system

Abstract

The system according to this embodiment aims to suggest appropriate qualifications and courses based on employees' work history and qualification information. [Solution] The system according to this embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects employee career history and qualification information. The analysis unit analyzes the information collected by the collection unit. The suggestion unit suggests qualifications and courses based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to efficiently support the reskilling of employees, and there are problems in improving the productivity of enterprises.

[0005] The system according to the embodiment aims to suggest appropriate qualifications and courses based on the work experience and qualification information of employees.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects the work experience and qualification information of employees. The analysis unit analyzes the information collected by the collection unit. The suggestion unit suggests qualifications and courses based on the analysis results obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can suggest appropriate qualifications and courses based on the employee's work history and qualification information. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the receiving device 38, the output device 40, and the camera 42 are 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) An embodiment of the present invention provides a reskilling support system for companies to support employee reskilling. This reskilling support system collects information such as employee career history and qualifications recorded by the company, and an AI analyzes this information to suggest qualifications that employees should acquire and courses they should take. This mechanism allows employees to efficiently improve their skills and enables companies to improve productivity. First, the reskilling support system collects information such as employee career history and qualifications recorded by the company. At this time, it collects detailed data such as the employee's past work experience, current position, and acquired qualifications. For example, it can understand what projects an employee has participated in in the past and what skills they possess. Next, the AI ​​analyzes the collected information. The AI ​​analyzes data such as the employee's career history and qualifications and suggests the most suitable way for the employee to improve their skills. For example, if an employee does not have the skills required for their current position, the AI ​​can suggest qualifications and courses to acquire those skills. This allows employees to efficiently acquire the skills they need. Furthermore, by employees taking the qualifications and courses suggested by the AI, the overall productivity of the company improves. For example, when employees acquire new skills, work efficiency increases and project progresses more smoothly. Furthermore, improved employee skills allow companies to seize new business opportunities. This system enables efficient skill development for employees, contributing to individual growth. Companies can expect to achieve increased productivity and strengthen their competitiveness. For instance, by having employees acquire new technologies, companies can offer products and services utilizing the latest technologies. Also, improved employee skills allow companies to enter new markets. Thus, AI-powered reskilling support services offer significant benefits to both employees and companies. Employees can efficiently improve their skills, and companies can increase productivity. This strengthens companies' competitiveness and enables sustainable growth. Therefore, reskilling support systems can efficiently support employee skill development and improve corporate productivity.

[0029] The reskilling support system according to this embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects employee career and qualification information. This information includes, but is not limited to, work history, educational background, and acquired qualifications. The collection unit can, for example, automatically retrieve information from a company's database. The collection unit can also collect information manually entered by employees. Furthermore, the collection unit can collect information about employees' past work experience and current positions. For example, the collection unit can retrieve an employee's work history from a company's database to understand what projects the employee has participated in in the past. The analysis unit analyzes the information collected by the collection unit. The analysis unit can, for example, analyze employee career and qualification information using statistical analysis or machine learning algorithms. For example, the analysis unit can identify employee skill gaps and suggest qualifications and courses to fill those gaps. The analysis unit can also predict the skills that will be needed in the future based on the employee's career path and suggest qualifications and courses to acquire those skills. The suggestion unit suggests qualifications and courses based on the analysis results obtained by the analysis unit. For example, the suggestion unit can propose the most suitable qualifications and courses based on an employee's current position and future career path. It can also propose qualifications and courses to acquire necessary skills based on an employee's skill gap. Furthermore, the suggestion unit may include a progress management unit to manage the progress of the suggested qualifications and courses. For example, the suggestion unit monitors the progress of qualifications and courses taken by employees and sends reminders as needed. As a result, the reskilling support system according to this embodiment can efficiently support employee skill development and improve the productivity of the company.

[0030] The data collection unit collects employee background and qualification information. This includes, but is not limited to, work history, educational background, and certifications. The data collection unit can automatically retrieve information from company databases, for example. Specifically, it can access company HR systems and databases to automatically extract information such as employee work history, educational background, and certifications. The data collection unit can also collect information manually entered by employees. For example, it can provide forms or applications where employees self-report their information and collect the information entered there. Furthermore, the data collection unit can collect information about employees' past work experience and current positions. For example, it can retrieve employee work histories from company databases to understand what projects employees have participated in in the past. This allows the data collection unit to gain a detailed understanding of employees' skill sets and experience. In addition, the data collection unit can collect information from external data sources. For example, it can retrieve information about external training and seminars that employees have attended and add it to their skill sets. This allows the data collection unit to gain a comprehensive understanding of employees' skills and experience and build a foundation for providing this information to the analytics and suggestion units.

[0031] The Analysis Department analyzes the information collected by the Data Collection Department. For example, the Analysis Department can analyze employee career and qualification information using statistical analysis and machine learning algorithms. Specifically, it evaluates current skill sets and identifies skill gaps based on employees' work history and acquired qualifications. For instance, it uses machine learning algorithms to analyze an employee's past work history and acquired qualifications and compares them to the skills required for their current position to identify skill gaps. The Analysis Department can also predict future skill needs based on an employee's career path and suggest qualifications and courses to acquire those skills. For example, it analyzes an employee's past career path and industry trends to predict future skill needs. Furthermore, the Analysis Department can suggest optimal learning methods considering an employee's learning style and history. For example, it analyzes what learning methods have been successful for an employee in the past and suggests the optimal learning method based on the results. This allows the Analysis Department to efficiently support employee skill development and provide information to improve corporate productivity.

[0032] The Suggestion Department suggests qualifications and courses based on the analysis results obtained by the Analysis Department. For example, the Suggestion Department can propose the most suitable qualifications and courses based on an employee's current position and future career path. Specifically, it proposes qualifications and courses to fill employee skill gaps. For example, if an employee lacks the skills required for their current position, it will propose qualifications and courses to acquire those skills. The Suggestion Department can also propose qualifications and courses to acquire skills that will be needed in the future, based on the employee's career path. For example, if an employee aspires to a management position in the future, it will propose courses to acquire management skills. Furthermore, the Suggestion Department can also have a Progress Management Department to manage the progress of the suggested qualifications and courses. For example, the Suggestion Department can monitor the progress of qualifications and courses taken by employees and send reminders as needed. This supports employees in progressing with their learning according to plan. The Suggestion Department can also collect employee feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can improve future suggestions based on employee evaluations and feedback on courses taken. This allows the suggestion department to efficiently support employee skill development and improve the company's productivity.

