn efficiently in a way that suits them. Furthermore, the plan generation unit can create learning plans that match the employee's learning pace. For example, it adjusts the learning schedule based on the employee's learning pace. This allows employees to learn at a comfortable pace. The plan generation unit can also monitor the progress of the learning plan and modify the plan as needed. For example, if an employee's learning progress is behind schedule, the plan generation unit reviews the learning plan and provides additional support and resources. This allows the plan generation unit to effectively support employee learning and help them achieve their goals. Furthermore, the plan generation unit can evaluate the effectiveness of the learning plan and incorporate that evaluation into the next learning plan. This allows the plan generation unit to consistently provide the optimal learning plan and promote employee growth.

[0033] The progress monitoring unit monitors progress based on the learning plans generated by the plan generation unit. For example, the progress monitoring unit periodically evaluates employees' learning progress. Specifically, it evaluates employees' learning progress weekly and monthly to understand their progress. The progress monitoring unit can also monitor learning progress in real time. For example, it evaluates employees' learning progress in real time and adjusts the learning plan as needed. This allows the unit to always have an up-to-date understanding of employees' learning progress and provide appropriate support. Furthermore, the progress monitoring unit can visualize employees' learning progress. For example, it can display learning progress in graphs and charts, allowing employees and supervisors to understand the progress at a glance. This allows employees to check their learning progress and maintain their motivation. The progress monitoring unit can also provide feedback according to learning progress. For example, it can provide feedback such as praise and areas for improvement according to employees' learning progress to increase their motivation to learn. In this way, the progress monitoring unit can effectively support employees' learning and help them achieve their goals. Furthermore, the progress monitoring unit can accumulate learning progress data and evaluate long-term learning effectiveness. This allows the progress monitoring unit to continuously support employee growth and improve the overall effectiveness of the system.

[0034] The effectiveness measurement department measures the effectiveness of learning based on progress data obtained by the progress monitoring department. The effectiveness measurement department evaluates the effectiveness of learning quantitatively and qualitatively, for example. Specifically, it evaluates the effectiveness of learning using numerical and statistical data. For example, the effectiveness measurement department quantifies the effectiveness of learning using employee skill test results and performance evaluation data. The effectiveness measurement department can also evaluate the effectiveness of learning through interviews and questionnaires. For example, the effectiveness measurement department conducts interviews with employees to evaluate the effectiveness of learning. In the interviews, they confirm what skills employees have acquired through learning and how the learning has been useful in their work. Furthermore, the effectiveness measurement department can also provide feedback on the effectiveness of learning. For example, the effectiveness measurement department evaluates the effectiveness of learning and reflects the results in the next learning plan. This allows the effectiveness measurement department to continuously evaluate the effectiveness of learning and improve the effectiveness of the entire system. The effectiveness measurement department can also visualize the effectiveness of learning. For example, the effectiveness measurement department displays the effectiveness of learning in graphs and charts so that employees and supervisors can grasp the effect at a glance. This allows employees to confirm their own growth and maintain their motivation. Furthermore, the effectiveness measurement unit can track the effects of learning over the long term and support sustainable growth. This allows the effectiveness measurement unit to continuously support employee growth and improve the overall effectiveness of the system.

[0035] The proposal department proposes learning content. For example, the proposal department can propose learning content that is best suited to each employee. For example, the proposal department can propose online courses that help improve employees' skills. The proposal department can also propose video materials tailored to employees' needs. For example, the proposal department can propose video materials that are appropriate to each employee's skill level. Furthermore, the proposal department can also propose interactive learning content. For example, the proposal department can propose interactive materials that allow employees to learn by actually working with them. This allows the proposal department to suggest learning content that is best suited to each employee. Learning content includes, but is not limited to, text materials, video materials, and interactive materials. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input employee skill data into a generating AI and have the generating AI propose the best learning content.

[0036] The adjustment unit adjusts the learning plan in real time. The adjustment unit can, for example, adjust the learning plan in real time. For example, the adjustment unit adjusts the learning plan according to the employee's learning progress. The adjustment unit can also adjust the learning plan to match the employee's learning pace. For example, the adjustment unit adjusts the learning schedule based on the employee's learning pace. Furthermore, the adjustment unit can also adjust the learning plan according to the employee's learning needs. For example, the adjustment unit adjusts the learning content based on the employee's learning needs. This allows the learning plan to be adjusted in real time. Real time includes, but is not limited to, seconds, minutes, hours, etc. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input employee learning data into a generating AI and have the generating AI perform real-time adjustments to the learning plan.

[0037] The data collection unit can collect employee profiles, past learning data, and evaluation data. For example, the data collection unit can collect profile data such as employee names, ages, job titles, and skills. It can also collect past learning data such as employees' learning history, learning outcomes, and learning time. Furthermore, the data collection unit can collect evaluation data such as employees' test results, feedback, and performance evaluations. This allows for more accurate analysis by collecting detailed employee data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee profile data into a generating AI and have the generating AI perform the data collection.

[0038] The analysis unit can analyze data using machine learning to evaluate each employee's skills and growth. For example, the analysis unit uses machine learning algorithms such as regression analysis, classification algorithms, and clustering to analyze data. The analysis unit can also evaluate employees' skills, including technical skills, soft skills, and specialized knowledge. Furthermore, the analysis unit can evaluate growth, such as the degree of skill improvement and changes in learning outcomes. This improves the accuracy of data analysis through the use of machine learning. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee data into a generating AI and have the generating AI perform the data analysis.

[0039] The plan generation unit can create personalized learning plans based on employees' goals. For example, the plan generation unit can create learning plans based on employees' short-term and long-term goals. It can also customize learning plans based on employees' individual learning needs. Furthermore, the plan generation unit can create learning plans that include components such as learning objectives, learning content, and learning schedules. This allows for the provision of optimal learning plans tailored to employees' goals. Some or all of the processes described above in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input employee goal data into a generation AI and have the generation AI create the learning plan.

[0040] The progress monitoring unit can periodically evaluate employees' learning progress. The progress monitoring unit evaluates learning progress at regular intervals, such as daily, weekly, or monthly. It can also evaluate learning progress based on progress evaluation criteria. Furthermore, the progress monitoring unit can provide feedback on the evaluation results. This allows for appropriate feedback to be provided by periodically evaluating employees' learning progress. Some or all of the above processes in the progress monitoring unit may be performed using AI, or not. For example, the progress monitoring unit can input employee learning data into a generating AI and have the generating AI perform the evaluation of learning progress.

[0041] The effectiveness measurement unit can quantitatively and qualitatively evaluate the effectiveness of learning and provide feedback on the results. For example, the effectiveness measurement unit can quantitatively evaluate the effectiveness of learning using numerical data or statistical data. It can also qualitatively evaluate the effectiveness of learning using interviews or questionnaires. Furthermore, the effectiveness measurement unit can evaluate the effectiveness of learning based on evaluation indicators. This allows for evaluation of the effectiveness of learning and reflection of it in the next learning plan. Some or all of the above-described processes in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input learning data into a generating AI and have the generating AI perform the evaluation of the effectiveness of learning.

[0042] The data collection unit can analyze past employee evaluation data and select the optimal data collection method. For example, the data collection unit can identify the most effective data collection method from past employee evaluation data and select that method. The data collection unit can also analyze employee evaluation data and optimize the frequency and timing of data collection. Furthermore, the data collection unit can select individually customized data collection methods based on employee evaluation data. This allows for the selection of the optimal data collection method by analyzing past evaluation data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee evaluation data into a generating AI and have the generating AI select the optimal data collection method.

[0043] The data collection unit can filter data based on an employee's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to a project the employee is currently working on. The data collection unit can also filter and collect highly relevant data based on the employee's areas of interest. Furthermore, the data collection unit can collect necessary data in a timely manner according to the employee's project progress. This allows for the collection of highly relevant data by filtering data based on the employee's projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee project data into a generating AI and have the generating AI perform data filtering.

[0044] The data collection unit can prioritize the collection of highly relevant data based on employees' geographical location information during data collection. For example, if an employee is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect region-specific data based on the employee's location information. Furthermore, if an employee is on the move, the data collection unit can collect data related to their destination. This enables efficient data collection by collecting highly relevant data based on the employee's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee location data into a generating AI and have the generating AI perform the data collection.

