system

Abstract

The system according to this embodiment aims to predict the skills that companies will need based on future industry changes and technological trends, and to identify skill gaps among employees. [Solution] The system according to the embodiment comprises a prediction unit, a skill identification unit, a gap identification unit, and a plan creation unit. The prediction unit predicts future industry changes and technological trends. The skill identification unit identifies the skills required by the company based on the information predicted by the prediction unit. The gap identification unit identifies the skill gaps of employees based on the skills identified by the skill identification unit. The plan creation unit creates individual education and training plans based on the skill gaps identified by the gap identification unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, predicting the skills required by a company based on future industry changes and technology trends and identifying the skill gaps of employees have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to predict the skills required by a company based on future industry changes and technology trends and identify the skill gaps of employees.

Means for Solving the Problems

[0006] Note: There seems to be an error in the original text where the "

発明の概要

Summary of the Invention

Summary of the Invention

[0007] The system according to this embodiment can predict the skills that a company will need based on future industry changes and technological trends, and can identify skill gaps among 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The skill prediction platform according to an embodiment of the present invention is a system that uses AI to predict the skills a company will need based on future industry changes and technological trends, and identifies skill gaps among employees. This skill prediction platform predicts future industry changes and technological trends, identifies the skills a company will need, and analyzes employees' current skill sets to identify gaps between their current skills and the required skills. Furthermore, the AI ​​creates individualized education and training plans to help employees acquire the necessary skills. This enables companies to cultivate highly skilled personnel who can adapt to change and maintain their competitiveness. For example, the AI ​​can analyze patterns of past technological innovations and predict upcoming technological trends. This allows companies to prepare for future changes. Next, based on the predicted information, the system identifies the skills a company will need. For example, based on the technological trends predicted by the AI, the system can identify the skill sets a company will need in the future. This allows companies to understand the skills needed to meet future needs. Furthermore, the system analyzes employees' current skill sets to identify gaps between their current skills and the required skills. For example, the AI ​​can analyze employee skill data to identify gaps between employee skill data and the skills a company needs. This allows companies to understand which employees need to acquire which skills. Finally, the AI ​​creates individualized education and training plans to help employees acquire the necessary skills. For example, AI can create optimal training plans for each employee, helping them efficiently acquire the necessary skills. This allows employees to learn at their own pace, and enables companies to cultivate highly skilled personnel. This system allows companies to develop highly skilled personnel who can adapt to change and maintain their competitiveness. For instance, by having employees acquire new skills based on technology trends predicted by AI, companies can respond quickly to market changes. Furthermore, individualized training plans allow employees to learn at their own pace, leading to efficient learning. This improves overall company productivity and helps maintain competitiveness.This allows the skills prediction platform to predict the skills a company needs, identify skill gaps among employees, and create individualized education and training plans, enabling companies to cultivate highly skilled talent that can adapt to change and maintain their competitiveness.

[0029] The skill prediction platform according to this embodiment comprises a prediction unit, a skill identification unit, a gap identification unit, and a plan creation unit. The prediction unit predicts future industry changes and technological trends. The prediction unit, for example, analyzes past data and current market trends to predict future changes. For example, the prediction unit can analyze patterns of past technological innovations to predict upcoming technological trends. The skill identification unit identifies the skills that a company needs based on the information predicted by the prediction unit. For example, the skill identification unit can identify the skill set that a company will need in the future based on technological trends predicted by AI. The gap identification unit identifies the skill gaps of employees based on the skills identified by the skill identification unit. For example, the gap identification unit can use AI to analyze employee skill data and identify the gap between the employee's skills and the skills required by the company. The plan creation unit creates individual education and training plans based on the skill gaps identified by the gap identification unit. For example, the plan creation unit can use AI to create an optimal education and training plan for each employee, helping them to efficiently acquire the necessary skills. This allows the skills prediction platform to predict the skills a company needs, identify skill gaps among employees, and create individualized education and training plans, enabling companies to cultivate highly skilled talent that can adapt to change and maintain their competitiveness.

[0030] The forecasting unit predicts future industry changes and technological trends. For example, it analyzes historical data and current market trends to predict future changes. Specifically, the forecasting unit meticulously analyzes patterns of technological innovation over the past several decades and utilizes advanced data analysis techniques to predict upcoming technological trends. For instance, it uses big data analysis to evaluate the timing and impact of past technological innovations and compares them with current market trends to predict the next technologies and industry changes to watch. The forecasting unit also uses natural language processing technology to analyze the latest industry news, research papers, and patent information to detect early signs of technological trends. This allows the forecasting unit to provide crucial information that enables companies to respond quickly to future market changes. Furthermore, the forecasting unit leverages AI to simulate multiple scenarios and identify the most likely future scenario. For example, it creates scenarios that consider different economic conditions and the pace of technological advancement, and predicts industry changes based on each scenario. This allows companies to develop strategies to flexibly respond to future uncertainties. The forecasting department regularly updates these forecast results and provides forecasts based on the latest information, helping companies stay up-to-date with the latest market trends.

[0031] The Skill Identification Department identifies the skills a company needs based on information predicted by the Prediction Department. For example, the Skill Identification Department can identify the skill sets a company will need in the future based on technology trends predicted by AI. Specifically, the Skill Identification Department identifies skill sets suitable for a company's operations and strategic objectives based on information on future technology trends and industry changes provided by the Prediction Department. For example, it uses data analyzed by AI to identify programming languages, data analysis techniques, and project management skills necessary to respond to next-generation technologies and industry changes. The Skill Identification Department also compares a company's current skill map with predicted future skill needs to clarify which skills are lacking. This allows companies to proactively understand the skills necessary to maintain future competitiveness and to plan talent development accordingly. Furthermore, the Skill Identification Department proposes the optimal skill set for a company, referencing industry best practices and successful case studies from other companies. This enables companies to develop concrete skill development plans to respond to the latest technology trends. The Skill Identification Department shares this information with the company's human resources and training departments, providing a foundation for developing concrete action plans for skill development.

[0032] The Gap Identification Department identifies employee skill gaps based on the skills identified by the Skill Identification Department. For example, the Gap Identification Department can use AI to analyze employee skill data and identify gaps between those skills and those required by the company. Specifically, the Gap Identification Department utilizes an employee skill database to analyze each employee's current skill set in detail. For instance, it collects data such as training programs employees have attended, qualifications they have obtained, and work experience, and uses AI to analyze this data. This clearly identifies the gaps between the skills each employee currently possesses and the skills the company will need in the future. Furthermore, the Gap Identification Department regularly conducts employee skill evaluations and monitors skill progress. For example, it conducts regular skill evaluation tests and self-assessment questionnaires to keep employees' skill levels up-to-date. This allows the Gap Identification Department to grasp changes in skill gaps in real time and respond quickly. The Gap Identification Department also uses AI to analyze the causes of skill gaps and proposes specific improvement measures. For example, if a lack of a particular skill is due to a lack of training programs, it proposes appropriate training programs. This allows the gap identification unit to effectively identify skill gaps among employees and provide a foundation for efficiently developing the skills that companies need.

[0033] The Planning Department creates individualized education and training plans based on the skill gaps identified by the Gap Identification Department. For example, the Planning Department can use AI to create optimal education and training plans for each employee, supporting them in efficiently acquiring the necessary skills. Specifically, the Planning Department designs the optimal education and training plan for each employee based on the skill gap information provided by the Gap Identification Department. For instance, it uses AI to analyze an employee's learning style and past learning history, suggesting the most suitable learning methods and materials. This allows employees to efficiently acquire skills in a way that suits them. Furthermore, the Planning Department provides comprehensive training plans by combining various educational methods, such as online courses, workshops, and on-the-job training. For example, it designs a step-by-step learning plan where employees learn foundational knowledge through online courses, acquire practical skills in workshops, and gain practical experience through on-the-job training. The Planning Department also monitors the progress of training and modifies the plan as needed. For example, if an employee is taking longer than expected to acquire a particular skill, it provides additional support or remedial instruction. This ensures that employees acquire the necessary skills. Furthermore, the planning department evaluates the effectiveness of the training and collects feedback for continuous improvement. For example, they conduct surveys after the training to evaluate employee satisfaction and learning effectiveness. This allows the planning department to improve the quality of education and training plans and raise the overall skill level of the company.