[0033] The data collection unit can automatically retrieve information from a company's database. For example, the data collection unit can automatically retrieve employee history and qualifications from the company's database using an API. The data collection unit can also perform periodic database queries to obtain the latest information. For example, the data collection unit can perform database queries based on a daily or weekly schedule to collect the latest employee information. Furthermore, the data collection unit can automatically receive notifications when there are changes in the company's database and update the information based on those notifications. This enables efficient information collection by automatically retrieving information from the company's database. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the information retrieved from the company's database into a generating AI, which can then collect and organize the information.

[0034] The suggestion function can suggest qualifications and courses based on an employee's current position and future career path. For example, it can identify the skills required for an employee's current position and suggest qualifications and courses to acquire those skills. It can also predict the skills an employee will need in the future based on their future career path and suggest qualifications and courses to acquire those skills. For example, if an employee aims for a management position in their future career path, the suggestion function will suggest qualifications and courses to improve their leadership skills. If an employee is pursuing a career in a technical field, the suggestion function can also suggest qualifications and courses to acquire the latest technical skills. This allows for more appropriate skill development by suggesting qualifications and courses based on an employee's position and career path. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input information about an employee's position and career path into a generating AI, which can then suggest the most suitable qualifications and courses.

[0035] The suggestion unit may include a progress management unit that manages the progress of suggested qualifications and courses. The progress management unit, for example, monitors the progress of qualifications and courses taken by employees and sends reminders as needed. The progress management unit can, for example, provide an interface for employees to report their progress on qualifications and courses. The progress management unit can also provide a dashboard that visualizes employee progress and makes it easier for managers to understand the progress. For example, the progress management unit can display employee progress in graphs and charts, allowing for quick identification of delays and problems. Furthermore, the progress management unit can provide additional support and resources based on employee progress. For example, if an employee falls behind in their progress on a qualification or course, the progress management unit can provide additional training and support. This makes it easier to understand the progress of employee skill development by managing the progress of qualifications and courses. Some or all of the above processes in the progress management unit may be performed using AI, for example, or not using AI. For example, the progress management unit can input employee progress into a generating AI, which can then monitor and manage the progress.

[0036] The data collection unit can analyze an employee's past work experience in detail and select the optimal information collection method. For example, the data collection unit can analyze data from projects an employee has participated in in the past and prioritize the collection of relevant information. The data collection unit can also collect information related to specific skill sets based on an employee's past work experience. Furthermore, the data collection unit can collect information related to future career paths based on an employee's past work history. For example, the data collection unit can analyze data from projects an employee has participated in in the past and prioritize the collection of relevant information. The data collection unit can also collect information related to specific skill sets based on an employee's past work experience. The data collection unit can also collect information related to future career paths based on an employee's past work history. This enables efficient information collection by selecting the optimal information collection method based on an employee's past work experience. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee past work experience data into a generating AI, which can then select the optimal information collection method.

[0037] The data collection unit can filter information based on an employee's current projects and areas of interest. For example, the data collection unit can prioritize collecting information related to a project the employee is currently working on. It can also collect information on relevant qualifications and courses based on the employee's areas of interest. Furthermore, the data collection unit can filter and collect information related to the employee's current job title. For example, the data collection unit can prioritize collecting information related to a project the employee is currently working on. It can also collect information on relevant qualifications and courses based on the employee's areas of interest. The data collection unit can also filter and collect information related to the employee's current job title. This allows for the collection of highly relevant information by filtering information based on the employee's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input information about an employee's current projects and areas of interest into a generating AI, which can then filter and collect the most relevant information.

[0038] The data collection unit can prioritize collecting highly relevant information by considering the geographical location of employees during information gathering. For example, if an employee is in a specific region, the data collection unit will prioritize collecting information related to that region. Furthermore, if an employee is on a business trip, the data collection unit can prioritize collecting information related to their business trip destination. Additionally, if an employee is working remotely, the data collection unit can prioritize collecting information about their home area. This allows for the efficient collection of highly relevant information by considering the geographical location of employees. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the employee's geographical location information into a generating AI, which can then prioritize collecting highly relevant information.

[0039] The data collection unit can analyze employees' social media activity and collect relevant information during data collection. For example, the data collection unit can collect information on relevant qualifications and courses based on information shared by employees on social media. The data collection unit can also analyze employees' areas of interest on social media and collect relevant information. Furthermore, the data collection unit can collect relevant information based on information about accounts that employees follow on social media. For example, the data collection unit can collect information on relevant qualifications and courses based on information shared by employees on social media. The data collection unit can also analyze employees' areas of interest on social media and collect relevant information. The data collection unit can also collect relevant information based on information about accounts that employees follow on social media. This allows for the collection of highly relevant information by analyzing employees' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee social media activity data into a generating AI, which can then collect relevant information.

[0040] The analysis department can adjust the level of detail of the analysis based on the importance of the employee. For example, the analysis department can provide detailed analysis results to employees in important positions. It can also provide basic analysis results to new employees. Furthermore, it can provide analysis results with an appropriate level of detail for mid-career employees, depending on their position. For example, the analysis department can provide detailed analysis results to employees in important positions. It can also provide basic analysis results to new employees. It can also provide analysis results with an appropriate level of detail for mid-career employees, depending on their position. By adjusting the level of detail of the analysis based on the importance of the employee, more appropriate analysis results can be provided. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input information on the importance of employees into a generating AI, which can then adjust the level of detail of the analysis.

[0041] The analysis department can apply different analysis algorithms depending on the employee's category during analysis. For example, the analysis department can apply an analysis algorithm specializing in technical skills to technical employees. It can also apply an analysis algorithm specializing in leadership skills to managerial employees. Furthermore, it can apply an analysis algorithm specializing in sales skills to sales employees. This allows for more appropriate analysis results by applying different analysis algorithms depending on the employee's category. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input information about employee categories into a generating AI, which can then apply the most suitable analysis algorithm.

[0042] The analysis department can prioritize analyses based on employee submission dates. For example, it can prioritize analyzing submissions with approaching deadlines. It can also prioritize analyzing submissions submitted earlier. Furthermore, it can postpone analyzing submissions submitted later. This allows for efficient analysis by prioritizing analyses based on employee submission dates. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input information about employee submission dates into a generating AI, which can then determine the analysis priorities.