[0045] The data collection unit can analyze employees' social media activities and collect relevant data during data collection. For example, the data collection unit can collect data related to topics of interest from employees' social media activities. The data collection unit can also analyze employees' social media posts and collect necessary data. Furthermore, the data collection unit can collect relevant data by referring to the activities of employees' social media followers and friends. This allows for the collection of highly relevant data by analyzing employees' social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employees' social media data into a generating AI and have the generating AI perform the data collection.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply an image recognition algorithm to image data. Furthermore, it can apply a statistical analysis algorithm to numerical data. This enables efficient data analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0048] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. This enables efficient data analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can optimize the order of analysis based on the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0050] The plan generation unit can adjust the level of detail of a learning plan based on the employee's goals when generating the plan. For example, the plan generation unit can provide a learning plan that includes specific steps based on the employee's short-term goals. It can also provide a learning plan that shows the overall direction based on the employee's long-term goals. Furthermore, the plan generation unit can adjust the level of detail of the plan according to the employee's goals to provide the optimal learning plan. This allows for efficient learning by adjusting the level of detail of the plan based on the employee's goals. Some or all of the above processing in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input employee goal data into a generation AI and have the generation AI perform the adjustment of the level of detail of the plan.

[0051] The plan generation unit can apply different plan generation algorithms depending on the employee's skill level when generating learning plans. For example, the plan generation unit can apply an algorithm containing basic content to learning plans for beginners. It can also apply an algorithm containing advanced content to learning plans for intermediate learners. Furthermore, it can apply an algorithm containing specialized content to learning plans for advanced learners. This enables efficient learning by applying different plan generation algorithms according to the employee's skill level. Some or all of the above processing in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input employee skill data into a generation AI and have the generation AI execute the application of the plan generation algorithm.

[0052] The plan generation unit can determine the priority of learning plans based on the employee's past learning history when generating learning plans. For example, the plan generation unit can provide a plan that prioritizes learning important skills based on the employee's past learning history. It can also provide a plan that prioritizes learning unlearned skills based on the employee's learning history. Furthermore, the plan generation unit can analyze the employee's past learning history and determine the optimal priority of learning plans. This enables efficient learning by determining plan priorities based on the employee's past learning history. Some or all of the above processes in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input employee learning history data into a generation AI and have the generation AI perform the determination of plan priorities.

[0053] The plan generation unit can adjust the order of learning plans based on employee relevance when generating learning plans. For example, the plan generation unit can provide a plan that prioritizes learning skills related to an employee's current work. It can also provide a plan that prioritizes learning skills related to an employee's future career. Furthermore, the plan generation unit can optimize the order of learning plans based on employee relevance. This allows for efficient learning by adjusting the order of plans based on employee relevance. Some or all of the above processing in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input employee relevance data into a generation AI and have the generation AI perform the adjustment of the order of plans.

[0054] The progress monitoring unit can adjust the level of detail of monitoring based on the employee's learning progress. For example, the progress monitoring unit can perform detailed monitoring on employees who are behind in their learning progress to identify problems. It can also perform simpler monitoring on employees who are progressing well to check their progress. Furthermore, the progress monitoring unit can adjust the frequency and level of detail of monitoring according to the learning progress. This allows for efficient progress monitoring by adjusting the level of detail of monitoring based on the employee's learning progress. Some or all of the above processes in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input employee learning progress data into a generating AI and have the generating AI adjust the level of detail of monitoring.

[0055] The progress monitoring unit can apply different monitoring algorithms depending on the employee's skill level during progress monitoring. For example, the progress monitoring unit can apply an algorithm containing basic content for monitoring beginners. It can also apply an algorithm containing advanced content for monitoring intermediate users. Furthermore, it can apply an algorithm containing specialized content for monitoring advanced users. This enables efficient progress monitoring by applying different monitoring algorithms according to the employee's skill level. Some or all of the above processing in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input employee skill data into a generating AI and have the generating AI execute the application of the monitoring algorithm.

[0056] The progress monitoring unit can determine monitoring priorities based on an employee's past learning history during progress monitoring. For example, the progress monitoring unit can prioritize monitoring the progress of important skills based on an employee's past learning history. It can also prioritize monitoring the progress of unacquired skills based on an employee's learning history. Furthermore, the progress monitoring unit can analyze an employee's past learning history to determine the optimal monitoring priorities. This enables efficient progress monitoring by determining monitoring priorities based on an employee's past learning history. Some or all of the above processes in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input employee learning history data into a generating AI and have the generating AI determine the monitoring priorities.

[0057] The progress monitoring unit can adjust the monitoring order based on employee relevance during progress monitoring. For example, the progress monitoring unit can prioritize monitoring the progress of skills related to an employee's current work. It can also prioritize monitoring the progress of skills related to an employee's future career. Furthermore, the progress monitoring unit can optimize the monitoring order based on employee relevance. This enables efficient progress monitoring by adjusting the monitoring order based on employee relevance. Some or all of the above processing in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input employee relevance data into a generating AI and have the generating AI perform the adjustment of the monitoring order.

[0058] The effectiveness measurement unit can adjust the level of detail of the measurement based on the employee's learning effectiveness during the effectiveness measurement process. For example, the effectiveness measurement unit can perform detailed effectiveness measurements for employees with high learning effectiveness. Conversely, it can also perform simplified effectiveness measurements for employees with low learning effectiveness. Furthermore, the effectiveness measurement unit can optimally allocate measurement resources according to the learning effectiveness. This allows for efficient effectiveness measurement by adjusting the level of detail of the measurement based on the employee's learning effectiveness. Some or all of the above processing in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input employee learning effectiveness data into a generating AI and have the generating AI perform the adjustment of the level of detail of the measurement.

[0059] The effectiveness measurement unit can apply different measurement algorithms depending on the employee's skill level during effectiveness measurement. For example, for effectiveness measurement for beginners, the effectiveness measurement unit applies an algorithm that includes basic content. Furthermore, for effectiveness measurement for intermediate users, the effectiveness measurement unit generates algorithms that include more advanced content. For example, the generating AI generates a beginner-level learning plan based on the employee's skill level. The generating AI can also generate a learning plan that reflects past learning achievements based on the employee's learning history. In addition, the generating AI can generate a long-term learning plan based on the employee's goals. This allows for the provision of optimal learning plans tailored to each employee's skill level, learning history, and goals.

[0060] The effectiveness measurement unit can determine measurement priorities based on employees' past learning history during effectiveness measurement. For example, the effectiveness measurement unit can prioritize measuring the effectiveness of important skills based on employees' past learning history. It can also prioritize measuring the effectiveness of unacquired skills based on employees' learning history. Furthermore, the effectiveness measurement unit can analyze employees' past learning history to determine the optimal measurement priorities. This enables efficient effectiveness measurement by determining measurement priorities based on employees' past learning history. Some or all of the above processes in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input employee learning history data into a generating AI and have the generating AI perform the determination of measurement priorities.

[0061] The effectiveness measurement unit can adjust the order of measurements based on employee relevance during effectiveness measurement. For example, the effectiveness measurement unit may prioritize measuring the effectiveness of skills related to an employee's current work. It can also prioritize measuring the effectiveness of skills related to an employee's future career. Furthermore, the effectiveness measurement unit can optimize the order of measurements based on employee relevance. This allows for efficient effectiveness measurement by adjusting the order of measurements based on employee relevance. Some or all of the above processing in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input employee relevance data into a generating AI and have the generating AI perform the adjustment of the measurement order.

[0062] The proposal department can adjust the level of detail in learning content proposals based on the employee's skill level. For example, the proposal department can propose learning content for beginners that includes basic information. It can also propose learning content for intermediate learners that includes more advanced information. Furthermore, it can propose learning content for advanced learners that includes more specialized information. By adjusting the level of detail in proposals based on the employee's skill level, efficient learning becomes possible. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input employee skill data into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0063] The suggestion department can apply different suggestion algorithms depending on the employee's goals when suggesting learning content. For example, the suggestion department can suggest specific learning content based on the employee's short-term goals. It can also suggest learning content that provides an overall direction based on the employee's long-term goals. Furthermore, the suggestion department can adjust the suggestion algorithm according to the employee's goals to provide the most suitable learning content. This enables efficient learning by applying different suggestion algorithms according to the employee's goals. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the suggestion department can input employee goal data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0064] The suggestion department can adjust the order of suggested learning content based on the employee's past learning history. For example, the suggestion department can suggest content that prioritizes learning important skills based on the employee's past learning history. It can also suggest content that prioritizes learning skills that the employee has not yet acquired, based on the employee's learning history. Furthermore, the suggestion department can analyze the employee's past learning history and determine the optimal order of learning content. This allows for efficient learning by adjusting the order of suggestions based on the employee's past learning history. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input employee learning history data into a generating AI and have the generating AI adjust the order of suggestions.

[0065] The suggestion department can adjust the order of learning content suggestions based on the employee's relevance. For example, the suggestion department can suggest content that prioritizes learning skills related to the employee's current work. It can also suggest content that prioritizes learning skills related to the employee's future career. Furthermore, the suggestion department can optimize the order of learning content based on the employee's relevance. This allows for more efficient learning by adjusting the order of suggestions based on the employee's relevance. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input employee relevance data into a generating AI and have the generating AI adjust the order of suggestions.