[0034] The plan creation department includes a delivery department that provides the created plans to employees. The delivery department can, for example, send the created plans to employees via email. It can also provide plans through an online platform. Furthermore, the delivery department can provide plans in paper format. This allows employees to acquire the necessary skills by providing them with the created plans. Some or all of the above processes in the delivery department may be performed using AI, for example, or not. For example, the delivery department can provide the created plans in the most optimal way using an AI model.

[0035] The plan creation unit includes a monitoring unit that monitors the progress of the plan. The monitoring unit can, for example, track employees' learning progress in real time. The monitoring unit can also collect employees' test results and evaluate their progress. Furthermore, the monitoring unit can collect feedback from employees and evaluate the progress of the plan. This allows for understanding employees' learning status and providing appropriate support by monitoring the progress of the plan. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input employee learning progress data into an AI model and have the AI ​​perform the progress evaluation.

[0036] The forecasting unit analyzes historical data and current market trends to predict future changes. For example, the forecasting unit can analyze historical sales data to predict future market trends. It can also analyze technology development data to predict future technology trends. Furthermore, it can analyze customer feedback to predict future consumer needs. This allows for more accurate prediction of future changes by analyzing historical data and current market trends. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input historical data into an AI model and have the AI ​​perform predictions of future changes.

[0037] The gap identification unit analyzes employee skill data and identifies the gap between employee skills and the skills required by the company. For example, the gap identification unit can analyze employee qualifications to identify the gap between required skills. It can also analyze past project experience to identify skill gaps. Furthermore, the gap identification unit can analyze employee self-assessment data to identify skill gaps. In this way, by analyzing employee skill data, the gap between employee skills and the skills required by the company can be identified. Some or all of the above processes in the gap identification unit may be performed using AI, for example, or not using AI. For example, the gap identification unit can input employee skill data into an AI model and have the AI ​​perform the skill gap identification.

[0038] The forecasting unit analyzes past technological innovation patterns in a specific industry in detail to make more precise future predictions. For example, the forecasting unit can analyze technological innovation data from the past 10 years to predict technological trends for the next 10 years. It can also analyze the cycle of technological innovation in a specific industry to predict the timing of the next innovation. Furthermore, the forecasting unit can analyze the success factors of past technological innovations to predict future technological trends. This enables more precise future predictions by analyzing past technological innovation patterns in a specific industry in detail. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or not. For example, the forecasting unit can input past technological innovation data into an AI model and have the AI ​​perform future predictions.

[0039] The prediction unit improves prediction accuracy by combining different data sources. For example, the prediction unit can predict technology trends by combining patent databases and academic papers. It can also predict future technology trends by combining corporate R&D data and market trend data. Furthermore, it can predict technology trends by combining social media trend data and patent data. This improves prediction accuracy by combining different data sources. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input different data sources into an AI model and have the AI ​​perform the improvement of prediction accuracy.

[0040] The forecasting unit predicts regional technology trends, taking geographical market trends into consideration. For example, the forecasting unit can analyze market trend data for each region and predict regional technology trends. It can also predict technology trends based on regional economic growth data. Furthermore, the forecasting unit can analyze regional consumer behavior data and predict technology trends. This allows for more accurate predictions of regional technology trends by considering geographical market trends. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input geographical market trend data into an AI model and have the AI ​​perform the prediction of regional technology trends.

[0041] The prediction unit analyzes social media trends and predicts future technology trends. For example, the prediction unit can analyze social media post data to predict technology trends. It can also predict technology trends based on social media hashtag data. Furthermore, the prediction unit can analyze posts from social media influencers to predict technology trends. This allows for more accurate predictions of future technology trends by analyzing social media trends. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input social media trend data into an AI model and have the AI ​​perform predictions of future technology trends.

[0042] The skills identification unit analyzes a company's past project data to identify the necessary skills. For example, the skills identification unit can analyze past project data to identify the skills required for successful projects. It can also analyze data from failed projects to identify the skills that were lacking. Furthermore, the skills identification unit can identify the necessary skills based on project progress data. In this way, the necessary skills can be identified by analyzing a company's past project data. Some or all of the above processes in the skills identification unit may be performed using AI, for example, or not using AI. For example, the skills identification unit can input past project data into an AI model and have the AI ​​perform the identification of the necessary skills.

[0043] The skills identification unit identifies skill sets by referring to industry best practices. For example, the skills identification unit can analyze industry best practice data to identify the required skill sets. It can also identify required skill sets based on industry success stories. Furthermore, the skills identification unit can identify required skills by referring to industry standard skill sets. In this way, the required skill sets can be identified by referring to industry best practices. Some or all of the above processes in the skills identification unit may be performed using AI, for example, or not using AI. For example, the skills identification unit can input industry best practice data into an AI model and have the AI ​​perform the skill set identification.

[0044] The skills identification unit identifies regional skill needs by considering the geographical location of companies. For example, the skills identification unit can identify regional skill needs by analyzing economic data for each region. It can also identify necessary skills based on regional industrial data. Furthermore, the skills identification unit can identify skill needs by analyzing regional labor market data. This allows for the identification of regional skill needs by considering the geographical location of companies. Some or all of the above processing in the skills identification unit may be performed using AI, for example, or without AI. For example, the skills identification unit can input geographical location information into an AI model and have the AI ​​perform the identification of regional skill needs.

[0045] The skill identification unit analyzes social media activity to identify necessary skills. For example, the skill identification unit can analyze social media post data to identify necessary skills. It can also identify skill needs based on social media hashtag data. Furthermore, the skill identification unit can analyze posts from social media influencers to identify necessary skills. In this way, necessary skills can be identified by analyzing social media activity. Some or all of the above processing in the skill identification unit may be performed using AI, for example, or without AI. For example, the skill identification unit can input social media activity data into an AI model and have the AI ​​perform the identification of necessary skills.

[0046] The gap identification unit analyzes employees' past performance data to identify skill gaps. For example, the gap identification unit can identify skill gaps by analyzing past performance data. It can also identify skill gaps based on employee evaluation data. Furthermore, the gap identification unit can identify skill gaps by analyzing project outcome data. This allows for the identification of skill gaps by analyzing employees' past performance data. Some or all of the above processes in the gap identification unit may be performed using AI, or not. For example, the gap identification unit can input past performance data into an AI model and have the AI ​​perform the skill gap identification.

[0047] The gap identification unit identifies skill gaps by referring to employees' self-assessment data. For example, the gap identification unit can identify skill gaps by analyzing employees' self-assessment data. It can also identify skill gaps by comparing employees' self-assessments with their supervisors' assessments. Furthermore, the gap identification unit can identify skill gaps based on employees' self-assessment data. In this way, skill gaps can be identified by referring to employees' self-assessment data. Some or all of the above processing in the gap identification unit may be performed using AI, for example, or without AI. For example, the gap identification unit can input self-assessment data into an AI model and have the AI ​​perform the skill gap identification.

[0048] The gap identification unit identifies regional skill gaps by considering the geographical location information of employees. For example, the gap identification unit can identify regional skill gaps by analyzing economic data for each region. It can also identify skill gaps based on regional industry data. Furthermore, it can identify skill gaps by analyzing regional labor market data. In this way, by considering the geographical location information of employees, regional skill gaps can be identified. Some or all of the above processing in the gap identification unit may be performed using AI, for example, or without AI. For example, the gap identification unit can input geographical location information into an AI model and have the AI ​​perform the identification of regional skill gaps.