[0043] The analysis department can adjust the order of analysis based on employee relevance during the analysis process. For example, the analysis department can prioritize analyzing data of employees related to important projects. It can also prioritize analyzing data of employees related to ongoing projects. Furthermore, it can postpone analyzing data of employees related to future projects. This allows for efficient analysis by adjusting the order of analysis based on employee relevance. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input information about employee relevance into a generating AI, which can then adjust the order of analysis.

[0044] The suggestion function can adjust the level of detail of its suggestions based on the importance of the qualifications and courses. For example, it can provide detailed suggestions for important qualifications and courses, concise suggestions for basic qualifications and courses, and suggestions with a moderate level of detail for intermediate-level qualifications and courses. By adjusting the level of detail of the suggestions based on the importance of the qualifications and courses, more appropriate suggestions can be made. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input information about the importance of qualifications and courses into a generating AI, which can then adjust the level of detail of the suggestions.

[0045] The suggestion function can apply different suggestion algorithms depending on the category of the qualification or course. For example, the suggestion function can apply a suggestion algorithm specializing in technical skills to technical qualifications and courses. It can also apply a suggestion algorithm specializing in leadership skills to management qualifications and courses. Furthermore, it can apply a suggestion algorithm specializing in sales skills to sales qualifications and courses. By applying different suggestion algorithms depending on the category of the qualification or course, more appropriate suggestions become possible. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion section inputs information about qualifications and course categories into a generating AI, which can then apply the most suitable suggestion algorithm.

[0046] The suggestion function can prioritize suggestions based on the submission deadlines for qualifications and courses. For example, it can prioritize suggesting qualifications and courses with approaching deadlines. It can also prioritize suggesting qualifications and courses with earlier submission deadlines. Furthermore, it can postpone suggesting qualifications and courses with later submission deadlines. This allows for efficient suggestions by prioritizing suggestions based on the submission deadlines of qualifications and courses. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input information about the submission deadlines of qualifications and courses into a generating AI, which can then determine the priority of the suggestions.

[0047] The suggestion function can adjust the order of suggestions based on the relevance of qualifications and courses. For example, the suggestion function can prioritize suggesting qualifications and courses related to important projects. It can also prioritize suggesting qualifications and courses related to ongoing projects. Furthermore, it can postpone suggesting qualifications and courses related to future projects. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of qualifications and courses. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input information about the relevance of qualifications and courses into a generating AI, which can then adjust the order of suggestions.

[0048] The progress management department can analyze an employee's past progress history to select the optimal management method. For example, the progress management department can adjust the frequency of progress management based on an employee's past progress history. Furthermore, the progress management department can prioritize progress management at specific times based on an employee's past progress history. In addition, the progress management department can analyze an employee's past progress history and propose the optimal progress management method. For example, the progress management department can adjust the frequency of progress management based on an employee's past progress history. The progress management department can also prioritize progress management at specific times based on an employee's past progress history. The progress management department can also analyze an employee's past progress history and propose the optimal progress management method. This allows the optimal progress management method to be selected by analyzing an employee's past progress history. Some or all of the above processes in the progress management department may be performed using AI, or not. For example, the progress management department can input employee past progress history data into a generating AI, which can then select the optimal progress management method.

[0049] The progress management department can select the optimal management method when managing progress, taking into account the geographical location information of employees. For example, if an employee is in a specific region, the progress management department can select a progress management method related to that region. Furthermore, if an employee is on a business trip, the progress management department can select a progress management method related to the business trip destination. Additionally, if an employee is working remotely, the progress management department can select a progress management method considering information about the area around their home. This allows the department to select the optimal progress management method by considering the geographical location information of employees. Some or all of the above processes in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input the geographical location information of employees into a generating AI, which can then select the optimal progress management method.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The risk-skilling support system can provide customized learning plans based on each employee's individual learning style. For example, employees who prefer visual learning can be provided with materials that heavily utilize videos and infographics. Employees who prefer auditory learning can be recommended podcasts and audiobooks. Furthermore, employees who prefer hands-on learning can be offered hands-on workshops or project-based learning. This maximizes learning effectiveness by providing the optimal learning plan tailored to each employee's learning style.

[0052] A risk-skilling support system can track employees' learning progress in real time and provide feedback based on that progress. For example, it can send a congratulatory message when an employee achieves their learning goals. It can also send a reminder if an employee falls behind in their learning. Furthermore, it can provide additional support and resources if an employee is struggling with a particular challenge. This allows for real-time monitoring of employee learning progress and the provision of appropriate feedback, thereby maintaining motivation for learning.

[0053] The risk-skilling support system can analyze an employee's learning history and suggest the optimal learning plan based on past learning outcomes. For example, it can suggest learning methods that have worked well for the employee in the past, or it can help the employee avoid learning methods that have caused them trouble in the past. Furthermore, it can suggest learning plans related to the employee's future career path based on their learning history. This allows for the provision of more effective learning plans by leveraging the employee's past learning history.

[0054] The risk-skilling support system can monitor employees' learning environments and provide the optimal learning environment. For example, if an employee prefers to study in a quiet environment, noise-canceling headphones can be provided. If an employee prefers a brighter environment, appropriate lighting can be provided. Furthermore, if an employee prefers to study in a comfortable chair, an ergonomic chair can be provided. By optimizing the employee's learning environment, the system can enhance learning effectiveness.

[0055] The risk-skilling support system can evaluate employees' learning outcomes and suggest the next learning plan based on the evaluation results. For example, if an employee has acquired a particular skill, it can suggest the next steps to utilize that skill. It can also suggest complementary learning plans if an employee is struggling with a particular skill. Furthermore, it can suggest learning plans related to future career paths based on the employee's learning outcomes. This allows for the provision of more effective learning plans by leveraging employees' learning outcomes.