[0066] The adjustment unit can adjust the level of detail of the adjustments based on the employee's learning progress when adjusting the learning plan. For example, the adjustment unit can perform detailed adjustments for employees who are behind in their learning progress to identify problems. The adjustment unit can also perform simpler adjustments for employees who are progressing well to check their progress. Furthermore, the adjustment unit can adjust the frequency and level of detail of the adjustments according to the learning progress. This allows for more efficient learning by adjusting the level of detail of the adjustments based on the employee's learning progress. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or not. For example, the adjustment unit can input employee learning progress data into a generating AI and have the generating AI perform the adjustment of the level of detail of the adjustments.

[0067] The adjustment unit can apply different adjustment algorithms to employees according to their skill level when adjusting learning plans. For example, the adjustment unit can apply an algorithm containing basic content for beginners. It can also apply an algorithm containing advanced content for intermediate learners. Furthermore, it can apply an algorithm containing specialized content for advanced learners. This enables efficient learning by applying different adjustment algorithms according to the employee's skill level. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input employee skill data into a generating AI and have the generating AI execute the application of the adjustment algorithm.

[0068] The adjustment unit can adjust the order of adjustments based on the employee's past learning history when adjusting the learning plan. For example, the adjustment unit can adjust a plan that prioritizes learning important skills based on the employee's past learning history. The adjustment unit can also adjust a plan that prioritizes learning unlearned skills based on the employee's learning history. Furthermore, the adjustment unit can analyze the employee's past learning history and determine the optimal order of the learning plan. This enables efficient learning by adjusting the order of adjustments based on the employee's past learning history. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the employee's learning history data into a generating AI and have the generating AI perform the adjustment of the order of adjustments.

[0069] The adjustment unit can adjust the order of adjustments based on employee relevance when adjusting learning plans. For example, the adjustment unit can adjust a plan that prioritizes learning skills related to an employee's current work. It can also adjust a plan that prioritizes learning skills related to an employee's future career. Furthermore, the adjustment unit can optimize the order of learning plans based on employee relevance. This enables efficient learning by adjusting the order of adjustments based on employee relevance. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input employee relevance data into a generating AI and have the generating AI perform the adjustment of the order of adjustments.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] The data collection unit can prioritize the collection of highly relevant data based on employees' geographical location information. For example, if an employee is in a specific region, it will prioritize the collection of data related to that region. It can also collect region-specific data based on the employee's location. Furthermore, if an employee is on the move, it can collect data related to their destination. This enables efficient data collection by collecting highly relevant data based on employees' geographical location information.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. Furthermore, it can optimally allocate analysis resources according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0073] The plan generation unit can apply different plan generation algorithms depending on the employee's skill level. For example, a learning plan for beginners can be created using an algorithm that includes basic content. A learning plan for intermediate learners can be created using an algorithm that includes more advanced content. Furthermore, a learning plan for advanced learners can be created using an algorithm that includes more specialized content. By applying different plan generation algorithms according to the employee's skill level, efficient learning becomes possible.

[0074] The progress monitoring department can prioritize monitoring based on employees' past learning history. For example, it can prioritize monitoring the progress of important skills based on an employee's past learning history. It can also prioritize monitoring the progress of skills that have not yet been acquired, based on an employee's learning history. Furthermore, it can analyze an employee's past learning history to determine the optimal monitoring priority. This enables efficient progress monitoring by prioritizing monitoring based on an employee's past learning history.

[0075] The effectiveness measurement unit can adjust the order of measurements based on employee relevance. For example, it can prioritize measuring the effectiveness of skills related to an employee's current work. It can also prioritize measuring the effectiveness of skills related to an employee's future career. Furthermore, it can optimize the order of measurements based on employee relevance. This allows for efficient effectiveness measurement by adjusting the order of measurements based on employee relevance.

[0076] The following briefly describes the processing flow for example form 1.

[0077] Step 1: The data collection unit collects data. For example, it collects employee profiles, past learning data, evaluation data, performance evaluations, skill test results, learning history, feedback data, and self-assessment data. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses machine learning to evaluate each employee's skills and growth rate, data mining techniques to analyze skill sets, and natural language processing techniques to analyze feedback data. Step 3: The plan generation unit generates a learning plan based on the analysis results obtained by the analysis unit. For example, it creates a personalized learning plan based on the employee's goals, suggests courses and training programs to improve specific skills, and creates a learning plan that matches the learning style and pace. Step 4: The progress monitoring unit monitors progress based on the learning plan generated by the plan generation unit. For example, it periodically evaluates employees' learning progress, monitors it in real time, adjusts the learning plan as needed, and visualizes the learning progress. Step 5: The effectiveness measurement unit measures the effectiveness of learning based on the progress data obtained by the progress monitoring unit. For example, it evaluates the effectiveness of learning quantitatively and qualitatively, using numerical data, statistical data, interviews, and questionnaires, and reflects the results in the next learning plan.

[0078] (Example of form 2) The human resource development system according to an embodiment of the present invention is a system that maximizes training efficiency in corporate human resource development by utilizing an AI agent to provide personalized training plans tailored to each employee's skills and level of growth. This human resource development system collects past evaluation data of employees, and the AI ​​agent analyzes this data. Next, based on the analysis results, it generates an optimal learning plan for each employee. Furthermore, the AI ​​agent automatically suggests effective learning content and methods, regularly monitors progress, and adjusts the plan in real time. Finally, it measures the effectiveness of learning and forms a feedback loop to achieve sustainable learning effects. For example, past evaluation data of employees is collected. In this process, employee profiles, past learning data, evaluation data, etc., are input into the system. For example, this includes employee performance evaluations and skill test results. This allows the AI ​​agent to understand each employee's skill set and level of growth. Next, the AI ​​agent analyzes the collected data. The AI ​​agent uses machine learning to analyze the data and evaluate each employee's skills and level of growth. For example, it can determine how much a particular skill has improved based on past evaluation data. This provides the basic data for generating an optimal learning plan for each employee. Based on the analysis results, the AI ​​agent generates an optimal learning plan for each employee. Specifically, it creates a personalized learning plan based on the employee's goals. For example, it can suggest courses or training programs to improve specific skills. This allows employees to efficiently progress in their learning according to the learning plan best suited to them. Furthermore, the AI ​​agent automatically suggests effective learning content and methods. For example, it can suggest learning methods tailored to the employee's needs, such as online courses, workshops, or on-the-job training. This allows employees to efficiently improve their skills using the learning method best suited to them. Progress is regularly monitored, and plans are adjusted in real time. The AI ​​agent regularly evaluates the employee's learning progress and adjusts the learning plan as needed. For example, if a particular skill improves more than expected, it can suggest a new plan to move on to the next step.This ensures that employees always progress through their learning according to the optimal learning plan. Finally, the effectiveness of learning is measured, and a feedback loop is formed. The AI ​​agent quantitatively and qualitatively evaluates the effectiveness of learning and provides feedback on the results. For example, by evaluating the effectiveness of a learning plan and reflecting it in the next learning plan, sustained learning effectiveness can be achieved. This allows for continuous support of employee skill improvement and growth. As a result, the talent development system can provide personalized training plans tailored to each employee's skills and level of growth, maximizing training efficiency.

[0079] The human resource development system according to this embodiment comprises a data collection unit, an analysis unit, a plan generation unit, a progress monitoring unit, and an effectiveness measurement unit. The data collection unit collects data. The data collection unit can collect, for example, employee profiles, past learning data, and evaluation data. For example, the data collection unit collects employee performance evaluations and skill test results. The data collection unit can also collect employee learning history and feedback data. For example, the data collection unit retrieves employee learning history from a database and collects feedback data. Furthermore, the data collection unit can also collect employee self-evaluation data. For example, the data collection unit collects data from self-evaluations conducted by employees. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses machine learning to analyze the data and evaluate each employee's skills and growth. For example, the analysis unit determines how much a particular skill has improved based on past evaluation data. The analysis unit can also use data mining techniques to analyze employees' skill sets. For example, the analysis unit uses data mining techniques to analyze employees' skill sets and identify skill gaps. Furthermore, the analysis unit can also analyze employee feedback data using natural language processing technology. For example, the analysis unit uses natural language processing technology to analyze employee feedback data and perform sentiment analysis. The plan generation unit generates learning plans based on the analysis results obtained by the analysis unit. The plan generation unit can, for example, create personalized learning plans based on employee goals. For example, the plan generation unit can suggest courses or training programs to improve specific skills. The plan generation unit can also create learning plans tailored to employees' learning styles. For example, the plan generation unit can suggest online courses, workshops, or in-person training based on employees' learning styles. Furthermore, the plan generation unit can also create learning plans tailored to employees' learning pace. For example, the plan generation unit adjusts the learning schedule based on employees' learning pace. The progress monitoring unit monitors progress based on the learning plans generated by the plan generation unit. The progress monitoring unit can, for example, periodically evaluate employees' learning progress.For example, the progress monitoring unit evaluates employees' learning progress weekly or monthly. The progress monitoring unit can also monitor learning progress in real time. For example, it evaluates employees' learning progress in real time and adjusts learning plans as needed. Furthermore, the progress monitoring unit can visualize employees' learning progress. For example, it displays learning progress in graphs or charts. The effectiveness measurement unit measures the effectiveness of learning based on the progress data obtained by the progress monitoring unit. The effectiveness measurement unit evaluates the effectiveness of learning quantitatively and qualitatively, for example. For example, it evaluates the effectiveness of learning using numerical or statistical data. The effectiveness measurement unit can also evaluate the effectiveness of learning through interviews or questionnaires. For example, it conducts interviews with employees to evaluate the effectiveness of learning. Furthermore, the effectiveness measurement unit can provide feedback on the effectiveness of learning. For example, it evaluates the effectiveness of learning and reflects the results in the next learning plan. This allows the human resource development system according to this embodiment to efficiently carry out a series of processes from data collection to effectiveness measurement.