[0049] The gap identification unit analyzes employees' social media activities to identify skill gaps. For example, the gap identification unit can identify skill gaps by analyzing social media post data. It can also identify skill gaps based on social media hashtag data. Furthermore, the gap identification unit can identify skill gaps by analyzing posts from social media influencers. In this way, skill gaps can be identified by analyzing employees' social media activities. Some or all of the above processing in the gap identification unit may be performed using AI, for example, or not using AI. For example, the gap identification unit can input social media activity data into an AI model and have the AI ​​perform the skill gap identification.

[0050] The planning department analyzes employees' learning styles and creates optimal education and training plans. For example, the planning department can analyze employees' past learning data to identify the optimal learning style. It can also provide customized plans based on employees' learning styles. Furthermore, the planning department can adjust learning styles based on employee feedback to create the optimal plan. In this way, the optimal education and training plan can be created by analyzing employees' learning styles. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input learning style data into an AI model and have the AI ​​create the optimal plan.

[0051] The planning department predicts the effectiveness of a plan by referring to past education and training data. For example, the planning department can analyze past education and training data to identify effective plans. It can also predict the effectiveness of a new plan based on the success rate of past plans. Furthermore, the planning department can predict the effectiveness of a plan by referring to past feedback data. In this way, the effectiveness of a plan can be predicted by referring to past education and training data. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input past education and training data into an AI model and have the AI ​​perform the prediction of the plan's effectiveness.

[0052] The planning department creates regional education and training plans, taking into account the geographical location of employees. For example, the planning department can analyze the educational resources of each region and create an optimal plan. It can also create plans for learning necessary skills based on regional industry data. Furthermore, the planning department can analyze regional labor market data and create an optimal plan. This allows for the creation of regional education and training plans that take into account the geographical location of employees. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input geographical location information into an AI model and have the AI ​​create regional plans.

[0053] The planning department analyzes employees' social media activity to create optimal education and training plans. For example, the planning department can analyze social media posting data to create the optimal plan. It can also create plans for learning necessary skills based on social media hashtag data. Furthermore, the planning department can analyze posts from social media influencers to create the optimal plan. In this way, by analyzing employees' social media activity, the optimal education and training plan can be created. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input social media activity data into an AI model and have the AI ​​create the optimal plan.

[0054] The delivery department selects the optimal delivery method by referring to the employee's past learning history. For example, the delivery department can analyze past learning history and select the optimal delivery method. Furthermore, the delivery department can provide customized delivery methods based on the employee's learning style. In addition, the delivery department can adjust the delivery method based on employee feedback. This allows the optimal delivery method to be selected by referring to the employee's past learning history. Some or all of the above processes in the delivery department may be performed using AI, or not. For example, the delivery department can input past learning history data into an AI model and have the AI ​​select the optimal delivery method.

[0055] The delivery department selects the optimal delivery method by considering the employee's device information. For example, if an employee is using a smartphone, the delivery department can provide a delivery method that is adapted to the screen size. If an employee is using a tablet, the delivery department can also provide a delivery method optimized for a larger screen. Furthermore, if an employee is using a smartwatch, the delivery department can provide a concise and highly visible delivery method. In this way, the optimal delivery method can be selected by considering the employee's device information. Some or all of the above processing in the delivery department may be performed using AI, for example, or not. For example, the delivery department can input device information into an AI model and have the AI ​​select the optimal delivery method.

[0056] The monitoring unit selects the optimal monitoring method by referring to the employee's past learning progress data. For example, the monitoring unit can analyze past learning progress data and select the optimal monitoring method. Furthermore, the monitoring unit can provide a customized monitoring method based on the employee's learning style. In addition, the monitoring unit can adjust the monitoring method based on employee feedback. This allows the optimal monitoring method to be selected by referring to the employee's past learning progress data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input past learning progress data into an AI model and have the AI ​​select the optimal monitoring method.

[0057] The monitoring unit selects the optimal monitoring method by considering the employee's device information. For example, if an employee is using a smartphone, the monitoring unit can provide a monitoring method that matches the screen size. If an employee is using a tablet, the monitoring unit can also provide a monitoring method optimized for larger screens. Furthermore, if an employee is using a smartwatch, the monitoring unit can provide a simple and highly visible monitoring method. This allows the optimal monitoring method to be selected by considering the employee's device information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input device information into an AI model and have the AI ​​select the optimal monitoring method.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The prediction unit can predict new skill needs by crossing over technology trends from different industries. For example, it can combine technology trends from the medical and IT industries to predict new skill needs in medical IT. It can also cross over technology trends from the energy and automotive industries to predict new skill needs in electric vehicles. Furthermore, it can combine technology trends from the agriculture and robotics industries to predict new skill needs in agricultural robotics. In this way, new skill needs can be predicted by crossing over technology trends from different industries. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input technology trend data from different industries into an AI model and have the AI ​​predict new skill needs.

[0060] The gap identification unit analyzes employees' past performance data to identify skill gaps. For example, the gap identification unit can identify skill gaps by analyzing past performance data. It can also identify skill gaps based on employee evaluation data. Furthermore, the gap identification unit can identify skill gaps by analyzing project outcome data. This allows for the identification of skill gaps by analyzing employees' past performance data. Some or all of the above processes in the gap identification unit may be performed using AI, or not. For example, the gap identification unit can input past performance data into an AI model and have the AI ​​perform the skill gap identification.

[0061] The delivery department selects the optimal delivery method by referring to the employee's past learning history. For example, the delivery department can analyze past learning history and select the optimal delivery method. Furthermore, the delivery department can provide customized delivery methods based on the employee's learning style. In addition, the delivery department can adjust the delivery method based on employee feedback. This allows the optimal delivery method to be selected by referring to the employee's past learning history. Some or all of the above processes in the delivery department may be performed using AI, or not. For example, the delivery department can input past learning history data into an AI model and have the AI ​​select the optimal delivery method.

[0062] The forecasting unit analyzes past technological innovation patterns in a specific industry in detail to make more precise future predictions. For example, the forecasting unit can analyze technological innovation data from the past 10 years to predict technological trends for the next 10 years. It can also analyze the cycle of technological innovation in a specific industry to predict the timing of the next innovation. Furthermore, the forecasting unit can analyze the success factors of past technological innovations to predict future technological trends. This enables more precise future predictions by analyzing past technological innovation patterns in a specific industry in detail. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or not. For example, the forecasting unit can input past technological innovation data into an AI model and have the AI ​​perform future predictions.

[0063] The skills identification unit analyzes a company's past project data to identify the necessary skills. For example, the skills identification unit can analyze past project data to identify the skills required for successful projects. It can also analyze data from failed projects to identify the skills that were lacking. Furthermore, the skills identification unit can identify the necessary skills based on project progress data. In this way, the necessary skills can be identified by analyzing a company's past project data. Some or all of the above processes in the skills identification unit may be performed using AI, for example, or not using AI. For example, the skills identification unit can input past project data into an AI model and have the AI ​​perform the identification of the necessary skills.

[0064] The planning department analyzes employees' learning styles and creates optimal education and training plans. For example, the planning department can analyze employees' past learning data to identify the optimal learning style. It can also provide customized plans based on employees' learning styles. Furthermore, the planning department can adjust learning styles based on employee feedback to create the optimal plan. In this way, the optimal education and training plan can be created by analyzing employees' learning styles. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input learning style data into an AI model and have the AI ​​create the optimal plan.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The forecasting unit predicts future industry changes and technological trends. The forecasting unit analyzes past data and current market trends to predict future changes. For example, it can analyze patterns of past technological innovations to predict upcoming technological trends. Step 2: The Skill Identification Unit identifies the skills that companies need based on the information predicted by the Prediction Unit. The Skill Identification Unit can identify the skill sets that companies will need in the future based on the technology trends predicted by the AI. Step 3: The Gap Identification Unit identifies employee skill gaps based on the skills identified by the Skill Identification Unit. The Gap Identification Unit uses AI to analyze employee skill data and identify the gap between their skills and the skills required by the company. Step 4: The planning department creates individual education and training plans based on the skill gaps identified by the gap identification department. The planning department can use AI to create optimal education and training plans for each employee and support them in efficiently acquiring the necessary skills.