[0056] The risk-skilling support system can suggest collaborations with external experts and coaches based on employees' learning needs. For example, it can suggest online sessions with experts in specific skills, regular meetings with career coaches, and even mentoring programs with industry leaders. This allows for enhanced learning effectiveness by leveraging external resources tailored to employees' learning needs.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects employee background and qualification information. This includes work history, educational background, and certifications. The data collection unit can automatically retrieve information from the company's database. It can also collect information manually entered by employees, as well as information about past work experience and current job titles. Step 2: The analysis department analyzes the information collected by the data collection department. The analysis department uses statistical analysis and machine learning algorithms to analyze employee career history and qualifications. For example, it can identify employee skill gaps and suggest qualifications and courses to fill those gaps. It can also predict the skills that will be needed in the future based on the employee's career path and suggest qualifications and courses to acquire those skills. Step 3: The suggestion department suggests qualifications and courses based on the analysis results obtained by the analysis department. The suggestion department proposes the most suitable qualifications and courses based on the employee's current position and future career path. It can also suggest qualifications and courses to acquire necessary skills based on the employee's skill gaps.

[0059] (Example of form 2) An embodiment of the present invention provides a reskilling support system for companies to support employee reskilling. This reskilling support system collects information such as employee career history and qualifications recorded by the company, and an AI analyzes this information to suggest qualifications that employees should acquire and courses they should take. This mechanism allows employees to efficiently improve their skills and enables companies to improve productivity. First, the reskilling support system collects information such as employee career history and qualifications recorded by the company. At this time, it collects detailed data such as the employee's past work experience, current position, and acquired qualifications. For example, it can understand what projects an employee has participated in in the past and what skills they possess. Next, the AI ​​analyzes the collected information. The AI ​​analyzes data such as the employee's career history and qualifications and suggests the most suitable way for the employee to improve their skills. For example, if an employee does not have the skills required for their current position, the AI ​​can suggest qualifications and courses to acquire those skills. This allows employees to efficiently acquire the skills they need. Furthermore, by employees taking the qualifications and courses suggested by the AI, the overall productivity of the company improves. For example, when employees acquire new skills, work efficiency increases and project progresses more smoothly. Furthermore, improved employee skills allow companies to seize new business opportunities. This system enables efficient skill development for employees, contributing to individual growth. Companies can expect to achieve increased productivity and strengthen their competitiveness. For instance, by having employees acquire new technologies, companies can offer products and services utilizing the latest technologies. Also, improved employee skills allow companies to enter new markets. Thus, AI-powered reskilling support services offer significant benefits to both employees and companies. Employees can efficiently improve their skills, and companies can increase productivity. This strengthens companies' competitiveness and enables sustainable growth. Therefore, reskilling support systems can efficiently support employee skill development and improve corporate productivity.

[0060] The reskilling support system according to this embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects employee career and qualification information. This information includes, but is not limited to, work history, educational background, and acquired qualifications. The collection unit can, for example, automatically retrieve information from a company's database. The collection unit can also collect information manually entered by employees. Furthermore, the collection unit can collect information about employees' past work experience and current positions. For example, the collection unit can retrieve an employee's work history from a company's database to understand what projects the employee has participated in in the past. The analysis unit analyzes the information collected by the collection unit. The analysis unit can, for example, analyze employee career and qualification information using statistical analysis or machine learning algorithms. For example, the analysis unit can identify employee skill gaps and suggest qualifications and courses to fill those gaps. The analysis unit can also predict the skills that will be needed in the future based on the employee's career path and suggest qualifications and courses to acquire those skills. The suggestion unit suggests qualifications and courses based on the analysis results obtained by the analysis unit. For example, the suggestion unit can propose the most suitable qualifications and courses based on an employee's current position and future career path. It can also propose qualifications and courses to acquire necessary skills based on an employee's skill gap. Furthermore, the suggestion unit may include a progress management unit to manage the progress of the suggested qualifications and courses. For example, the suggestion unit monitors the progress of qualifications and courses taken by employees and sends reminders as needed. As a result, the reskilling support system according to this embodiment can efficiently support employee skill development and improve the productivity of the company.

[0061] The data collection unit collects employee background and qualification information. This includes, but is not limited to, work history, educational background, and certifications. The data collection unit can automatically retrieve information from company databases, for example. Specifically, it can access company HR systems and databases to automatically extract information such as employee work history, educational background, and certifications. The data collection unit can also collect information manually entered by employees. For example, it can provide forms or applications where employees self-report their information and collect the information entered there. Furthermore, the data collection unit can collect information about employees' past work experience and current positions. For example, it can retrieve employee work histories from company databases to understand what projects employees have participated in in the past. This allows the data collection unit to gain a detailed understanding of employees' skill sets and experience. In addition, the data collection unit can collect information from external data sources. For example, it can retrieve information about external training and seminars that employees have attended and add it to their skill sets. This allows the data collection unit to gain a comprehensive understanding of employees' skills and experience and build a foundation for providing this information to the analytics and suggestion units.

[0062] The Analysis Department analyzes the information collected by the Data Collection Department. For example, the Analysis Department can analyze employee career and qualification information using statistical analysis and machine learning algorithms. Specifically, it evaluates current skill sets and identifies skill gaps based on employees' work history and acquired qualifications. For instance, it uses machine learning algorithms to analyze an employee's past work history and acquired qualifications and compares them to the skills required for their current position to identify skill gaps. The Analysis Department can also predict future skill needs based on an employee's career path and suggest qualifications and courses to acquire those skills. For example, it analyzes an employee's past career path and industry trends to predict future skill needs. Furthermore, the Analysis Department can suggest optimal learning methods considering an employee's learning style and history. For example, it analyzes what learning methods have been successful for an employee in the past and suggests the optimal learning method based on the results. This allows the Analysis Department to efficiently support employee skill development and provide information to improve corporate productivity.

[0063] The Suggestion Department suggests qualifications and courses based on the analysis results obtained by the Analysis Department. For example, the Suggestion Department can propose the most suitable qualifications and courses based on an employee's current position and future career path. Specifically, it proposes qualifications and courses to fill employee skill gaps. For example, if an employee lacks the skills required for their current position, it will propose qualifications and courses to acquire those skills. The Suggestion Department can also propose qualifications and courses to acquire skills that will be needed in the future, based on the employee's career path. For example, if an employee aspires to a management position in the future, it will propose courses to acquire management skills. Furthermore, the Suggestion Department can also have a Progress Management Department to manage the progress of the suggested qualifications and courses. For example, the Suggestion Department can monitor the progress of qualifications and courses taken by employees and send reminders as needed. This supports employees in progressing with their learning according to plan. The Suggestion Department can also collect employee feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can improve future suggestions based on employee evaluations and feedback on courses taken. This allows the suggestion department to efficiently support employee skill development and improve the company's productivity.