[0080] The data collection department collects data. For example, the data collection department can collect employee profiles, past learning data, and evaluation data. Specifically, it collects basic employee information such as name, age, department, position, and date of joining the company. It also collects past learning data such as the history of training and courses taken, acquired qualifications and skills, and learning progress. As evaluation data, it can collect performance evaluations from supervisors and colleagues, skill test results, and project results. For example, the data collection department collects employee performance evaluations and skill test results. This allows for an understanding of employee performance and skill levels. The data collection department can also collect employee learning history and feedback data. For example, the data collection department retrieves employee learning history from a database and collects feedback data. Learning history includes the content and progress of courses taken, study time, and test scores. Feedback data includes comments and evaluations from supervisors and colleagues, as well as self-evaluations. Furthermore, the data collection department can also collect employee self-evaluation data. For example, the data collection department collects data from self-evaluations conducted by employees. Self-assessment data includes employees' perceived strengths and weaknesses in skills, as well as their future goals and challenges. This allows the data collection unit to gather multifaceted data on employees and gain a detailed understanding of each employee's situation. The collected data is stored in a central database and made accessible to the analysis and plan generation units. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0081] The analysis department analyzes the data collected by the data collection department. For example, the analysis department uses machine learning to analyze the data and evaluate each employee's skills and growth. Specifically, it determines how much a particular skill has improved based on past evaluation data. For instance, the analysis department uses past evaluation data to analyze the degree of skill improvement and growth patterns of employees. The analysis department can also analyze employees' skill sets using data mining techniques. For example, the analysis department uses data mining techniques to analyze employees' skill sets and identify skill gaps. This makes it clear which skills employees need to strengthen. Furthermore, the analysis department can analyze employee feedback data using natural language processing techniques. For example, the analysis department uses natural language processing techniques to analyze employee feedback data and perform sentiment analysis. Sentiment analysis allows for understanding employee motivation, satisfaction, and stress levels. This enables the analysis department to analyze the collected data from multiple perspectives and evaluate employees' skills and growth in detail. Additionally, the analysis department can utilize past data and statistical information to predict long-term growth trends and performance fluctuations. For example, based on past data, it can analyze how specific skills have improved and predict future growth. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early problem identification. This allows the analysis unit to not only monitor the situation in real time but also handle long-term growth management and anomaly detection, improving the overall reliability and security of the system.

[0082] The plan generation unit generates learning plans based on the analysis results obtained by the analysis unit. For example, the plan generation unit creates personalized learning plans based on employees' goals. Specifically, it proposes the optimal learning plan according to the employee's career goals and skill development goals. For example, the plan generation unit proposes courses and training programs to improve specific skills. The plan generation unit can also create learning plans tailored to the employee's learning style. For example, it proposes online courses, workshops, and in-person training based on the employee's learning style. This allows employees to learn efficiently in a way that suits them. Furthermore, the plan generation unit can create learning plans that match the employee's learning pace. For example, it adjusts the learning schedule based on the employee's learning pace. This allows employees to learn at a comfortable pace. The plan generation unit can also monitor the progress of the learning plan and modify the plan as needed. For example, if an employee's learning progress is behind schedule, the plan generation unit reviews the learning plan and provides additional support and resources. This allows the plan generation unit to effectively support employee learning and help them achieve their goals. Furthermore, the plan generation unit can evaluate the effectiveness of the learning plan and incorporate that evaluation into the next learning plan. This allows the plan generation unit to consistently provide the optimal learning plan and promote employee growth.

[0083] The progress monitoring unit monitors progress based on the learning plans generated by the plan generation unit. For example, the progress monitoring unit periodically evaluates employees' learning progress. Specifically, it evaluates employees' learning progress weekly and monthly to understand their progress. The progress monitoring unit can also monitor learning progress in real time. For example, it evaluates employees' learning progress in real time and adjusts the learning plan as needed. This allows the unit to always have an up-to-date understanding of employees' learning progress and provide appropriate support. Furthermore, the progress monitoring unit can visualize employees' learning progress. For example, it can display learning progress in graphs and charts, allowing employees and supervisors to understand the progress at a glance. This allows employees to check their learning progress and maintain their motivation. The progress monitoring unit can also provide feedback according to learning progress. For example, it can provide feedback such as praise and areas for improvement according to employees' learning progress to increase their motivation to learn. In this way, the progress monitoring unit can effectively support employees' learning and help them achieve their goals. Furthermore, the progress monitoring unit can accumulate learning progress data and evaluate long-term learning effectiveness. This allows the progress monitoring unit to continuously support employee growth and improve the overall effectiveness of the system.

[0084] The effectiveness measurement department measures the effectiveness of learning based on progress data obtained by the progress monitoring department. The effectiveness measurement department evaluates the effectiveness of learning quantitatively and qualitatively, for example. Specifically, it evaluates the effectiveness of learning using numerical and statistical data. For example, the effectiveness measurement department quantifies the effectiveness of learning using employee skill test results and performance evaluation data. The effectiveness measurement department can also evaluate the effectiveness of learning through interviews and questionnaires. For example, the effectiveness measurement department conducts interviews with employees to evaluate the effectiveness of learning. In the interviews, they confirm what skills employees have acquired through learning and how the learning has been useful in their work. Furthermore, the effectiveness measurement department can also provide feedback on the effectiveness of learning. For example, the effectiveness measurement department evaluates the effectiveness of learning and reflects the results in the next learning plan. This allows the effectiveness measurement department to continuously evaluate the effectiveness of learning and improve the effectiveness of the entire system. The effectiveness measurement department can also visualize the effectiveness of learning. For example, the effectiveness measurement department displays the effectiveness of learning in graphs and charts so that employees and supervisors can grasp the effect at a glance. This allows employees to confirm their own growth and maintain their motivation. Furthermore, the effectiveness measurement unit can track the effects of learning over the long term and support sustainable growth. This allows the effectiveness measurement unit to continuously support employee growth and improve the overall effectiveness of the system.

[0085] The proposal department proposes learning content. For example, the proposal department can propose learning content that is best suited to each employee. For example, the proposal department can propose online courses that help improve employees' skills. The proposal department can also propose video materials tailored to employees' needs. For example, the proposal department can propose video materials that are appropriate to each employee's skill level. Furthermore, the proposal department can also propose interactive learning content. For example, the proposal department can propose interactive materials that allow employees to learn by actually working with them. This allows the proposal department to suggest learning content that is best suited to each employee. Learning content includes, but is not limited to, text materials, video materials, and interactive materials. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input employee skill data into a generating AI and have the generating AI propose the best learning content.

[0086] The adjustment unit adjusts the learning plan in real time. The adjustment unit can, for example, adjust the learning plan in real time. For example, the adjustment unit adjusts the learning plan according to the employee's learning progress. The adjustment unit can also adjust the learning plan to match the employee's learning pace. For example, the adjustment unit adjusts the learning schedule based on the employee's learning pace. Furthermore, the adjustment unit can also adjust the learning plan according to the employee's learning needs. For example, the adjustment unit adjusts the learning content based on the employee's learning needs. This allows the learning plan to be adjusted in real time. Real time includes, but is not limited to, seconds, minutes, hours, etc. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input employee learning data into a generating AI and have the generating AI perform real-time adjustments to the learning plan.

[0087] The data collection unit can collect employee profiles, past learning data, and evaluation data. For example, the data collection unit can collect profile data such as employee names, ages, job titles, and skills. It can also collect past learning data such as employees' learning history, learning outcomes, and learning time. Furthermore, the data collection unit can collect evaluation data such as employees' test results, feedback, and performance evaluations. This allows for more accurate analysis by collecting detailed employee data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee profile data into a generating AI and have the generating AI perform the data collection.