[0067] (Example of form 2) The skill prediction platform according to an embodiment of the present invention is a system that uses AI to predict the skills a company will need based on future industry changes and technological trends, and identifies skill gaps among employees. This skill prediction platform predicts future industry changes and technological trends, identifies the skills a company will need, and analyzes employees' current skill sets to identify gaps between their current skills and the required skills. Furthermore, the AI ​​creates individualized education and training plans to help employees acquire the necessary skills. This enables companies to cultivate highly skilled personnel who can adapt to change and maintain their competitiveness. For example, the AI ​​can analyze patterns of past technological innovations and predict upcoming technological trends. This allows companies to prepare for future changes. Next, based on the predicted information, the system identifies the skills a company will need. For example, based on the technological trends predicted by the AI, the system can identify the skill sets a company will need in the future. This allows companies to understand the skills needed to meet future needs. Furthermore, the system analyzes employees' current skill sets to identify gaps between their current skills and the required skills. For example, the AI ​​can analyze employee skill data to identify gaps between employee skill data and the skills a company needs. This allows companies to understand which employees need to acquire which skills. Finally, the AI ​​creates individualized education and training plans to help employees acquire the necessary skills. For example, AI can create optimal training plans for each employee, helping them efficiently acquire the necessary skills. This allows employees to learn at their own pace, and enables companies to cultivate highly skilled personnel. This system allows companies to develop highly skilled personnel who can adapt to change and maintain their competitiveness. For instance, by having employees acquire new skills based on technology trends predicted by AI, companies can respond quickly to market changes. Furthermore, individualized training plans allow employees to learn at their own pace, leading to efficient learning. This improves overall company productivity and helps maintain competitiveness.This allows the skills prediction platform to predict the skills a company needs, identify skill gaps among employees, and create individualized education and training plans, enabling companies to cultivate highly skilled talent that can adapt to change and maintain their competitiveness.

[0068] The skill prediction platform according to this embodiment comprises a prediction unit, a skill identification unit, a gap identification unit, and a plan creation unit. The prediction unit predicts future industry changes and technological trends. The prediction unit, for example, analyzes past data and current market trends to predict future changes. For example, the prediction unit can analyze patterns of past technological innovations to predict upcoming technological trends. The skill identification unit identifies the skills that a company needs based on the information predicted by the prediction unit. For example, the skill identification unit can identify the skill set that a company will need in the future based on technological trends predicted by AI. The gap identification unit identifies the skill gaps of employees based on the skills identified by the skill identification unit. For example, the gap identification unit can use AI to analyze employee skill data and identify the gap between the employee's skills and the skills required by the company. The plan creation unit creates individual education and training plans based on the skill gaps identified by the gap identification unit. For example, the plan creation unit can use AI to create an optimal education and training plan for each employee, helping them to efficiently acquire the necessary skills. This allows the skills prediction platform to predict the skills a company needs, identify skill gaps among employees, and create individualized education and training plans, enabling companies to cultivate highly skilled talent that can adapt to change and maintain their competitiveness.

[0069] The forecasting unit predicts future industry changes and technological trends. For example, it analyzes historical data and current market trends to predict future changes. Specifically, the forecasting unit meticulously analyzes patterns of technological innovation over the past several decades and utilizes advanced data analysis techniques to predict upcoming technological trends. For instance, it uses big data analysis to evaluate the timing and impact of past technological innovations and compares them with current market trends to predict the next technologies and industry changes to watch. The forecasting unit also uses natural language processing technology to analyze the latest industry news, research papers, and patent information to detect early signs of technological trends. This allows the forecasting unit to provide crucial information that enables companies to respond quickly to future market changes. Furthermore, the forecasting unit leverages AI to simulate multiple scenarios and identify the most likely future scenario. For example, it creates scenarios that consider different economic conditions and the pace of technological advancement, and predicts industry changes based on each scenario. This allows companies to develop strategies to flexibly respond to future uncertainties. The forecasting department regularly updates these forecast results and provides forecasts based on the latest information, helping companies stay up-to-date with the latest market trends.

[0070] The Skill Identification Department identifies the skills a company needs based on information predicted by the Prediction Department. For example, the Skill Identification Department can identify the skill sets a company will need in the future based on technology trends predicted by AI. Specifically, the Skill Identification Department identifies skill sets suitable for a company's operations and strategic objectives based on information on future technology trends and industry changes provided by the Prediction Department. For example, it uses data analyzed by AI to identify programming languages, data analysis techniques, and project management skills necessary to respond to next-generation technologies and industry changes. The Skill Identification Department also compares a company's current skill map with predicted future skill needs to clarify which skills are lacking. This allows companies to proactively understand the skills necessary to maintain future competitiveness and to plan talent development accordingly. Furthermore, the Skill Identification Department proposes the optimal skill set for a company, referencing industry best practices and successful case studies from other companies. This enables companies to develop concrete skill development plans to respond to the latest technology trends. The Skill Identification Department shares this information with the company's human resources and training departments, providing a foundation for developing concrete action plans for skill development.

[0071] The Gap Identification Department identifies employee skill gaps based on the skills identified by the Skill Identification Department. For example, the Gap Identification Department can use AI to analyze employee skill data and identify gaps between those skills and those required by the company. Specifically, the Gap Identification Department utilizes an employee skill database to analyze each employee's current skill set in detail. For instance, it collects data such as training programs employees have attended, qualifications they have obtained, and work experience, and uses AI to analyze this data. This clearly identifies the gaps between the skills each employee currently possesses and the skills the company will need in the future. Furthermore, the Gap Identification Department regularly conducts employee skill evaluations and monitors skill progress. For example, it conducts regular skill evaluation tests and self-assessment questionnaires to keep employees' skill levels up-to-date. This allows the Gap Identification Department to grasp changes in skill gaps in real time and respond quickly. The Gap Identification Department also uses AI to analyze the causes of skill gaps and proposes specific improvement measures. For example, if a lack of a particular skill is due to a lack of training programs, it proposes appropriate training programs. This allows the gap identification unit to effectively identify skill gaps among employees and provide a foundation for efficiently developing the skills that companies need.

[0072] The Planning Department creates individualized education and training plans based on the skill gaps identified by the Gap Identification Department. For example, the Planning Department can use AI to create optimal education and training plans for each employee, supporting them in efficiently acquiring the necessary skills. Specifically, the Planning Department designs the optimal education and training plan for each employee based on the skill gap information provided by the Gap Identification Department. For instance, it uses AI to analyze an employee's learning style and past learning history, suggesting the most suitable learning methods and materials. This allows employees to efficiently acquire skills in a way that suits them. Furthermore, the Planning Department provides comprehensive training plans by combining various educational methods, such as online courses, workshops, and on-the-job training. For example, it designs a step-by-step learning plan where employees learn foundational knowledge through online courses, acquire practical skills in workshops, and gain practical experience through on-the-job training. The Planning Department also monitors the progress of training and modifies the plan as needed. For example, if an employee is taking longer than expected to acquire a particular skill, it provides additional support or remedial instruction. This ensures that employees acquire the necessary skills. Furthermore, the planning department evaluates the effectiveness of the training and collects feedback for continuous improvement. For example, they conduct surveys after the training to evaluate employee satisfaction and learning effectiveness. This allows the planning department to improve the quality of education and training plans and raise the overall skill level of the company.

[0073] The plan creation department includes a delivery department that provides the created plans to employees. The delivery department can, for example, send the created plans to employees via email. It can also provide plans through an online platform. Furthermore, the delivery department can provide plans in paper format. This allows employees to acquire the necessary skills by providing them with the created plans. Some or all of the above processes in the delivery department may be performed using AI, for example, or not. For example, the delivery department can provide the created plans in the most optimal way using an AI model.

[0074] The plan creation unit includes a monitoring unit that monitors the progress of the plan. The monitoring unit can, for example, track employees' learning progress in real time. The monitoring unit can also collect employees' test results and evaluate their progress. Furthermore, the monitoring unit can collect feedback from employees and evaluate the progress of the plan. This allows for understanding employees' learning status and providing appropriate support by monitoring the progress of the plan. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input employee learning progress data into an AI model and have the AI ​​perform the progress evaluation.