[0064] The data collection unit can automatically retrieve information from a company's database. For example, the data collection unit can automatically retrieve employee history and qualifications from the company's database using an API. The data collection unit can also perform periodic database queries to obtain the latest information. For example, the data collection unit can perform database queries based on a daily or weekly schedule to collect the latest employee information. Furthermore, the data collection unit can automatically receive notifications when there are changes in the company's database and update the information based on those notifications. This enables efficient information collection by automatically retrieving information from the company's database. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the information retrieved from the company's database into a generating AI, which can then collect and organize the information.

[0065] The suggestion function can suggest qualifications and courses based on an employee's current position and future career path. For example, it can identify the skills required for an employee's current position and suggest qualifications and courses to acquire those skills. It can also predict the skills an employee will need in the future based on their future career path and suggest qualifications and courses to acquire those skills. For example, if an employee aims for a management position in their future career path, the suggestion function will suggest qualifications and courses to improve their leadership skills. If an employee is pursuing a career in a technical field, the suggestion function can also suggest qualifications and courses to acquire the latest technical skills. This allows for more appropriate skill development by suggesting qualifications and courses based on an employee's position and career path. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input information about an employee's position and career path into a generating AI, which can then suggest the most suitable qualifications and courses.

[0066] The suggestion unit may include a progress management unit that manages the progress of suggested qualifications and courses. The progress management unit, for example, monitors the progress of qualifications and courses taken by employees and sends reminders as needed. The progress management unit can, for example, provide an interface for employees to report their progress on qualifications and courses. The progress management unit can also provide a dashboard that visualizes employee progress and makes it easier for managers to understand the progress. For example, the progress management unit can display employee progress in graphs and charts, allowing for quick identification of delays and problems. Furthermore, the progress management unit can provide additional support and resources based on employee progress. For example, if an employee falls behind in their progress on a qualification or course, the progress management unit can provide additional training and support. This makes it easier to understand the progress of employee skill development by managing the progress of qualifications and courses. Some or all of the above processes in the progress management unit may be performed using AI, for example, or not using AI. For example, the progress management unit can input employee progress into a generating AI, which can then monitor and manage the progress.

[0067] The data collection unit can estimate employees' emotions and adjust the timing of information collection based on the estimated emotions. For example, if an employee is feeling stressed, the unit will collect information during a relaxed period. It can also collect information when an employee is concentrating. Furthermore, if an employee is tired, the unit can collect information after a break. For instance, the unit can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. It calculates an emotion score based on changes in facial expression and adjusts the timing of information collection. The unit can also record an employee's voice and estimate their emotions using voice analysis technology. It analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of information collection. Additionally, the unit can collect employee biometric data (heart rate and skin electrical activity) using sensors and estimate their emotions using an emotion estimation algorithm. It calculates an emotion score based on fluctuations in heart rate and adjusts the timing of information collection. This allows for more effective information gathering by adjusting the timing of information collection according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input employee emotion data into the generative AI, which can then adjust the timing of information collection.

[0068] The data collection unit can analyze an employee's past work experience in detail and select the optimal information collection method. For example, the data collection unit can analyze data from projects an employee has participated in in the past and prioritize the collection of relevant information. The data collection unit can also collect information related to specific skill sets based on an employee's past work experience. Furthermore, the data collection unit can collect information related to future career paths based on an employee's past work history. For example, the data collection unit can analyze data from projects an employee has participated in in the past and prioritize the collection of relevant information. The data collection unit can also collect information related to specific skill sets based on an employee's past work experience. The data collection unit can also collect information related to future career paths based on an employee's past work history. This enables efficient information collection by selecting the optimal information collection method based on an employee's past work experience. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee past work experience data into a generating AI, which can then select the optimal information collection method.

[0069] The data collection unit can filter information based on an employee's current projects and areas of interest. For example, the data collection unit can prioritize collecting information related to a project the employee is currently working on. It can also collect information on relevant qualifications and courses based on the employee's areas of interest. Furthermore, the data collection unit can filter and collect information related to the employee's current job title. For example, the data collection unit can prioritize collecting information related to a project the employee is currently working on. It can also collect information on relevant qualifications and courses based on the employee's areas of interest. The data collection unit can also filter and collect information related to the employee's current job title. This allows for the collection of highly relevant information by filtering information based on the employee's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input information about an employee's current projects and areas of interest into a generating AI, which can then filter and collect the most relevant information.

[0070] The data collection unit can estimate an employee's emotions and prioritize the information to collect based on the estimated emotions. For example, if an employee is stressed, the unit will prioritize collecting information that promotes relaxation. It can also prioritize collecting important information if the employee is focused. Furthermore, if the employee is tired, it can prioritize collecting lighter information. For instance, the unit can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. Based on changes in facial expression, the unit calculates an emotion score and determines the priority of the information to collect. The unit can also record an employee's voice and estimate their emotions using voice analysis technology. It analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of the information to collect. Additionally, the unit can collect employee biometric data (heart rate and skin electrical activity) using sensors and estimate their emotions using an emotion estimation algorithm. Based on fluctuations in heart rate, the unit calculates an emotion score and determines the priority of the information to collect. This enables more effective information gathering by prioritizing information according to employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not using AI. For example, the collection unit can input employee emotion data into a generative AI, which can then determine the priority of the information.

[0071] The data collection unit can prioritize collecting highly relevant information by considering the geographical location of employees during information gathering. For example, if an employee is in a specific region, the data collection unit will prioritize collecting information related to that region. Furthermore, if an employee is on a business trip, the data collection unit can prioritize collecting information related to their business trip destination. Additionally, if an employee is working remotely, the data collection unit can prioritize collecting information about their home area. This allows for the efficient collection of highly relevant information by considering the geographical location of employees. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the employee's geographical location information into a generating AI, which can then prioritize collecting highly relevant information.

[0072] The data collection unit can analyze employees' social media activity and collect relevant information during data collection. For example, the data collection unit can collect information on relevant qualifications and courses based on information shared by employees on social media. The data collection unit can also analyze employees' areas of interest on social media and collect relevant information. Furthermore, the data collection unit can collect relevant information based on information about accounts that employees follow on social media. For example, the data collection unit can collect information on relevant qualifications and courses based on information shared by employees on social media. The data collection unit can also analyze employees' areas of interest on social media and collect relevant information. The data collection unit can also collect relevant information based on information about accounts that employees follow on social media. This allows for the collection of highly relevant information by analyzing employees' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee social media activity data into a generating AI, which can then collect relevant information.