[0088] The analysis unit can analyze data using machine learning to evaluate each employee's skills and growth. For example, the analysis unit uses machine learning algorithms such as regression analysis, classification algorithms, and clustering to analyze data. The analysis unit can also evaluate employees' skills, including technical skills, soft skills, and specialized knowledge. Furthermore, the analysis unit can evaluate growth, such as the degree of skill improvement and changes in learning outcomes. This improves the accuracy of data analysis through the use of machine learning. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee data into a generating AI and have the generating AI perform the data analysis.

[0089] The plan generation unit can create personalized learning plans based on employees' goals. For example, the plan generation unit can create learning plans based on employees' short-term and long-term goals. It can also customize learning plans based on employees' individual learning needs. Furthermore, the plan generation unit can create learning plans that include components such as learning objectives, learning content, and learning schedules. This allows for the provision of optimal learning plans tailored to employees' goals. Some or all of the processes described above in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input employee goal data into a generation AI and have the generation AI create the learning plan.

[0090] The progress monitoring unit can periodically evaluate employees' learning progress. The progress monitoring unit evaluates learning progress at regular intervals, such as daily, weekly, or monthly. It can also evaluate learning progress based on progress evaluation criteria. Furthermore, the progress monitoring unit can provide feedback on the evaluation results. This allows for appropriate feedback to be provided by periodically evaluating employees' learning progress. Some or all of the above processes in the progress monitoring unit may be performed using AI, or not. For example, the progress monitoring unit can input employee learning data into a generating AI and have the generating AI perform the evaluation of learning progress.

[0091] The effectiveness measurement unit can quantitatively and qualitatively evaluate the effectiveness of learning and provide feedback on the results. For example, the effectiveness measurement unit can quantitatively evaluate the effectiveness of learning using numerical data or statistical data. It can also qualitatively evaluate the effectiveness of learning using interviews or questionnaires. Furthermore, the effectiveness measurement unit can evaluate the effectiveness of learning based on evaluation indicators. This allows for evaluation of the effectiveness of learning and reflection of it in the next learning plan. Some or all of the above-described processes in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input learning data into a generating AI and have the generating AI perform the evaluation of the effectiveness of learning.

[0092] The data collection unit can estimate employees' emotions and adjust the timing of data collection based on the estimated emotions. For example, if an employee is stressed, the data collection unit can postpone data collection and collect it when the employee is relaxed. It can also collect data when an employee is focused, ensuring efficient data acquisition. Furthermore, if an employee is tired, the data collection unit can collect data after a break to obtain accurate data. This allows for efficient data acquisition by adjusting the timing of data collection according to employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 data collection unit may be performed using AI, or not. For example, the data collection unit can input employee emotion data into a generative AI and have the generative AI adjust the data collection timing.

[0093] The data collection unit can analyze past employee evaluation data and select the optimal data collection method. For example, the data collection unit can identify the most effective data collection method from past employee evaluation data and select that method. The data collection unit can also analyze employee evaluation data and optimize the frequency and timing of data collection. Furthermore, the data collection unit can select individually customized data collection methods based on employee evaluation data. This allows for the selection of the optimal data collection method by analyzing past evaluation data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee evaluation data into a generating AI and have the generating AI select the optimal data collection method.

[0094] The data collection unit can filter data based on an employee's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to a project the employee is currently working on. The data collection unit can also filter and collect highly relevant data based on the employee's areas of interest. Furthermore, the data collection unit can collect necessary data in a timely manner according to the employee's project progress. This allows for the collection of highly relevant data by filtering data based on the employee's projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee project data into a generating AI and have the generating AI perform data filtering.

[0095] The data collection unit can estimate employees' emotions and prioritize the data to be collected based on those estimated emotions. For example, if an employee is stressed, the data collection unit will postpone the collection of less important data. Conversely, if an employee is relaxed, the data collection unit can prioritize the collection of more important data. Furthermore, if an employee is focused, the data collection unit can prioritize the collection of more urgent data. This enables efficient data collection by prioritizing data according to employees' 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 data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into a generative AI and have the generative AI determine the data prioritization.

[0096] The data collection unit can prioritize the collection of highly relevant data based on employees' geographical location information during data collection. For example, if an employee is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect region-specific data based on the employee's location information. Furthermore, if an employee is on the move, the data collection unit can collect data related to their destination. This enables efficient data collection by collecting highly relevant data based on the employee's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee location data into a generating AI and have the generating AI perform the data collection.

[0097] The data collection unit can analyze employees' social media activities and collect relevant data during data collection. For example, the data collection unit can collect data related to topics of interest from employees' social media activities. The data collection unit can also analyze employees' social media posts and collect necessary data. Furthermore, the data collection unit can collect relevant data by referring to the activities of employees' social media followers and friends. This allows for the collection of highly relevant data by analyzing employees' social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employees' social media data into a generating AI and have the generating AI perform the data collection.

[0098] The analysis unit can estimate employees' emotions and adjust the data analysis method based on the estimated emotions. For example, if an employee is stressed, the analysis unit can select a simpler analysis method to reduce the burden. If an employee is relaxed, the analysis unit can select a more detailed analysis method to perform a more accurate analysis. Furthermore, if an employee is focused, the analysis unit can select a more complex analysis method to gain deeper insights. This allows for efficient data analysis by adjusting the data analysis method 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 analysis unit may be performed using AI, or not. For example, the analysis unit can input employee emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0099] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0100] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply an image recognition algorithm to image data. Furthermore, it can apply a statistical analysis algorithm to numerical data. This enables efficient data analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0101] The analysis unit can estimate employees' emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if an employee is stressed, the analysis unit provides a simple and highly visible display method. If an employee is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if an employee is focused, the analysis unit can provide a display method that focuses on the key points. By adjusting the display method of the analysis results according to the employee's emotions, efficient data analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input employee emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0102] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. This enables efficient data analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0103] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can optimize the order of analysis based on the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0104] The plan generation unit can estimate an employee's emotions and adjust the presentation of the learning plan based on the estimated emotions. For example, if an employee is stressed, the plan generation unit provides a simple and highly visual learning plan. If the employee is relaxed, the plan generation unit can also provide a learning plan with more detailed information. Furthermore, if the employee is focused, the plan generation unit can provide a concise learning plan. By adjusting the presentation of the learning plan according to the employee's emotions, efficient learning becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the plan generation unit may be performed using AI, or not using AI. For example, the plan generation unit can input employee emotion data into the generative AI and have the generative AI adjust the presentation of the learning plan.

[0105] The plan generation unit can adjust the level of detail of a learning plan based on the employee's goals when generating the plan. For example, the plan generation unit can provide a learning plan that includes specific steps based on the employee's short-term goals. It can also provide a learning plan that shows the overall direction based on the employee's long-term goals. Furthermore, the plan generation unit can adjust the level of detail of the plan according to the employee's goals to provide the optimal learning plan. This allows for efficient learning by adjusting the level of detail of the plan based on the employee's goals. Some or all of the above processing in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input employee goal data into a generation AI and have the generation AI perform the adjustment of the level of detail of the plan.

[0106] The plan generation unit can apply different plan generation algorithms depending on the employee's skill level when generating learning plans. For example, the plan generation unit can apply an algorithm containing basic content to learning plans for beginners. It can also apply an algorithm containing advanced content to learning plans for intermediate learners. Furthermore, it can apply an algorithm containing specialized content to learning plans for advanced learners. This enables efficient learning by applying different plan generation algorithms according to the employee's skill level. Some or all of the above processing in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input employee skill data into a generation AI and have the generation AI execute the application of the plan generation algorithm.

[0107] The plan generation unit 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 stressed, the plan generation unit can provide a learning plan that can be achieved in a short period of time. It can also provide a longer-term learning plan if the employee is relaxed. Furthermore, if the employee is focused, the plan generation unit can provide a learning plan of appropriate length. This allows for efficient learning by adjusting the length of the learning plan according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the plan generation unit may be performed using AI, or not. For example, the plan generation unit can input employee emotion data into the generative AI and have the generative AI adjust the length of the learning plan.

[0108] The plan generation unit can determine the priority of learning plans based on the employee's past learning history when generating learning plans. For example, the plan generation unit can provide a plan that prioritizes learning important skills based on the employee's past learning history. It can also provide a plan that prioritizes learning unlearned skills based on the employee's learning history. Furthermore, the plan generation unit can analyze the employee's past learning history and determine the optimal priority of learning plans. This enables efficient learning by determining plan priorities based on the employee's past learning history. Some or all of the above processes in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input employee learning history data into a generation AI and have the generation AI perform the determination of plan priorities.