[0075] The forecasting unit analyzes historical data and current market trends to predict future changes. For example, the forecasting unit can analyze historical sales data to predict future market trends. It can also analyze technology development data to predict future technology trends. Furthermore, it can analyze customer feedback to predict future consumer needs. This allows for more accurate prediction of future changes by analyzing historical data and current market trends. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input historical data into an AI model and have the AI ​​perform predictions of future changes.

[0076] The gap identification unit analyzes employee skill data and identifies the gap between employee skills and the skills required by the company. For example, the gap identification unit can analyze employee qualifications to identify the gap between required skills. It can also analyze past project experience to identify skill gaps. Furthermore, the gap identification unit can analyze employee self-assessment data to identify skill gaps. In this way, by analyzing employee skill data, the gap between employee skills and the skills required by the company can be identified. Some or all of the above processes in the gap identification unit may be performed using AI, for example, or not using AI. For example, the gap identification unit can input employee skill data into an AI model and have the AI ​​perform the skill gap identification.

[0077] The prediction unit estimates the user's emotions and adjusts the display method of the prediction results based on the estimated emotions. For example, if the user is stressed, the prediction unit can provide a simple and highly visible display method. If the user is relaxed, the prediction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can provide a concise display method. By adjusting the display method of the prediction results according to the user's emotions, a user-friendly display is made 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 prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input user emotion data into an AI model and have the AI ​​perform the adjustment of the display method.

[0078] The forecasting unit analyzes past technological innovation patterns in a specific industry in detail to make more precise future predictions. For example, the forecasting unit can analyze technological innovation data from the past 10 years to predict technological trends for the next 10 years. It can also analyze the cycle of technological innovation in a specific industry to predict the timing of the next innovation. Furthermore, the forecasting unit can analyze the success factors of past technological innovations to predict future technological trends. This enables more precise future predictions by analyzing past technological innovation patterns in a specific industry in detail. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or not. For example, the forecasting unit can input past technological innovation data into an AI model and have the AI ​​perform future predictions.

[0079] The prediction unit improves prediction accuracy by combining different data sources. For example, the prediction unit can predict technology trends by combining patent databases and academic papers. It can also predict future technology trends by combining corporate R&D data and market trend data. Furthermore, it can predict technology trends by combining social media trend data and patent data. This improves prediction accuracy by combining different data sources. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input different data sources into an AI model and have the AI ​​perform the improvement of prediction accuracy.

[0080] The prediction unit estimates the user's emotions and determines the priority of prediction results based on the estimated emotions. For example, if the user is feeling anxious, the prediction unit can prioritize displaying the most important prediction results. If the user is relaxed, the prediction unit can also display detailed prediction results in order. Furthermore, if the user is in a hurry, the prediction unit can prioritize displaying concise prediction results. In this way, by prioritizing prediction results according to the user's emotions, information important to the user can be provided preferentially. 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 prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input user emotion data into an AI model and have the AI ​​perform the priority determination.

[0081] The forecasting unit predicts regional technology trends, taking geographical market trends into consideration. For example, the forecasting unit can analyze market trend data for each region and predict regional technology trends. It can also predict technology trends based on regional economic growth data. Furthermore, the forecasting unit can analyze regional consumer behavior data and predict technology trends. This allows for more accurate predictions of regional technology trends by considering geographical market trends. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input geographical market trend data into an AI model and have the AI ​​perform the prediction of regional technology trends.

[0082] The prediction unit analyzes social media trends and predicts future technology trends. For example, the prediction unit can analyze social media post data to predict technology trends. It can also predict technology trends based on social media hashtag data. Furthermore, the prediction unit can analyze posts from social media influencers to predict technology trends. This allows for more accurate predictions of future technology trends by analyzing social media trends. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input social media trend data into an AI model and have the AI ​​perform predictions of future technology trends.

[0083] The skill identification unit estimates the user's emotions and adjusts the skill identification method based on the estimated emotions. For example, if the user is stressed, the skill identification unit can provide a simple skill identification method. If the user is relaxed, the skill identification unit can also provide a detailed skill identification method. Furthermore, if the user is in a hurry, the skill identification unit can provide a quick skill identification method. This allows for optimal skill identification for the user by adjusting the skill identification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the skill identification unit may be performed using AI or not using AI. For example, the skill identification unit can input user emotion data into an AI model and have the AI ​​perform the adjustment of the skill identification method.

[0084] The skills identification unit analyzes a company's past project data to identify the necessary skills. For example, the skills identification unit can analyze past project data to identify the skills required for successful projects. It can also analyze data from failed projects to identify the skills that were lacking. Furthermore, the skills identification unit can identify the necessary skills based on project progress data. In this way, the necessary skills can be identified by analyzing a company's past project data. Some or all of the above processes in the skills identification unit may be performed using AI, for example, or not using AI. For example, the skills identification unit can input past project data into an AI model and have the AI ​​perform the identification of the necessary skills.

[0085] The skills identification unit identifies skill sets by referring to industry best practices. For example, the skills identification unit can analyze industry best practice data to identify the required skill sets. It can also identify required skill sets based on industry success stories. Furthermore, the skills identification unit can identify required skills by referring to industry standard skill sets. In this way, the required skill sets can be identified by referring to industry best practices. Some or all of the above processes in the skills identification unit may be performed using AI, for example, or not using AI. For example, the skills identification unit can input industry best practice data into an AI model and have the AI ​​perform the skill set identification.

[0086] The skill identification unit estimates the user's emotions and determines the priority of skill identification based on the estimated emotions. For example, if the user is feeling anxious, the skill identification unit can prioritize identifying the most important skills. If the user is relaxed, the skill identification unit can also prioritize identifying detailed skills in order. Furthermore, if the user is in a hurry, the skill identification unit can prioritize identifying essential skills. In this way, by determining the priority of skill identification according to the user's emotions, the skills that are important to the user can be prioritized. 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 skill identification unit may be performed using AI, or not using AI. For example, the skill identification unit can input user emotion data into an AI model and have the AI ​​perform the priority determination.

[0087] The skills identification unit identifies regional skill needs by considering the geographical location of companies. For example, the skills identification unit can identify regional skill needs by analyzing economic data for each region. It can also identify necessary skills based on regional industrial data. Furthermore, the skills identification unit can identify skill needs by analyzing regional labor market data. This allows for the identification of regional skill needs by considering the geographical location of companies. Some or all of the above processing in the skills identification unit may be performed using AI, for example, or without AI. For example, the skills identification unit can input geographical location information into an AI model and have the AI ​​perform the identification of regional skill needs.

[0088] The skill identification unit analyzes social media activity to identify necessary skills. For example, the skill identification unit can analyze social media post data to identify necessary skills. It can also identify skill needs based on social media hashtag data. Furthermore, the skill identification unit can analyze posts from social media influencers to identify necessary skills. In this way, necessary skills can be identified by analyzing social media activity. Some or all of the above processing in the skill identification unit may be performed using AI, for example, or without AI. For example, the skill identification unit can input social media activity data into an AI model and have the AI ​​perform the identification of necessary skills.

[0089] The gap identification unit estimates the user's emotions and adjusts the gap identification method based on the estimated emotions. For example, if the user is stressed, the gap identification unit can provide a simple gap identification method. If the user is relaxed, the gap identification unit can also provide a more detailed gap identification method. Furthermore, if the user is in a hurry, the gap identification unit can provide a quick gap identification method. By adjusting the gap identification method according to the user's emotions, optimal gap identification for the user 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 gap identification unit may be performed using AI, or not using AI. For example, the gap identification unit can input user emotion data into an AI model and have the AI ​​perform the adjustment of the gap identification method.