[0073] The analysis department can estimate employees' emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is relaxed, the analysis department can provide detailed analysis results. If an employee is in a hurry, the analysis department can provide concise analysis results that get straight to the point. Furthermore, if an employee is excited, the analysis department can provide analysis results with visually stimulating effects. For example, the analysis department can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The analysis department calculates an emotion score based on changes in facial expression and adjusts the presentation of the analysis. The analysis department can also record an employee's voice and estimate their emotions using voice analysis technology. The analysis department analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the presentation of the analysis. Furthermore, the analysis department can collect employees' biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The analysis department calculates an emotion score based on fluctuations in heart rate and adjusts the presentation of the analysis. This allows for the provision of more effective analysis results by adjusting the presentation of the analysis according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis department may be performed using AI or not using AI. For example, the analysis department can input employee emotion data into a generative AI, which can then adjust the presentation of the analysis.

[0074] The analysis department can adjust the level of detail of the analysis based on the importance of the employee. For example, the analysis department can provide detailed analysis results to employees in important positions. It can also provide basic analysis results to new employees. Furthermore, it can provide analysis results with an appropriate level of detail for mid-career employees, depending on their position. For example, the analysis department can provide detailed analysis results to employees in important positions. It can also provide basic analysis results to new employees. It can also provide analysis results with an appropriate level of detail for mid-career employees, depending on their position. By adjusting the level of detail of the analysis based on the importance of the employee, more appropriate analysis results can be provided. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input information on the importance of employees into a generating AI, which can then adjust the level of detail of the analysis.

[0075] The analysis department can apply different analysis algorithms depending on the employee's category during analysis. For example, the analysis department can apply an analysis algorithm specializing in technical skills to technical employees. It can also apply an analysis algorithm specializing in leadership skills to managerial employees. Furthermore, it can apply an analysis algorithm specializing in sales skills to sales employees. This allows for more appropriate analysis results by applying different analysis algorithms depending on the employee's category. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input information about employee categories into a generating AI, which can then apply the most suitable analysis algorithm.

[0076] The analysis department can estimate an employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if an employee is in a hurry, the analysis department can provide a short, concise analysis. If an employee is relaxed, the analysis department can provide a longer analysis with detailed explanations. Furthermore, if an employee is excited, the analysis department can provide an analysis with visually stimulating effects. For example, the analysis department can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The analysis department calculates an emotion score based on changes in facial expression and adjusts the length of the analysis. The analysis department can also record an employee's voice and estimate their emotions using voice analysis technology. The analysis department analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the analysis. Furthermore, the analysis department can collect employee biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The analysis department calculates an emotion score based on heart rate fluctuations and adjusts the length of the analysis. This allows for more effective analysis results by adjusting the length of the analysis according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input employee emotion data into a generative AI, which can then adjust the length of the analysis.

[0077] The analysis department can prioritize analyses based on employee submission dates. For example, it can prioritize analyzing submissions with approaching deadlines. It can also prioritize analyzing submissions submitted earlier. Furthermore, it can postpone analyzing submissions submitted later. This allows for efficient analysis by prioritizing analyses based on employee submission dates. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input information about employee submission dates into a generating AI, which can then determine the analysis priorities.

[0078] The analysis department can adjust the order of analysis based on employee relevance during the analysis process. For example, the analysis department can prioritize analyzing data of employees related to important projects. It can also prioritize analyzing data of employees related to ongoing projects. Furthermore, it can postpone analyzing data of employees related to future projects. This allows for efficient analysis by adjusting the order of analysis based on employee relevance. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input information about employee relevance into a generating AI, which can then adjust the order of analysis.

[0079] The suggestion unit can estimate an employee's emotions and adjust the way suggestions are presented based on those emotions. For example, if an employee is relaxed, the suggestion unit can provide detailed suggestions. If an employee is in a hurry, it can provide concise suggestions that get straight to the point. Furthermore, if an employee is excited, the suggestion unit can provide suggestions with visually stimulating effects. For example, the suggestion unit can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit calculates an emotion score based on changes in facial expression and adjusts the way suggestions are presented. The suggestion unit can also record an employee's voice and estimate their emotions using voice analysis technology. The suggestion unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the way suggestions are presented. Furthermore, the suggestion unit can collect employee biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The suggestion unit calculates an emotion score based on heart rate fluctuations and adjusts the way suggestions are presented. This allows for more effective suggestions by adjusting the presentation of suggestions according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input employee emotion data into the generative AI, which can then adjust the presentation of suggestions.

[0080] The suggestion function can adjust the level of detail of its suggestions based on the importance of the qualifications and courses. For example, it can provide detailed suggestions for important qualifications and courses, concise suggestions for basic qualifications and courses, and suggestions with a moderate level of detail for intermediate-level qualifications and courses. By adjusting the level of detail of the suggestions based on the importance of the qualifications and courses, more appropriate suggestions can be made. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input information about the importance of qualifications and courses into a generating AI, which can then adjust the level of detail of the suggestions.

[0081] The suggestion function can apply different suggestion algorithms depending on the category of the qualification or course. For example, the suggestion function can apply a suggestion algorithm specializing in technical skills to technical qualifications and courses. It can also apply a suggestion algorithm specializing in leadership skills to management qualifications and courses. Furthermore, it can apply a suggestion algorithm specializing in sales skills to sales qualifications and courses. By applying different suggestion algorithms depending on the category of the qualification or course, more appropriate suggestions become possible. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion section inputs information about qualifications and course categories into a generating AI, which can then apply the most suitable suggestion algorithm.

[0082] The suggestion unit can estimate an employee's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if an employee is in a hurry, the suggestion unit will provide short, concise suggestions. If an employee is relaxed, the suggestion unit can provide longer suggestions with more detailed explanations. Furthermore, if an employee is excited, the suggestion unit can provide suggestions with visually stimulating effects. For example, the suggestion unit can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit calculates an emotion score based on changes in facial expression and adjusts the length of the suggestions. The suggestion unit can also record an employee's voice and estimate their emotions using voice analysis technology. The suggestion unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the suggestions. Furthermore, the suggestion unit can collect employee biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The suggestion unit calculates an emotion score based on heart rate fluctuations and adjusts the length of the suggestions. This allows for more effective suggestions by adjusting the length of the suggestions according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input employee emotion data into the generative AI, which can then adjust the length of the suggestions.