[0109] The plan generation unit can adjust the order of learning plans based on employee relevance when generating learning plans. For example, the plan generation unit can provide a plan that prioritizes learning skills related to an employee's current work. It can also provide a plan that prioritizes learning skills related to an employee's future career. Furthermore, the plan generation unit can optimize the order of learning plans based on employee relevance. This allows for efficient learning by adjusting the order of plans based on employee relevance. Some or all of the above processing in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input employee relevance data into a generation AI and have the generation AI perform the adjustment of the order of plans.

[0110] The progress monitoring unit can estimate employees' emotions and adjust the progress monitoring method based on the estimated emotions. For example, if an employee is stressed, the progress monitoring unit can provide a simplified progress monitoring method. It can also provide a more detailed progress monitoring method if the employee is relaxed. Furthermore, if the employee is focused, it can provide a concise progress monitoring method. This allows for efficient progress monitoring by adjusting the progress monitoring method 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 progress monitoring unit may be performed using AI, or not. For example, the progress monitoring unit can input employee emotion data into the generative AI and have the generative AI adjust the progress monitoring method.

[0111] The progress monitoring unit can adjust the level of detail of monitoring based on the employee's learning progress. For example, the progress monitoring unit can perform detailed monitoring on employees who are behind in their learning progress to identify problems. It can also perform simpler monitoring on employees who are progressing well to check their progress. Furthermore, the progress monitoring unit can adjust the frequency and level of detail of monitoring according to the learning progress. This allows for efficient progress monitoring by adjusting the level of detail of monitoring based on the employee's learning progress. Some or all of the above processes in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input employee learning progress data into a generating AI and have the generating AI adjust the level of detail of monitoring.

[0112] The progress monitoring unit can apply different monitoring algorithms depending on the employee's skill level during progress monitoring. For example, the progress monitoring unit can apply an algorithm containing basic content for monitoring beginners. It can also apply an algorithm containing advanced content for monitoring intermediate users. Furthermore, it can apply an algorithm containing specialized content for monitoring advanced users. This enables efficient progress monitoring by applying different monitoring algorithms according to the employee's skill level. Some or all of the above processing in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input employee skill data into a generating AI and have the generating AI execute the application of the monitoring algorithm.

[0113] The progress monitoring unit can estimate employees' emotions and adjust the display method of progress monitoring based on the estimated emotions. For example, if an employee is stressed, the progress monitoring unit can provide a simple and highly visible display method. If an employee is relaxed, the progress monitoring unit can also provide a display method that includes detailed information. Furthermore, if an employee is focused, the progress monitoring unit can provide a display method that focuses on the key points. By adjusting the display method of progress monitoring according to employees' emotions, efficient progress monitoring becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress monitoring unit may be performed using AI, for example, or not using AI. For example, the progress monitoring unit can input employee emotion data into a generative AI and have the generative AI adjust the display method of progress monitoring.

[0114] The progress monitoring unit can determine monitoring priorities based on an employee's past learning history during progress monitoring. For example, the progress monitoring unit can prioritize monitoring the progress of important skills based on an employee's past learning history. It can also prioritize monitoring the progress of unacquired skills based on an employee's learning history. Furthermore, the progress monitoring unit can analyze an employee's past learning history to determine the optimal monitoring priorities. This enables efficient progress monitoring by determining monitoring priorities based on an employee's past learning history. Some or all of the above processes in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input employee learning history data into a generating AI and have the generating AI determine the monitoring priorities.

[0115] The progress monitoring unit can adjust the monitoring order based on employee relevance during progress monitoring. For example, the progress monitoring unit can prioritize monitoring the progress of skills related to an employee's current work. It can also prioritize monitoring the progress of skills related to an employee's future career. Furthermore, the progress monitoring unit can optimize the monitoring order based on employee relevance. This enables efficient progress monitoring by adjusting the monitoring order based on employee relevance. Some or all of the above processing in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input employee relevance data into a generating AI and have the generating AI perform the adjustment of the monitoring order.

[0116] The effectiveness measurement unit can estimate employees' emotions and adjust the effectiveness measurement method based on the estimated emotions. For example, if an employee is stressed, the effectiveness measurement unit can provide a simple effectiveness measurement method. Furthermore, if an employee is relaxed, the effectiveness measurement unit can provide a more detailed effectiveness measurement method. Additionally, if an employee is focused, the effectiveness measurement unit can provide a concise effectiveness measurement method. This allows for efficient effectiveness measurement by adjusting the effectiveness measurement method 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 effectiveness measurement unit may be performed using AI, or not. For example, the effectiveness measurement unit can input employee emotion data into the generative AI and have the generative AI adjust the effectiveness measurement method.

[0117] The effectiveness measurement unit can adjust the level of detail of the measurement based on the employee's learning effectiveness during the effectiveness measurement process. For example, the effectiveness measurement unit can perform detailed effectiveness measurements for employees with high learning effectiveness. Conversely, it can also perform simplified effectiveness measurements for employees with low learning effectiveness. Furthermore, the effectiveness measurement unit can optimally allocate measurement resources according to the learning effectiveness. This allows for efficient effectiveness measurement by adjusting the level of detail of the measurement based on the employee's learning effectiveness. Some or all of the above processing in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input employee learning effectiveness data into a generating AI and have the generating AI perform the adjustment of the level of detail of the measurement.

[0118] The effectiveness measurement unit can apply different measurement algorithms depending on the employee's skill level during effectiveness measurement. For example, for effectiveness measurement for beginners, the effectiveness measurement unit applies an algorithm that includes basic content. Furthermore, for effectiveness measurement for intermediate users, the effectiveness measurement unit generates algorithms that include more advanced content. For example, the generating AI generates a beginner-level learning plan based on the employee's skill level. The generating AI can also generate a learning plan that reflects past learning achievements based on the employee's learning history. In addition, the generating AI can generate a long-term learning plan based on the employee's goals. This allows for the provision of optimal learning plans tailored to each employee's skill level, learning history, and goals.

[0119] The effectiveness measurement unit can estimate employees' emotions and adjust the display method of effectiveness measurements based on the estimated emotions. For example, if an employee is stressed, the effectiveness measurement unit can provide a simple and highly visible display method. If an employee is relaxed, the effectiveness measurement unit can also provide a display method that includes detailed information. Furthermore, if an employee is focused, the effectiveness measurement unit can provide a display method that focuses on the key points. By adjusting the display method of effectiveness measurements according to employees' emotions, efficient effectiveness measurement becomes possible. Some or all of the above processing in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input employee emotion data into a generating AI and have the generating AI perform the adjustment of the display method of effectiveness measurements.

[0120] The effectiveness measurement unit can determine measurement priorities based on employees' past learning history during effectiveness measurement. For example, the effectiveness measurement unit can prioritize measuring the effectiveness of important skills based on employees' past learning history. It can also prioritize measuring the effectiveness of unacquired skills based on employees' learning history. Furthermore, the effectiveness measurement unit can analyze employees' past learning history to determine the optimal measurement priorities. This enables efficient effectiveness measurement by determining measurement priorities based on employees' past learning history. Some or all of the above processes in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input employee learning history data into a generating AI and have the generating AI perform the determination of measurement priorities.

[0121] The effectiveness measurement unit can adjust the order of measurements based on employee relevance during effectiveness measurement. For example, the effectiveness measurement unit may prioritize measuring the effectiveness of skills related to an employee's current work. It can also prioritize measuring the effectiveness of skills related to an employee's future career. Furthermore, the effectiveness measurement unit can optimize the order of measurements based on employee relevance. This allows for efficient effectiveness measurement by adjusting the order of measurements based on employee relevance. Some or all of the above processing in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input employee relevance data into a generating AI and have the generating AI perform the adjustment of the measurement order.

[0122] The suggestion department can estimate employees' emotions and adjust how learning content is suggested based on those emotions. For example, if an employee is stressed, the suggestion department can suggest simple and easy-to-understand learning content. If an employee is relaxed, the suggestion department can also suggest detailed learning content. Furthermore, if an employee is focused, the suggestion department can suggest concise learning content. By adjusting the way learning content is suggested according to the employee's emotions, efficient learning becomes possible. 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 suggestion department may be performed using AI or not. For example, the suggestion department can input employee emotion data into a generative AI and have the generative AI adjust how learning content is suggested.