[0090] The gap identification unit analyzes employees' past performance data to identify skill gaps. For example, the gap identification unit can identify skill gaps by analyzing past performance data. It can also identify skill gaps based on employee evaluation data. Furthermore, the gap identification unit can identify skill gaps by analyzing project outcome data. This allows for the identification of skill gaps by analyzing employees' past performance data. Some or all of the above processes in the gap identification unit may be performed using AI, or not. For example, the gap identification unit can input past performance data into an AI model and have the AI ​​perform the skill gap identification.

[0091] The gap identification unit identifies skill gaps by referring to employees' self-assessment data. For example, the gap identification unit can identify skill gaps by analyzing employees' self-assessment data. It can also identify skill gaps by comparing employees' self-assessments with their supervisors' assessments. Furthermore, the gap identification unit can identify skill gaps based on employees' self-assessment data. In this way, skill gaps can be identified by referring to employees' self-assessment data. Some or all of the above processing in the gap identification unit may be performed using AI, for example, or without AI. For example, the gap identification unit can input self-assessment data into an AI model and have the AI ​​perform the skill gap identification.

[0092] The gap identification unit estimates the user's emotions and determines the priority of gap identification based on the estimated user emotions. For example, if the user is feeling anxious, the gap identification unit can prioritize identifying the most important skill gaps. If the user is relaxed, the gap identification unit can also prioritize identifying detailed skill gaps in order. Furthermore, if the user is in a hurry, the gap identification unit can prioritize identifying essential skill gaps. In this way, by determining the priority of gap identification according to the user's emotions, the skill gaps that are important to the user can be identified preferentially. 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 gap identification unit may be performed using AI, for example, or not using AI. For example, the gap identification unit can input user emotion data into an AI model and have the AI ​​perform the priority determination.

[0093] The gap identification unit identifies regional skill gaps by considering the geographical location information of employees. For example, the gap identification unit can identify regional skill gaps by analyzing economic data for each region. It can also identify skill gaps based on regional industry data. Furthermore, it can identify skill gaps by analyzing regional labor market data. In this way, by considering the geographical location information of employees, regional skill gaps can be identified. Some or all of the above processing in the gap identification unit may be performed using AI, for example, or without AI. For example, the gap identification unit can input geographical location information into an AI model and have the AI ​​perform the identification of regional skill gaps.

[0094] The gap identification unit analyzes employees' social media activities to identify skill gaps. For example, the gap identification unit can identify skill gaps by analyzing social media post data. It can also identify skill gaps based on social media hashtag data. Furthermore, the gap identification unit can identify skill gaps by analyzing posts from social media influencers. In this way, skill gaps can be identified by analyzing employees' social media activities. Some or all of the above processing in the gap identification unit may be performed using AI, for example, or not using AI. For example, the gap identification unit can input social media activity data into an AI model and have the AI ​​perform the skill gap identification.

[0095] The planning unit estimates the user's emotions and adjusts the content of the education / training plan based on the estimated emotions. For example, if the user is feeling stressed, the planning unit can provide a simple and low-burden plan. If the user is relaxed, the planning unit can provide a detailed and comprehensive plan. Furthermore, if the user is in a hurry, the planning unit can provide a short-term and effective plan. In this way, by adjusting the content of the education / training plan according to the user's emotions, the optimal plan can be provided to the user. 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 planning unit may be performed using AI, or not using AI. For example, the planning unit can input user emotion data into an AI model and have the AI ​​perform the adjustment of the plan content.

[0096] The planning department analyzes employees' learning styles and creates optimal education and training plans. For example, the planning department can analyze employees' past learning data to identify the optimal learning style. It can also provide customized plans based on employees' learning styles. Furthermore, the planning department can adjust learning styles based on employee feedback to create the optimal plan. In this way, the optimal education and training plan can be created by analyzing employees' learning styles. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input learning style data into an AI model and have the AI ​​create the optimal plan.

[0097] The planning department predicts the effectiveness of a plan by referring to past education and training data. For example, the planning department can analyze past education and training data to identify effective plans. It can also predict the effectiveness of a new plan based on the success rate of past plans. Furthermore, the planning department can predict the effectiveness of a plan by referring to past feedback data. In this way, the effectiveness of a plan can be predicted by referring to past education and training data. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input past education and training data into an AI model and have the AI ​​perform the prediction of the plan's effectiveness.

[0098] The planning unit estimates the user's emotions and determines the priority of the education and training plan based on the estimated emotions. For example, if the user is feeling anxious, the planning unit can provide a plan that prioritizes learning the most important skills. If the user is relaxed, the planning unit can provide a plan that prioritizes learning detailed skills sequentially. Furthermore, if the user is in a hurry, the planning unit can provide a plan that prioritizes learning essential skills. In this way, by determining the priority of the education and training plan according to the user's emotions, the user can prioritize learning the skills that are important to them. 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 planning unit may be performed using AI or not using AI. For example, the planning unit can input user emotion data into an AI model and have the AI ​​perform the priority determination.

[0099] The planning department creates regional education and training plans, taking into account the geographical location of employees. For example, the planning department can analyze the educational resources of each region and create an optimal plan. It can also create plans for learning necessary skills based on regional industry data. Furthermore, the planning department can analyze regional labor market data and create an optimal plan. This allows for the creation of regional education and training plans that take into account the geographical location of employees. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input geographical location information into an AI model and have the AI ​​create regional plans.

[0100] The planning department analyzes employees' social media activity to create optimal education and training plans. For example, the planning department can analyze social media posting data to create the optimal plan. It can also create plans for learning necessary skills based on social media hashtag data. Furthermore, the planning department can analyze posts from social media influencers to create the optimal plan. In this way, by analyzing employees' social media activity, the optimal education and training plan can be created. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input social media activity data into an AI model and have the AI ​​create the optimal plan.

[0101] The service provider estimates the user's emotions and adjusts the plan delivery method based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and less burdensome delivery method. If the user is relaxed, the service provider can provide a detailed and comprehensive delivery method. Furthermore, if the user is in a hurry, the service provider can provide a quick delivery method. In this way, by adjusting the plan delivery method according to the user's emotions, the service provider can provide the optimal delivery method for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into an AI model and have the AI ​​perform the adjustment of the delivery method.

[0102] The delivery department selects the optimal delivery method by referring to the employee's past learning history. For example, the delivery department can analyze past learning history and select the optimal delivery method. Furthermore, the delivery department can provide customized delivery methods based on the employee's learning style. In addition, the delivery department can adjust the delivery method based on employee feedback. This allows the optimal delivery method to be selected by referring to the employee's past learning history. Some or all of the above processes in the delivery department may be performed using AI, or not. For example, the delivery department can input past learning history data into an AI model and have the AI ​​select the optimal delivery method.

[0103] The service provider estimates the user's emotions and determines the priority of plan delivery based on the estimated emotions. For example, if the user is feeling anxious, the service provider can prioritize providing the most important skills. If the user is relaxed, the service provider can also prioritize providing detailed skills in order. Furthermore, if the user is in a hurry, the service provider can prioritize providing concise skills. In this way, by prioritizing plan delivery according to the user's emotions, the service provider can prioritize providing skills that are important to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into an AI model and have the AI ​​perform the priority determination.

[0104] The delivery department selects the optimal delivery method by considering the employee's device information. For example, if an employee is using a smartphone, the delivery department can provide a delivery method that is adapted to the screen size. If an employee is using a tablet, the delivery department can also provide a delivery method optimized for a larger screen. Furthermore, if an employee is using a smartwatch, the delivery department can provide a concise and highly visible delivery method. In this way, the optimal delivery method can be selected by considering the employee's device information. Some or all of the above processing in the delivery department may be performed using AI, for example, or not. For example, the delivery department can input device information into an AI model and have the AI ​​select the optimal delivery method.

[0105] The monitoring unit estimates the user's emotions and adjusts the monitoring method based on the estimated emotions. For example, if the user is stressed, the monitoring unit can provide a simple and less burdensome monitoring method. If the user is relaxed, the monitoring unit can also provide a detailed and comprehensive monitoring method. Furthermore, if the user is in a hurry, the monitoring unit can provide a rapid monitoring method. In this way, by adjusting the monitoring method according to the user's emotions, the optimal monitoring method can be provided for the user. 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 monitoring unit may be performed using AI, or not using AI. For example, the monitoring unit can input user emotion data into an AI model and have the AI ​​perform the adjustment of the monitoring method.