[0083] The suggestion function can prioritize suggestions based on the submission deadlines for qualifications and courses. For example, it can prioritize suggesting qualifications and courses with approaching deadlines. It can also prioritize suggesting qualifications and courses with earlier submission deadlines. Furthermore, it can postpone suggesting qualifications and courses with later submission deadlines. This allows for efficient suggestions by prioritizing suggestions based on the submission deadlines of qualifications and courses. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input information about the submission deadlines of qualifications and courses into a generating AI, which can then determine the priority of the suggestions.

[0084] The suggestion function can adjust the order of suggestions based on the relevance of qualifications and courses. For example, the suggestion function can prioritize suggesting qualifications and courses related to important projects. It can also prioritize suggesting qualifications and courses related to ongoing projects. Furthermore, it can postpone suggesting qualifications and courses related to future projects. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of qualifications and courses. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input information about the relevance of qualifications and courses into a generating AI, which can then adjust the order of suggestions.

[0085] The progress management department can estimate employees' emotions and adjust progress management methods based on those estimates. For example, if an employee is stressed, the progress management department can reduce the frequency of progress management. Conversely, if an employee is relaxed, the progress management department can conduct detailed progress management. Furthermore, if an employee is in a hurry, the progress management department can conduct concise progress management. For example, the progress management department can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The progress management department calculates an emotion score based on changes in facial expression and adjusts progress management methods accordingly. The progress management department can also record employees' voices and estimate their emotions using voice analysis technology. The progress management department analyzes the tone and speed of the voice, calculates an emotion score, and adjusts progress management methods accordingly. Furthermore, the progress management department can collect employees' biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The progress management department calculates an emotion score based on fluctuations in heart rate and adjusts progress management methods accordingly. This allows for more effective progress management by adjusting the progress management method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress management department may be performed using AI or not. For example, the progress management department can input employee emotion data into a generative AI, which can then adjust the progress management method.

[0086] The progress management department can analyze an employee's past progress history to select the optimal management method. For example, the progress management department can adjust the frequency of progress management based on an employee's past progress history. Furthermore, the progress management department can prioritize progress management at specific times based on an employee's past progress history. In addition, the progress management department can analyze an employee's past progress history and propose the optimal progress management method. For example, the progress management department can adjust the frequency of progress management based on an employee's past progress history. The progress management department can also prioritize progress management at specific times based on an employee's past progress history. The progress management department can also analyze an employee's past progress history and propose the optimal progress management method. This allows the optimal progress management method to be selected by analyzing an employee's past progress history. Some or all of the above processes in the progress management department may be performed using AI, or not. For example, the progress management department can input employee past progress history data into a generating AI, which can then select the optimal progress management method.

[0087] The progress management department can estimate employees' emotions and determine progress management priorities based on those estimated emotions. For example, if an employee is stressed, the progress management department can lower the priority of their progress management. Conversely, if an employee is relaxed, the progress management department can raise the priority of their progress management. Furthermore, if an employee is in a hurry, the progress management department can adjust the priority of their progress management. For example, the progress management department can capture an employee's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. Based on the changes in facial expression, the progress management department calculates an emotion score and determines the priority of their progress management. The progress management department can also record an employee's voice and estimate their emotions using voice analysis technology. Based on the tone and speed of their voice, the progress management department calculates an emotion score and determines the priority of their progress management. Furthermore, the progress management department can collect employees' biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. Based on fluctuations in heart rate, the progress management department calculates an emotion score and determines the priority of their progress management. This enables more effective progress management by prioritizing progress management based on employee emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress management department may be performed using AI or not. For example, the progress management department can input employee emotion data into a generative AI, which can then determine the priority of progress management.

[0088] The progress management department can select the optimal management method when managing progress, taking into account the geographical location information of employees. For example, if an employee is in a specific region, the progress management department can select a progress management method related to that region. Furthermore, if an employee is on a business trip, the progress management department can select a progress management method related to the business trip destination. Additionally, if an employee is working remotely, the progress management department can select a progress management method considering information about the area around their home. This allows the department to select the optimal progress management method by considering the geographical location information of employees. Some or all of the above processes in the progress management department may be performed using AI, for example, or without AI. For example, the progress management department can input the geographical location information of employees into a generating AI, which can then select the optimal progress management method.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The risk-skilling support system can provide customized learning plans based on each employee's individual learning style. For example, employees who prefer visual learning can be provided with materials that heavily utilize videos and infographics. Employees who prefer auditory learning can be recommended podcasts and audiobooks. Furthermore, employees who prefer hands-on learning can be offered hands-on workshops or project-based learning. This maximizes learning effectiveness by providing the optimal learning plan tailored to each employee's learning style.

[0091] The risk-skilling support system can monitor employees' health status and provide learning plans tailored to their needs. For example, if an employee is feeling fatigued, a lighter learning plan can be provided. Conversely, if an employee is healthy and energetic, a more challenging learning plan can be offered. Furthermore, if an employee is feeling stressed, a relaxing learning plan can be provided. By providing learning plans that match employees' health status, the system can enhance the effectiveness of their learning.

[0092] A risk-skilling support system can track employees' learning progress in real time and provide feedback based on that progress. For example, it can send a congratulatory message when an employee achieves their learning goals. It can also send a reminder if an employee falls behind in their learning. Furthermore, it can provide additional support and resources if an employee is struggling with a particular challenge. This allows for real-time monitoring of employee learning progress and the provision of appropriate feedback, thereby maintaining motivation for learning.

[0093] The risk-skilling support system can analyze an employee's learning history and suggest the optimal learning plan based on past learning outcomes. For example, it can suggest learning methods that have worked well for the employee in the past, or it can help the employee avoid learning methods that have caused them trouble in the past. Furthermore, it can suggest learning plans related to the employee's future career path based on their learning history. This allows for the provision of more effective learning plans by leveraging the employee's past learning history.

[0094] The risk-skilling support system can estimate an employee's emotions and adjust the difficulty level of the learning plan based on those emotions. For example, if an employee is relaxed, it can provide a more challenging learning plan. If an employee is stressed, it can provide an easier learning plan. Furthermore, if an employee is excited, it can provide a more challenging learning plan. By providing learning plans tailored to the employee's emotions, the system can enhance the effectiveness of their learning.