[0123] The proposal department can adjust the level of detail in learning content proposals based on the employee's skill level. For example, the proposal department can propose learning content for beginners that includes basic information. It can also propose learning content for intermediate learners that includes more advanced information. Furthermore, it can propose learning content for advanced learners that includes more specialized information. By adjusting the level of detail in proposals based on the employee's skill level, efficient learning becomes possible. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input employee skill data into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0124] The suggestion department can apply different suggestion algorithms depending on the employee's goals when suggesting learning content. For example, the suggestion department can suggest specific learning content based on the employee's short-term goals. It can also suggest learning content that provides an overall direction based on the employee's long-term goals. Furthermore, the suggestion department can adjust the suggestion algorithm according to the employee's goals to provide the most suitable learning content. This enables efficient learning by applying different suggestion algorithms according to the employee's goals. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the suggestion department can input employee goal data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0125] The suggestion department can estimate employees' emotions and prioritize learning content based on those emotions. For example, if an employee is stressed, the suggestion department will postpone less important learning content. Conversely, if an employee is relaxed, the suggestion department can prioritize suggesting more important learning content. Furthermore, if an employee is focused, the suggestion department can prioritize suggesting more urgent learning content. This allows for more efficient learning by prioritizing learning content according to employees' 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 suggestion department may be performed using AI or not. For example, the suggestion department can input employee emotion data into a generative AI and have the generative AI determine the priority of learning content.

[0126] The suggestion department can adjust the order of suggested learning content based on the employee's past learning history. For example, the suggestion department can suggest content that prioritizes learning important skills based on the employee's past learning history. It can also suggest content that prioritizes learning skills that the employee has not yet acquired, based on the employee's learning history. Furthermore, the suggestion department can analyze the employee's past learning history and determine the optimal order of learning content. This allows for efficient learning by adjusting the order of suggestions based on the employee's past learning history. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input employee learning history data into a generating AI and have the generating AI adjust the order of suggestions.

[0127] The suggestion department can adjust the order of learning content suggestions based on the employee's relevance. For example, the suggestion department can suggest content that prioritizes learning skills related to the employee's current work. It can also suggest content that prioritizes learning skills related to the employee's future career. Furthermore, the suggestion department can optimize the order of learning content based on the employee's relevance. This allows for more efficient learning by adjusting the order of suggestions based on the employee's relevance. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input employee relevance data into a generating AI and have the generating AI adjust the order of suggestions.

[0128] The adjustment unit can estimate an employee's emotions and adjust the learning plan adjustment method based on the estimated emotions. For example, if an employee is stressed, the adjustment unit can provide a simple adjustment method. If an employee is relaxed, the adjustment unit can also provide a more detailed adjustment method. Furthermore, if an employee is focused, the adjustment unit can provide a concise adjustment method. This allows for more efficient learning by adjusting the learning plan adjustment method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit can input employee emotion data into a generative AI and have the generative AI perform the adjustment of the learning plan adjustment method.

[0129] The adjustment unit can adjust the level of detail of the adjustments based on the employee's learning progress when adjusting the learning plan. For example, the adjustment unit can perform detailed adjustments for employees who are behind in their learning progress to identify problems. The adjustment unit can also perform simpler adjustments for employees who are progressing well to check their progress. Furthermore, the adjustment unit can adjust the frequency and level of detail of the adjustments according to the learning progress. This allows for more efficient learning by adjusting the level of detail of the adjustments based on the employee's learning progress. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or not. For example, the adjustment unit can input employee learning progress data into a generating AI and have the generating AI perform the adjustment of the level of detail of the adjustments.

[0130] The adjustment unit can apply different adjustment algorithms to employees according to their skill level when adjusting learning plans. For example, the adjustment unit can apply an algorithm containing basic content for beginners. It can also apply an algorithm containing advanced content for intermediate learners. Furthermore, it can apply an algorithm containing specialized content for advanced learners. This enables efficient learning by applying different adjustment algorithms according to the employee's skill level. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input employee skill data into a generating AI and have the generating AI execute the application of the adjustment algorithm.

[0131] The adjustment unit can estimate employees' emotions and prioritize learning plans based on those emotions. For example, if an employee is stressed, the adjustment unit will postpone less important learning plans. Conversely, if an employee is relaxed, the adjustment unit can prioritize more important learning plans. Furthermore, if an employee is focused, the adjustment unit can prioritize more urgent learning plans. This allows for more efficient learning by prioritizing learning plans according to employees' 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 adjustment unit may be performed using AI or not. For example, the adjustment unit can input employee emotion data into a generative AI and have the generative AI determine the priority of learning plans.

[0132] The adjustment unit can adjust the order of adjustments based on the employee's past learning history when adjusting the learning plan. For example, the adjustment unit can adjust a plan that prioritizes learning important skills based on the employee's past learning history. The adjustment unit can also adjust a plan that prioritizes learning unlearned skills based on the employee's learning history. Furthermore, the adjustment unit can analyze the employee's past learning history and determine the optimal order of the learning plan. This enables efficient learning by adjusting the order of adjustments based on the employee's past learning history. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the employee's learning history data into a generating AI and have the generating AI perform the adjustment of the order of adjustments.

[0133] The adjustment unit can adjust the order of adjustments based on employee relevance when adjusting learning plans. For example, the adjustment unit can adjust a plan that prioritizes learning skills related to an employee's current work. It can also adjust a plan that prioritizes learning skills related to an employee's future career. Furthermore, the adjustment unit can optimize the order of learning plans based on employee relevance. This enables efficient learning by adjusting the order of adjustments based on employee relevance. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input employee relevance data into a generating AI and have the generating AI perform the adjustment of the order of adjustments.

[0134] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0135] The data collection unit can estimate employees' emotions and adjust the timing of data collection based on those estimates. For example, if an employee is stressed, data collection can be postponed and collected when they are relaxed. Conversely, if an employee is focused, data can be collected at that time for efficient data acquisition. Furthermore, if an employee is tired, data can be collected after a break to obtain accurate data. In this way, by adjusting the timing of data collection according to employees' emotions, efficient data collection becomes possible.

[0136] The analysis unit can estimate employees' emotions and adjust the data analysis method based on those estimates. For example, if an employee is stressed, a simpler analysis method can be selected to reduce their burden. If an employee is relaxed, a more detailed analysis method can be selected for higher accuracy. Furthermore, if an employee is focused, a more complex analysis method can be selected to gain deeper insights. By adjusting the data analysis method according to employees' emotions, efficient data analysis becomes possible.

[0137] The plan generation unit can estimate employees' emotions and adjust the presentation of the learning plan based on those estimates. For example, if an employee is stressed, it can provide a simple and highly visual learning plan. If an employee is relaxed, it can provide a learning plan with more detailed information. Furthermore, if an employee is focused, it can provide a learning plan that focuses on the essentials. By adjusting the presentation of the learning plan according to the employee's emotions, efficient learning becomes possible.

[0138] The progress monitoring department can estimate employees' emotions and adjust the progress monitoring method based on those estimates. For example, if an employee is stressed, it can provide a simplified progress monitoring method. If an employee is relaxed, it can provide a more detailed progress monitoring method. Furthermore, if an employee is focused, it can provide a concise progress monitoring method. By adjusting the progress monitoring method according to employees' emotions, efficient progress monitoring becomes possible.

[0139] The effectiveness measurement department can estimate employees' emotions and adjust the effectiveness measurement method based on those estimates. For example, if an employee is stressed, it can provide a simplified effectiveness measurement method. If an employee is relaxed, it can provide a more detailed effectiveness measurement method. Furthermore, if an employee is focused, it can provide a concise effectiveness measurement method. By adjusting the effectiveness measurement method according to employees' emotions, efficient effectiveness measurement becomes possible.

[0140] The data collection unit can prioritize the collection of highly relevant data based on employees' geographical location information. For example, if an employee is in a specific region, it will prioritize the collection of data related to that region. It can also collect region-specific data based on the employee's location. Furthermore, if an employee is on the move, it can collect data related to their destination. This enables efficient data collection by collecting highly relevant data based on employees' geographical location information.

[0141] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. Furthermore, it can optimally allocate analysis resources according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0142] The plan generation unit can apply different plan generation algorithms depending on the employee's skill level. For example, a learning plan for beginners can be created using an algorithm that includes basic content. A learning plan for intermediate learners can be created using an algorithm that includes more advanced content. Furthermore, a learning plan for advanced learners can be created using an algorithm that includes more specialized content. By applying different plan generation algorithms according to the employee's skill level, efficient learning becomes possible.

[0143] The progress monitoring department can prioritize monitoring based on employees' past learning history. For example, it can prioritize monitoring the progress of important skills based on an employee's past learning history. It can also prioritize monitoring the progress of skills that have not yet been acquired, based on an employee's learning history. Furthermore, it can analyze an employee's past learning history to determine the optimal monitoring priority. This enables efficient progress monitoring by prioritizing monitoring based on an employee's past learning history.

[0144] The effectiveness measurement unit can adjust the order of measurements based on employee relevance. For example, it can prioritize measuring the effectiveness of skills related to an employee's current work. It can also prioritize measuring the effectiveness of skills related to an employee's future career. Furthermore, it can optimize the order of measurements based on employee relevance. This allows for efficient effectiveness measurement by adjusting the order of measurements based on employee relevance.

[0145] The following briefly describes the processing flow for example form 2.