[0106] The monitoring unit selects the optimal monitoring method by referring to the employee's past learning progress data. For example, the monitoring unit can analyze past learning progress data and select the optimal monitoring method. Furthermore, the monitoring unit can provide a customized monitoring method based on the employee's learning style. In addition, the monitoring unit can adjust the monitoring method based on employee feedback. This allows the optimal monitoring method to be selected by referring to the employee's past learning progress data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input past learning progress data into an AI model and have the AI ​​select the optimal monitoring method.

[0107] The monitoring unit estimates the user's emotions and determines monitoring priorities based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can prioritize monitoring the most important skills. If the user is relaxed, the monitoring unit can sequentially monitor detailed skills. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring essential skills. In this way, by determining monitoring priorities according to the user's emotions, monitoring of skills important to the user can be prioritized. 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 monitoring unit may be performed using AI, or not using AI. For example, the monitoring unit can input user emotion data into an AI model and have the AI ​​perform the priority determination.

[0108] The monitoring unit selects the optimal monitoring method by considering the employee's device information. For example, if an employee is using a smartphone, the monitoring unit can provide a monitoring method that matches the screen size. If an employee is using a tablet, the monitoring unit can also provide a monitoring method optimized for larger screens. Furthermore, if an employee is using a smartwatch, the monitoring unit can provide a simple and highly visible monitoring method. This allows the optimal monitoring method to be selected by considering the employee's device information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input device information into an AI model and have the AI ​​select the optimal monitoring method.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The prediction unit can predict new skill needs by crossing over technology trends from different industries. For example, it can combine technology trends from the medical and IT industries to predict new skill needs in medical IT. It can also cross over technology trends from the energy and automotive industries to predict new skill needs in electric vehicles. Furthermore, it can combine technology trends from the agriculture and robotics industries to predict new skill needs in agricultural robotics. In this way, new skill needs can be predicted by crossing over technology trends from different industries. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input technology trend data from different industries into an AI model and have the AI ​​predict new skill needs.

[0111] The skill identification unit estimates the user's emotions and adjusts the skill identification method based on the estimated emotions. For example, if the user is stressed, the skill identification unit can provide a simple skill identification method. If the user is relaxed, the skill identification unit can also provide a detailed skill identification method. Furthermore, if the user is in a hurry, the skill identification unit can provide a quick skill identification method. This allows for optimal skill identification for the user by adjusting the skill identification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the skill identification unit may be performed using AI or not using AI. For example, the skill identification unit can input user emotion data into an AI model and have the AI ​​perform the adjustment of the skill identification method.

[0112] The gap identification unit analyzes employees' past performance data to identify skill gaps. For example, the gap identification unit can identify skill gaps by analyzing past performance data. It can also identify skill gaps based on employee evaluation data. Furthermore, the gap identification unit can identify skill gaps by analyzing project outcome data. This allows for the identification of skill gaps by analyzing employees' past performance data. Some or all of the above processes in the gap identification unit may be performed using AI, or not. For example, the gap identification unit can input past performance data into an AI model and have the AI ​​perform the skill gap identification.

[0113] The planning unit estimates the user's emotions and adjusts the content of the education / training plan based on the estimated emotions. For example, if the user is feeling stressed, the planning unit can provide a simple and low-burden plan. If the user is relaxed, the planning unit can provide a detailed and comprehensive plan. Furthermore, if the user is in a hurry, the planning unit can provide a short-term and effective plan. In this way, by adjusting the content of the education / training plan according to the user's emotions, the optimal plan can be provided to the user. 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 planning unit may be performed using AI, or not using AI. For example, the planning unit can input user emotion data into an AI model and have the AI ​​perform the adjustment of the plan content.

[0114] The delivery department selects the optimal delivery method by referring to the employee's past learning history. For example, the delivery department can analyze past learning history and select the optimal delivery method. Furthermore, the delivery department can provide customized delivery methods based on the employee's learning style. In addition, the delivery department can adjust the delivery method based on employee feedback. This allows the optimal delivery method to be selected by referring to the employee's past learning history. Some or all of the above processes in the delivery department may be performed using AI, or not. For example, the delivery department can input past learning history data into an AI model and have the AI ​​select the optimal delivery method.

[0115] The monitoring unit estimates the user's emotions and adjusts the monitoring method based on the estimated emotions. For example, if the user is stressed, the monitoring unit can provide a simple and less burdensome monitoring method. If the user is relaxed, the monitoring unit can also provide a detailed and comprehensive monitoring method. Furthermore, if the user is in a hurry, the monitoring unit can provide a rapid monitoring method. In this way, by adjusting the monitoring method according to the user's emotions, the optimal monitoring method can be provided for the user. 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 monitoring unit may be performed using AI, or not using AI. For example, the monitoring unit can input user emotion data into an AI model and have the AI ​​perform the adjustment of the monitoring method.

[0116] The forecasting unit analyzes past technological innovation patterns in a specific industry in detail to make more precise future predictions. For example, the forecasting unit can analyze technological innovation data from the past 10 years to predict technological trends for the next 10 years. It can also analyze the cycle of technological innovation in a specific industry to predict the timing of the next innovation. Furthermore, the forecasting unit can analyze the success factors of past technological innovations to predict future technological trends. This enables more precise future predictions by analyzing past technological innovation patterns in a specific industry in detail. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or not. For example, the forecasting unit can input past technological innovation data into an AI model and have the AI ​​perform future predictions.

[0117] The skills identification unit analyzes a company's past project data to identify the necessary skills. For example, the skills identification unit can analyze past project data to identify the skills required for successful projects. It can also analyze data from failed projects to identify the skills that were lacking. Furthermore, the skills identification unit can identify the necessary skills based on project progress data. In this way, the necessary skills can be identified by analyzing a company's past project data. Some or all of the above processes in the skills identification unit may be performed using AI, for example, or not using AI. For example, the skills identification unit can input past project data into an AI model and have the AI ​​perform the identification of the necessary skills.

[0118] The gap identification unit estimates the user's emotions and adjusts the gap identification method based on the estimated emotions. For example, if the user is stressed, the gap identification unit can provide a simple gap identification method. If the user is relaxed, the gap identification unit can also provide a more detailed gap identification method. Furthermore, if the user is in a hurry, the gap identification unit can provide a quick gap identification method. By adjusting the gap identification method according to the user's emotions, optimal gap identification for the user 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 gap identification unit may be performed using AI, or not using AI. For example, the gap identification unit can input user emotion data into an AI model and have the AI ​​perform the adjustment of the gap identification method.