[0095] The risk-skilling support system can monitor employees' learning environments and provide the optimal learning environment. For example, if an employee prefers to study in a quiet environment, noise-canceling headphones can be provided. If an employee prefers a brighter environment, appropriate lighting can be provided. Furthermore, if an employee prefers to study in a comfortable chair, an ergonomic chair can be provided. By optimizing the employee's learning environment, the system can enhance learning effectiveness.

[0096] The risk-skilling support system can estimate an employee's emotions and adjust the timing of learning based on those emotions. For example, if an employee is relaxed, it can suggest a good time to start learning. If an employee is stressed, it can suggest a time to start learning after they have relaxed. Furthermore, if an employee is focused, it can suggest starting learning at that time. By providing learning timing that is tailored to the employee's emotions, the system can enhance the effectiveness of learning.

[0097] The risk-skilling support system can evaluate employees' learning outcomes and suggest the next learning plan based on the evaluation results. For example, if an employee has acquired a particular skill, it can suggest the next steps to utilize that skill. It can also suggest complementary learning plans if an employee is struggling with a particular skill. Furthermore, it can suggest learning plans related to future career paths based on the employee's learning outcomes. This allows for the provision of more effective learning plans by leveraging employees' learning outcomes.

[0098] The risk-skilling support system can estimate an employee's emotions and adjust learning feedback based on those emotions. For example, if an employee is relaxed, it can provide detailed feedback. If an employee is stressed, it can provide concise feedback. Furthermore, if an employee is excited, it can provide visually stimulating feedback. This enhances the effectiveness of learning by providing feedback tailored to the employee's emotions.

[0099] The risk-skilling support system can suggest collaborations with external experts and coaches based on employees' learning needs. For example, it can suggest online sessions with experts in specific skills, regular meetings with career coaches, and even mentoring programs with industry leaders. This allows for enhanced learning effectiveness by leveraging external resources tailored to employees' learning needs.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The data collection unit collects employee background and qualification information. This includes work history, educational background, and certifications. The data collection unit can automatically retrieve information from the company's database. It can also collect information manually entered by employees, as well as information about past work experience and current job titles. Step 2: The analysis department analyzes the information collected by the data collection department. The analysis department uses statistical analysis and machine learning algorithms to analyze employee career history and qualifications. For example, it can identify employee skill gaps and suggest qualifications and courses to fill those gaps. It can also predict the skills that will be needed in the future based on the employee's career path and suggest qualifications and courses to acquire those skills. Step 3: The suggestion department suggests qualifications and courses based on the analysis results obtained by the analysis department. The suggestion department proposes the most suitable qualifications and courses based on the employee's current position and future career path. It can also suggest qualifications and courses to acquire necessary skills based on the employee's skill gaps.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit described above, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and automatically acquires employee career history and qualification information from the company's database. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies employee skill gaps using statistical analysis and machine learning algorithms. The suggestion unit is implemented by the control unit 46A of the smart device 14 and suggests the most suitable qualifications and courses for employees. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit described above, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and automatically retrieves employee career history and qualification information from the company's database. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies employee skill gaps using statistical analysis and machine learning algorithms. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 and suggests the most suitable qualifications and courses for employees. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and automatically acquires employee career history and qualification information from the company's database. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies employee skill gaps using statistical analysis and machine learning algorithms. The suggestion unit is implemented by the control unit 46A of the headset terminal 314 and suggests the most suitable qualifications and courses to employees. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit described above, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and automatically acquires employee career history and qualification information from the company's database. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and identifies employee skill gaps using statistical analysis and machine learning algorithms. The suggestion unit is implemented by, for example, the control unit 46A of the robot 414 and suggests the most suitable qualifications and courses for employees. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] (Note 1) A collection department that collects employee background and qualification information, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a suggestion unit suggests qualifications and courses, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Automatically retrieve information from a company's database. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned suggestion section is, Suggest qualifications and courses based on employees' current positions and future career paths. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned suggestion section is, It has a progress management department that manages the progress of suggested qualifications and courses. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We estimate employees' emotions and adjust the timing of information gathering based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We will conduct a detailed analysis of employees' past work experience and select the most suitable method for gathering information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When gathering information, filter it based on the employee's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Estimate employee sentiment and prioritize the information to collect based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information by considering the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information, we analyze employees' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is We estimate employee sentiment and adjust the presentation of the analysis based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of each employee. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the employee category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is Estimate employee sentiment and adjust the length of the analysis based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when the employee submitted their data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, adjust the order of the analysis based on employee relevance. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned suggestion section is, The system estimates employee sentiment and adjusts the way suggestions are presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned suggestion section is, When suggesting qualifications or courses, adjust the level of detail based on their importance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned suggestion section is, When suggesting, different suggestion algorithms are applied depending on the category of qualifications or courses. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned suggestion section is, The system estimates employee sentiment and adjusts the length of suggestions based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned suggestion section is, When suggesting options, the system prioritizes suggestions based on the submission deadlines for qualifications and courses. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned suggestion section is, When suggesting options, the order of suggestions is adjusted based on the relevance of the qualifications and courses. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned progress management unit, Estimate employee sentiment and adjust progress management methods based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned progress management unit, When managing progress, analyze the employee's past progress history to select the most suitable management method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned progress management unit, Estimate employee sentiment and prioritize progress management based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned progress management unit, When managing progress, select the optimal management method by considering the geographical location information of employees. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 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. A collection department that collects employee background and qualification information, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a suggestion unit suggests qualifications and courses, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Automatically retrieve information from a company's database. The system according to feature 1.

3. The aforementioned suggestion section is, Suggest qualifications and courses based on employees' current positions and future career paths. The system according to feature 1.

4. The aforementioned suggestion section is, It has a progress management department that manages the progress of suggested qualifications and courses. The system according to feature 1.

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

6. The aforementioned collection unit is We will conduct a detailed analysis of employees' past work experience and select the most suitable method for gathering information. The system according to feature 1.

7. The aforementioned collection unit is When gathering information, filter it based on the employee's current projects and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is Estimate employee sentiment and prioritize the information to collect based on the estimated employee sentiment. The system according to feature 1.

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