[0146] Step 1: The data collection unit collects data. For example, it collects employee profiles, past learning data, evaluation data, performance evaluations, skill test results, learning history, feedback data, and self-assessment data. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses machine learning to evaluate each employee's skills and growth rate, data mining techniques to analyze skill sets, and natural language processing techniques to analyze feedback data. Step 3: The plan generation unit generates a learning plan based on the analysis results obtained by the analysis unit. For example, it creates a personalized learning plan based on the employee's goals, suggests courses and training programs to improve specific skills, and creates a learning plan that matches the learning style and pace. Step 4: The progress monitoring unit monitors progress based on the learning plan generated by the plan generation unit. For example, it periodically evaluates employees' learning progress, monitors it in real time, adjusts the learning plan as needed, and visualizes the learning progress. Step 5: The effectiveness measurement unit measures the effectiveness of learning based on the progress data obtained by the progress monitoring unit. For example, it evaluates the effectiveness of learning quantitatively and qualitatively, using numerical data, statistical data, interviews, and questionnaires, and reflects the results in the next learning plan.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, plan generation unit, progress monitoring unit, effectiveness measurement unit, proposal unit, and adjustment unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects employee profiles, past learning data, and evaluation data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The plan generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a learning plan based on the analysis results. The progress monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the learning progress. The effectiveness measurement unit is implemented by the specific processing unit 290 of the data processing unit 12 and measures the effectiveness of the learning. The proposal unit is implemented by the control unit 46A of the smart device 14 and proposes optimal learning content. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the learning plan in real time. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0151] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

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

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

[0161] In 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.

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

[0163] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0165] The data processing system 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.

[0166] Each of the multiple elements described above, including the data collection unit, analysis unit, plan generation unit, progress monitoring unit, effectiveness measurement unit, proposal unit, and adjustment unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects employee profiles, past learning data, and evaluation data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The plan generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a learning plan based on the analysis results. The progress monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the learning progress. The effectiveness measurement unit is implemented by the specific processing unit 290 of the data processing unit 12 and measures the effectiveness of the learning. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes optimal learning content. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the learning plan in real time. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0167] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Each of the multiple elements described above, including the data collection unit, analysis unit, plan generation unit, progress monitoring unit, effectiveness measurement unit, proposal unit, and adjustment unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects employee profiles, past learning data, and evaluation data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The plan generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a learning plan based on the analysis results. The progress monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the learning progress. The effectiveness measurement unit is implemented by the specific processing unit 290 of the data processing unit 12 and measures the effectiveness of the learning. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes optimal learning content. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the learning plan in real time. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0183] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.).

[0196] 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.

[0197] 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.

[0198] 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.

[0199] Each of the multiple elements described above, including the data collection unit, analysis unit, plan generation unit, progress monitoring unit, effectiveness measurement unit, proposal unit, and adjustment unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects employee profiles, past learning data, and evaluation data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The plan generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a learning plan based on the analysis results. The progress monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the learning progress. The effectiveness measurement unit is implemented by the specific processing unit 290 of the data processing unit 12 and measures the effectiveness of the learning. The proposal unit is implemented by the control unit 46A of the robot 414 and proposes optimal learning content. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the learning plan in real time. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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."

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A plan generation unit generates a learning plan based on the analysis results obtained by the analysis unit, A progress monitoring unit monitors the progress based on the learning plan generated by the plan generation unit, The system includes an effectiveness measurement unit that measures the effectiveness of learning based on the progress data obtained by the progress monitoring unit. A system characterized by the following features. (Note 2) It has a proposal department that suggests learning content. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features an adjustment unit that adjusts the learning plan in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect employee profiles, past learning data, and evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We use machine learning to analyze data and evaluate each employee's skills and growth rate. The system described in Appendix 1, characterized by the features described herein. (Note 6) The plan generation unit, Create personalized learning plans based on employee goals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned progress monitoring unit, Regularly evaluate employees' learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned effect measurement unit is Evaluate the effectiveness of learning quantitatively and qualitatively, and provide feedback on the results. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate employees' emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze past employee performance data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, 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 12) The aforementioned collection unit is We estimate employees' emotions and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data based on employees' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, analyze employees' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, We estimate employees' emotions and adjust the data analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system estimates employees' emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The plan generation 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 22) The plan generation unit, When generating a learning plan, adjust the level of detail in the plan based on the employee's goals. The system described in Appendix 1, characterized by the features described herein. (Note 23) The plan generation unit, When generating learning plans, different plan generation algorithms are applied depending on the employee's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 24) The plan generation 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 25) The plan generation unit, When generating a learning plan, the priority of the plan is determined based on the employee's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The plan generation unit, When generating learning plans, adjust the order of the plans based on employee relevance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit is, We estimate employees' emotions and adjust progress monitoring methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned progress monitoring unit, When monitoring progress, adjust the level of detail based on the employee's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned monitoring unit is, During progress monitoring, different monitoring algorithms are applied depending on the employee's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned monitoring unit is, The system estimates employee sentiment and adjusts the display method of progress monitoring based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned monitoring unit is, During progress monitoring, monitoring priorities are determined based on the employee's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned monitoring unit is, During employee monitoring, adjust the monitoring order based on employee relevance. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned effect measurement unit is We estimate employees' emotions and adjust the effectiveness measurement method based on the estimated emotions of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned effect measurement unit is When measuring effectiveness, adjust the level of detail based on the employees' learning impact. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned effect measurement unit is When measuring effectiveness, different measurement algorithms are applied depending on the skill level of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned effect measurement unit is We estimate employees' emotions and adjust the display method of performance measurement based on the estimated emotions of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned effect measurement unit is When measuring effectiveness, prioritize measurements based on employees' past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned effect measurement unit is When measuring effectiveness, adjust the order of measurements based on employee relevance. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned proposal section is, We estimate employees' emotions and adjust how learning content is suggested based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned proposal section is, When proposing learning content, adjust the level of detail in the proposal based on the employee's skill level. The system described in Appendix 2, characterized by the features described herein. (Note 41) The aforementioned proposal section is, When proposing learning content, apply different proposal algorithms according to the goals of employees. The system according to Appendix 2, characterized in that (Appendix 42) The proposal department Estimate the emotions of employees and determine the priority of learning content based on the estimated emotions of employees. The system according to Appendix 2, characterized in that (Appendix 43) The proposal department When proposing learning content, adjust the order of proposals based on the past learning history of employees. The system according to Appendix 2, characterized in that (Appendix 44) The proposal department When proposing learning content, adjust the order of proposals based on the relevance of employees. The system according to Appendix 2, characterized in that (Appendix 45) The adjustment department Estimate the emotions of employees and adjust the adjustment method of the learning plan based on the estimated emotions of employees. The system according to Appendix 3, characterized in that (Appendix 46) The adjustment department When adjusting the learning plan, adjust the degree of detail of the adjustment based on the learning progress of employees. The system according to Appendix 3, characterized in that (Appendix 47) The adjustment department When adjusting the learning plan, apply different adjustment algorithms according to the skill level of employees. The system according to Appendix 3, characterized in that (Appendix 48) The adjustment department Estimate the emotions of employees and determine the priority of the learning plan based on the estimated emotions of employees. The system according to Appendix 3, characterized in that (Appendix 49) The adjustment department When adjusting the learning plan, adjust the order of adjustment based on the past learning history of employees The system according to Appendix 3, characterized by the above (Appendix 50) The adjustment unit is When adjusting the learning plan, adjust the order of adjustment based on the relevance of employees The system according to Appendix 3, characterized by the above

Explanation of symbols

[0219] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A plan generation unit generates a learning plan based on the analysis results obtained by the analysis unit, A progress monitoring unit monitors the progress based on the learning plan generated by the plan generation unit, The system includes an effectiveness measurement unit that measures the effectiveness of learning based on the progress data obtained by the progress monitoring unit. A system characterized by the following features.

2. It has a proposal department that suggests learning content. The system according to feature 1.

3. It features an adjustment unit that adjusts the learning plan in real time. The system according to feature 1.

4. The aforementioned collection unit is Collect employee profiles, past learning data, and evaluation data. The system according to feature 1.

5. The aforementioned analysis unit, We use machine learning to analyze data and evaluate each employee's skills and growth rate. The system according to feature 1.

6. The plan generation unit, Create personalized learning plans based on employee goals. The system according to feature 1.

7. The aforementioned progress monitoring unit, Regularly evaluate employees' learning progress. The system according to feature 1.

8. The aforementioned effect measurement unit is Evaluate the effectiveness of learning quantitatively and qualitatively, and provide feedback on the results. The system according to feature 1.

9. The aforementioned collection unit is We estimate employees' emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is Analyze past employee performance data and select the optimal data collection method. The system according to feature 1.

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