[0119] The planning department analyzes employees' learning styles and creates optimal education and training plans. For example, the planning department can analyze employees' past learning data to identify the optimal learning style. It can also provide customized plans based on employees' learning styles. Furthermore, the planning department can adjust learning styles based on employee feedback to create the optimal plan. In this way, the optimal education and training plan can be created by analyzing employees' learning styles. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input learning style data into an AI model and have the AI ​​create the optimal plan.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The forecasting unit predicts future industry changes and technological trends. The forecasting unit analyzes past data and current market trends to predict future changes. For example, it can analyze patterns of past technological innovations to predict upcoming technological trends. Step 2: The Skill Identification Unit identifies the skills that companies need based on the information predicted by the Prediction Unit. The Skill Identification Unit can identify the skill sets that companies will need in the future based on the technology trends predicted by the AI. Step 3: The Gap Identification Unit identifies employee skill gaps based on the skills identified by the Skill Identification Unit. The Gap Identification Unit uses AI to analyze employee skill data and identify the gap between their skills and the skills required by the company. Step 4: The planning department creates individual education and training plans based on the skill gaps identified by the gap identification department. The planning department can use AI to create optimal education and training plans for each employee and support them in efficiently acquiring the necessary skills.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the prediction unit, skill identification unit, gap identification unit, plan creation unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the prediction unit is implemented by the processor 46 of the smart device 14 and the processor 28 of the data processing unit 12, and analyzes past data and current market trends to predict future changes. The skill identification unit is implemented by the control unit 46A of the smart device 14 and the identification processing unit 290 of the data processing unit 12, and identifies the skills required by the company based on the predicted information. The gap identification unit is implemented by the control unit 46A of the smart device 14 and the identification processing unit 290 of the data processing unit 12, and analyzes employee skill data to identify skill gaps. The plan creation unit is implemented by the control unit 46A of the smart device 14 and the identification processing unit 290 of the data processing unit 12, and creates individual education and training plans. The provision unit is implemented by the control unit 46A of the smart device 14 and the identification processing unit 290 of the data processing unit 12, and provides the created plans to employees. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the prediction unit, skill identification unit, gap identification unit, plan creation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the prediction unit is implemented by the processor 46 of the smart glasses 214 and the processor 28 of the data processing unit 12, and predicts future changes by analyzing past data and current market trends. The skill identification unit is implemented by the control unit 46A of the smart glasses 214 and the identification processing unit 290 of the data processing unit 12, and identifies the skills required by the company based on the predicted information. The gap identification unit is implemented by the control unit 46A of the smart glasses 214 and the identification processing unit 290 of the data processing unit 12, and identifies skill gaps by analyzing employee skill data. The plan creation unit is implemented by the control unit 46A of the smart glasses 214 and the identification processing unit 290 of the data processing unit 12, and creates individual education and training plans. The provision unit is implemented by the control unit 46A of the smart glasses 214 and the identification processing unit 290 of the data processing unit 12, and provides the created plans to employees. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the prediction unit, skill identification unit, gap identification unit, plan creation unit, and provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the prediction unit is implemented by the processor 46 of the headset terminal 314 and the processor 28 of the data processing unit 12, and analyzes past data and current market trends to predict future changes. The skill identification unit is implemented by the control unit 46A of the headset terminal 314 and the identification processing unit 290 of the data processing unit 12, and identifies the skills required by the company based on the predicted information. The gap identification unit is implemented by the control unit 46A of the headset terminal 314 and the identification processing unit 290 of the data processing unit 12, and analyzes employee skill data to identify skill gaps. The plan creation unit is implemented by the control unit 46A of the headset terminal 314 and the identification processing unit 290 of the data processing unit 12, and creates individual education and training plans. The provision unit is implemented by the control unit 46A of the headset terminal 314 and the identification processing unit 290 of the data processing unit 12, and provides the created plans to employees. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the prediction unit, skill identification unit, gap identification unit, plan creation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the prediction unit is implemented by the processor 46 of the robot 414 and the processor 28 of the data processing unit 12, and predicts future changes by analyzing past data and current market trends. The skill identification unit is implemented by the control unit 46A of the robot 414 and the identification processing unit 290 of the data processing unit 12, and identifies the skills required by the company based on the predicted information. The gap identification unit is implemented by the control unit 46A of the robot 414 and the identification processing unit 290 of the data processing unit 12, and identifies skill gaps by analyzing employee skill data. The plan creation unit is implemented by the control unit 46A of the robot 414 and the identification processing unit 290 of the data processing unit 12, and creates individual education and training plans. The provision unit is implemented by the control unit 46A of the robot 414 and the identification processing unit 290 of the data processing unit 12, and provides the created plans to employees. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) The forecasting department predicts future industry changes and technological trends, A skills identification unit identifies the skills required by a company based on the information predicted by the aforementioned prediction unit, A gap identification unit identifies the skill gaps of employees based on the skills identified by the aforementioned skill identification unit, The system includes a plan creation unit that creates individual education and training plans based on the skill gaps identified by the gap identification unit. A system characterized by the following features. (Note 2) The aforementioned plan creation unit, The company has a department that provides the plans it has created to its employees. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned plan creation unit, It includes a monitoring unit to monitor the progress of the plan. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, By analyzing past data and current market trends, we can predict future changes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The gap identification unit is, Analyze employee skill data to identify the gap between employee skills and the skills required by the company. The system described in Appendix 1, characterized by the features described herein. (Note 6) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The prediction unit, By conducting a detailed analysis of past technological innovation patterns in specific industries, we can make more precise future predictions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The prediction unit, Combining different data sources improves prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 9) The prediction unit, It estimates the user's emotions and prioritizes the prediction results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The prediction unit, Predicting regional technology trends by considering geographical market trends. The system described in Appendix 1, characterized by the features described herein. (Note 11) The prediction unit, Analyzing social media trends and predicting future technology trends. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned skill identification unit is We estimate the user's emotions and adjust the skill-specification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned skill identification unit is Analyze the company's past project data to identify the necessary skills. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned skill identification unit is Identify skill sets by referring to industry best practices. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned skill identification unit is It estimates the user's emotions and determines skill-specific priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned skill identification unit is Identify regional skill needs by considering the geographical location of companies. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned skill identification unit is Analyze social media activity and identify necessary skills. The system described in Appendix 1, characterized by the features described herein. (Note 18) The gap identification unit is, We estimate user sentiment and adjust the gap identification method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The gap identification unit is, Analyze employees' past performance data to identify skill gaps. The system described in Appendix 1, characterized by the features described herein. (Note 20) The gap identification unit is, Identify skill gaps by referring to employee self-assessment data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The gap identification unit is, It estimates user sentiment and determines priority for identifying gaps based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The gap identification unit is, Identify regional skill gaps by considering employees' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The gap identification unit is, Analyze employees' social media activity to identify skill gaps. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned plan creation unit, The system estimates user emotions and adjusts the content of the education and training plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned plan creation unit, We analyze employees' learning styles and create optimal education and training plans. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned plan creation unit, Predict the effectiveness of the plan by referring to past education and training data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned plan creation unit, It estimates user emotions and prioritizes education and training plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned plan creation unit, We create regional education and training plans that take into account the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned plan creation unit, Analyze employees' social media activity to create optimal education and training plans. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, We estimate the user's emotions and adjust the plan delivery method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We will select the optimal delivery method by referring to the employee's past learning history. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned supply unit is, The system estimates user sentiment and prioritizes plan delivery based on that estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned supply unit is, We will select the optimal delivery method considering the employee's device information. The system described in Appendix 2, characterized by the features described herein. (Note 34) The monitoring unit, We estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The monitoring unit, Select the optimal monitoring method by referring to employees' past learning progress data. The system described in Appendix 3, characterized by the features described herein. (Note 36) The monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 37) The monitoring unit, Select the optimal monitoring method considering the employee's device information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The forecasting department predicts future industry changes and technological trends, A skills identification unit identifies the skills required by a company based on the information predicted by the aforementioned prediction unit, A gap identification unit identifies the skill gaps of employees based on the skills identified by the aforementioned skill identification unit, The system includes a plan creation unit that creates individual education and training plans based on the skill gaps identified by the gap identification unit. A system characterized by the following features.

2. The aforementioned plan creation unit, The company has a department that provides the plans it has created to its employees. The system according to feature 1.

3. The aforementioned plan creation unit, It includes a monitoring unit to monitor the progress of the plan. The system according to feature 1.

4. The prediction unit, By analyzing past data and current market trends, we can predict future changes. The system according to feature 1.

5. The gap identification unit is, Analyze employee skill data to identify the gap between employee skills and the skills required by the company. The system according to feature 1.

6. The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system according to feature 1.

7. The prediction unit, By conducting a detailed analysis of past technological innovation patterns in specific industries, we can make more precise future predictions. The system according to feature 1.

8. The prediction unit, Combining different data sources improves prediction accuracy. The system according to feature 1.

9. The prediction unit, It estimates the user's emotions and prioritizes the prediction results based on the estimated user emotions. The system according to feature 1.

10. The prediction unit, Predicting regional technology trends by considering geographical market trends. The system according to feature 1.

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